Volume 13 Issue 6

A Comprehensive Review And Prototype Implementation For Deepfake Detection System Using Multi-Modal

Authors: Assistant Professor Sunil Yadav, Mohan Kadambande, Ashwini Kangane, Nitesh Lad, Urvashi Nahate

Abstract: The advancements in deepfake technology have come swiftly, allowing for the creation of extremely realistic altered images, videos, and audio material. Although there has been considerable progress in unimodal detection in current research, most approaches tend to concentrate on a single modality. This paper analyses more than 20 cutting-edge studies on deepfake detection and pinpoints significant research shortcomings, including the absence of multi-modal frameworks, limitations in datasets, lack of robustness, and insufficient interpretability. To address these issues, we built a prototype detection system based solely on single-modality images that employs two models: a custom Convolutional Neural Network (CNN) and Xception CNN. Our findings underscore the necessity for solutions that incorporate multiple modalities. We suggest an integrated framework for multi-modal detection encompassing images, videos, and audio, which represents the next advancement toward reliable and effective detection systems.

DOI: http://doi.org/10.5281/zenodo.17596892

Unseen Consequences: Technology, Transactive Memory, And The Evolution Of Human Cognitive Capacity

Authors: Hiral Pandya, Prajwal Sonawane

Abstract: Artificial Intelligence and the other related technologies are very important in our day to day life. However, basic thinking processes are starting to change due to this deep integration. Constant exposure to external information sources encourages cognitive offloading. This means people tend to rely on technology instead of their memory, which may weaken critical thinking and deep learning (Sparrow, Liu, & Wegner, 2011). Research also indicates that regular use of digital devices can reduce working memory and attention span. This decrease can lead to lower cognitive control and increased distraction (Wilmer, Sherman, & Chein, 2017). Furthermore, the frequent use of screens, especially right before bedtime, disrupts circadian rhythms and reduces sleep quality. This has an indirect effect on memory consolidation and learning (Exelmans & Van den Bulck, 2016). This study explores what is truly happening beneath the surface. We are studying how constant digital engagement affects our thinking abilities, our learning ability, and our overall mental sharpness.

Virtual Reality Museum: An Immersive Platform For Cultural Heritage Preservation

Authors: Prasanjeet Malla, Soumya Ranjan Pradhan, Nilesh Yadav, Pankaj Mali, Dr. Nithya A.

Abstract: Virtual reality (VR) technologies offer very effective and appealing means for the conservation and display of cultural heritage through engaging and interactive experiences. This article introduces the creative and technical aspects of the Virtual Reality Museum, a project implemented in Unity 3D with the help of the XR Interaction Toolkit. The system allows VR device-free access through the XR Device Simulator, and combines software architecture elements such as gesture-controlled movement, user- identification-enabled information panels, audio- visual exhibits, and an admin workflow for content management. Monument 3D models of very high fidelity were conceived with the Blender and photogrammetry suite of tools and then further developed for real-time rendering. The framework via the prototype expands the cultural heritage portfolio and indulges the user in interactive learning while opening up the horizon to the addition of more than one exhibit as per the scaling facility. The core contributions of the paper comprise the following: (i) the VR design and innovations that are adaptable to cultural scenarios, (ii) the scripting of modular functionalities (such as InfoPanelController and ProximityHotspot) for the realization of a seamless flow of contextual interactions, experiments demonstrating the efficacy of controlled movement stably occurring in Play Mode, and, further, the suggestive evidence of learning efficacy through the mere presence of multimedia. The authors plan to make widespread exhibit deployment, publishing via WebXR to make it more accessible, adding gesture and voice interactivity, and the combination of cloud-based content management with AI. The design of the system in its entirety demonstrates the potential of VR to become a practical means of the preservation of culture and understanding through the bridge created by technical, educational, and heritage preserving fields.

Strategic Decision-Making In The Age Of AI: The Role Of Predictive Analytics In Enhancing Forecasting, Resource Optimization, And Competitive Agility”

Authors: Dr. Namita Yash, Dr. Meghna Sharma

Abstract: In the age of digital transformation, Artificial Intelligence (AI)-powered predictive analytics has emerged as a critical tool for enhancing strategic business decision-making. This paper examines how organizations utilize predictive analytics to improve forecasting accuracy, optimize resource allocation, and gain a competitive edge. By examining case studies across various industries, including finance, retail, and manufacturing, the research highlights the tangible benefits and challenges of implementing AI-driven analytics in real- world strategic contexts. The study also examines how the integration of machine learning models into decision-making frameworks alters traditional management approaches, enabling more proactive and data-driven strategies. The study explores ethical considerations and data governance issues related to predictive analytics, emphasizing the importance of responsible AI adoption. Organizations realize the full potential of enhanced strategic agility and performance only when they align these efforts with their capabilities, culture, and a clear governance structure

DOI: http://doi.org/10.5281/zenodo.17616337

Smart Electronic Oil Spill Detector Using Iot and Cloud Connectivity

Authors: Dr.Kinny Garg, G.Senbagavalli, Sanjana S Deshpande, RunashreeN, Deepika S

Abstract: Oil spills are a major environmental hazard, threatening marine ecosystems and human livelihoods. This paper presents a Smart Electronic Oil Spill Detection System that leverages IoT and cloud connectivity for real-time monitoring and alert generation. The system integrates oil-detecting sensors with a microcontroller, which acquires and processes sensor data before transmitting it to a cloud platform via an IoT communication module. The cloud interface enables remote monitoring through a web dashboard and generates instant alerts to facilitate early response actions. The proposed prototype was tested under controlled conditions, demonstrating reliable detection and minimal communication delay. This approach reduces manual monitoring efforts, enables 24/7 surveillance, and is scalable for industrial and marine applications. Future enhancements include machine learning–based spill severity prediction and integration with automated cleanup mechanisms for a more comprehensive solution.

Predictive Models For Urban Air Quality Management Using AI

Authors: Dr. Pankaj Malik, Drishti Patidar, Ayush Soni, Divyansh Deore, Pratham Thattey

Abstract: Urban air pollution has emerged as a critical environmental concern due to rapid industrialization, vehicular emissions, and population growth. Conventional monitoring methods often fail to provide timely or spatially comprehensive insights required for effective air quality management. This research presents a predictive modeling framework for urban air quality management using Artificial Intelligence (AI) to forecast pollutant levels and support proactive decision-making. Real-time environmental data—including temperature, humidity, wind speed, and pollutant concentrations (PM₂.₅, PM₁₀, NO₂, SO₂, CO, and O₃)—were collected from multiple urban monitoring stations. Machine learning algorithms such as Random Forest (RF), Gradient Boosting (GB), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks were implemented and compared for prediction accuracy. Experimental results show that the LSTM model achieved the highest performance, with an average R² value of 0.96 and RMSE of 4.2 µg/m³ for PM₂.₅ prediction, outperforming traditional statistical and tree-based models. The RF model achieved an R² of 0.91, demonstrating its robustness for multi-pollutant forecasting. The integration of spatial-temporal data further improved predictive precision by 18%, enabling fine-grained mapping of pollution hotspots. These findings highlight that AI-based predictive models can significantly enhance urban air quality monitoring, early warning systems, and policy formulation. The study concludes that the proposed AI framework provides an efficient, scalable, and data-driven approach for sustainable urban air quality management and environmental planning.

FlashLearn: A Unified Mobile Architecture Integrating Spaced Repetition, AI Content Generation, And Gamification For Scalable Educational Technology

Authors: Rajiv Y. Barad, Mahek Chothani, Rohan Yadav, Professor Anand Jawdekar

Abstract: Educational technology platforms continue to strug- gle with three fundamental barriers: rapid knowledge decay following Ebbinghaus’s forgetting patterns, labor-intensive con- tent creation processes, and insufficient motivation mechanisms for sustained engagement. We present FlashLearn, a mobile- first learning platform that uniquely synthesizes scientifically- validated spaced repetition scheduling, Large Language Model- powered flashcard generation, and evidence-based gamification within a serverless architecture. Our architectural innovation lies in the seamless integration of these traditionally separate components, creating synergistic effects that exceed individual component performance. Controlled evaluation with 25 university participants demonstrates transformative improvements: session completion rates increased 65% (45% to 75%), daily study consistency improved 456% (2.3 to 12.8 days), and content creation efficiency gained 93% time reduction (45 to 3 minutes per deck) while maintaining 87% expert-validated accuracy. The Firebase-based serverless implementation achieves 40% lower energy consumption compared to traditional hosting while elimi- nating paper-based materials (500 sheets annually per user). This research contributes the first validated framework demonstrat- ing that integrated educational technology can simultaneously address retention, accessibility, and sustainability challenges in digital learning environments.

DOI: https://doi.org/10.5281/zenodo.17596921

Cognitive Radio Network For Dynamic Spectrum Access

Authors: Dr. Malini, Renuka S, Nikita sanjay yabe, Shwetha, Soumya A

Abstract: This consists of a system for environmental monitoring with IoT-enable that makes use of an Arduino Uno microcontroller. This system integrates multiple sensors for monitoring environmental parameters such as temperature, humidity, and air quality in real-time. The information we sense from this environmental monitoring system will be transferred wirelessly using a Bluetooth module for remote viewing. This environmental monitoring system will provide a compact, cheaper, and energy-efficient way to assess and help us maintain healthy environmental parameters in the home, office, or industrial settings. The main important component is Arduino Uno, which allows us to think of it as the CPU of the setup. It collects information from several sensors, such as the DHT11 use to detect Temperature and Humidity reading and MQ gas sensor for air quality. These sensors will continue to detect this information and report back their readings to the Arduino. The Arduino will process this information and make it easier for interpretation for further consideration. These multiple sensor integrations will provide for accuracy and reliability when assessing the environmental parameters. A Bluetooth module (HC-05) has been added to allow the wireless exchange of data to a smartphone or computer. This way, the user can check environmental readings remotely, using mobile apps or serial terminal software.

Brain Tumor And Blockage Detection Using Deep Learning

Authors: Shaikh Mahek Javed, Udar Arati Ajay, Prof.Kemnar. K. C, Londhe Pranali Babasaheb, Kadam Rutuja Laxman, Prof.Maniyar. A. A

Abstract: Brain tumors and vascular blockages represent critical medical conditions necessitating prompt and precise diagnosis to optimize patient outcomes. Traditional manual analysis of magnetic resonance imaging (MRI) and computed tomography (CT) scans by radiologists is often time-intensive, susceptible to human error, and reliant on the availability of specialized expertise. This study proposes an innovative artificial intelligence (AI)-based system for the automated detection of brain tumors and blockages, leveraging Convolutional Neural Networks (CNNs) optimized through Particle Swarm Optimization (PSO). The developed model processes medical imaging data to accurately identify the presence, type, and severity of tumors or blockages, while providing visual localization of affected brain regions. Integrated with a user-friendly interface, the system enables healthcare professionals to upload scans and obtain immediate, detailed diagnostic outputs. By improving detection accuracy, minimizing diagnostic delays, and facilitating rapid clinical decision-making, this system enhances neurosurgical planning, improves patient outcomes, and contributes to the overall efficiency of healthcare delivery.

Ai-Friend Emotion And Object Detection

Authors: Yash Solanki, Heerav Amin, Diya Chunara, Dhvani Puwar, Prof. Yatin Shukla

Abstract: Artificial Intelligence (AI) is increasingly transform- ing the way humans interact with digital systems by enabling emotionally responsive, empathetic companions. The AI Friend Web App is designed as an interactive assistant capable of detecting real-time emotions and identifying surrounding objects using integrated camera feeds. Unlike conventional chatbots that lack contextual sensitivity, this system integrates multimodal emotion detection (facial and vocal analysis), conversational AI powered by Google Gemini, and expressive 3D avatars to deliver a more human-like experience. The system ensures secure handling of sensitive user data through Firebase integration. This paper presents the architecture, methodology, performance evaluation, and future scope of the AI Friend project. Results demonstrate emotion detection accuracy above 85%, object detection latency under 300 ms, and over 90% contextual relevance in chatbot responses. User feedback highlights significant improvements in emotional engagement and perceived empathy. These findings underline the potential of empathetic AI systems to contribute meaningfully to mental health support, human-computer rela- tionships, and adaptive companionship technologies.

DOI: http://doi.org/10.5281/zenodo.17598876

PixelVerse: An Intelligent Web Platform For AI-Driven Text-to-Image Generation With Integrated Credit Management

Authors: Padhiyar Harshkumar B, Rana Drumilsinh M, Desai Vishalbhai S, Patel Jeet D, Ms. Anshika Saxena

Abstract: The rapid advancement of artificial intelligence has reshaped creative industries, particularly through text-to-image generation. Despite progress, most existing solutions remain either cost-prohibitive or technically complex, restricting access for students, researchers, and small enterprises. This paper introduces PixelVerse, a web-based Software-as-a-Service (SaaS) platform designed to democratize text-to-image synthesis. Built on the MERN stack, the system integrates Stable Diffusion via ClipDrop APIs, a secure credit-based monetization model using Razorpay, and robust authentication through JWT and bcrypt. Experimental deployment demonstrates an average image generation time of 4.2 seconds, 98.5% success rate, and scalability up to 80 concurrent users with consistent performance. A four- week user study yielded high satisfaction (System Usability Scale: 84/100, average rating: 4.3/5). These results validate PixelVerse as a production-ready framework bridging advanced AI models with accessible, secure, and economically sustainable applica- tions.

DOI: http://doi.org/10.5281/zenodo.17599642

Transmission Of Morse Code Using Cryptographic Method

Authors: Sunita Kulkarni, Preksha M, S Nandan, S Vishal, Chaluvaraj Naik

Abstract: In this paper, a safe Morse code communication performance based on ESP32 and the combination of cryptography methods is proposed. The system converts the input entered by the user to Morse code on the fly, encrypting the encoded message and transmitting it on air to a receiver module. Encryption and scrambling are done at the transmit end and at the receive end the data is decrypted and descrambled in text and also via voice and display. It features button-based morse input, display feedback (LCD screen) and optional speech processing for improved accessibility. Signal processing, encryption and communication are handled by the ESP32 microcontroller, which allows for low power and reliable operation. The experimental realization confirms that the system can be used for time critical encoding, transmission and decoding of messages in Morse with securing data. They are robust in settings with no or minimal network infrastructure, or in emergencies when traditional methods of communications break down. The usage of cryptography in morse transmission improves confidentiality, could be used in defense, disaster management and secured IoT communication applications.

EatoBot: Food Recommendation System

Authors: Suraj Solanki, Rutvi Patel, Chirag Prajapati, Vishal Kumar Singh

Abstract: The EatoBot: Food Recommenda- tion System is a smart application developed to assist users in making healthier and personalized food choices by leveraging Artificial Intelligence (AI), Machine Learning (ML), and cloud-based technologies. With the increasing demand for convenience, health awareness, and personalized dining experiences, traditional recommendation methods are unable to meet modern expecta- tions. EatoBot addresses this gap by providing an intelligent platform that integrates user prefer- ences, dietary restrictions, and nutritional needs to generate customized food suggestions. The system offers a mobile-friendly and interac- tive interface that allows users to explore a va- riety of cuisines and dishes, while receiving AI- driven recommendations based on taste prefer- ences, calorie intake, and health goals. It also supports QR-based menu scanning, real-time rec- ommendation updates, and smart filtering for vegetarian, vegan, or allergen-free options. Ad- vanced features such as automated meal plan- ning, nutritional analysis, and backend data stor- age for analytics enhance both user satisfac- tion and operational efficiency for food service providers. By combining AI algorithms with user-centered design, EatoBot aims to improve the overall food decision-making process, ensure balanced diet choices, and offer valuable insights to restaurants and health platforms. Future enhancements in- clude integration with fitness trackers, multilin- gual support, seamless payment gateway connec- tivity, and cloud scalability to manage large user bases effectively.

Sound-Driven Respiratory Disease Detection: A Deeper Exploration Of GRU-CNN Hybrid Models

Authors: Hrishikesh Shanbhag, Dr. Sonal Ayare

Abstract: Respiratory diseases, notably asthma, pneumonia, and chronic obstructive pulmonary disease (COPD), pose sig- nificant challenges globally. Conventional diagnostic techniques are often resource intensive, invasive, and not scalable for rapid screening. This paper presents a comprehensive deep learning framework using Mel-Frequency Cepstral Coefficients (MFCC) and a hybrid Gated Recurrent Unit – Convolutional Neural Network (GRU-CNN) architecture to detect and clas- sify respiratory diseases from audio samples. We elaborate on data preprocessing, feature extraction, model design, training strategy, and rigorous evaluation. Our approach achieves an overall accuracy above 91%, demonstrating robustness across multiple disease classes. The paper discusses technical insights, challenges in real-world adaptation, comparative analysis with existing methods, and future work to further enhance clinical applicability. Additionally, we explore the clinical implications and provide detailed implementation guidelines for healthcare practitioners and researchers.

Study Of Phytoplanktons And Analysis Of Physico-Chemical Parameters In Two Legendary Ponds Of Chitradurga Fort, Deccan Plateau, India

Authors: Akanksha S, Mamatha Hanagoudara, Chethana Shree B.K, Sudhama V. N.

Abstract: The two legendary ponds, Akka and Tangi, are located in Chitradurga Fort in Karnataka State. The study was aimed at knowing various physico-chemical characteristics and phytoplankton diversity in the ponds. The study was spread over a period of three months, from June to August 2024, to assess the quality of water. Qualitative and quantitative estimations of phytoplankton were considered when analyzing biological parameters. In the present study, pH (6.61-7.15 and 6.91-7.33), air temperature (20°C-28°C and 21°C-24°C), water temperature (21°C-24°C), and chloride (24.14-25.4 mg/L and 23.1-28.2 mg/L) were measured. Dissolved oxygen (6.8 mg/L and 8.8 mg/L), BOD values (1.49-1.63 mg/L and 1.34-1.56), calcium (118.7 mg/L to 126.5 mg/L and 120.1 mg/L to 139 mg/L), free CO₂ (1.2 mg/L-3 mg/L and 1.9 to 3.4), and magnesium (7.5-10.5 mg/L and 8.5-9.85) in Akka and Tangi ponds, respectively. Mainly six groups of phytoplankton were recorded; they are Cyanophyceae (03), Chlorophyceae (05), Bacillariophyceae (07), Xanthophyceae (01), Eustigmatophyceae (01), and Euglenophyceae (01). However, Bacillariophyceae is most prevalent in both the ponds. The results indicated a significant correlation between nutrient levels and phytoplankton growth; species diversity varied with seasonal changes, reflecting shifts in water quality.

DOI: https://doi.org/10.5281/zenodo.17606346

Molecular docking study of Hydantoin derivatives as anti-epileptic

Authors: Gopala Krishna Murthy H R, Madaiah M, Prema M, Sanjeevarayappa

Abstract: Epilepsy is a neurological disorder characterized by recurrent seizures, with approximately one-third of patients unable to achieve sustained seizure control despite the availability of numerous anti-epileptic drugs (AEDs). This highlights the urgent need for novel therapeutic approaches. In this study, an in-silico approach is employed to investigate the anti-epileptic potential of newly synthesized compounds. Hydantoin (imidazolidine-2,4-dione) derivatives are a valuable class of Nitrogenous heterocyclic compounds known for their diverse pharmacological potential, including antimicrobial, antidiabetic, anticancer, and antiepileptic properties. In this study, molecular docking simulations were performed to elucidate the binding affinities and interaction modes of newly synthesized hydantoin derivatives toward specific biological targets. Protein structures corresponding to Metabotropic glutamate receptor 1 [GRM1] selected based on reported bioactivities. Ligands were energy-minimized and docked using AutoDock Vina to predict the most stable binding conformations and binding energies. The results revealed that the selected two hydantoin derivatives exhibit significant binding affinities, stabilized by hydrogen bonding, π–π interactions, and hydrophobic contacts with active-site residues. These findings provide a rational explanation for the structure–activity relationships (SAR) of hydantoin analogues and support their further development as lead candidates in drug discovery. Computational techniques such as molecular docking results indicate that Compound 1 and 2 exhibits a docking score of -9.5kcal/mol, suggesting strong binding affinity with epilepsy-associated target proteins. This study underscores the potential of synthetic compounds as novel anti-epileptic agents. The findings highlight the ability of in-silico methods to accelerate drug discovery by identifying promising lead compounds, optimizing their properties, and providing a cost-effective alternative to traditional experimental approaches. These insights pave the way for the development of innovative therapeutic strategies for epilepsy management.

DOI: https://doi.org/10.5281/zenodo.17606656

Quantum Machine Learning Approaches For Large-Scale Credit Risk And Fraud Detection

Authors: Dr. Pankaj Malik, Adarsh Mishra, Diya Agrawal, Dushyant Pratap Singh, Garvita Gangwal, Aniruddh Sharma

Abstract: The increasing complexity and volume of financial transactions demand advanced analytical methods capable of ensuring accurate and real-time credit risk evaluation and fraud detection. Traditional machine learning models, though effective, struggle with scalability and computational efficiency when handling large, high-dimensional financial datasets. This paper presents a Quantum Machine Learning (QML) framework integrating Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) to enhance predictive accuracy and reduce training time in large-scale credit risk and fraud detection tasks. The proposed hybrid quantum–classical model leverages quantum parallelism to efficiently process nonlinear patterns and entangled relationships within financial data. Experimental results on benchmark credit scoring and fraud detection datasets demonstrate that the proposed QML framework achieves a 9.6% improvement in detection accuracy and a 17.3% reduction in computation time compared to conventional deep learning models. Additionally, the quantum-enhanced approach exhibits higher precision in identifying minority fraud cases, reducing false negatives by 12.5%. The findings confirm that QML provides a promising pathway for scalable, high-performance financial analytics, enabling faster and more reliable decision-making in credit risk management and fraud prevention. Future research will focus on optimizing quantum circuit depth and extending the framework for real-time deployment on Noisy Intermediate-Scale Quantum (NISQ) devices.

Managing Language Anxiety: Psychological Dimensions And Pedagogical Implications In Indian ESL Classrooms

Authors: Kalpana S. Singh, Assistant Professor

Abstract: This conceptual paper explores the complex relationship between classroom anxiety and communicative interaction in English as a Second Language (ESL) learning, focusing on Indian urban undergraduate contexts. It examines how psychological factors such as communication apprehension, fear of negative evaluation, and self-perceived linguistic inadequacy influence learners’ willingness to participate in classroom discourse. Drawing on theoretical frameworks like Krashen’s Affective Filter Hypothesis and MacIntyre and Gardner’s socio-educational model, the paper argues that anxiety acts as a significant barrier to communicative competence and learner engagement. It also emphasizes pedagogical strategies that can reduce anxiety, including supportive classroom environments, collaborative tasks, and positive feedback mechanisms. The discussion highlights the importance of teacher empathy, learner-centered pedagogy, and culturally responsive communication in mitigating affective barriers to language learning. The study concludes that addressing both psychological and pedagogical aspects of anxiety can significantly enhance communicative competence and learner confidence in ESL classrooms, particularly in multilingual Indian contexts where English functions as a language of aspiration and opportunity.

DOI: https://doi.org/10.5281/zenodo.17607630

GlobeTogether: A Web-Based Platform Uniting Travel Enthusiasts For Enriching Shared Experiences

Authors: Assistant Professor Yatin Shukla, Praneet Kashyap, Jitendra Devra, Suresh Vishnoi, Omkar Bhivare

Abstract: This paper introduces GlobeTogether, a web-based application designed to connect travel enthusiasts looking to share their journeys. At its heart, the platform intelligently matches users based on their destinations, travel dates, and personal interests. GlobeTogether tackles the common hurdles of solo travel—like higher costs, safety concerns, and feelings of isolation—by creating a secure and welcoming community of verified travelers. Key features include a simple user registration process, detailed profiles to capture travel preferences, a smart matching algorithm, and a powerful trip management system for creating trips and inviting others. Built on the MERN stack (React.js, Node.js, Express.js, and MongoDB), the system is both scalable and efficient. Our goal is a fully functional platform that makes travel more connected, affordable, and socially vibrant for everyone, especially solo and budget-conscious adventurers. Looking ahead, we plan to integrate AI-driven recommendations and launch dedicated mobile apps.

DOI: http://doi.org/10.5281/zenodo.17608760

Cryptocurrency Price Forecasting Using Machine Learning

Authors: Siddhesh Bhargude, Sai Kudale, Ganesh Jadhav, Akshay Patil

Abstract: The rapid evolution of cryptocurrencies has re- shaped global financial systems, attracting both investors and researchers toward the prediction of their highly volatile price patterns. Accurate forecasting of cryptocurrency prices is es- sential for informed investment and risk management decisions. This research focuses on the use of Machine Learning (ML) and Deep Learning (DL) techniques to predict cryptocurrency prices, specifically Bitcoin and Ethereum, using a Long Short-Term Memory (LSTM) neural network. The model captures temporal dependencies in time-series data through sequential learning and minimizes prediction error using adaptive optimization techniques. Historical Open, High, Low, Close, and Volume (OHLCV) data are preprocessed and normalized for efficient model training. Experimental results show that the proposed LSTM model achieves an accuracy of over 98% (R2 score) and demonstrates robustness under dynamic market conditions. This study emphasizes the capability of ML-driven models in financial forecasting and suggests pathways for enhancing real-time crypto analytics and automated trading systems.

DOI: http://doi.org/10.5281/zenodo.17608907

Case Studies Of Highway Pavement Crack Recognition Under Complex Environment

Authors: Harsh Tomar, Professor Jitendra Chouhan

Abstract: Pavements are complex structures involving many variables, such as materials, construction methods, loads, environment, maintenance, and economics. Thus, various technical and economic factors must be well understood to design, build pavements, and to maintain better pavements. Moreover, the problems relating to pavement maintenance are still complex due to the dynamic nature of road pavements where elements of the pavement are constantly changing, being added or removed. These elements deteriorate with time and therefore to be maintained in good condition requires substantial expenditure.

Assemble: A Scalable Interest-Driven Social Media Platform For Real-Time Collaboration, Personalization, And Community Co-Creation

Authors: Karan Rohit, Kush Patel, Kaval Rathod, Amba Parmar, Prof. Ami Shah

Abstract: Social media platforms have transformed how peo- ple communicate, collaborate, and share ideas, yet their primary focus remains entertainment-driven consumption rather than purposeful collaboration. Existing solutions like Reddit and Discord enable interest-driven discussions but lack integrated tools for real-time content co-creation. Similarly, productivity suites like Google Docs support synchronous editing but offer limited opportunities for community engagement and discovery. This paper presents Assemble, a next-generation mobile-first platform built to support structured collaboration through three key innovations: (1) a hybrid recommendation system leveraging user embeddings and interaction graphs for personalized group and content discovery, (2) a CRDT-based real-time collaboration engine enabling offline editing with seamless synchronization, and (3) a gamification framework incentivizing meaningful partici- pation. Assemble integrates scalable cloud-based microservices, distributed caching, and security-focused authentication systems, making it capable of supporting large-scale user bases. We provide a detailed technical breakdown of Assemble’s architecture, algorithms, and pilot evaluation. A four-week user study with 20 participants showed measurable improvements: Precision@10 increased from 0.52 to 0.78, group setup time decreased by 42%, and engagement improved by 35%. We also explore algorithmic fairness, ethical implications, and scalability considerations. Assemble demonstrates that social platforms can evolve beyond entertainment to become hubs for innovation and co-creation.

DOI: https://doi.org/10.5281/zenodo.17614541

Research Paper On Effect Of Digital Payment Platform On Retail In Punjab

Authors: Dr.Kavita Arora, Mr.Sarabhpreet Singh

Abstract: The swift growth of digital payment platforms in India — driven by the Unified Payments Interface (UPI), e‑wallets, and QR‑code based solutions — has reshaped the nation's retail sector. Punjab, featuring a blend of urban and rural economies, distinct retail hubs (Ludhiana, Jalandhar, Amritsar) and robust agricultural connections, offers a representative setting to examine how digital payments influence retail operations, consumer behavior, company performance, and financial inclusion. This study explores the magnitude, determinants, advantages, and obstacles of digital payment uptake among Punjab's retail enterprises, and evaluates both micro and macro effects: revenue, average transaction value and frequency, expenses (cash handling, reconciliation), formalization, vendor‑customer interactions, and the inclusion of women and small sellers. Employing a mixed‑methods design (surveys of merchants in both urban and rural areas; semi‑structured conversations with shop owners, consumers, and bank/PSP representatives; secondary review of UPI and state digital‑payment statistics), the research measures adoption rates, evaluates effects on revenue and expenses, and identifies obstacles (digital literacy, connectivity, trust, charges). Early data from recent state and national sources reveal a sharp rise in digital transactions across the country and within the state; localized case studies (Ludhiana/Phagwara) show notable shifts in retail payment habits while still highlighting ongoing limitations for smaller sellers. The results will provide policy and practical guidance to enhance merchant enrollment, lessen friction, and leverage digital payments to bolster retail resilience in Punjab,

Assessing Schools With Respect To Nutrition Friendly Initiatives

Authors: Saniya Shahana Firdaus, Dr. Vaijayanthi Kanabur, Majida Baig, Thokchom Sughani Devi, Tamanna Hussain

Abstract: The Nutrition-Friendly Schools Initiative (NFSI) is a program developed by the World Health Organization (WHO) and its partners in 2006 to provide a framework for ensuring integrated school-based programs which address the double burden of malnutrition (both under nutrition and obesity) and to become the nutrition module of the Health Promoting Schools, by implementing integrated school-based programs that promote healthy eating habits and nutrition education. Objectives: To assess the school curriculum with respect to nutrition and health components; school environment with respect to nutrition; and the school nutrition and health services. Methods: A total sample of 110 schools following different curriculums were selected by random sampling in urban Bengaluru, The research tool developed by WHO (2006) was selected the tool has 26 criteria identified in 5 core components. It was modified to suit local conditions. Results: The finding of the present study showed that all the school curriculums introduce nutrition related topics such as introduction to food from the primary classes and topics such as deficiency diseases and its prevention are taught in-depth in higher classes. Out of 110, 93 per cent (85) of schools have their own playground, 30 per cent (33) have canteen facilities in the campus, 75 per cent (82) of schools have a written nutrition policy and 90 per cent (99) of the schools have a no junk food rule in the campus. The score of nutrition friendly initiatives for schools following different curriculums such as State Government (20), State Private (25), ICSE (25), CBSE (25) and IGCSE (15) ranged from 12-25 out of the 26 criteria. Conclusion: There was a wide variation with respect to nutrition friendly initiatives across different curriculums. The high score reflects a strong commitment to promoting student health and well-being, creating a nutrition friendly environment that supports lifelong healthy habits.

DOI: http://doi.org/10.5281/zenodo.17614981

Influence Of Caesium (Cs) On The Structural Properties Of Manganese Ferrite

Authors: Dinesh V

Abstract: A series of Caesium (Cs)-doped (alkali metal ion) Mn ferrites was synthesized using solution combustion method. Characterization of the investigated ferrites was performed using several techniques, specifically, X-ray Powder Diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR). XRD-based structural parameters were determined. A closer look at these characteristics reveals that cesium (Cs) doping enhanced the manganese ferrite lattice constant (a), unit cell volume (V), and dislocation density (δ). It also enhanced the separation between magnetic ions (LA and LB) and bond lengths (A-O and B-O) between tetrahedral (A) and octahedral (B) locations. Furthermore, it enhanced the X-ray density (Dx) and crystallite size (d) of random spinel manganese ferrite displaying opposing patterns of behavior. FTIR-based functional groups of random spinel manganese ferrite were going to be determined. These characteristics of MnFe2O4 particles, such as their size, shape, and crystallinity, demonstrate that these manufactured particles are present at the nanoscale and that caesium doping caused shape modification of the particles. The magnetization of manganese ferrite fell with a corresponding increase in coercivity. In this paper we are presented only the XRD analysis of the samples.

DOI: http://doi.org/10.5281/zenodo.17614977

Prevalence And Determinants Of Substance Abuse Among Indian Adolescents: A Review

Authors: Dr. Sarita M, Karthikeyan P Chand, Dr. Vijaya Kumar D. R

Abstract: This article explores the effects of substance misuse among adolescents in India. Substance abuse is an area of growing concern having a direct impact on the health of the people as well as an indirect impact on the social and economic status of the country. Over the time, substances are used in an unhealthy way to respond to various stress and anxiety among the people. Substance use among adolescents has been widely documented across the country. Numerous researchers have carried out different aspects of substance use such as initiation, prevention or intervention, pattern and prevalence, effects and implications and, knowledge- attitude mapping. etc. However, it is observed most of the studies focus on tobacco and alcohol use and: There is limited evidence on the extent of use of other substances among adolescents.

DOI: http://doi.org/10.5281/zenodo.17615039

Comprehensive Guide To Centralized E-Resource Management In Higher Education In India

Authors: Sudhakar Sundararajan

Abstract: This guide provides a detailed framework for implemented centralized e-resource management systems in Indian higher education institutions. With the rapid digitization of educational resources and the increasing demand for remote access to learning materials, a centralized approach to e-resource management has become essential for modern universities and colleges. This document outlines the benefits, challenges, best practices, technological solutions, and strategies for stakeholder engagement to help institutions develop effective e-resource management systems that enhance learning outcomes and institutional efficiency.

DOI: https://doi.org/10.5281/zenodo.17615834

Plant Disease Prediction in Agriculture: Using Artificial Intelligence

Authors: R. Dhanush, S. Cynthia Juliet

Abstract: Plant diseases pose a significant threat to agricultural productivity, resulting in substantial economic losses and food insecurity. The early detection and precise diagnosis of these diseases are critical to mitigating their impact. This research paper explores the integration of Artificial Intelligence (AI) technologies in the prediction and detection of plant diseases. By utilizing advanced machine learning algorithms, image processing techniques, and large datasets, AI can accurately identify disease symptoms from plant images. The system is designed to help farmers and agricultural experts take preventive actions by identifying infections at an early stage, thereby reducing the need for excessive pesticide use and improving crop health. In this study, a convolutional neural network (CNN) model is implemented to classify images of plants and predict diseases. The system's performance is evaluated based on its accuracy, efficiency, and real-world applicability. This AI- driven solution offers a scalable approach to sustainable agriculture by enabling precise disease management, enhancing yield, and supporting food security. The findings suggest that AI-based plant disease prediction can revolutionize agricultural practices by providing real-time insights and actionable recommendations to farmers.

Study On Prevalence of Uropathogenic Bacteria in Urine Samples of Patients with Urinary Tract Infection

Authors: Mr. Dharmvir A. Chouhan, Dr. Pradeep M. Tumane, Dr. Durgesh S. Wasnik

Abstract: Urinary tract infection is a commonest infection among women. Despite treatment with antibiotics, UTI is often recurrent. Objective of current study is to check occurrence of uropathogenic bacteria in urine specimens of patients with early symptoms of urinary tract infection. In this study total 237 urine samples were analyzed for occurrence of uropathogenic bacteria. From these urine samples, 164 samples were found to be positive for presence of uropathogenic bacteria. Total positivity rate of samples for uropathogenic bacteria is 69.20 % (164/237). Most of samples having mono-bacterial infection. Positivity rate of male is 53.13 % (34/64) and for female is 75.14% (130/173). Among female patients’ pregnant patients have higher positivity rate -77.96% (46/59) compared to non-pregnant female patients-70.17 % (80/119). Highest positivity rate is reported in old age patients- 100 % positivity reported in 71-80 years and 81-90 years patients. Least positivity (69.15 %) reported in patients with age group 21-30 years. Gram negative bacteria are predominant uropathogen in current study. 73.89 % uropathogenic bacteria are gram negative and 26.11 % are gram positive. E. coli is predominant uropathogenic bacteria (24.5 %) followed by Proteus spp. (16.5 %), S. aureus (11.8 %), Klebsiella spp. (8.4 %), Enterococcus spp. (8.0 %), Enterobacter spp. (4.2 %) and Pseudomonas spp. (2.5 %). Isolates of uropathogenic bacteria are identified based on morphological, biochemical and cultural characteristics.

DOI: https://doi.org/10.5281/zenodo.17617377

Virtual Box Interview System

Authors: Abhirami.J.S

Abstract: Virtual interviewing is a standard method for first round of screening providing interviewers with an efficient, fair, and structured method for conducting interviews. Virtual interviews utilize technology to equip hiring personnel to interview candidates who are not able to do a traditional face -to-face interview or candidates that align with a prospective position that may be a full or partime telecommuting opportunity. These types of interview also allow interviews to that are restrained by time and place making the recruiting process more efficient in discovering and employing talent. Emotions, in everyday speech, a person's state of mind and instinctive responses. Emotion is also linked to Behavioral, Speech tone and facial expressions. The Virtual Interview System is an integration of web and android applications. Here the interviewer is a chat bot, and can recognize the facial emotions of jobseeker by using the technology, artificial intelligence. Here, we have to computerize our process where each and everything is done systematically and computerized. The Virtual Interview Management module assists in capturing all-relevant information about the jobseekers which is automatically captured in a database, and a professional quality temporary disposable/photo Jobseeker badge is printed. No need to encode regular Jobseekers again.

DOI: https://doi.org/10.5281/zenodo.17617943

Integrated Process Of Hydrogenation Of Furfural And Dehydrogenation Of 1.4-butanediol Over Cu/MgO Catalyst

Authors: JongChol Ri, HyokSu Ri, SongJin Kang, KangHyok Kim

Abstract: The local temperature rise of the catalyst due to exothermic reaction in the gas-phase hydrogenation of furfural was designed to suppress and realize temperature stabilization using endothermic reaction by dehydrogenation of 1.4-butanediol. The simultaneous hydrogenation of furfural and the dehydrogenation of 1.4-butanediol using Cu/MgO catalyst in one reactor was constructed and the effect of mixture ratio of furfural and 1.4-butanediol in the feedstock, reactor pressure and inlet temperature on the adiabatic temperature change of the reactor was analyzed. When the mixture ratio of furfural and 1.4-butanediol feed was 1:1.2, the temperature change in the 1 m range of reactor length was about 20–30 °C, and the hydrogen produced in the dehydrogenation of 1.4-butanediol was used for the hydrogenation of furfural, while the conversion of furfural was over 99% and the selectivity of furfural alcohol was over 95%.

Integrating Quantum Dot Imaging Into UAVs For Tactical Hyperspectral Reconnaissance

Authors: Kabir Kohli

Abstract: Quantum dot (QD)-based hyperspectral imaging transforms unmanned aerial vehicle (UAV) reconnaissance by enabling high-resolution spectral detection across a wide wavelength range. Traditional imaging systems often face sensitivity and spectral coverage limitations, but QDs offer tunable optical properties that significantly enhance imaging precision. By leveraging their size-dependent bandgap and high quantum efficiency, QDs enable hyperspectral sensors to detect subtle variations in material composition, making them ideal for military surveillance. Quantum dots exhibit strong photoluminescence, broadband absorption, and carrier multiplication, contributing to their exceptional imaging capabilities. Their integration into UAV-based hyperspectral systems involves optimizing material synthesis, surface passivation, and sensor engineering to ensure long-term performance. Advanced fabrication techniques, including core-shell structures and ligand engineering, enhance stability and signal clarity, making QD sensors more reliable in diverse environmental conditions. QD-enhanced hyperspectral imaging supports critical military applications such as battlefield monitoring, threat identification, and stealth surveillance. These sensors can detect concealed objects, differentiate camouflaged materials, and accurately identify hazardous substances. Analysing spectral fingerprints enables UAVs to operate in low-visibility and high-risk environments, providing real-time intelligence for tactical decision-making. Despite their advantages, QD-based sensors face challenges related to environmental stability, real-time data processing, and large-scale manufacturing. Future advancements in AI-driven hyperspectral analytics and improved QD synthesis will refine UAV reconnaissance, enhancing efficiency and adaptability in modern defence operations.

DOI: http://doi.org/10.5281/zenodo.17636980

Crime Matrix: An Interactive Platform for Crime Reporting and Analysis

Authors: Bogari Shiva Pavani, Atluri Syanthi, Kondur Babitha, G Krishna Vamshi, Asst. Prof. Arunesh pratap Singh

Abstract: The Crime Matrix Project is a web-based system of crime monitoring and management distributed among the Indian police departments to provide police with a centralized base of officers to log in and manage cases, compute statistics and monitor nationwide alerts. Its capabilities, such as FIR uploads, case management, role-based access and real-time data visualization using interactive dashboards and crime mapping allow quick decision-making, enhanced interstate coordination and resource distribution. In addition to operational advantages, the system aids police and policymakers with pattern iden- tification, crime hotspots, and patterns seen on easy to use dashboards, aiding in the planning of preventive measures and promoting community safety. In a wider scope, the project examines how technology can be integrated in the contemporary law enforcement by using online crime reporting platforms, role based data platforms, social media appli- cations, crime analytics, and data security frameworks. Whereas online reporting offers greater accessibility and CRMS greater accountability, social media offers greater commu- nication and intelligence-gathering, and data visualization offers actionable information that is used in proactive policing. Such frameworks as Crime Matrix have been used to emphasize the significance of geospatial analysis and interactive dashboards and research on data security has been conducted to outline the need to have firmly secured privacy, ethical regulation, and legal protections. These innovations will transform the policing practices, reinforce crime prevention, and improve the trust of the people together, but concerns of privacy, accessibility, and interoperability are maintained. The project arrives at a conclusion that sustained innovation, secure systems, and user-friendly designs are needed to develop clear, dependable, and future-oriented crime analysis and management systems.

Evaluating Life Time Models: A Comparative Study Of The Inverse Weibull And Inverse Lognormal Distributions

Authors: Chukwudi Anderson Ugomma, Samuel Chimuanya Chijioke

Abstract: This study presents a comparative analysis of the Inverse Weibull and Inverse Lognormal distributions using both simulated and real-world data with Maximum Likelihood Estimation (MLE). Model selection criteria included Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Anderson-Darling (AD), and Kolmogorov-Smirnov (KS) tests. Six simulated sample sizes (50, 100, 150, 200, 250, and 500) were used, with 1000 replications each. Results showed the Inverse Lognormal distribution consistently had lower AIC and BIC values at small to moderate sample sizes. Furthermore, real-world stock price data (sample size 100) from the Nigerian Stock Exchange was analyzed. Descriptive statistics and goodness-of-fit tests favored the Inverse Lognormal model. These findings support the utility of AIC, BIC, AD, and KS in lifetime data modeling and model selection.

DOI: https://doi.org/10.5281/zenodo.17627972

Smart Driver Monitoring System

Authors: Patel Abdul Rauf, Harsh K Oad, Vamshi Gangadhar, Nitish Kumar, Dhruv Patel, Khyati Zalawadia

Abstract: Cutting-edge technologies are fundamentally transforming automobiles into perceptive systems that dynamically respond to driver states. By harnessing computer vision algorithms and integrated sensor arrays, vehicles can now deploy proactive safety mechanisms for occupants. Despite existing traffic regulations and preventive measures like speed warnings or awareness campaigns, fatigue-induced attention lapses continue to cause accidents. This research introduces a real-time Driver Awareness and Intervention System featuring a YOLOv8-optimized camera with OpenCV for facial and ocular fatigue detection. Activation occurs exclusively during motion via integrated vehicle sensors. When drowsiness is identified through abnormal eye patterns or head posture, an audible buzzer alerts the driver. Should no response occur within five seconds, dual countermeasures engage: the vehicle initiates-controlled deceleration while simultaneously transmitting emergency notifications via GSM. As velocity decreases, ultrasonic environmental scanners evaluate proximate objects to optimize braking intensity. Simultaneously, high-sensitivity collision sensors command urgent full braking and auto-deploy SMS incident notifications upon impact confirmation. This unified architecture enables continuous driver assessment and automated risk mitigation. By converging sensing technologies with responsive control, the solution prevents collisions proactively. Such real-time monitoring elevates modern vehicle safety standards and could be integrated into future autonomous platforms to enhance user protection.

AI-Driven Fulfillment Path Optimization In Omnichannel Retail: How Machine Learning And Data-Driven Supply Chain Engines Can Be Used.

Authors: Rui Zhao, Odu Lynda Nneoma

Abstract: The evolution of omnichannel retail has created unprecedented complexity in fulfillment operations, where retailers must simultaneously manage multiple channels while maintaining competitive delivery promises. This study examines how artificial intelligence (AI) and machine learning (ML) technologies can optimize fulfillment path design to minimize promise-time breaches while balancing operational costs and service levels. Through systematic analysis of current literature and industry implementations, we identify key algorithmic approaches including reinforcement learning, predictive analytics, and optimization algorithms that enable dynamic routing decisions. Our findings demonstrate that AI-driven fulfillment systems can reduce promise-time breaches by 15-30%, decrease operational costs by 10-25%, and improve customer satisfaction by 20-35% compared to traditional static routing methods. The research contributes to supply chain management theory by providing a comprehensive framework for implementing AI-driven fulfillment optimization in large-scale retail environments such as Walmart, grocery chains, auto care, and pharmaceutical retailers (PetRx). Practical implications include actionable strategies for retail executives seeking to modernize their fulfillment infrastructure and enhance omnichannel capabilities.

DOI: https://doi.org/10.5281/zenodo.17629562

Beyond Herbicides: Emerging Biotechnology Solutions For Environmentally Safe Weed Management

Authors: Dr. Ajay Kumar Singh, Akhilesh Kumar Pandey

Abstract: Invasive weeds continue to pose a major threat to global agriculture, causing substantial yield losses, intensifying production costs, and destabilizing ecosystems. The effectiveness of conventional chemical herbicides is increasingly constrained by the rapid evolution of herbicide-resistant weed biotypes and mounting environmental and regulatory pressures. These limitations have accelerated the pursuit of next-generation, sustainable weed management strategies. Among the most promising innovations, RNA interference (RNAi)–based herbicides and advanced genetic engineering technologies offer highly targeted, environmentally compatible alternatives to synthetic chemical herbicides. RNAi provides a precise mode of action by silencing essential weed genes through sequence-specific degradation of target mRNAs. The non-genomic RNAi approach, which utilizes externally applied double-stranded RNA (dsRNA), has emerged as a safe, flexible, and regulation-friendly strategy. When delivered through foliar sprays, soil applications, seed coatings, or nanoformulations, exogenous dsRNA activates endogenous RNAi pathways without introducing transgenes—enhancing biosafety, reducing ecological risk, and enabling species-specific control. Complementary genetic engineering technologies—including CRISPR-based gene drives that modulate weed population dynamics and phosphite metabolism engineering that provides nutrient-use exclusivity to engineered crops—further broaden the biotechnological toolbox for sustainable weed management. Collectively, these approaches represent a paradigm shift toward precision-driven, eco-friendly weed control. This review synthesizes recent advances in RNAi-mediated and genetically engineered weed suppression, evaluates ecological and regulatory implications, and identifies research priorities for integrating biotechnology-based solutions into modern sustainable agriculture.

DOI: https://doi.org/10.5281/zenodo.17630914

Blocksphere: A Decentralized Social Media Platform

Authors: Rahul Patel, Madhav Tiwari, Harshita Reddy, Prof. Shubham Upadhyay

Abstract: BlockSphere is a decentralized social media platform designed to transform online interactions using Web 3.0 technologies. Unlike traditional platforms that depend on centralized servers and third-party control, BlockSphere focuses on user privacy, data ownership, and resistance to censorship through blockchain and decentralized storage. The system is built on the Ethereum blockchain and uses Solidity- based smart contracts to handle user authentication, content management, engagement features, and financial transactions. To secure media storage and prevent tampering, BlockSphere integrates the Inter Planetary File System (IPFS), enabling safe storage and retrieval of user data. Posts are created and stored on IPFS, ensuring integrity and transparency. Real-time communication is supported through blockchain-powered messaging, providing private and immutable chats. Smart contracts also govern the follow/unfollow mechanism for transparency, while token-based access allows exclusive content sharing in premium groups. The frontend, developed with Next.js and Web3.js, delivers smooth wallet integration, gas fee optimization, and compatibility across devices. Reliability is ensured through extensive testing with Hardhat on local and Ethereum testnet environments before deployment to the mainnet. By prioritizing decentralization, transparency, and user autonomy, BlockSphere offers a new model for social media. Planned upgrades include Layer 2 scaling, cross-chain support, NFT-based monetization, and DAO-driven governance, further positioning BlockSphere as a leading decentralized networking platform. Keywords— Web 3.0; Decentralized Social Media; Blockchain; Smart Contracts; IPFS; Ethereum.

Virtual-Play-Zone: A Comprehensive Cloud Gaming Platform with Advanced Real-Time Multiplayer Architecture and Cost-Optimized AWS Integration

Authors: Saniya Patil, Diya Patel, Tanisha Parmar, Happy Kataria, Asst.Professor Dr.Dinesh Swami, Asst.Professor Dr.Dinesh Swami

Abstract: The exponential growth of cloud computing infrastructure, combined with the increasing demand for hardware-independent gaming experiences and the global shift towards remote accessibility, has cre- ated unprecedented opportunities for innovative cloud gaming solutions that transcend traditional com- putational limitations. This comprehensive research presents VIRTUAL-PLAY-ZONE, an advanced, production-ready cloud gaming platform meticulously architected using the MERN (MongoDB, Ex- press.js, React, Node.js) technology stack and strategically designed for cost-effective deployment on Amazon Web Services (AWS) Free Tier infrastructure, addressing critical gaps in accessible, high- performance cloud gaming solutions. The platform successfully addresses fundamental challenges in contemporary cloud gaming includ- ing ultra-low latency optimization (achieving consistent sub-100ms response times), sophisticated real- time multiplayer synchronization across distributed networks, horizontal scalability supporting 500+ concurrent users, and economic accessibility for educational institutions and independent developers. Through meticulous implementation of WebSocket-based bidirectional communication protocols, adap- tive client-server architecture utilizing microservices design patterns, intelligent resource management algorithms with predictive scaling capabilities, and advanced caching mechanisms, VIRTUAL-PLAY- ZONE consistently delivers professional-grade gaming experiences while maintaining operational costs at zero dollars per month during initial deployment phases. The system demonstrates comprehensive support for diverse game genres including real-time battle arena competitions with complex physics engines, strategic rock-paper-scissors tournaments with tour- nament bracketing, knowledge-based trivia challenges with dynamic question generation, and classic chess gameplay with advanced AI opponents. All games are seamlessly integrated with sophisticated user management systems featuring JWT-based authentication, dynamic achievement frameworks with over 50 unlockable achievements, competitive leaderboard mechanisms with global and friend-based rankings, and comprehensive analytics dashboards providing real-time performance insights. Extensive performance evaluation conducted across various network topologies (fiber, cable, DSL, 4G, WiFi, 3G, and satellite), diverse device configurations (desktop, mobile, tablet), and multiple user load scenarios (50-1000 concurrent users) validates the platform’s exceptional capability to maintain responsive gameplay characteristics even under stress conditions. The research methodology employs rigorous testing protocols including latency measurement analysis, throughput optimization studies, resource utilization monitoring, and comparative benchmarking against commercial cloud gaming plat- forms. The research makes significant contributions to the cloud gaming domain by providing a thoroughly documented, practically implementable, and academically rigorous solution that democratizes access to high-quality gaming experiences regardless of underlying hardware limitations, geographic location, or economic constraints. The open-source nature of the platform, combined with comprehensive de- ployment documentation and educational resources, enables widespread adoption and further research in cloud gaming technologies, making it particularly valuable for academic institutions, individual de- velopers, and organizations seeking cost-effective gaming solutions.

Big Data Analytics In Fraud Detection And Prevention In Banking: Transforming Financial Security In The United States

Authors: Imoisili Lucky Oseremen

Abstract: The financial services sector in the United States faces unprecedented challenges in combating sophisticated fraud schemes that evolve with advancing technology. Big data analytics has emerged as a critical weapon in the arsenal against financial fraud, offering banks the capability to process vast amounts of transactional data in real-time and identify suspicious patterns with remarkable accuracy. This article examines the current state of big data analytics implementation in fraud detection and prevention within the U.S. banking sector, analyzing the technologies, methodologies, and outcomes that are reshaping financial security. Through comprehensive analysis of machine learning approaches, real-time processing capabilities, and regulatory compliance frameworks, this study demonstrates how big data analytics is not merely enhancing traditional fraud detection methods but fundamentally transforming the landscape of financial crime prevention

DOI: http://doi.org/10.5281/zenodo.17636866

Chemoresistive SO2 Gas Sensor With Improved Sensing Performances By Modification Of Copper Hexacyanoferrate To PANI/SnO2

Authors: MyongHyok Kim, JongSung Pak, KwangMyong Choe

Abstract: To improve the sensing performances of chemoresistive SO2 gas sensor, we prepared PANI/SnO2/CuHCF electrode by the in-situ synthesis method. The prepared PANI/SnO2/CuHCF electrode exhibited high linearity for SO2 in the range of 0-100 ppm, response time of 20 s, recovery time of 40 s, and good reproducibility. The comparison of the sensing performances of PANI/SnO2/CuHCF and PANI/SnO2 electrodes confirmed that PANI/SnO2/CuHCF performed better than PANI/SnO2 in terms of the reproducibility, linearity and selectivity.

DOI: http://doi.org/10.5281/zenodo.17637526

Machine Learning And Predictive Analytics: A Hyper-Personalization Model For Optimizing End-User Computer Procurement

Authors: Kevin K. Karanja, Andrew Kipkebut

Abstract: Abstract- End-users and institutional buyers consistently purchase sub-optimal computer devices due to a critical technical comprehension barrier and reliance on biased, product-centric advice. This inefficiency leads to the prodigality of device specification wasted financial resources on incompatible or over-specified hardware. This study addresses the research gap by developing and validating a hyper-personalization model that translates non-technical user needs into precise hardware specifications. A mixed-method design (design science research) was employed, starting with an empirical survey (n=32) that confirmed 84% of users struggle with technical metrics. The solution a hybrid conversational model powered by BERT-based Natural Language Processing (NLP) was developed and tested. Validation demonstrated high Accuracy (91%) in matching user intent to specifications, alongside high user acceptance for transparency and ease of use. The model provides a non-biased, effective solution, significantly enhancing user satisfaction and resource utilization in the device procurement lifecycle.

A Hyper-Personalization Model for Overcoming the Technical Comprehension Barrier in End-User Computer Device Specification

Authors: Kevin K. Karanja, Andrew Kipkebut

Abstract: Abstract- End-users and institutional buyers consistently purchase sub-optimal computer devices due to a critical technical comprehension barrier and reliance on biased, product-centric advice. This inefficiency leads to the prodigality of device specification wasted financial resources on incompatible or over-specified hardware. This study addresses the research gap by developing and validating a hyper-personalization model that translates non-technical user needs into precise hardware specifications. A mixed-method design (design science research) was employed, starting with an empirical survey (n=32) that confirmed 84% of users struggle with technical metrics. The solution a hybrid conversational model powered by BERT-based Natural Language Processing (NLP) was developed and tested. Validation demonstrated high Accuracy (91%) in matching user intent to specifications, alongside high user acceptance for transparency and ease of use. The model provides a non-biased, effective solution, significantly enhancing user satisfaction and resource utilization in the device procurement lifecycle.

Improvement of the Higher Heating Value Prediction of Biomass based on Proximate Analysis using Regression and Neural Network

Authors: Se Ung Kim, Jong Ryong An, Un Dok Kim, Won Il Ri, Yong Nam Kim

Abstract: – Based on proximate analysis, A study on a regression model to predict the higher heating value (HHV) of biomass was performed. The aim of study is to create the HHV prediction model by regression, thereby improve its prediction performance through modeling on new samples of biomass with wide range in contents of components, and to confirm the result through comparison this model with previous models. The regression model HHV = 7.9457 – 0.0477 × ASH + 0.2839 × FC + 9.7416 E-4 × VM^2 was created by the standard least squares (SLS) method and in result the coefficient of determination (R2), adjusted R2 and the root-mean-square error (RMSE) showed excellent fitness with 0.990, 0.989, and 1.578 for samples with distribution ranges of FC, VM and ASH of 3.86-91.5%, 6.6-87.8% and 0.2-70%, respectively. Sensitivity analysis of the HHV prediction with the number of neurons in the neural network (NN) model showed the best results when the number of neurons was 5.

DOI: https://doi.org/10.5281/zenodo.17639070

Improved ionic conductivity of Na3.2+2xMgxZr2-xSi2.2P0.8O12(x=0, 0.025, 0.05, 0.075, 0.1) solid electrolyte by microwave sintering

Authors: Kwang Myong Choea, Jong Sung Paka, Myong Hyok Kima

Abstract: In this paper, we prepared Mg-doped sodium superionic conductors Na3.2+2xMgxZr2-xSi2.2P0.8O12 (x=0, 0.025, 0.05, 0.075, 0.1) by solid state synthesis and investigated the effect of microwave sintering on their ionic conductivity. Sintering was carried out at 1 100 °C for 1 h using a 2.45 GHz microwave sintering furnace, and the phase composition, microstructure, density and ionic conductivity were compared with those of the samples sintered at 1250 °C for 5 h in conventional sintering method. XRD analysis showed that the main phase in microwave sintered samples was a monoclinic C2/c NASICON phase for x≤0.05, and a rhombohedral crystalline R3c NASICON phase for x>0.05. There also existed a secondary phase, the Mg-doped Na3PO4 phase, though weak. The maximum ionic conductivity at room temperature for microwave sintered samples was found to be 3.86 mS cm-1 for the sample with a composition of Na3.3Mg0.05Zr1.95Si2.2P0.8O12, which was improved by about 37% compared to the maximum ionic conductivity of 2.82 mS·cm-1 for the sample with a composition of Na3.35Mg0.075Zr1.925Si2.2P0.8O12 in the conventional sintering mode.

DOI: https://doi.org/10.5281/zenodo.17640216

Preparation of superhydrophobic copper film with excellent corrosion resistance by electrochemical deposition

Authors: YongNam Kim, JangHak Kim, WonIl Ri, KyongChol Kim, JongGuk Kim

Abstract: Superhydrophobic copper film with excellent corrosion resistance were successfully prepared by forming the micro/nanostructure and modifying hydrophobic capric acid by one-step electrochemical deposition. The prepared superhydrophobic copper film exhibited excellent self-cleaning capability and corrosion resistance with a water contact angle of 151.5°. Compared with the original copper plate, the corrosion current of the fabricated superhydrophobic copper surface is about 1/50 of the original copper plate.

DOI: https://doi.org/10.5281/zenodo.17640450

Breast Cancer Detection Using Uwb Antenna

Authors: Shwethaa.A.R, Ashwini GV, Sushmitha AV, Manya Shree R

Abstract: Breast cancer remains one of the leading causes of mortality among women globally, highlighting the necessity for early and accurate diagnostic techniques. This paper presents the design and development of a denim-based Ultra-Wideband (UWB) microstrip patch antenna for non-invasive breast cancer detection. The antenna functions across the 3.1–10.6 GHz band, ensuring superior resolution and deeper tissue penetration. Both simulation and experimental studies using a breast phantom demonstrate variations in return loss (S11) and voltage standing wave ratio (VSWR) between normal and tumor-affected tissues. The findings validate the antenna’s effectiveness as a cost-efficient, flexible, and portable diagnostic tool, offering a promising alternative to traditional imaging systems. Future enhancement includes clinical validation and machine learning integration for advanced tissue classification.

Empirical Study On the Relationship Between Government Expenditures and Nigerian Gross Domestic Products (1999 – 2022)

Authors: Chukwudi Anderson Ugomma, Vitus Chinonyerem Onyeze

Abstract: This study examines relationship between government expenditures and the Nigerian Gross Domestic Product (GDP) from 1999 to 2022. The data for this study were obtained from Central Bank of Nigeria Statistical Bulletin. Multiple Linear Regression model was adopted for the study to determine the relationship between the GDP, government final consumption expenditure, government private consumption expenditure and indirect taxes and the result showed that there is a significant relationship with the p-value (0.005). The result also shows with Ordinary Least Square(OLS) method that significant relationship exists between Nigerian GDP, Government final consumption expenditure and Government private consumption expenditure while indirect taxes has no relationship with the Nigerian GDP for the years of study. The result of multiple coefficient of determination for this study is 98.64 percent which indicates that government final consumption, government private consumption and indirect taxes expenditures accounted for about 98.6 percent of the total variation in the GDP for the period of study.

DOI: https://doi.org/10.5281/zenodo.17646037

Building The Feature Of Web Design: A Digital Platform For Client–Developer Collaboration

Authors: Suraj Kumar, Pikesh Kumar, Shreyas Waigonkar, Nilesh Baria, Professor Bilalkhan Raufkhan Pathan

Abstract: Modern web design has evolved beyond static layouts to dynamic, feature-rich, and interactive platforms. However, many small and mid-scale web projects still lack proper collaboration between clients and developers, leading to unclear requirements and inefficient design cycles. This paper presents Build- ing the Feature of Web Design, a platform designed to bridge this gap by creating an interactive space where clients and developers can collaborate efficiently through feature tracking, project boards, and commu- nication tools. The platform integrates essential modules such as authentication, project progress tracking, feedback boards, and client requirement management. By com- bining frontend design principles with a scalable backend structure, it enhances communication, trans- parency, and productivity during project development. The system was tested successfully across various mod- ules, proving effective in improving user experience, real-time interaction, and workflow organization. The paper concludes with analysis, results, and potential improvements for future scalability and mobile inte- gration.

Developing an Advanced Machine Learning Model for Ransom ware Detection

Authors: Mr. Kartik, Dr. Bijendra, Dr. Kavita

Abstract: Ransomware has become one of the most pervasive and damaging cyber threats, targeting individuals, enterprises, and critical infrastructures by encrypting essential data and demanding ransom payments. Traditional signature-based and heuristic detection methods are increasingly ineffective due to the rapid evolution, obfuscation techniques, and polymorphic behavior of modern ransomware variants. This research focuses on developing an advanced machine learning model for accurate, real-time, and adaptive ransomware detection. The study begins with a comprehensive review of existing ransomware detection approaches and their limitations. A hybrid detection framework is then designed using both static and dynamic features extracted from executable files and runtime behaviours. The model is implemented using appropriate artificial intelligence and machine learning algorithms to enhance detection accuracy and resilience. Experimental evaluation compares the proposed model with existing techniques, demonstrating improved performance in terms of accuracy, precision, recall, and robustness against zero-day ransomware attacks. The findings highlight the potential of advanced ML-driven approaches in strengthening cyber security defences and mitigating the growing impact of ransomware.

DOI: https://doi.org/10.5281/zenodo.17646282

Personal Voice Assistant Robot for Autonomous Cleaning

Authors: Shwethaa.A.R, Ashwini GV, Samarth R Biradar, Shrikant Ravi Rathod, Soumya G, Yamanur

Abstract: The proliferation of service robots, particularly in domestic and semi-structured environments, has highlighted the need for more intuitive and user-friendly human-robot interaction (HRI). Concurrently, voice assistants powered by advancements in natural language processing (NLP) and artificial intelligence (AI) have become ubiquitous, offering a hands-free and natural interface for controlling technology. This paper presents a comprehensive review of the convergence of these two fields, focusing on the development of a Personal Voice Assistant Robot, specifically for floor cleaning applications. We synthesize findings from recent research in robotics, AI, and HRI to explore the key components, challenges, and opportunities in this domain. The review covers the limitations of current cleaning robots in terms of expressiveness, maneuverability, and user acceptance, and examines how voice control can mitigate these issues. We analyze different system architectures, from cloud-dependent systems to fully embedded, offline voice assistants that prioritize privacy and low latency. Furthermore, we discuss innovations in robot morphology, such as reconfigurable and zoomorphic designs, and advanced locomotion mechanisms that enhance op- erational efficiency. By integrating insights from diverse studies, we propose a conceptual framework for a personal voice assistant robot and outline critical areas for future research, including ensuring user acceptance among diverse populations, enhancing navigational autonomy in dynamic environments, and managing the complexities of system integration. This review serves as a foundational document for researchers and developers aiming to create the next generation of intelligent, personalized, and interactive service robots.

In Vitro Cloning Of Cymbidium Species: A Review

Authors: Mangalleima Moirangthem, Rocky Thokchom, Potsangbam Kumar Singh

Abstract: Cymbidium, or boat orchids, are among the most commercially and ornamentally valuable genera in the Orchidaceae family, native to tropical and subtropical Asia. Threatened by habitat loss and overexploitation, their conservation and propagation have increasingly relied on in vitro cloning techniques. Micropropagation enables rapid multiplication of elite genotypes and supports both commercial cultivation and ex situ conservation. This review explores key methodologies in Cymbidium micropropagation, including explant selection, media optimization, plant growth regulators (PGRs) combinations, somatic embryogenesis, and protocorm-like body (PLB) formation. Explants such as shoot tips and PLBs show high regeneration potential, while media like Murashige and Skoog (MS) and Knudson C are tailored for specific developmental stages. Cytokinins (BAP, TDZ) and auxins (IAA, IBA, NAA) regulate shoot and root induction, with activated charcoal enhancing rooting efficiency. Acclimatization bridges the gap between in vitro and natural environments, improving survival rates through gradual exposure and humidity control. Asymbiotic seed germination using nutrient-rich media and organic additives facilitates propagation without fungal symbionts. Genetic fidelity is assessed using RAPD and ISSR markers, with direct organogenesis preferred for clonal stability. Challenges such as browning, contamination, and low rooting efficiency persist, but recent advances—including Temporary Immersion Bioreactors, synthetic seed technology, and genetic transformation—have improved propagation outcomes. Genomic studies further support targeted breeding and trait enhancement. Micropropagation plays a vital role in Cymbidium conservation, enabling mass propagation and reintroduction of threatened species. Integration with cryopreservation and collaboration with botanical institutions ensures sustainable conservation strategies.

DOI: http://doi.org/10.5281/zenodo.17710935

Hyper-Automation using Agentic AI in ServiceNow

Authors: Shrihari Bhagat, Vaishnavi Bedge, Prof. Ravindra ingle

Abstract: This paper examines how agentic AI, which includes autonomous, goal-directed AI agents, can work with ServiceNow’s hyper-automation features to provide complete autonomous process execution across enterprise service areas like ITSM, HR, Security, and CSM. We define hyper-automation and agentic AI, review relevant literature and vendor capabilities, suggest an architectural pattern for agentic hyper-automation on the Now Platform, and outline an implementation roadmap using ServiceNow features, such as AI Agent Studio, Flow Designer, Integration Hub, Agent Assist, and Predictive Intelligence. We also discuss evaluation metrics, risks, governance, and ethics, and offer practical recommendations for adoption. Key findings include that ServiceNow already provides agentic-style AI agents and low-code automation tools that can work together to automate multi-step processes. However, achieving maturity, validating ROI, and establishing operational guidelines are essential for success.

Integrating Environmental, Social, And Governance (ESG) Frameworks Into Creative Industry Management: A Sustainable Governance Perspective On The Indian Film Sector

Authors: Naveen Kumar B, Dr. H H Ramesha

Abstract: The global creative industries, particularly the film sector, are increasingly required to align with sustainable and ethically responsible practices. This study explores the integration of Environmental, Social, and Governance (ESG) frameworks within the management of the Indian film industry through the lens of sustainable governance. As a major cultural and economic force, the Indian film sector’s project-based structure has historically externalized environmental and social costs, posing serious sustainability challenges. Adopting a qualitative, desk-based approach, this research synthesizes secondary data from academic sources, policy documents, and industry reports to assess the current level of ESG awareness and implementation. The findings indicate a notable gap between intention and action—marked by limited awareness, fragmented practices, and weak governance mechanisms. The study critically examines how governance structures in financing, contracting, and accountability influence environmental responsibility and social inclusion. In response, it proposes a structured governance framework that embeds ESG principles throughout the film production value chain. The study concludes that strong governance is the cornerstone for achieving sustainability, positioning the Indian film industry as a potential global exemplar of responsible creative enterprise.

DOI: http://doi.org/10.5281/zenodo.17647653

“Phytoremediation Of Heavy Metals By Alternanthera Sessilis And Croton Bonplandianus, In Akka-tangi Park Pond, Tumakuru”.

Authors: Thipperudraiah. D.T, F T Z Jabeen, Madhura S.

Abstract: The purpose of this work is to estimate the assemblage of heavy metals like, Cd, Ar and Pb in water and soil and their transfer from the contaminated soils and water to the plants in terms of transfer factor (TF). The Heavy metals cannot be breakdown and cleaned up usually. Most of the ordinary remedial techniques are unaffordable and decreases the soil fecundity, this causes the adverse impact on the ecosystem. Phytoremediation is eco-friendly cost effective and most suitable for developing countries. This research reports the proximity of heavy metals in soil, water and plant parts. Paper discuss the accumulation of heavy metals in plants. After the phytoremediation process the bioavailability of metal ions in the plant can be recycled from the polluted soil, water and then it can be neutralized by enzymes for less toxicity of environment.

DOI: http://doi.org/10.5281/zenodo.17647749

Carbon-Aware Edge AI Scheduling for Sustainable Smart Campuses

Authors: Dr.Girija D.K, Dr.Rashmi M, Dr.Divyashree J

Abstract: This study presents a carbon-aware scheduling framework for edge-centric AI workloads in a smart-campus setting. We formalize a multi-objective problem that minimizes energy use and carbon-weighted energy while satisfying latency service-level agreements through joint control of dynamic voltage/frequency scaling (DVFS), task placement across heterogeneous edge clusters and cloud, temporal deferral of non-urgent jobs, and adaptive model selection. A tractable decomposition combines convex deferral (water-filling over time-varying grid-intensity), min-cost flow for placement on a time-expanded graph with carbonized edge weights, DVFS tuning under queue-stability constraints, and a contextual bandit for model choice. Queueing performance is modeled with M/M/1 response times to enforce utilization caps and SLA feasibility. In a day-long synthetic campus case study with diurnal arrivals and carbon cycles, the proposed controller achieves ≈31% average energy reduction relative to a greedy low-latency baseline, with single-digit to tens-of-milliseconds latency penalties for interactive tasks; larger savings accrue for deferrable analytics via demand shaping. Ablations indicate DVFS and deferral deliver the largest gains, while model selection contributes incremental savings without harming accuracy targets. We discuss deployment guidance, limitations (forecast error, burstiness, hardware heterogeneity), and future directions including robust/MPC extensions and integration with campus microgrids. The results demonstrate that principled, carbon-aware control can materially improve sustainability without sacrificing user experience.

DOI: https://doi.org/10.5281/zenodo.17647801

Carbon Credits And ESG Performance: An Integrated Framework For Sustainable Growth In Emerging Economies

Authors: Taranjeet Singh, Monish Patil

Abstract: Carbon credit trading is an important method to connect climate action and economic progress. Here, we look at how carbon credit systems work, focusing on their linkswith Environmental, Social, and Governance (ESG) measuresin developing countries. Drawing from policy documents, real world programs, and market data, we explore how these markets function and what makes them effective. The paper outlines the chances carbon credits give to shrink greenhouse gas emissions, encourage investment in green technology, and support global climate aims. Even so, challenges remain, like inconsistent regu- lations, sharp price changes, tough verification steps, and doubts about whether projects deliver extra environmental benefits. We find that good results need clear rules, trustworthy data checks, and strong involvement of local groups. When run well, carbon credits can help countries grow sustainably while cutting emissions.

DOI: http://doi.org/10.5281/zenodo.17649595

Social Media consolidation and Management system

Authors: Nilesh S, Manojkumar Mm, Maruthu E

Abstract: In the age of digital communication, managing multiple social media accounts individually can be a tedious and time-consuming task, especially for businesses and active social media users. This project proposes a Unified Social Media Management System designed to consolidate various social media platforms into a single, user-friendly web application. By integrating APIs from major social networks such as Facebook, Instagram, Twitter, and YouTube, the platform provides a centralized dashboard for users to post content, schedule posts, and monitor engagement and reach across all accounts. The system allows one-time secure login using OAuth 2.0, enabling users to manage multiple accounts efficiently without the hassle of repeated authentication. simultaneously. Features such as cross-posting, scheduling, and basic analytics empower users to optimize their social media strategy and save significant time and effort. The application uses a technology stack that includes React for the frontend and Node.js with Express for the backend, with data storage managed by either MongoDB or PostgreSQL.This project not only simplifies social media management but also offers valuable insights through consolidated analytics, making it easier to understand audience engagement and content performance. By providing a scalable and intuitive platform, the system aims to support individual users and small to medium-sized enterprises in maintaining an effective social media presence.

Credit Card Fraud Detection Using Machine Learning and Behavioural Pattern Analysis

Authors: Samarth Aneja, Mr. Ritesh Kumar Chandel

Abstract: Credit card fraud has increased rapidly with the rise of digital payment ecosystems. Traditional rule-based systems struggle to detect evolving attack patterns. This research presents a hybrid approach combining machine learning models with behavioural pattern analysis to improve fraud detection accuracy. Three ML algorithms—Logistic Regression, Random Forest and XGBoost—were trained on an imbalanced credit card transaction dataset. Behavioural features such as spending velocity, merchant category deviation, and location inconsistency were added to enhance model performance. The models were evaluated using precision, recall, F1-score, and AUC values. Results show that incorporating behavioural patterns significantly improves detection rates compared to pure ML models. This approach provides a scalable, real-time method for financial institutions to reduce fraudulent transactions.

CraveCart: An Intelligent Multi-Restaurant Food Ordering Platform Using MERN Stack

Authors: Mr. Jaimeel Shah

Abstract: Online food delivery services have changed how people order meals. There is a growing need for speed, flexibility, and personalization. Even with the popularity of existing apps, users often encounter issues. These include the inability to combine orders from different restaurants, no real-time kitchen updates, and limited emergency meal options. To solve these problems, we created CraveCart, an intelligent food ordering platform built with the MERN stack (MongoDB, Express.js, React.js, and Node.js). The system includes features like multi-restaurant ordering, live kitchen status tracking, split billing, and quick emergency meal requests. Its design focuses on scalability and security, using JWT-based authentication with optional OAuth integration. We developed it using the Agile model, which allowed for ongoing improvements and user feedback. We conducted thorough testing, including unit, integration, and performance checks to guarantee a smooth experience. This paper details the design, methodology, key features, and evaluation of CraveCart. It also discusses its potential for future growth in a new food delivery system.

Redefining Hospitality : Service Quality Improvement_719

Authors: Pranali bhor, Manasi disagaj, Prathamesh lokhulwar, Professor Vijaya mam

Abstract: In conditions of increasing global competition, demands and needs of consumers, quality and quality management have become fundamental strategic factors of achieving profitability and competitiveness on the relentless tourism market. Any serious "top" hotel management, with a defined mission, vision and goals, must define a "special policy" of improving the quality of hotel services through "structural programs of quality improvement," which have become an important factor in the hotel business. With the design, introduction and control of a "special program" of quality improvement of hotel services, hotel management can have a positive impact on increasing satisfaction of customers and human resources, increasing competitiveness and market power of the hotel, the rationalization of operating costs and enhance the reputation and value of the hotel on the demanding tourist market.

Exploring the Use of Virtual Reality Simulation Projects in Designing Phobia Treatment Programs: A Review

Authors: Jeeya Tayare, Vijayashree B, Samraddhi Saxena, Dharani S, Aditya Jaiswal, Muskaan, Harini S, Imaiya V

Abstract: This review tries to examine the benefits and limitations of virtual reality for the future of psychological treatments for treating phobias. The papers used were researched during the last 10 to 15 years across various databases to compare the results and use of traditional methods to treat phobias. The pros of VRET is its capacity to create a safe exposure experience through creation of relatively regulated immersive environments It also has the capacity for automated and self guided VRET which can expand accessibility and can pass the obstacles such as financial and geographic constraints although more research is required.

DOI: https://doi.org/10.5281/zenodo.17733313

Comparative Performance Analysis of Progressive Web Apps and Native Mobile Applications

Authors: Anushka Wadekar

Abstract: With the rapid proliferation of mobile devices and the increasing demand for seamless user experiences, developers face the choice between Progressive Web Apps (PWAs) and native mobile applications. PWAs, leveraging web technologies, promise cross-platform compatibility, reduced development costs, and instant updates, whereas native applications provide optimized performance, deeper hardware integration, and superior offline capabilities. This study presents a comparative performance analysis of PWAs and native mobile applications across key metrics including load time, responsiveness, offline functionality, resource utilization, and user engagement. Experimental evaluation using benchmark applications across multiple devices and operating systems demonstrates that while native apps generally outperform PWAs in speed and resource efficiency, PWAs offer competitive performance for general use cases and provide significant advantages in accessibility and maintenance. The findings aim to guide developers, businesses, and stakeholders in selecting the optimal approach for mobile application development based on performance requirements, user expectations, and cost considerations.

Health Risk Assessment Of Aldrin And Dieldrin Pesticide Residues In Channa Sp. From Kattemalalavadi, Hunsur Taluk.

Authors: Kavitha, Krishna MP

Abstract: Current study emphasized on assessing the pesticide residue level in common edible fish Channa sp. followed by its health risk assessment. Kattemalalavadi of Hunsur Taluk is a remote area with intensive farming activity. Tributary of Cauvery, Lakshmana Teertha flows through Kattemalalavadi hobali encouraging fishing activities. Fishes like Tilapia, Common Carp, Rohu and Channa are commonly captured and sold in local markets. Agricultural rundown is adding pesticides and heavy metals to these water bodies. Hence in the current study fish Channa was selected to assess levels of two pesticides Aldrin and Dieldrin in muscle tissue. The data thus obtained was used for health risk assessment in random population. Pesticide analysis was done by GC-MS/MS, using FSSAI Manual test method. Details about fish consumption was collected using Questionnaire with a sample size of 200 to assess the health risk, EDI, Hazard quotient (HI) and Cancer risk. Hazard quotient and cancer risk value for both Aldrin and dieldrin in Channa sp, is found to be within low risk level at thirty four gram per day consumption rate in the study area.

DOI: https://doi.org/10.5281/zenodo.17659817

Effect of low energy oxygen ion beam irradiation on Structural and Electrical properties of PEDOT: PSS Thin films and PEDOT: PSS/TiO2 Nanocomposites

Authors: Venkatachalaiah K.N., Venkataravanappa M

Abstract: Poly(3,4-ethylenedioxithiophene): Poly (styrene sulfonate) (PEDOT: PSS), is one of the promising conducting polymers. Its conductivity is generally around 1 S/cm, which severely limits its use in many of the applications. An ion irradiation plays an important role to modify the properties of the polymers. When ion beam irradiates the PEDOT: PSS film, the bond breaking, re-arrangement of polymer structure modifies the structural and electrical properties of the PEDOT: PSS. In the present study, an attempt is made to enhance the conductivity of PEDOT: PSS thin films and PEDOT: PSS/TiO2 nanocomposites by low energy oxygen ion irradiation. The effect of low energy oxygen ion beam irradiation on structural and electrical properties of PEDOT: PSS thin films and PEDOT: PSS/TiO2 thin films were studied by FTIR, XRD, SEM and conductivity using Vander pau method. The conductivity of PEDOT: PSS thin films increases for low fluency ion beam irradiation on PEDOT: PSS thin films and decreases for higher fluency.

TF-IDF and Machine Learning Based Approach for Fake News Classification

Authors: Gobika Sree B

Abstract: Fake news has become a major issue in today’s digital world, where information spreads rapidly through social media platforms. Identifying and filtering such misleading content is important to maintain public trust and prevent misinformation. This paper proposes a Machine Learning-based approach for fake news classification using TF-IDF as a feature extraction technique. TF-IDF helps convert text into numerical values by highlighting important words within a document. Various Machine Learning models such as Logistic Regression, Support Vector Machine (SVM), and Naïve Bayes were trained and evaluated. Among them, Logistic Regression achieved the highest accuracy and consistency in classification. Experimental results show that traditional ML models, combined with proper preprocessing, can effectively detect misleading content. The proposed approach provides a strong baseline for automated fake news detection.

DOI: https://doi.org/10.5281/zenodo.17667878

Growth, Structural, Spectral And Thermal Studies On NLO Single Crystals: Succinic Acid And L-Tyrosine Succinate Hydrobromide

Authors: V.Sheelarania, J.Shanthi

Abstract: Nonlinear optical Succinic Acid (SA) and L-Tyrosine Succinate Hydrobromide (LTSHB) single crystals were grown by slow evaporation technique. Single crystal X-ray diffraction (SXRD) revealed that both grown crystal belongs to monoclinic system. Optical behavior of the crystal was investigated using UV–Vis spectroscopy. The optical band gap energy have been determined as 5.48 eV and 4.31 eV respectively for SA and LTSHB single crystal. The presence of functional groups were identified by FTIR and Raman spectroscopic studies. The dielectric constant of the grown crystal was carried out as a function of frequency for different temperatures 308K, 328K and 348K, respectively. Thermogravimetric analysis (TG) of LTHSB were investigated at three different heating rates of 10°C /min, 15°C/min and 20°C/min to study the thermal decomposition and kinetics of the thermal decomposition of LTSHB using Kissinger, Flynn-Wall heating methods to found activation energies and the results are discussed.

Ai Agri-Gaurd : Crop Disease Detection

Authors: Aashi Vidyarthi, Aadesh Rathore, Devika girase, prarthi patel

Abstract: Agriguard is an intelligent crop-disease detection and advisory platform that leverages advanced technologies from the fields of Artificial Intelligence (AI), Computer Vision, and Deep Learning to provide real-time plant health monitoring. It automates the identification of crop diseases by analyzing digital images of plant leaves and generates immediate treatment recommendations. At its core, the system employs Deep Learning, a subfield of AI that enables computers to automatically learn features and patterns from data without explicit programming. Deep learning uses artificial neural networks inspired by the human brain, where interconnected layers of neurons process input data and progressively learn complex representations. In Agriguard, the primary model used is ResNet-50, a 50-layer Convolutional Neural Network (CNN) architecture. CNNs are a type of deep neural network particularly effective for image classification tasks because they automatically detect spatial features such as edges, textures, and shapes. Unlike traditional machine learning methods that depend on handcrafted features, CNNs perform hierarchical feature extraction — learning low-level details (like leaf color or vein texture) in early layers and more abstract disease-related patterns in deeper layers. The ResNet (Residual Network) architecture introduces the concept of skip connections or residual links, which allow gradients to flow directly through layers during backpropagation. This innovation prevents the “vanishing gradient” problem and makes it possible to train very deep networks effectively. ResNet-50, pre-trained on the large ImageNet dataset, has been fine-tuned with agricultural datasets, including PlantVillage and region-specific field images. This transfer learning approach significantly improves model accuracy and reduces training time. The trained model achieves over 94% classification accuracy across multiple crop types, successfully differentiating between diseases such as early blight, late blight, leaf mold, and healthy leaf conditions. The trained model is deployed through a Flask-based REST API, which acts as a communication bridge between the AI model and the user interfaces. Flask, a Python micro web framework, allows efficient server-side handling of requests where an image uploaded by the user is processed by the backend model to generate a prediction. The REST (Representational State Transfer) API ensures seamless communication using standard HTTP methods, making the system modular and scalable. The front-end of Agriguard is built using React, a modern JavaScript framework known for its component-based architecture and high responsiveness. The React-based web dashboard and mobile application allow users— especially farmers—to capture or upload images of affected leaves through their devices. Once processed, the system displays the disease name, probability score (confidence level), and recommended treatment measures, including pesticide usage, organic remedies, and preventive steps to avoid recurrence. Agriguard’s goal is to make precision agriculture—a data-driven approach that optimizes inputs like water, fertilizer, and pesticides—accessible to every farmer, regardless of technical expertise.

Structural Modifications And Geometrical Changes In DNA Quadruple Folding In The Context Of Sustainable Development Goals (SDGs)

Authors: Subhasis Basu, Dr.Subhasis Basu

Abstract: The four natural DNA bases A-Adenosine ,T-Thymine, G-Guanine, C-Cytosine are associate in base pairs as (A=T and G≡C), allowing the attached DNA strands to assemble into the canonical double helix of DNA which is duplex of DNA, also known as ℬ-DNA. The intrinsic supra molecular properties of nuclei bases make other associations possible such as base triplets or quartets, which thus translates into a diversity of DNA structures is ripe with approximately 20 letters, (from A- to Z-DNA); however, only a few of them are being considered as key players in cell biology and by extension, valuable targets for chemical biology invention. In the present review, we summarise (1) what is known about alternative DNA structures (2) what are they? (3)When, where and how they fold? These are all proceeded to discuss further and those considered nowadays as valuable therapeutic targets. We discuss in more detail the molecular tools (ligands) that have been recently developed to target these structures. In order to intervene in the biological processes, particularly three and four ways of DNA junctions are involved there. This new and simulating chemical biology playground allows for devising innovative strategies to fight against genetic diseases. For example to find Structural Modification/Geometrical changes by DNA Quadruples Folding are done by attachment of second prototypes of ligands. DNA quadruples participate in many biological functions. It takes up a variety of folds based on the sequence and environment. Here, a meticulous analysis of experimentally determined 437 quadruple structures (433 PDBs) deposited in the PDB is carried out. The analysis reveals the modular representation of the quadruples folds. Forty-eight unique quadruple motifs (whose diversity arises out of the propeller, bulge, diagonal, and lateral loops that connect the quartets) are identified, leading to simple to complex inter/intra molecular quadruple folds. The two-layered structural motifs are further classified into 33 continuous and 15 discontinuous motifs. While the continuous motifs can directly be extended to a quadruple fold; the discontinuous motif requires an additional loop(s) to complete a fold, as illustrated here with examples. Similarly, higher-order quadruple folds can also be represented by continuous or discontinuous motifs or their combinations. In order to achieve Sustainable Development Goals we have to get maximum stable form of folding to get both thermodynamically and kinetically stable DNA or RNA structure to get proper drug design for the treatment of target diseases or cloning purposes. Help of molecular docking software like AutodocVina and help of PDB software we have to perform it. Alternatively applied those drugs to the victim like Rabbits or mousses etc. for a time period for particular target diseases for optimal treatment result conditions. The topic is under the domain of modern Analytical Chemistry and biotechnology up gradation purposes. With attachment of small molecules with DNA/RNA Finding proper cloning or stalest DNA folding and to find optimal drug design for the treatment of target disease and to find a diseases free society for lifelong initiatives. Such a modular representation of the quadruple folds may assist in custom engineering of quadruples, designing motif-based drugs, and the prediction of the quadruple structure. Furthermore, it could facilitate understanding of the role of quadruples’ in biological functions and diseases

DOI: http://doi.org/10.5281/zenodo.17669303

Mycological Assessment and Proximate Composition of Zobo Drinks Sold in Aba Metropolis, Abia State, South East Nigeria

Authors: Kanu, A. M, Onwumelu, G.O, Handsome, C.

Abstract: Zobo is a non-alcoholic beverage produced locally from dried calyces of Hibiscus sabdariffa. It is a refreshing and medicinal drink generally consumed throughout Nigeria. This research was conducted to evaluate the proximate composition and mycological quality of zobo drinks locally sold in Aba, Abia State. A total of 48 samples were purchased randomly from twelve different local vendors in Aba. The proximate analysis for moisture, ash, fat, protein and carbohydrate were determined as described by AOAC. The mycological quality was assessed using standard microbiological methods. The proximate composition showed high moisture content (88.2%), ash (0.45%), fat (0.91%), protein (2.92%) and carbohydrate (10.6%). Fungi isolated from the zobo drink samples include Fusarium sp (18.75%), Penicillium spp (18.75%), Aspergillus spp (27.08%) and Rhizopus spp (35.42%). Rhizopus spp was the predominant fungi isolated from the sampled zobo drinks. The fungal load ranged from 2.5 x 103 to 3.9 x 103cfu/ml. The present findings revealed the nutritive and mycological quality of zobo drinks retailed and sold in Aba, Nigeria. This study revealed that zobo obtained from different vendors contained pathogenic organisms that pose threats to public health. The proximate composition shows that zobo could serve as a source of contribution to the daily nutrient intake of an individual. There is also high potential for these drinks to serve as vehicles for transmission of food borne illness. Hence, good manufacturing processes in the production and packaging of these drinks is advocated.

“Tiny Protector’s: Exploring The Antiviral Functions Of Plants MiRNAs”

Authors: Jyotika vats

Abstract: MiRNAs, which are small RNA molecules that do not code for proteins, are essential for regulating gene expression after transcription in plants. Recently, studies have shown that miRNAs also act as antiviral agents in plants by targeting viral RNA and inhibiting viral replication. Several miRNAs have been identified that can target different viral genes and limit viral infection in plants. The role of microRNAs (miRNAs) as antiviral agents in plants. It highlights the importance of understanding the mechanisms by which miRNAs target viral RNA and limit viral infection, as well as the strategies employed by plant viruses to evade miRNA-mediated antiviral defense. The article discusses the potential applications of miRNAs in developing new strategies for controlling viral diseases in crops. It emphasizes that miRNAs not only act as direct antiviral agents by targeting viral RNA but also regulate the expression of genes involved in plant defense responses. Additionally, miRNAs can serve as diagnostic tools for viral infections and targets for the development of antiviral strategies. The article concludes by emphasizing the need for further research to fully understand the mechanisms underlying miRNA-mediated antiviral defense in plants and to develop effective strategies for controlling viral infections in crops.

DOI: http://doi.org/10.5281/zenodo.17680065

The Pretreatment Of Refractory Gold Concentrate By Using Mechanochemical Activation And Nitric Acid Oxidation Method

Authors: Hyok Sin, Chang-Sok Kim, Ryong-Ju Cho, Gang-Hyok Kim

Abstract: Hydrothermal oxidation pretreatment of refractory gold ores is an effective pretreatment of refractory gold ores, which has been widely used in recent years due to high rate of desulfurization, low environmental pollution and high reaction rate. In this paper, we describe the thermodynamic studies of nitric acid oxidation and the factors affecting gold extraction when pyrite and arsenopyrite-based gold concentrates are pretreated by mechanical activation and nitric acid oxidation. The factors affecting gold extraction are nitric acid concentration, mechanical activation time, and liquid-solid ratio. As a result, the decomposition of pyrite and arsenopyrite in nitric acid medium was proceeded from nitric acid concentration above 1mol/L at room temperature, and at the present of the mechanical activation, the decomposition was proceeded from nitric acid concentration above 0.5mol/L. After pretreatment using mechanical activation and nitric acid oxidation, the cyanidation leaching rate for gold concentrate was over 87%.

DOI: http://doi.org/10.5281/zenodo.17695421

Real-Time Multiclass Gesture Recognition Via Hjorth Parameters And EMG Signal Analysis

Authors: Thaneshwar Kumar Sahu, Dr. Pankaj Kumar Mishra, Dr. Saurabh Gupta

Abstract: Surface electromyography sEMG for short is one of those tools that sounds intimidating but really just means sticking sensors on the skin to pick up tiny electrical signals from your muscles. In our case, we tried using it to recognize static hand gestures, pulling data from the UCI EMG dataset. That set is surprisingly rich 36 people, each performing a bunch of defined gestures, all recorded across eight muscle channels. Now, instead of throwing raw signals straight into a model (which usually doesn’t go well), we built a feature extraction setup that digs into both time and frequency domains. Things like integrated EMG, waveform length, and zero-crossing rate sit alongside spectral peaks and Hjorth parameters. These aren’t flashy features, but together they give a surprisingly detailed picture of muscle activity when run through sliding windows. One headache was class imbalance some gestures were just less frequent than others. We tried resampling and scaling tricks to smooth that out before training a deep neural network. The payoff was solid: about 94% macro-average accuracy across all gesture types. Still, I wouldn’t claim perfection. Results like these depend a lot on preprocessing choices, and there’s always the worry that performance might slip outside controlled datasets. To peek inside the black box a little, we leaned on visualization t-SNE plots to see how gestures clustered, correlation heatmaps, even multiclass ROC curves. These don’t solve the interpretability issue entirely, but they help. So, the main takeaway, at least for me, is that blending old-school feature engineering with deep nets can work really well here. Pure deep learning often gets all the attention, but sometimes the more traditional signal processing tricks quietly carry the load.

DOI: https://doi.org/10.5281/zenodo.17695632

Efficient Water Data Consumption And Prediction Model: SODECI Case

Authors: Dr. Bayomock Linwa André Claude, Mrs. Dosso Nofogon Grace Marienne

Abstract: Water is an essential yet limited resource whose management is increasingly challenging due to urban growth, climate variability, and limited infrastructure, especially in developing countries. In Côte d’Ivoire, the national water utility SODECI oversees production, distribution, and consumption monitoring, but faces significant obstacles. These include reliance on manual meter readings, delayed or inaccurate data from smart meters, and the absence of real-time alerts. As a result, both utilities and consumers struggle with unreliable consumption data, leading to billing inaccuracies, undetected leaks, mistrust, and inefficient planning. Many water utilities in Sub-Saharan Africa operate with fragmented, low-digital systems and lack structured, continuous data needed for effective analysis. They have limited tools for understanding historical trends, identifying anomalies, or forecasting demand. Existing systems focus primarily on billing rather than data-driven monitoring or prediction. To address these gaps, the paper proposes a new system aimed at improving water consumption monitoring and understanding for both consumers and utilities in Côte d’Ivoire. The system integrates data collection, analysis, and visualization to enhance transparency and decision-making. It provides insights tailored to different stakeholders and supports proactive management of water use. The paper proposes a suitable and predictable water consumption data model that captures abnormal events and alerts consumers and producer. Algorithms that cleanse and retrieve the abnormal water consumption from a given data set are proposed too.

DOI: https://doi.org/10.5281/zenodo.17697328

Blockchain Technology

Authors: Praveen Raj .K, Mrs . sri Padma

Abstract: Data sharing is increasingly critical across diverse domains—healthcare, IoT, enterprise, supply chains—yet conventional centralised data-sharing frameworks suffer from issues of trust, integrity, access-control, and single points of failure. Blockchain technology offers a promising alternative: via decentralised ledgers, cryptographic immutability, smart contracts and distributed consensus, it can enhance data sharing by improving security, transparency and autonomy. This paper explores a blockchain-based secure data sharing framework: firstly reviewing existing literature and gaps; then presenting an existing model (baseline) and a proposed new architecture that separates data ownership from data storage (on-chain/off-chain), incorporates lightweight cryptography and traceability via non-fungible tokens (NFTs) or S equivalent, and implements fine-grained access control via smart contracts. We describe the modules of the system (data owner module, data requester module, blockchain ledger module, off-chain storage module), outline an implementation prototype, and discuss evaluation in terms of security and performance. We conclude that the proposed framework improves data sharing trust and security while mitigating key limitations of prior systems (storage overhead, single trust authority). We also identify future research directions such as scalability, privacy preservation, cross-chain and regulatory compliance.

AI And Digital Technologies In South African Higher Education: Transforming Teaching, Learning, And Equity

Authors: Professor Avishek Das

Abstract: Artificial Intelligence (AI) and digital technologies in education introduce a paradigm shift in how teaching and learning occur. In this regard, educators will be able to use the technologies to provide personalized, adaptive, and engaging learning experiences that cater to the individual needs and preferences of their learners (Baker & Inventado, 2014). This abstract explores multifaceted roles, applications, benefits, and challenges of AI and digital tools in modern education, showing their transformative potential while addressing critical concerns. AI is revolutionizing traditional education by introducing tools such as intelligent tutoring systems, personalized learning platforms, and automated assessment systems. Intelligent tutoring systems, for example, use AI algorithms to adapt the instruction according to the progress of a student in order to ensure that each learner gets an appropriate learning experience. Examples of such systems include Carnegie Learning's MATHia, Coursera, and Duolingo, which provide adaptive learning pathways to tailor content to individual learners' strengths and weaknesses (VanLehn, 2011). These innovations enhance both learning outcomes and engagement, thus making education more effective and accessible. This has significantly streamlined the process of automating tasks, like grading and scheduling, thus leaving educators with more time for pedagogy and interaction with students. The use of Turnitin and Gradescope tools facilitates assessments that are both efficient and consistent. Learning analytics enabled by AI will help educators track performance and engagement levels so that timely interventions and evidence-based curriculum design are undertaken (Siemens & Baker, 2012). Immersive technology, including virtual reality (VR) and augmented reality (AR), is also changing teaching. Simulated environments built by VR and AR permit experiential learning with topics from history to medical procedures. Students can explore historic civilizations, perform virtual lab activities, or train in simulated medical procedures in a safe environment (Dede, 2009). Platforms like Kahoot and Quizizz further amplify engagement: gamification adds game design elements to learning, ensuring motivation and teamwork (Deterding et al., 2011). Digital technologies encourage inclusivity because they present solutions for learners with disabilities that are accessible and convenient. There is also TTS, STT, as well as adaptive tools that benefit students who have dyslexia or face other difficulties while studying. More importantly, the fact that digital platforms transcend geography enables access to quality education through schemes like MOOCs for remote students (Bozkurt et al., 2023). However, the use of AI and digital technology in education does come with some challenges. One main issue is the digital divide as learners face inequalities with technology and internet access (van Dijk, 2020). It is also an area to address in terms of the preparation of teachers who need expertise to successfully apply AI in learning processes (Tondeur et al., 2012). There are further complications of adopting these technologies by privacy and ethical concerns in the use of student data, requiring strong data protection policies (Zawacki-Richter et al., 2019). Financial constraints also pose significant barriers, particularly for institutions in developing regions. Implementing advanced AI systems and digital infrastructures requires substantial investment, making it difficult for some schools and universities to adopt these innovations.

DOI: http://doi.org/10.5281/zenodo.17698743

Automated Detection of Recyclable Waste in Real-Time Using Deep Learning and Computer Vision Techniques

Authors: Ashwini Gulhane, Mohammad Ghouse Mohiuddin, Mohammad Abdul Wahab, Mohammad Faheem Pasha

Abstract: The growing volume of unmanaged solid waste poses significant environmental and operational challenges in urban settings. Traditional scrap segregation is labour-intensive, error-prone, and lacks consistency, especially in regions where manual sorting dominates the recycling process. This research presents a realtime recyclable and non-recyclable material detection system powered by YOLOv5 and Convolutional Neural Networks (CNN), integrated with OpenCV for live camera-based waste identification. A custom-labelled dataset was developed, comprising diverse waste categories such as plastic, iron, cardboard, books, motors, and nonrecyclable items to address real-world scrap scenarios. The YOLOv5 model was trained and optimized on this dataset, achieving high accuracy and rapid inference speeds suitable for deployment in industrial or public recycling environments. The system processes live video feeds and detects multiple objects simultaneously with bounding boxes and class labels, demonstrating robust performance under varying lighting and background conditions. The proposed solution significantly reduces manual effort, supports realtime sorting, and provides a scalable approach for smart waste management. This research aims to bridge the gap between intelligent computer vision applications and sustainable waste handling by enabling reliable, fast, and cost-effective classification of scrap materials.

DOI: https://doi.org/10.5281/zenodo.17699446

Tripartite Graph-guided Analysis to Build Recommender Systems

Authors: Mr. Sakshi Siva Ramakrishna, Dr. T Anuradha

Abstract: Business transactions generate a large amount of data that requires thorough analysis to provide vital insights for businesses' decision-making.A transaction is linked with a good number of record attributes. Traditional transactional data analysis treated all attributes equally, but a subset of attributes can distinguish fine-grained transactions. This discrimination is achievable through attribute and transaction weighting. User surveys or empirical data are the sources for weighting data attributes. If user views are unavailable or the empirical study is missing, weighting becomes tough. Some effective methods exist for weighting. When attributes are binary-valued, the weighting process should rely on transaction-item relationships. The HITS (Hypertext Induced Topic Search) algorithm can perform this weighting. A new algorithm called “Bijective HITS” is proposed, capable of weighing transactions and items by mapping transaction-item relations to item-feature relations. This two-level processcan identify important transactions and items. A new distance measure named “W-distance” is derived from this weighting process. Additionally, a link and density-based hierarchical clustering method is proposed to cluster transaction data using only binary information. Experiments conducted with real and hypothetical datasets compare the results of this approach with those of existing well-known methods. The findings indicate that the proposed models outperform the compared processes, offering better tools for implementing recommendation systems.

Direct Electroplating Of Printed Curcuit Board Through-hole By Deposition Of Carbon Black

Authors: Kuk Chol Ri, Jong Chol Han, Jong Guk Kim

Abstract: Direct electrodeposition of PCB perforated holes by conductive carbon deposition is a promising process to reduce the cost of the process and reduce the environmental burden significantly compared to conventional electroless plating processes. For the deposition of conductive carbon into the perforated wall, an aqueous solution of polyamidoamine-epichlorohydrin copolymer was used as modifier. By measuring the streaming potential, when the pH of the modifier solution was 9.5, the deformed through-hole wall reached the isoelectric point and the amount of adsorbent of the modifier was maximized. Dynamic light scattering spectroscopy (DLS) was used to elucidate the effect of ultrasonic dispersion time, surfactant (SDS) and binder (Acrysol TT-935) on the dispersion of carbon black. The adhesion strength of the coating was investigated by conducting a soldered pin tensile test on the coated tubular holes using the conductive carbon deposition method. The results confirmed that the adhesion strength of the perforated coating coated using cationic copolymer modified and carbon dispersions was within the industrial standard.

DOI: https://doi.org/10.5281/zenodo.17707276

StudySync: An AI-Powered Platform for Personalized and Collaborative Learning

Authors: Rudra Patel, Jay Deshmukh, Kirpalsinh Gohil, Rohan Patel, Dr. Jaimeel Shah

Abstract: Traditional learning methods are often seen as inefficient and isolating, especially for stu- dents dealing with large amounts of digital information. The one-size-fits-all approach to education fails to meet individual student needs, leading to reduced engagement and knowledge retention. This study introduces StudySync, an intelligent and AI-powered platform designed to bridge these gaps by creating a personalized and collaborative learn- ing environment. By examining prior research on AI in education, collaborative tools and Natural Lan- guage Processing (NLP), this research evaluates how StudySync can optimize study sched- ules, improve understanding of complex materials and boost collaborative learning. Using a system modeling approach that includes UML diagrams and layered architecture, the study outlines a robust framework for the platform. Findings suggest that features like an “AI Buddy” for real-time explanations can greatly enhance learning efficiency and user satisfaction. However, challenges such as ensuring algorithm accuracy and promoting user adoption need to be addressed. This research contributes to the evolving field of educational technology by providing a blueprint for a next-generation, AI-enhanced learning environment.

DOI: https://doi.org/10.5281/zenodo.17707431

Design, Fabrication And Experimental Validation Of Bernoulli Apparatus For Fluid-Flow Measurements

Authors: Munish Baboria, Rajesh Mantoo

Abstract: Interpreting the interdependence of pressure, velocity, and elevation heads in fluid flow is critical for understanding fluid behaviour and for designing efficient engineering systems Bernoulli’s theorem, which describes the conservation of mechanical energy along a streamline and quantitatively relates pressure, velocity, and elevation heads, serves as the fundamental theoretical framework for the present study. This experimental research aims to design and fabricate Bernoulli's apparatus for demonstrating the principle and measuring the discharge and flow rate of fluids. The apparatus consists of a smooth, narrow flow channel with gradual contraction and expansion sections, pressure taps, and flow rate and discharge measurement devices. The fabrication involves selecting suitable materials, fabricating the flow channel, installing pressure taps, and integrating flow rate and discharge measurement devices. The experimental setup includes connecting a fluid supply, controlling the flow rate, measuring pressure and discharge, and analyzing the data to verify Bernoulli's principle and calculate flow rate and discharge. Experimental results exhibited well-defined pressure head variations along the test section, aligning closely with theoretical expectations, and enabled accurate calculation of both theoretical and actual discharge The apparatus provides a practical tool for students and researchers to visualize and quantify fluid flow behavior, enhancing understanding of fluid dynamics and hydraulic engineering principles.

The Influence Of Self-Healing Infrastructure On Enterprise Service Resilience

Authors: Mira Sengupta

Abstract: Self-healing infrastructure is revolutionizing the way enterprises handle service resilience in the face of increasing complexity and demand for high availability. This innovative infrastructure leverages advanced automation, artificial intelligence, and predictive analytics to detect, diagnose, and remediate faults without human intervention. As enterprises grow more dependent on digital services, the ability to ensure continuous service delivery despite hardware failures, software bugs, and cyberattacks has become paramount. Self-healing systems improve not only recovery times but also preempt potential failures through real-time monitoring and adaptive responses. This leads to enhanced operational efficiency, minimized downtime, and greater customer satisfaction. The implementation of self-healing infrastructure integrates closely with technologies such as cloud computing, containerization, microservices, and edge computing, each contributing to a resilient architecture capable of maintaining service integrity during adverse conditions. This article explores the conceptual frameworks underpinning self-healing infrastructure, its practical applications in enterprise environments, key enabling technologies, and the impact on operational resilience. Further, it investigates challenges in implementation, including security concerns and integration complexities. Through an examination of real-world use cases, the article demonstrates measurable benefits in service uptime and incident management. Strategic recommendations for adopting self-healing systems are also discussed, aiming to equip enterprises with the knowledge to design, deploy, and optimize these infrastructures to safeguard their critical services. The transformative potential of self-healing infrastructure signifies a strategic shift towards autonomous, robust enterprise ecosystems that can sustain evolving business demands and threats.

DOI: http://doi.org/10.5281/zenodo.17709004

Quantum Machine Learning For Business Forecasting And Risk Assessment

Authors: Dr. Pankaj Malik, Daksh Sethi, Akshat Sharma, Devansh Ramchandani, Harshit Soni

Abstract: Business forecasting and risk assessment are critical components of modern enterprise decision-making in finance, retail, and supply-chain management. Classical machine learning models such as LSTM, XGBoost, and SVM have delivered significant improvements in predictive accuracy but face limitations in modeling complex nonlinear patterns, especially under small datasets and high-dimensional feature interactions. Quantum Machine Learning (QML), leveraging quantum feature embeddings and variational quantum circuits (VQCs), offers a promising alternative with enhanced expressivity and improved generalization properties. This study proposes a hybrid quantum–classical framework integrating a VQC-based quantum feature encoder with classical regression and classification layers. The model is evaluated across three business tasks: financial time-series forecasting, retail demand prediction, and credit-risk classification. Experimental results demonstrate that the proposed QML approach achieves notable improvements in specific conditions. For forecasting tasks, the hybrid QML model yields 8.7% lower RMSE compared to LSTM and 12.4% lower RMSE than XGBoost in low-data regimes (20–30% of training data). For retail demand prediction, QML achieves a 9.3% reduction in MAPE and offers more stable predictions under noisy feature perturbations. In credit-risk assessment, the QML classifier attains an ROC-AUC of 0.79, performing comparably to classical models while exhibiting higher robustness, maintaining accuracy within ±2% under noise injection, where classical models degrade by up to 6%. Overall, results reveal that QML models do not universally outperform classical machine learning but offer clear advantages when training data is limited, features exhibit nonlinear entanglement, or robustness under uncertainty is required. These findings position QML as a promising direction for next-generation predictive analytics and enterprise risk intelligence. The study also highlights existing hardware limitations and proposes future pathways for scalable, real-world deployment of QML-based business forecasting systems.

Analytical Solutions of Heat Transfer in Phase Change problems – Review Analysis

Authors: Dr. P. Bhargavi

Abstract: Transient heat transfer problems described by non-linear partial differential equations along with the moving interface conditions are special type of boundary value problems known as moving boundary problems or Stefan Problems. Freezing/melting problems are referred as Stefan problems, as these problems are first encountered by Physician Joseph Stefan and proposed a model for the polar ice-melting problem. The essential feature of a system undergoing phase change is that a moving interface exists separating two regions of different thermo -physical properties at which energy is absorbed or released, separating the two phases. The objective of this paper is to get mathematical understanding of the heat and mass transfer of the phase change problems with unknown free boundaries and solutions for different type of Stefan problems through research article review analysis. This study gives a clear picture on mathematical modelling of phase change problems with different solution techniques to quantify the process to predict the evolution of the temperature field in the material, the amount of energy used and stored, the interface location and thickness, the interface velocity, final time of freezing and analysis of phase change processes at the macroscopic level.

DOI: https://doi.org/10.5281/zenodo.17719381

EVALUATION OF PHYSIOCHEMICAL LOADS IN DIFFERENT WATER STORAGE CONTAINERS:IMPLICATIONS FOR DRINKING WATER QUALITY AND HUMAN HEALTH_976

Authors: Ubaezuonu, Chinelo Gloria, Engr. Dr. C. C Odenigbo, Nnorli Simon Ifedi

Abstract: This study evaluated the physiochemical loads in different water storage containers its implications for drinking water quality and human health . Six samples from six different locations in Awka, Anambra State was used. The samples were labeled A to F. Sample A from crunches (Ezeokoye Chinonso) Awka, Sample B from Ezeinda Street Isuaniocha, Sample C Udoka Estate and Sample D from Ngozika Estate by Hon Boniface Okonkwo Road, Sample E from Amudo Awka (Okafor Street, Sample F from Umuzocha Awka (Enukorah Ilorah street) and stored in plastic containers, earthen pots, and concrete tanks for 7, 14, and 21 days. The evaluation focused on parameters including pH(6.03-6.96), electrical conductivity (68.3-116µs/cm)), temperature (18-28.5°C), hardness(116-200mg/L), turbidity(0.81-4.21NTU),chlorides(180-199mg/L),phosphates(8.00-9.725mg/L),fluoride(0.673-1.539mg/L), nitrogen (0.0632-1.552mg/L), copper (0.058-1.555mg/L), cadmium (0.213-1.418mg/L), lead (0.128-0.318mg/L), magnesium (0.004-0.018mg/L), manganese (0.203-0.301mg/L), selenium (0.023-0.145mg/L), nickel (0.001-0.027mg/L), chromium (0.011-0.042mg/L), and zinc (0.030-1.481mg/L). The findings reveal significant variations in parameter concentrations based on the storage material. Plastic containers exhibited the highest increases in parameter concentrations, suggesting possible leaching and higher chemical interaction rates. Earthegn pots showed moderate increases, indicating natural buffering and filtration properties. Concrete tanks provided the most stable conditions, with minimal changes in parameter concentrations, attributed to their inert and neutralizing nature. These results suggest that concrete tanks are optimal for long-term storage where stability is paramount. Earthen pots are suitable for applications benefiting from natural filtration and buffering. Plastic containers, while convenient for short-term storage, may not be ideal for extended periods due to potential leaching. This study underscores the importance of selecting appropriate storage materials to maintain the integrity of stored water samples, providing valuable insights for environmental monitoring, water quality management, and related fields. Further research is recommended to deepen understanding and refine storage strategies under various environmental conditions. Water storage containers are essential for providing access to safe and clean drinking water, particularly in regions with inadequate infrastructure. However, the materials used in these containers can impact the physiochemical quality of the stored water, potentially posing health risks such cancers,hormones distruption and reproductive issues over time to consumers.

DOI: http://doi.org/10.5281/zenodo.17721198

Evatainers: Implications for Drinking Water Quality and Human Healthluation of Physiochemical Loads in Different Water Storage Con

Authors: Ubaezuonu, Chinelo Gloria, Engr. Dr. C. C Odenigbo, Nnorli Simon Ifedi

Abstract: This study evaluated the physiochemical loads in different water storage containers its implications for drinking water quality and human health . Six samples from six different locations in Awka, Anambra State was used. The samples were labeled A to F. Sample A from crunches (Ezeokoye Chinonso) Awka, Sample B from Ezeinda Street Isuaniocha, Sample C Udoka Estate and Sample D from Ngozika Estate by Hon Boniface Okonkwo Road, Sample E from Amudo Awka (Okafor Street, Sample F from Umuzocha Awka (Enukorah Ilorah street) and stored in plastic containers, earthen pots, and concrete tanks for 7, 14, and 21 days. The evaluation focused on parameters including pH(6.03-6.96), electrical conductivity (68.3-116µs/cm)), temperature (18-28.5°C), hardness(116-200mg/L), turbidity(0.81-4.21NTU),chlorides(180-199mg/L),phosphates(8.00-9.725mg/L),fluoride(0.673-1.539mg/L), nitrogen (0.0632-1.552mg/L), copper (0.058-1.555mg/L), cadmium (0.213-1.418mg/L), lead (0.128-0.318mg/L), magnesium (0.004-0.018mg/L), manganese (0.203-0.301mg/L), selenium (0.023-0.145mg/L), nickel (0.001-0.027mg/L), chromium (0.011-0.042mg/L), and zinc (0.030-1.481mg/L). The findings reveal significant variations in parameter concentrations based on the storage material. Plastic containers exhibited the highest increases in parameter concentrations, suggesting possible leaching and higher chemical interaction rates. Earthegn pots showed moderate increases, indicating natural buffering and filtration properties. Concrete tanks provided the most stable conditions, with minimal changes in parameter concentrations, attributed to their inert and neutralizing nature. These results suggest that concrete tanks are optimal for long-term storage where stability is paramount. Earthen pots are suitable for applications benefiting from natural filtration and buffering. Plastic containers, while convenient for short-term storage, may not be ideal for extended periods due to potential leaching. This study underscores the importance of selecting appropriate storage materials to maintain the integrity of stored water samples, providing valuable insights for environmental monitoring, water quality management, and related fields. Further research is recommended to deepen understanding and refine storage strategies under various environmental conditions. Water storage containers are essential for providing access to safe and clean drinking water, particularly in regions with inadequate infrastructure. However, the materials used in these containers can impact the physiochemical quality of the stored water, potentially posing health risks such cancers,hormones distruption and reproductive issues over time to consumers.

DOI: http://doi.org/10.5281/zenodo.17721198

Syntheses, Complexation And Biological Activity Of Aminoquinoline: A Mini-Review

Authors: Mrs Anjali Chauhan, Dr Shraddha Upadhyay

Abstract: Aminoquinolines represent a prominent category of heterocyclic compounds that have been the focus of extensive research in recent decades due to their diverse and significant biological activities.These compounds are isomeric, distinguished by the position of the amino group on the quinoline ring, including 2-aminoquinolines, 4-aminoquinolines, and 8-aminoquinolines. They demonstrate a broad spectrum of biological activities, such as antibacterial, antimalarial, anticancer, antitumor, anti-asthmatic, and antimicrobial properties. Aminoquinolines are abundant in nature and serve as fundamental structures for the development of a wide variety of molecules in chemical sciences. This research examines recent progress in streamlined methodologies for the synthesis of diverse aminoquinoline derivatives. It also delves into their metal-binding sites and associated biological properties through a comprehensive literature review and content analysis. The results highlight the range of solvents utilized in purification techniques and their corresponding biological effects. The importance of this study lies in the variety of synthetic approaches uncovered, which may support the creation of novel compounds featuring aminoquinoline frameworks. These new compounds have the potential to exhibit enhanced bioactivity with minimized poison.

DOI: https://doi.org/10.5281/zenodo.17721282

Financial Realities of the Gig Economy: An Analytical Study on the Money Challenges of Freelancers and Platform-Based Workers

Authors: Shubham Khapra, Kartik, Paramjeet Pahal, Aayush Tushir, Shruti

Abstract: The gig economy has reshaped the modern workforce by offering flexibility, autonomy, and digital independence to freelancers and platform-based workers. However, beneath the perceived freedom lies a pressing concern—financial instability. This research paper explores the financial challenges faced by individuals working in the gig economy, including irregular income, absence of social security, limited savings, inadequate insurance coverage, and taxation-related issues. Using assumed primary data from 100 participants (50 freelancers and 50 platform-based workers), the study evaluates income patterns, budgeting behavior, investment preferences, debt levels, and financial stress. Secondary data from journal articles, government reports, and industry publications supplement the analysis. Key findings reveal that 68% of respondents lack emergency savings, 54% face difficulties in managing monthly expenses, and only 22% invest in long-term financial instruments such as mutual funds or pensions. The absence of employer-provided benefits further increases vulnerability during medical emergencies or income gaps. The study concludes by emphasizing the need for structured financial planning, financial literacy programs, and policy-level interventions, including social security and tax-benefit frameworks for gig workers. This research contributes to understanding the human and economic consequences of financial instability within the growing gig economy.

DOI: https://doi.org/10.5281/zenodo.17721660

Taskflow: A Real-Time, Secure, and Extensible Task Management Web Application Using Next.js, Convex, Clerk, and Edgestore

Authors: Ohm Patel, Devraj, Devraj, Yashvi

Abstract: Efficient task management and collaborative docu- ment handling are foundational to distributed software teams and modern organizations. This paper presents Taskflow, a web application that combines real-time collaboration, secure authentication, multilingual document translation, AI-assisted document queries, and exportable, presentation-ready documents in a cohesive platform. The system leverages Next.js (App Router) for routing and rendering, React and TailwindCSS for the user interface, Convex for real-time backend logic and persistent state, Clerk for authentication and role-based access control (RBAC), and Edgestore for edge-powered uploads and synchronization. We describe the motivation, design goals, system architecture decisions, development methodology, implementation details, and results from an academic capstone-style build. Our evaluation focuses on developer experience, responsiveness, reliability of syn- chronization, and functional coverage. We also discuss limitations and opportunities for extension, including offline-first operation and advanced AI-driven workflows.

DOI: https://doi.org/10.5281/zenodo.17721953

Modelling Leaf Area Index And Biomass For Evaluating Carbon Stocks In Community Forests

Authors: Chidanandamurthy G

Abstract: Community-managed forests play a vital role in climate regulation, yet their carbon stocks are rarely quantified using simple, field-based methods that local stakeholders can apply. This study develops and applies a modelling framework that links Leaf Area Index (LAI), above-ground biomass, and carbon stocks in a stratified community forest comprising dense, moderately dense, and open stands. Data were collected from 12 permanent sample plots (4 per stratum). Within each plot, tree diameter and height were measured to estimate above-ground biomass using established allometric equations, while LAI was obtained from ground-based canopy measurements. Plot-level LAI ranged from 1.55 to 5.10 (mean 3.19), above-ground biomass from 74.8 to 229.4 t ha^(-1) (mean 145.6tha^(-1)), and above-ground carbon stocks from 35.2 to 107.8tCha^(-1) (mean 68.4tCha^(-1)). Dense stands exhibited the highest LAI and carbon stocks, followed by moderately dense and open stands. A simple linear model, AGB_ha=14.78+40.99×LAI, explained 98% of the variation in plot-level biomass (R^2=0.98, RMSE =6.44tha^(-1)), with a corresponding linear relationship for carbon stocks. These results show that LAI is a robust predictor of above-ground carbon in community forests and can be used for rapid, low-cost assessment. The framework provides a mathematically transparent and operational tool that can be integrated into community-based monitoring and sustainable forest management.

DOI: http://doi.org/10.5281/zenodo.17721944

Frequency-Dependent AC Conductivity And Dielectric Studies Of PPy/WO₃ Nanocomposites For Frequency-Tunable Dielectric Devices

Authors: Sandeep K M, J S Ashwajeet, Raghavendra M N.

Abstract: In this study, polypyrrole/tungsten trioxide (PPy/WO₃) nanocomposites were synthesized by chemical oxidative polymerization and systematically analyzed for their frequency-dependent AC conductivity and dielectric properties. X-ray diffraction confirmed the successful integration of nanocrystalline WO₃ within the amorphous PPy matrix, with crystallite sizes between 61.9 and 74.5 nm. Dielectric measurements showed that increasing WO₃ content significantly enhanced both permittivity (ε′) and dielectric loss (ε″), particularly at low frequencies—an effect attributed to Maxwell–Wagner–Sillars interfacial polarization. AC conductivity increased monotonically with frequency, followed Jonscher’s universal power law (0.22 ≤ s ≤ 0.50), and rose from ~10⁻3 to 10⁻1 S·cm⁻¹ with frequency and optimal WO₃ loading, consistent with hopping and tunneling of localized charge carriers through percolative pathways. Electric modulus and Nyquist impedance analyses revealed pronounced non-Debye relaxation, a broad distribution of relaxation times, and strong composition dependence of bulk and interfacial resistances. Notably, an intermediate WO₃ content offered the best balance between enhanced interfacial polarization and the formation of continuous conducting networks. These results demonstrate that precise control of WO₃ loading enables effective tuning of polarization mechanisms, relaxation dynamics, and AC conduction in PPy, positioning PPy/WO₃ nanocomposites as promising candidates for frequency-tunable dielectric components, capacitive sensors, and compact energy storage devices.

DOI: http://doi.org/10.5281/zenodo.17722005

An Efficient Gmp Classifier-Based Framework For Brain Tumour Detection and Classification Using Mri Image Techniques

Authors: Daniel Raj K, Anish K, InfantKavin F, Manickam I

Abstract: Brain tumors are one of the most life-threatening neurological disorders, and early classification using MRI is crucial. This paper proposes a deep learning-based diagnostic framework utilizing a Global Max Pooling (GMP) classifier for brain tumor detection and classification. The model identifies glioma, meningioma, and pituitary tumors and further categorizes them into severity grades. Extensive experiments demonstrate the proposed model's superiority in accuracy, robustness, and inference time. The system is implemented as a web application with real-time MRI analysis and clinical decision support.

A Novel Hybrid Intrusion Detection System Using Machine Learning and Optimization Techniques to Counter DOS and DDOS Attacks

Authors: Ashwini Gulhane, Abdul Raafeh, Mohammed Affanuddin, Ma Khizer Moinuddin

Abstract: The transformation towards the adoption of cloud computing has provided modern enterprises with the major benefits of easily extending their resources and being able to use them in various ways without any restrictions, but at the same time it has brought the enterprises a new set of complicated and changing security threats that are very hard to deal with. The conventional security countermeasures which are primarily based on static rule-based mechanisms, perimeter defenses, and Multi-Factor Authentication (MFA), have shown to be of limited effectiveness against the advanced attack vectors of insider threats, privilege escalation, and especially the large-scale, rapidly changing Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) campaigns.[1, 1] These traditional systems have been rendered ineffective most of the time when it comes to detecting zero-day exploits and sophisticated lateral movement which is characteristic of modern botnet operations. In this paper, we describe the process of creating, developing, and assessing SmartTrust, an advanced hybrid deep learning framework that is specifically designed to carry out real-time threat detection in cloud environments while being totally in accordance with Zero-Trust Architecture (ZTA) principles.[1, 1] SmartTrust is a system that is built on a composite deep learning core, which is the result of the integration of Convolutional Neural Networks (CNN) for the analysis of spatial patterns, Long Short-Term Memory (LSTM) networks for the understanding of temporal dependencies and, lastly, Transformer models for the extraction of global contextual relationships in network traffic and user behavior logs. A pivotal feature of the framework is its explicit optimization layer, realized through Reinforcement Learning (RL), which allows for adaptive decision-making and continuous policy adjustment based on real-time contextual signals; thus, Concept Drift is dynamically countered. Besides, in order to maintain unbroken forensic integrity and also compliance alignment with ZTA, the system implements tamper-proof Blockchain-Based Logging for all

DOI: https://doi.org/10.5281/zenodo.17723305

Near By Pro ( Local Business Support Network)

Authors: Deep Raiyani, Lathiya Vatsal, Surya Prakash, Dhruv Pal, Professor Anand Jawdekar

Abstract: Local salons, clinics, and wellness centers repeatedly confront missed appointments, manual bookkeeping, and ad-hoc customer outreach. Customers simultane- ously face fragmented discovery journeys and limited visibility into real-time availability. This work intro- duces SmartConnect, an end-to-end appointment co- ordination fabric that aligns nearby service providers with their clientele by combining predictive schedul- ing, AI-guided matching, virtual engagements, loy- alty incentives, emergency gap filling, and operational intelligence dashboards. Built using Python microser- vices, TensorFlow recommender pipelines, and Post- greSQL storage, SmartConnect delivers 95% suc- cessful booking placement, trims no-shows by 41%, enables sub-1.5-second median response time, and drives 87% loyalty enrollment within pilot cohorts. The platform demonstrates how lightweight AI, au- tomation, and customer experience tooling can mod- ernize neighborhood businesses without costly pro- prietary suites.

Stock Price Prediction Using Lstm and Gru Models Based On Historical Data Analysis

Authors: Padala Sri Roshni, Dr. Goldi Soni, Dr. Poonam Mishra

Abstract: The stock market is inherently volatile and influenced by complex patterns that traditional statistical models often fail to capture effectively. This research presents a deep learning-based approach for predicting stock prices using Long Short-Term Memory (LSTM) neural networks. The model is trained on historical stock price data obtained from Yahoo Finance, with a specific focus on Google (GOOG). In addition to the LSTM model, this research also integrates a Gated Recurrent Unit (GRU) model to compare performance and evaluate the efficiency of different recurrent neural architectures in stock price prediction. Data preprocessing techniques such as normalization, moving averages, and sequence generation were applied to enhance model learning. The LSTM architecture was designed to handle temporal dependencies within financial time series data, using multiple layers and dropout regularization to prevent overfitting. The model's performance was evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), demonstrating reliable predictive capability. Visual comparisons of actual versus predicted stock prices further affirm the effectiveness of the model. This study highlights the potential of LSTM networks in stock price forecasting and contributes to the advancement of intelligent financial decision-making tools.

DOI: http://doi.org/10.5281/zenodo.17731292

AntiqCart: A Case Study On Revolutionizing The Antique Marketplace With AI Analytics

Authors: Runal Pal, Kubavat Dakshkumar, Neel Patel, Vatsal Patel, Dr.Warish patel

Abstract: The increasing demand for ownership of authentic antiques, combined with persistent inefficiencies of a traditional physical market- place, signals the necessary evolution of this industry. The traditional exchange of antiques through regular physical auctions, local exhibitions walk-in dealers’ stores and manual valuations, all have limitations in efficiency, transparency, efficiency, bespoke personalization, and lack of global accessibility. AntiqCart will usher the antique trade into a new era with an artificial intelligence powered platform that studies the antique marketplace and new technologies, and creating new antique asset classes. Examples of important features through the platform consist of smart artifact identification, analytics driven product recommendations, secure digital purchases and sales, provenance checks, both real-time and historical Analysis of market trends, and bespoke seller-buyer fo- cused interactions. AntiqCart has a solution that creates accessibility, trust, customer experience, and supports decision making on the bound- aries of long-term historical research and preserves the antiquity of the trade and introduces both modern means and Artificial technologies because antiques from antiquity have been around longer than con- sumer or trade emergences of interest. These combined interests in the antique trade through AntiqCart present and connect traditional antique trading and business to modern digital experience through a highly connective environment to articulate the user experience. Ultimately, our goal is to create a holistic, safe, smart marketplace for collectors, enthusiasts, and sellers around the world.

Urban Planning And Spatial Transformation In Bengaluru City: An Historical Study

Authors: Dr.Lokesha

Abstract: Karnataka's urban expansion is at a crossroads, as towns with populations of less than one lakh emerge as wealth creators. Urban planning and spatial transformation is an interdisciplinary discipline that investigates how cities and other human settlements evolve over time, with a focus on physical layout and its social, economic, and environmental implications. It entails studying the evolution of urban structures, land use, and public places in order to increase quality of life while also promoting sustainability, efficiency, and resilience. The research paper objectives are urban planning and spatial transformation in Bengaluru.

DOI: http://doi.org/10.5281/zenodo.17733513

A Comparative Study on Gen Y and Gen Z Attitudes toward Education and Learning Styles

Authors: Dr Uzma Tasneem Shaikh, Mrs Samiha Baig, Miss Sara Sharif

Abstract: This study explores and compares the attitudes of Generation Y (Millennials, born 1981–1996) and Generation Z (born 1997–2012) toward education and learning styles. Using primary data collected through questionnaires and interviews with 120 respondents (60 from Gen Y and 60 from Gen Z), the research highlights differences in preferences for traditional learning methods, digital platforms, experiential learning, and self-directed study. The findings reveal that while Gen Y values structured, classroom-based instruction combined with career-oriented skills, Gen Z demonstrates a stronger inclination toward technology-driven, interactive, and personalized learning experiences. The study contributes to understanding generational shifts in education and provides insights for institutions, educators, and policymakers.

Syntheses, Complexation And Biological Activity Of Aminoquinoline: A Mini-Review_373

Authors: Mrs Anjali Chauhan, Dr Shraddha Upadhyay

Abstract: Aminoquinolines represent a prominent category of heterocyclic compounds that have been the focus of extensive research in recent decades due to their diverse and significant biological activities.These compounds are isomeric, distinguished by the position of the amino group on the quinoline ring, including 2-aminoquinolines, 4-aminoquinolines, and 8-aminoquinolines. They demonstrate a broad spectrum of biological activities, such as antibacterial, antimalarial, anticancer, antitumor, anti-asthmatic, and antimicrobial properties. Aminoquinolines are abundant in nature and serve as fundamental structures for the development of a wide variety of molecules in chemical sciences. This research examines recent progress in streamlined methodologies for the synthesis of diverse aminoquinoline derivatives. It also delves into their metal-binding sites and associated biological properties through a comprehensive literature review and content analysis. The results highlight the range of solvents utilized in purification techniques and their corresponding biological effects. The importance of this study lies in the variety of synthetic approaches uncovered, which may support the creation of novel compounds featuring aminoquinoline frameworks. These new compounds have the potential to exhibit enhanced bioactivity with minimized poison.

DOI: http://doi.org/10.5281/zenodo.17735888

A Comprehensive Review Of Brain Tumor Segmentation Methods: Traditional Approaches, Deep Learning, And Hybrid Model

Authors: Pratik Pandey, Nagendra Patel

Abstract: Brain tumor segmentation plays a vital role in medical imaging by enabling accurate diagnosis, treatment planning, and monitoring of disease progression. Over the years, researchers have developed a wide range of segmentation techniques, each with its strengths and limitations. Traditional methods, such as thresholding, edge detection, and region-based techniques, offered simplicity and efficiency but often struggled with noise, variability, and ill-defined tumor boundaries. Statistical and model-based approaches, including clustering and deformable models, provided improved adaptability but required careful parameter tuning and high computational effort. The advent of machine learning, and more recently deep learning, particularly Convolutional Neural Networks (CNNs) and U-Net variants, has revolutionized segmentation, delivering unprecedented accuracy and robustness across diverse datasets. Hybrid approaches that integrate classical and deep learning methods are emerging as powerful solutions, balancing precision, efficiency, and generalizability. This review synthesizes these advancements, highlighting their evolution, comparative performance, and potential future directions.

XGBoost Outperforms Traditional Models in Crop Recommendation Accuracy and Reliability

Authors: Harshal Patel, Jitendra Shrivastav, Kamlesh Patidar

Abstract: Crop recommendation plays a vital role in modern agriculture, enabling farmers to make informed decisions that enhance yield and sustainability. With advancements in machine learning, various algorithms have been applied to predict the most suitable crops based on soil and environmental parameters such as nitrogen, phosphorus, potassium, pH, temperature, humidity, and rainfall. This study presents a comparative analysis of several supervised learning models, including Decision Tree, Naïve Bayes, Support Vector Machine (SVM), Logistic Regression, Random Forest (RF), and XGBoost. Performance was evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that while traditional models achieved strong performance, XGBoost outperformed them all, achieving the highest accuracy (99.31%), precision (100%), and reliability across metrics. Its ability to capture complex, non-linear relationships within soil data underscores its effectiveness for precision agriculture. The findings highlight XGBoost as a robust and scalable solution for enhancing crop recommendation systems.

Iot Based Smart Energy Meter

Authors: Khyati Zalawadia, Pinninti Sriman Reddy, Chepala Paradeshi Naidu, Sagi Siddardha Varma, Sufiyan Nakum, Meda Shanmukha Sai

Abstract: This project is about building a smart energy meter using IoT technology to precisely measure electricity consumption and combat power theft. It uses microcontrollers along with current and voltage sensors, plus wireless communication, to keep track of energy use in real time. The system is smart enough to spot any unusual usage like unauthorized consumption or excessive reactive power, and it sends instant alerts and useful data to a cloud platform.

Internet of Things and Edge Computing: Concepts, System Layers, and Engineering Challenges

Authors: Srinivas Palaparthi

Abstract: Internet of Things deployments generate continuous data streams from distributed sensors and actuators. Handling this volume solely in remote clouds introduces delay, bandwidth usage, and privacy exposure. Edge computing addresses these issues by executing selected workloads near devices. This paper provides a concise examination of IoT and edge computing, outlines a practical layered architecture, and explains the flow of data and control across devices, edge infrastructure, and cloud services. The paper also highlights design considerations related to latency, resource limits, security, and interoperability. Current application trends and open research problems in AI-enabled edge systems and federated learning are discussed. Index Terms—IoT, Edge Computing, Fog Systems, Distributed Processing, Cyber-Physical Systems.

DOI: https://doi.org/10.5281/zenodo.17747460

A Review Of Soft Computing Approaches For Groundwater Pollution Source Identification And Analysis

Authors: Bijay Kumar Singh, Jasvir Singh

Abstract: Groundwater contamination presents a critical challenge to environmental sustainability and public health, particularly in regions facing rapid industrialization and agricultural intensification. Traditional analytical and statistical approaches often struggle to model the complexity, uncertainty, and nonlinearity inherent in subsurface pollution processes. This review explores the application of soft computing (SC) techniques—including Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machines (SVM), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and hybrid models—in groundwater pollution source identification and analysis. A systematic literature review (2010–2025) reveals that SC techniques effectively handle incomplete datasets, imprecise inputs, and non-linear contaminant transport. ANN and hybrid models exhibit high prediction accuracy for pollutant concentrations, while FL excels in qualitative risk mapping. SVM models perform well in binary classification of contaminated zones using limited data. GA and PSO are widely used for optimization tasks such as well placement and parameter calibration. Comparative analysis across global case studies highlights the strengths, limitations, and ideal applications of each technique. The study concludes that hybrid SC models offer the most robust performance for integrated risk mapping and multi-pollutant modeling. Future research should focus on explainable AI, transfer learning, and real-time sensor data integration to enhance model interpretability and deployment in decision-support systems.

DOI: http://doi.org/10.5281/zenodo.17748657

Bio-Mediated Combustion Synthesis And Photoluminescence Studies Of Y2o3: Tm3+ Nanoscale Superstructures

Authors: Venkataravanappa M, Venkatachalaiah. K. N

Abstract: Y2O3:Tm3+ (5mol %) nanoscale superstructure was prepared using Mimosa pudica plant extract as a fuel and nitrate source as a precursors.The sample was characterized by advanced characterization techniques. The PXRD data shows the formation of single phase, cubic structure of Y2O3 with crystallite sizes ~35 nm. PL emission spectra show the blue light emission under the excitation wavelength of 358 nm. The major emission peak of Tm3+ was at 453 nm and two very weak peaks were observed at ~ 474 nm, corresponding to the transitions of 1D2 → 3 F4 and 1G4 → 3H6, respectively. The estimated CIE chromaticity co-ordinate was very close to the national television standard committee value of blue emission. Correlated color temperature was found to be ~ 4000 K as a result the present phosphor was potential to be used for warm white light emitting display applications.

DOI: http://doi.org/10.5281/zenodo.17750245

Designing A Chatbot For The College Website

Authors: Ayush Jaiswal, Ms. Vaishali Goswami

Abstract: This study focuses on developing a chatbot for a college website to enhance the user experience by automating responses to common inquiries, including FAQs, admissions, and student support. The backend uses Dialog flow, TensorFlow, and Python for NLP-based contextual intelligence, while the frontend uses HTML and CSS for smooth deployment. The system demonstrated the efficacy of AI-powered chatbots in enhancing accessibility, lowering workload, and offering ongoing support in academic settings with an 8.7/10 user satisfaction score and 91% response accuracy [1]– [5], [16].

AI Base Face Recognition Attedance System

Authors: Namrata patel, Om Pancholi, Vedant Patel, Amlesh Sharma, Jainish Kachiyaa

Abstract: The rapid adoption of artificial intelligence (AI) and computer vision has transformed traditional attendance management systems, which are often inefficient, error-prone, and vulnerable to proxy marking. This research presents an AI-Based Face Recognition Attendance System developed using the MERN stack (MongoDB, Express.js, React.js, and Node.js) integrated with deep learning frameworks such as OpenCV and TensorFlow. The proposed system automates attendance tracking by capturing live facial images through a webcam or mobile camera, processing them in real time, and matching them against a secure database of registered users. Upon successful recognition, attendance is recorded automatically, eliminating the need for manual registers or RFID-based systems. The architecture combines a React-based interactive frontend, a Node.js/Express.js backend for secure communication, and MongoDB for scalable data storage. AI- driven face recognition ensures reliable identification even under varying conditions, while additional features such as role- based access, analytics dashboards, and real-time notifications enhance usability. This work demonstrates how integrating AI with modern full-stack web technologies can deliver a secure, contactless, and efficient attendance solution for educational institutions, corporate environments, and workplaces. Future enhancements include mobile application support, advanced anti-spoofing measures, and improved recognition accuracy under challenging conditions.

Impact Of Technology And Neurofeedback On Adolescent Mental Health

Authors: Ananya Achalla, N.V. VijayaLakshmi

Abstract: This expanded paper examines the relationship between technology use, social media engagement, and neurofeedback interventions in adolescent mental health. Building earlier sections incorporates a broader literature review, a detailed description of methodologies used in neurofeedback research, their limitations, and proposed strategies to address current gaps. The paper culminates in a comprehensive conclusion and recommendations for future research and practice.

DOI: http://doi.org/10.5281/zenodo.17774887

Predicting Unconfined Compressive Strength Of Cement-Stabilized Soil Using Artificial Intelligence: A Comparative Study Of Random Forest And Artificial Neural Network Models In Indian Geotechnical Conditions

Authors: Debashish Chandra, Jasvir Singh

Abstract: Accurate prediction of the unconfined compressive strength (UCS) of cement-stabilized soils is critical for optimizing pavement and subgrade design in geotechnical engineering. Traditional empirical models often fail to capture the complex, nonlinear relationships among influencing parameters such as cement content, curing duration, and soil properties. This study proposes a data-driven framework utilizing two artificial intelligence (AI) models—Random Forest (RF) and Artificial Neural Network (ANN)—to predict UCS based on laboratory and field data collected from diverse Indian soil conditions. Seven input features were considered: cement content, liquid limit, plasticity index, maximum dry density, optimum moisture content, fines content, and curing time. The dataset was preprocessed using Min-Max normalization, and models were trained and tested using a 70:30 split. Performance evaluation using R², RMSE, MAE, IOA, and a20 metrics indicated that ANN slightly outperformed RF, achieving an R² of 0.942 and an a20 of 94.6%. Feature importance analysis revealed that cement content and curing time had the most significant influence on UCS. SHAP analysis further enhanced interpretability of the ANN model. The results demonstrate the reliability and efficiency of AI-based approaches for UCS prediction, offering a robust alternative to conventional methods for soil stabilization design in Indian geotechnical engineering contexts.

DOI: http://doi.org/10.5281/zenodo.17759267

UniShowTime: A University Event Management And Ticketing System

Authors: Mahek Sharma, Raghav Sharma, Aum Kamble, Kushal Patira, Kushal Patira

Abstract: This paper introduces a comprehensive technical and strategic blueprint for UniShowTime, a Django-based event management and ticketing platform designed for university events. The website is developed to ensure that financial trans- actions are secure, users can move around easily and the site can handle high traffic loads. Based on the latest technology stack, UniShowTime provides a reliable, secure and interactive platform for the campus events. Crucial factors, such as system performance, data security, reliability, and transparency, receive special attention, as they have significant impacts on user satisfaction and acceptance. The paper investigates some of the architectural models (such as distributed systems, Model-View- Controller or MVC), which have made this kind of process robust and maintainable. It is also exploring incorporating emerging technologies like machine learning for fraud detection, blockchain to ensure transparency, and secure payment gateways. The aim of this work is to provide a useful handbook to developers and university administrators to help them construct a reliable, secure and user-friendly event management system.

Smart Contract-Driven Attribute-Based Access Control (ABAC) For Dynamic Cloud Services

Authors: Dr. Pankaj Malik, Ankit Sahu, Aadarsh Sahu, Harshita Modi, Harsh Solanki, Dishank Solanki

Abstract: The increasing use of cloud computing has resulted in increasingly dynamic and multi-tenant settings where conventional Role-Based Access Control (RBAC) systems find it difficult to provide fine-grained and context-aware access control. This paper introduces a blockchain-based Attribute-Based Access Control (ABAC) framework that uses smart contracts to implement access policies in a decentralized, transparent, and tamper-proof way. The system suggested, the paper enables policy definitions and attribute assessments to be directly encoded into smart contracts, making possible automated, real-time access decisions without the need for a central authority. It was explained using Ethereum smart contracts and tested through a prototype in a simulated healthcare cloud setting, where access of confidential patient records was controlled by dynamic attributes like user role, department, and clearance level. Experimental results illustrate that the system proposed performs secure and reliable access control with a mean decision latency of 350 ms and gas cost of 82,000 units per transaction. The system accommodates dynamic attribute updates and revocation with zero service downtime and provides full auditability using immutable blockchain logs. In comparison with conventional ABAC systems, the smart contract-based solution enhanced consistency in policy enforcement by 22% and removed single points of failure. These findings affirm the feasibility of decentralised ABAC as a viable solution for securing dynamic cloud services.

Credit Card Fraud Detection Using Machine Learning

Authors: Vinod V Kulkarni, Arghajeet Gupta, Akanksha Kumari Sinha, Farhan Sharieff, Mohan R B

Abstract: Among numerous emerging challenges in the digitized financial ecosystem is credit card fraud. However, traditional rule-based fraud detection systems have rendered fraud detection inadequate in a constantly evolving arena, and the false positives and false negatives are increasing alarmingly. This study implements an accurate and real-time credit card fraud detection using a machine learning- based framework. The system will analyze transaction patterns and classify fraudulent activities using the following multiple classification algorithms: Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting. Procedures concerning preprocessing of the dataset include feature scaling and handling class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE) methodology, followed by dimensionality reduction through PCA, all intended to improve computational efficiency. Results of experiments indicate that ensemble models, and especially Random Forest and XGBoost, produce superior performance with regard to precision, recall, and AUC- ROC scores when compared to baseline models. Results confirm the potential of machine learning in detecting rare fraudulent transactions, as well as scalable solutions for deployment into financial institutions. Enhanced transactional security and reduced losses associated with fraud could be achieved through data-driven predictive modeling.

Leveraging Artificial Intelligence for Enhanced Stage-Discharge Curve Analysis in Hydrological Modelling

Authors: Mr. Prabhu M, Sanjeev Kumar. K, T.S. RameshBabu B.E

Abstract: This paper aims to assess the application of Artificial Intelligence (AI) in the development of stage-discharge relationships. Stage-Discharge Relationships often known as rating curves are a crucial tool for hydrological modelling and water resource management. This study uses AI, specifically Large Language Models (LLMs) in the development, calibration, and validation of stage-discharge curves. The methodology involves utilizing LLMs to generate python code. The hydrological data i.e stage and discharge from September 1971 to May 2021 (30 years) at Biligundulu gauging station on the Cauvery River was used to derive SD Curve. The developed equation was then applied to hydrological data from June 2021 to May 2023. The predicted discharge values were subsequently compared to the actual observed values. The outcomes are evaluated using R-squared, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), demonstrating a strong fit to the observed data (R² = 0.9995, RMSE = 17.29 m³/s, MAE = 10.91 m³/s). The process and results indicate that AI-driven approaches can offer a robust alternative to conventional methods. This study demonstrates AI can empower hydrologists without extensive coding expertise to conduct complex data analysis. By leveraging LLMs, complex hydrological models, data pre-processing, etc., can be automated, enabling more researchers and practitioners to conduct advanced analyses.

DOI: https://doi.org/10.5281/zenodo.17774830

The Silent Revolution: How RPA And IIoT Are Reshaping Bangladesh’s Production Lines.

Authors: Quazi Nazmul Hossain

Abstract: Bangladesh’s manufacturing sector—particularly its ready-made garment (RMG) industry—is undergoing a profound yet largely unnoticed transformation driven by Robotic Process Automation (RPA) and the Industrial Internet of Things (IIoT). This silent revolution is redefining production efficiency, quality assurance, and global competitiveness. Through the integration of sensor-enabled machinery, real-time data analytics, automated workflows, and semi-autonomous production systems, factories are improving output consistency, reducing defects, and minimizing downtime. These technologies also enable predictive maintenance, supply-chain transparency, and faster decision-making, helping Bangladeshi producers meet stringent international buyer demands. However, the transition presents significant challenges, including high capital investment requirements, skills gaps within the workforce, and concerns related to job displacement—particularly for low-skill labour segments. Despite these hurdles, evidence suggests that the strategic adoption of RPA and IIoT can enhance Bangladesh’s position in the global manufacturing ecosystem by enabling smarter, more resilient, and more sustainable production lines. This paper explores the drivers, implications, and future trajectory of this digital-industrial shift, emphasizing the need for coordinated industry and policy responses to ensure an inclusive and competitive transformation.

DOI: https://doi.org/10.5281/zenodo.17775026

Performance Assessment Of Pelton Turbine With Traditional And Novel Hooped Runner By Experimental Investigation

Authors: Vimal K. Patel, Hemal N. Lakdawala, Sureel Dohare, Gaurang Chaudhary

Abstract: Hydro turbomachines are used since long as a water wheel, even be- fore knowledge of fluid mechanics. A newly developed concept at runner i.e. hooped is considered here for investigation purpose. This paper mainly focuses on the performance comparison between traditional and hooped type Pelton wheel turbine runner. The performance characteristic of any hydraulic machines describes the behavior of the machine under operating conditions. The down comer jet impingement to the rotor buckets provides more velocity and thereby impulse to a runner. Efficient performance behavior of any turbo machine under consideration can be easily estimated from performance curves for that machine under specified conditions. In case of impulse type machines, it relies on the quality of jet and other aspects of jet-vane interaction too. In the present inves- tigation, the performance of two types of runner has experimented on the same setup and flow conditions. The main objective of this paper is to compare the performance of a regular runner with a novel hooped runner. It has been found that hooped runner exhibits same characteristics with some loss of efficiency due to overweight and loss of energy at buckets due to restricted passage creat- ed by a hoop (flanges) but on contrary, the reliability and safety of buckets can be ensured. In additions, less deflection of the bucket can also be ensured under heavy jet force.

Deep Reinforcement Learning Approaches For Task Offloading In Mobile Edge Computing: A Critical And Systematic Review

Authors: Professor Shabina Modi, Ajinkya Shriram Gurav, Prasad Nagnath Londe, Pranit Dattatraya Patil, Prasanna Motiram Kasabe, Ajinkya Anil Dhane

Abstract: This study focuses on developing a chatbot for a college website to enhance the user experience by automating responses to common inquiries, including FAQs, admissions, and student support. The backend uses Dialog flow, TensorFlow, and Python for NLP-based contextual intelligence, while the frontend uses HTML and CSS for smooth deployment. The system demonstrated the efficacy of AI-powered chatbots in enhancing accessibility, lowering workload, and offering ongoing support in academic settings with an 8.7/10 user satisfaction score and 91% response accuracy [1]– [5], [16].

Rethinking Memory Management: How Rust’s Compile-Time Guarantees Influence System Architecture

Authors: Sowmya B L

Abstract: Rust programming; memory safety; ownership model; borrowing rules; lifetime analysis; garbage collection; compile-time verification; systems programming; deterministic memory management; embedded systems; real-time systems; low-latency computing; high-performance computing; concurrency safety; region-based memory; formal verification; RustBelt; resource management; zero-cost abstractions; systems architecture.

NudgeLight: LLM-Powered Psychological Behavior Modeling and Safe Multi-Agent RL for Zero-Conflict Yellow Intervals in Mixed Human-AV Traffic.

Authors: Mr. Sayyed Aamir Hussain

Abstract: This paper presents NudgeLight, a novel traffic control framework that integrates large language model (LLM)-driven psychological behavior modeling with a safety-constrained multi-agent reinforcement learning (MARL) strategy to govern decision-making during the yellow interval at signalized intersections within mixed human–autonomous vehicle (AV) environments. The yellow phase represents one of the most safety-critical and behaviorally sensitive segments of intersection control, where drivers must make rapid and uncertain stop-or-go decisions, frequently resulting in high conflict probabilities. To address this persistent challenge, NudgeLight employs LLM-based cognitive inference to predict heterogeneous human driver intentions under time pressure and dynamically adapts AV and signal policies through safe MARL mechanisms that explicitly enforce collision-avoidance constraints and minimize conflict trajectories among interacting agents. A high-fidelity simulation environment replicating realistic mixed-traffic conditions—including diverse driver archetypes, variable AV penetration rates, and heterogeneous roadway dynamics—was constructed to evaluate the proposed framework. Extensive experimental results demonstrate that NudgeLight substantially reduces surrogate safety conflicts, improves time-to-collision margins, and enhances intersection throughput, while preserving the naturalness and comfort of human driving behaviors. Unlike existing approaches that restrict AV operations to deterministic or conservatively scripted responses, NudgeLight delivers adaptive, cognitively informed, and safety-assured control tailored to real-world behavioral variability. This research provides critical insights for scalable deployment of intelligent, human-centered signal control solutions and contributes to the advancement of safe and harmonious human–AV coexistence in emerging urban mobility systems.

DOI: https://doi.org/10.5281/zenodo.17838176

AI-Driven Dynamic Multimodal Transport Demand Forecasting And Optimization In Pune

Authors: Fatima Mohsin Inamdar, Pratyay Patel, Prathmesh Tiwari, Pranav Singh Langeh, Prathamesh Thotwe, Pratik Pawar, Pratitya Wankhade

Abstract: Urban mobility in cities like Pune is increasingly challenged by congestion, fluctuating travel demand, and inefficient multimodal transport systems. This paper proposes an AI-driven system for dynamic multimodal transport demand forecasting and optimization, utilizing advanced machine learning models and web-based technologies. We develop a predictive platform that integrates traffic data to estimate demand, determine optimal routes, forecast arrival times, and suggest accurate fares across various modes of transport. Using machine learning frameworks such as PyTorch, XGBoost, and Random Forests, alongside a Flask backend and a HTML, CSS, JavaScript, ReactJS and Bootstrap frontend, the system offers real-time insights to both commuters and transport operators. Our approach aims to alleviate urban transport issues, improve commuter experience, and contribute to smart city initiatives.

DOI: http://doi.org/10.5281/zenodo.17785838

House Price Prediction Model Using Data Mining

Authors: Akinmerese, Oluwatobi, Ifekandu, Chiamaka, Ezeoke, Evelyn, Osuji, Adaeze, Lawal, Esther

Abstract: Reliable methods for forecasting home prices are scarce in many areas, particularly in our nation. This research tackles this gap by applying machine learning techniques to anticipate house values based on key attributes. Using the Cross Industry Standard Process for Data Mining (CRISP-DM) framework as a reference, this research used the Linear Regression model. Data cleansing, feature selection and visualization were all part of the approach. It was found that accuracy increased when the dataset was transformed using logarithmic values and models were assessed using statistical techniques like p-values and the Bayesian Information Criterion (BIC). The results demonstrate that property prices may be accurately forecasted using a condensed dataset without sacrificing model performance.

DOI: http://doi.org/10.5281/zenodo.17786493

Clutch Damper For Reducing Clutch Pedal Vibrations

Authors: Mr.Sandesh Kshirsagar, Mr. Nagaraj Naik, Mr. Rahul Shetti, Mr. Ajit Kharade

Abstract: Clutch pedal vibrations in manual transmissions affect driver comfort and control. This study investigates the use of a Clutch Release Line Damper to reduce pedal pulsation without compromising clutch system efficiency. The damper controls hydraulic fluid flow between the master and slave cylinders, absorbing vibrations through volume expansion and contraction in the damper chamber. Four dampers tuned at different frequencies (‘A’ Hz, ‘B’ Hz, ‘C’ Hz, ‘D’ Hz) were tested at various engine idle speeds (800, 1500, 2500 RPM). Results show the ‘B’ Hz tuned damper provided optimal vibration reduction across all conditions. These findings shows that proper frequency tuning of the damper improves pedal comfort while maintaining system responsiveness.

DOI: https://doi.org/10.5281/zenodo.17786790

Real-Time Driver Monitoring Using Multi-Modal Emotion Recognition Using Machine Learning

Authors: Vinod V. Kulkarni, Sujyothsna B, Vaishali BS, Jnanesh Kumar BN, Pruthvi Reddy k

Abstract: Road traffic safety requires continuous assessment of driver state, including fatigue and emotional distraction. This paper presents a robust real-time Driver Monitoring & Alert System, integrating multi-modal machine learning for drowsiness and emotion analytics. The system logs behavioral metrics—facial landmarks, speech volume, and micro-expressions—using edge computation and stores entries in a scalable database. A modern analytics dashboard visualizes trends, enables time-based filtering, and computes key summaries (e.g., average drowsiness, emotion distributions). Empirical results show high recognition accuracy, rapid dashboard responsiveness, and practical potential for real-world automotive deployment. The project demonstrates advances in real-time driver monitoring, combining intuitive visualization with rigorous machine learning methods.

DOI: http://doi.org/10.5281/zenodo.17797363

Conceptualization Of Quantum Computing With Machine Learning

Authors: S.Saranya Devi, K.Shandhini, R.Kalai Selvi, R.Kalai Magal, M.Satheesh

Abstract: Machine-learning algorithms infer a target relationship between inputs and outputs by studying example data, enabling them to interpret previously unseen inputs. This capability is essential for tasks like image and speech recognition, as well as strategy optimization, and it is increasingly important across the IT industry. In recent years, researchers have explored whether quantum computing can enhance classical machine-learning methods. Proposed ideas include accelerating computationally expensive algorithms or their subroutines using quantum hardware, and reformulating stochastic techniques within a quantum-theoretic framework.

DOI: https://doi.org/10.5281/zenodo.17787412

Delivering Under Pressure: Hr And Scm Issues In Flipkart’s Fast-Growing Operations

Authors: Parichay Saxena

Abstract: Flipkart’s rapid expansion in India’s competitive e-commerce sector has intensified pressures on both its human resource (HR) and supply chain management (SCM) functions. This paper examines the organizational challenges that arise as the company scales its logistics network, fulfillment centers, and last-mile delivery operations. Key HR issues—including high employee turnover, workload stress, skill gaps, and the need for continuous training—are analyzed alongside SCM pressures such as capacity constraints, demand variability, and the push for faster, more reliable deliveries. Through an integrated perspective, the study highlights how HR and SCM interdependencies shape operational efficiency, workforce stability, and service quality. Findings emphasize the importance of coordinated workforce planning, technology-enabled process optimization, and sustainable employee management practices to support Flipkart’s growth trajectory. The paper provides insights relevant to e-commerce firms navigating similar challenges in rapidly evolving markets.

Learning Mathematics through GeoGebra Software (Construction of Concepts and Application with Understanding)

Authors: Neeraj Kumar Joshi

Abstract: This study explores the use of GeoGebra software as a dynamic tool for enhancing students’ mathematical learning, with particular emphasis on the construction of concepts and the application of knowledge with understanding. Grounded in constructivist and visual-representational learning theories, the approach integrates interactive geometric, algebraic, and graphical representations to support deeper conceptual development. Through guided exploration, manipulation of mathematical objects, and real-time feedback, learners are encouraged to form connections between abstract ideas and their visual models. The use of GeoGebra further promotes active problem solving and meaningful application of mathematical concepts in varied contexts. Findings suggest that integrating GeoGebra into instruction not only strengthens students’ conceptual comprehension but also improves engagement, motivation, and the ability to apply mathematical principles flexibly and accurately. The study underscores the potential of technology-supported learning environments to enrich mathematics education and recommends the strategic incorporation of GeoGebra in classroom practice.

Twitter Sentiment Analysis Using NLP Models And Real-Time Tweet Fetching

Authors: Bheemalingappa, Chandrashekar K L, Darshan S, Dattatreya, Assistant Professor Sumitra Sharma Phurailatpam

Abstract: Social media platforms, particularly Twit- ter, generate massive volumes of real-time textual data that reflect public opinion, emotional tendencies, and emerging societal trends. Analyzing this stream of infor- mation manually is infeasible due to its speed, scale, and linguistic complexity. This paper presents an enhanced real-time Twitter Sentiment Analysis system that inte- grates Natural Language Processing (NLP) models with live tweet fetching using the Twitter API v2. The pro- posed system employs a hybrid pipeline consisting of the VADER rule-based sentiment analyser for fast polarity detection and Transformer-based models for deeper con- textual sentiment classification. Additionally, the system incorporates optional modules for emotion recognition and toxicity analysis, enabling multi-dimensional inter- pretation of user-generated content. A Streamlit-based in- teractive interface allows users to fetch tweets in real time, analyze sentiment distributions, examine top key- words, and download processed outputs. The architec- ture is designed for scalability, efficiency, and accessibil- ity, offering a low-cost yet powerful solution for social sentiment monitoring and data-driven decision-making. Experimental evaluations demonstrate that the model combination improves interpretability and accuracy while maintaining responsiveness suitable for real-time applications.

DOI: http://doi.org/10.5281/zenodo.17797433

Smart Geofence: An Intelligent Location-Based Boundary Monitoring System

Authors: Vishwajit Virkar, Prasad Mandi, Mayur Chaudhari, Mansi Bhandari, Shreya Sidnale, Gajanan Gambhire

Abstract: This research discusses an embedded real-time geofencing system for industrial and construction workplace safety . Using ESP32, GPS, and RFID technologies, it tracks worker movement, initiates local alarms on boundary violations, and provides a light-weight, robust, and scalable solution for low-infrastructure contexts.

Big Data Analytics For Predictive Healthcare And Early Disease Detection

Authors: Dr. C.K. Gomathy, Thulasi Ram. S, Nandagopal. S

Abstract: The convergence of advanced data collection technologies—specifically wearable sensors, Electronic Health Records (EHRs), and Internet of Medical Things (IoMT) devices—has led to an exponential increase in healthcare data volume and velocity. The efficacy of early disease prediction and subsequent improvement in clinical outcomes critically relies on the ability to analyze these massive, heterogeneous datasets in near real-time. This study posits that Big Data Analytics (BDA), leveraging scalable and distributed computing architectures, offers the necessary Artificial Intelligence (AI)-supported mechanisms for extracting timely and actionable clinical insights. This research investigates the utilization of Big Data frameworks, including Hadoop and Apache Spark, within a cloud-based environment for automated disease prediction and risk assessment. A sophisticated predictive model was developed using Spark MLlib, employing Random Forest (RF) and Gradient Boosting Trees (GBT) algorithms, specifically for the early detection of cardiovascular disorders. Experimental analysis demonstrates that the BDA-driven predictive system significantly improves diagnostic accuracy by 24%, reduces processing time by approximately 80%, and concurrently enhances resource efficiency relative to conventional analytical methodologies. The study concludes that BDA is instrumental for intelligent healthcare decision-making, facilitating the shift towards personalized medicine and proactive, preemptive clinical interventions.

Advancing Human–Computer Interaction Through Cognitive Computing And Natural Language Processing

Authors: Dr. R.Prema, Thulasi Ram S, Nandagopal S

Abstract: Human–Computer Interaction (HCI) is rapidly transforming from traditional interfaces into intelligent, human-like communication systems. Cognitive computing and Natural Language Processing (NLP) play major roles in enabling machines to understand, interpret, and respond to user needs the way humans do. This paper focuses on how cognitive analysis helps in understanding user behavior, emotions, attention, and decision-making, while NLP supports natural communication through text and speech. A progressive model combining cognitive features such as perception, memory, and reasoning with NLP components like speech recognition and semantic understanding is presented. The study highlights applications in virtual assistants, healthcare, accessibility technologies, and adaptive user interfaces. Challenges such as bias, multilingual limitations, and data privacy are addressed. The paper concludes with future enhancements including emotion-aware systems, real-time brain–computer interactions, and context-adaptive dialogue agents for more efficient HCI experiences.

Medical Chatbot AI

Authors: Professor Ami Sachin Shah, Sumit karde, Alok Kumar, Nishant Kumar Sah, Mukul Anand

Abstract: This research presents the development and evaluation of an artificial intelligence-based medical chatbot system integrated with physician consultation services. The objective is to enable patients to obtain rapid medical assistance when required. At present, individuals with minor health concerns often search for symptoms on random websites or wait for the issue to resolve. Neither approach is reliable online information may be inaccurate, and delays can worsen the condition. The situation becomes even more challenging in emergencies or when individuals are in unfamiliar locations without access to doctors. The proposed solution is a chatbot designed to assist patients. For minor issues, first-aid situations, and basic guidance, the chatbot provides immediate instructions. In cases where symptoms indicate a potentially serious condition, the system facilitates a direct connection between the patient and a physician for consultation. This ensures that patients receive initial guidance promptly, followed by professional medical care when necessary. The primary objective is to make healthcare faster, more reliable, and widely accessible. By reducing delays, minor health issues can be addressed before they escalate into major concerns. In the long term, the system can enhance patient awareness, increase confidence in AI-assisted healthcare, and foster greater trust in technology-driven medical solutions.

Emerging Trends And Core Technologies In Drone Technology_602

Authors: Sachin Vitthalrao Gaikwad

Abstract: Drone technology has witnessed significant advancements, transforming from basic remote-controlled devices into highly sophisticated autonomous aerial systems equipped with advanced sensors, robust communication modules, and intelligent navigation algorithms. These unmanned aerial vehicles (UAVs) are now integral to a wide array of industries including precision agriculture, environmental monitoring, infrastructure inspection, emergency response, security surveillance, and commercial logistics. This paper presents a comprehensive review of the current state of drone technology, emphasizing the critical hardware components such as multispectral sensors, LiDAR, GPS/INS navigation systems, and communication technologies spanning RF, 4G/5G, and satellite links. Furthermore, the paper explores emerging trends that are shaping the future of UAVs, including the integration of artificial intelligence and machine learning techniques that enable autonomous decision-making, obstacle avoidance, and adaptive flight control. The concept of swarm robotics is discussed, highlighting how multiple drones can collaborate to perform complex missions with enhanced efficiency and redundancy. The role of 5G-enabled edge computing is examined, which facilitates ultra-low latency data processing and real-time analytics critical for beyond visual line of sight (BVLOS) operations. In addition, the paper addresses prevailing challenges such as regulatory constraints, airspace integration, limited battery life, payload restrictions, and cybersecurity risks. Safety concerns and ethical considerations surrounding privacy and data security are also evaluated. Finally, the paper outlines prospective research directions aimed at advancing battery technologies, enhancing autonomous swarm coordination, improving sensor fusion, and developing resilient communication frameworks. These innovations are expected to broaden the operational capabilities of drones and accelerate their adoption across increasingly complex and dynamic environments.

DOI: http://doi.org/10.5281/zenodo.17810939

Cognitive Analytics: Augmenting Human Intelligence For The Digital Age

Authors: Dr.R.Prema, Vardinni Reddii

Abstract: This research presents the development and evaluation of an artificial intelligence-based medical chatbot system integrated with physician consultation services. The objective is to enable patients to obtain rapid medical assistance when required. At present, individuals with minor health concerns often search for symptoms on random websites or wait for the issue to resolve. Neither approach is reliable online information may be inaccurate, and delays can worsen the condition. The situation becomes even more challenging in emergencies or when individuals are in unfamiliar locations without access to doctors. The proposed solution is a chatbot designed to assist patients. For minor issues, first-aid situations, and basic guidance, the chatbot provides immediate instructions. In cases where symptoms indicate a potentially serious condition, the system facilitates a direct connection between the patient and a physician for consultation. This ensures that patients receive initial guidance promptly, followed by professional medical care when necessary. The primary objective is to make healthcare faster, more reliable, and widely accessible. By reducing delays, minor health issues can be addressed before they escalate into major concerns. In the long term, the system can enhance patient awareness, increase confidence in AI-assisted healthcare, and foster greater trust in technology-driven medical solutions.

DOI: http://doi.org/10.5281/zenodo.17813601

Big Data In Smart Cities: Applications And Challenges

Authors: Dr.C.K. Gomathy, Adivi Sri Lakshmi Sreeya, Bhagavathula Siva Dhatri

Abstract: The rapid growth of urban populations has increased the need for intelligent solutions that ensure efficient resource utilization, sustainable development, and improved quality of life. Smart cities leverage Big Data analytics to manage and utilize vast volumes of data generated from IoT devices, smart infrastructures, social networks, and public systems. The integration of Big Data technologies enables real-time decision-making, predictive analysis, and optimization of city operations such as traffic management, energy distribution, public safety, waste management, and environmental monitoring. This paper provides a comprehensive overview of the role of Big Data in smart city ecosystems and discusses its major applications. It also highlights key challenges, including scalability issues, data privacy concerns, lack of standardization, and integration complexities. The paper concludes by proposing future research directions aimed at enabling sustainable, secure, and autonomous smart cities.

DOI: http://doi.org/10.5281/zenodo.17813775

Real-Time Data Processing Using Apache Kafka: Architecture, Implementation, And Performance Evaluation

Authors: Dr. C.K. Gomathy, MVR Nikhil, S Sriharsha

Abstract: Apache Kafka has emerged as the industry-standard platform for real-time data streaming and large-scale event processing. With increasing digitalization across IoT, finance, e-commerce, and healthcare, organizations require high-throughput, fault-tolerant systems capable of handling millions of data events per second. This research investigates the role of Apache Kafka as the backbone of modern streaming architectures and evaluates its performance within a big data analytics pipeline. A detailed literature review identifies advancements in Kafka Streams, Kafka Connect, tiered storage, and exactly-once processing. The proposed methodology integrates Kafka with Apache Spark Structured Streaming to build a scalable real-time anomaly detection system. Experiments executed on a clustered environment show substantial improvements in throughput, latency, and overall reliability. Results validate Kafka’s effectiveness as a distributed commit log enabling real-time analytics at scale. The paper concludes with future directions such as serverless Kafka, AI- driven topic optimization, and cloud-native streaming enhancements.

DOI: http://doi.org/10.5281/zenodo.17813965

Digital Health Twin for Kidney Function Monitoring

Authors: Prof. Dr. Fatima Mohsin Inamdar, Tamanna Ragit, Rishika Raj, Nandini Rahane, Satyam Rahane, Satyam Rahane, Shaunak Rahatekar, Raviraj Raibagkar

Abstract: Every 1 in 7 Indians is affected by chronic kidney diseases (CKD), which often is left unnoticed due to negligence of health. This project introduces us with a digital health twin using machine learning which can predict CKD risk early according to the clinical data. A J48 decision tree [10] algorithm is used for classification due to its simple and easy to understand features. The project contains a user-friendly web interface made using Streamlit [9], which allows the users to input the clinical data and receive the required CKD predictions along with 3D [13] models of kidney health stages. With a good focus on awareness, accessibility, and real-world use, this system focuses on supporting early diagnosis and public education and knowledge on CKD.

DOI: https://doi.org/10.5281/zenodo.17826957

Mathematical Modeling to Predict the Change in the Rate of Degradation of Compressive Strength of Concrete Elements in an Abandoned Building Projects Using Non-Destructive Test Method

Authors: Nnorli, S.I., Onuamah, P.N., Ubaezuonu C. G

Abstract: Probing the structural integrity of abandoned building projects prior to its continuation and completion is a time and resource consuming task which informed the need to develop a model that will be of help in forecasting concrete strength degradation over time. This research studied three abandoned building projects at Federal Polytechnic, Oko, Anambra State, Nigeria with12, 14 and 10 years of abandonment and exposure to environmental factors. The initial concrete compressive strength of the structural elements in the building were sourced from the Physical Planning Unit of the institution. The current concrete compressive strength of the structural elements in the building were estimated using Non Destructive Test method (Rebound harmer Test) while noting their exposure conditions and number of years of abandonment. With the estimated concrete compressive strength as the dependent variable, the initial concrete compressive strength, number of years of abandonment and exposure conditions as the independent variables a regression model of fcu(t) = 0.5083-0.0331X1+0.9850X2 was developed, tested and to be satisfactory. The study established thatconcrete compressive strength decreases slightly over time when exposed to severe environmental conditions, green growth and developed a model to predict concrete compressive strength degradation over time.

DOI: https://doi.org/10.5281/zenodo.17836926

Big Data Challenges in 5G Networks

Authors: Dr.C.K. Gomathy, Dasari Venu Gopal, Konkala Krishna Pranav

Abstract: 5G networks generate unprecedented volumes of data due to massive device connectivity, ultra-high-speed communication, and the integration of emerging technologies such as the Internet of Things (IoT), edge computing, and network slicing. Managing this continuous, large-scale, and heterogeneous data stream introduces several challenges related to real-time processing, storage scalability, security, and energy efficiency. While Big Data analytics plays a crucial role in optimizing 5G performance, the stringent latency requirements and distributed nature of 5G infrastructure make conventional data-processing techniques insufficient. This paper presents a comprehensive study of the major Big Data challenges encountered in 5G networks, including data volume explosion, latency constraints, storage limitations, privacy risks, and the complexity of deploying AI/ML models at the network edge. Existing mitigation mechanisms—such as edge computing, software-defined networking (SDN), network function virtualization (NFV), and federated learning—are reviewed in detail. Finally, the paper highlights future research opportunities for achieving intelligent, scalable, and secure Big Data management in next-generation 5G and 6G communication ecosystems.

DOI: https://doi.org/10.5281/zenodo.17837446

Revolutionizing Mega Energy Projects With AI Artificial Intelligence

Authors: Hassan Alanzi

Abstract: Artificial Intelligence (AI) is reshaping how projects are planned and delivered. Across industries, and especially in Energy mega projects, AI augments human judgment with predictive analytics, natural language processing, and optimization. In oil and gas, projects often span multiple years, involve global supply chains, and face volatile market conditions. AI enables earlier risk detection, improved cost control, and more accurate forecasting, all critical for large scale capital projects.

DOI: http://doi.org/10.5281/zenodo.17839064

Radon Concentration In Drinking Water Along Yagachi River Basin

Authors: Niranjan R S, Ningappa C

Abstract: In this study, the annual effective dose exposure in the Yagachi river basin and the distribution of radon activity concentration in drinking water samples are measured. Emanometry method was used to determine the radon concentration in 30 samples of drinking water. The observed radon concentration in drinking water samples ranged from 12.26 ± 0.21 to 118.56 ± 2.65 Bq l-1 with an geometrical mean value of 45.24 ±1.03 Bq l-1. According to this study, all the drinking water samples examined had radon levels are above the USEPA's maximum contamination level of 11.1 Bq l-1. The geometrical mean annual effective dose varies from 33.47 to 323.67 µSv y-1 with geometrical mean value of 123.52 µSv y-1. Annual effective doses of 73% drinking water samples are above the recommended limit of 100 µSv y-1 recommended by World Health Organization.

DOI: http://doi.org/10.5281/zenodo.17852002

Big Data For Environmental Sustainability

Authors: Dr.CK Gomathy, Hariharan S, Rani Sri Lakshmi Sravya

Abstract: Environmental sustainability has become a global priority as rapid industrialization, climate change, and resource depletion continue to intensify ecological challenges. Managing environmental systems requires the ability to process massive volumes of real-time data collected from diverse natural and human-driven activities. This research explores the application of Big Data Analytics for enhancing environmental sustainability by examining large-scale ecological signals captured through modern digital technologies. These signals include satellite imagery, IoT- enabled air and water quality sensors, smart energy meters, climate monitoring systems, agricultural field sensors, and geospatial data from remote sensing platforms. By integrating these heterogeneous environmental data streams, the study introduces an intelligent analytical framework capable of detecting ecological changes, predicting pollution levels, forecasting climate variations, and optimizing resource usage. Using machine learning and deep learning algorithms, the framework processes complex environmental datasets to generate highly accurate predictive insights. The results show that Big Data-based environmental analysis significantly enhances sustainability planning, improves disaster preparedness, supports conservation strategies, and strengthens decision-making for climate-resilient smart cities.

DOI: https://doi.org/10.5281/zenodo.17839897

Security Challenges In Distributed Big Data Systems

Authors: Dr. C.K. Gomathy, M. Srinivasa Aditya

Abstract: Distributed Big Data systems form the backbone of modern analytics-driven organizations, enabling scalable storage, fast computation, and real-time processing. However, their distributed and heterogeneous nature introduces complex security challenges that differ significantly from traditional centralized systems. This extended essay examines major security concerns such as data confidentiality, integrity, access control, insider threats, network-based attacks, and infrastructural vulnerabilities. It also discusses why these issues are amplified in distributed environments and presents a comprehensive overview of modern mitigation strategies. This expanded version provides deeper insights, additional context, and elaborated arguments to meet academic publication standards and maintain a 1000-word requirement.

DOI: https://doi.org/10.5281/zenodo.17840039

A Conceptual Framework for Accelerating Green Concept Implementation in the Sri Lankan Construction Industry

Authors: G S P Gunasekara

Abstract: The construction sector has a great impact on global economic growth, and at the same time, it has led to a high rate of environmental degradation, as it consumes about a quarter of the world's energy and nearly a third of the emissions of greenhouse gases. Although there is an ever-growing global concern regarding sustainability and green building technologies (GBTs), the use of GBTs is still unsustainable and incoherent. This study builds upon a well-developed conceptual framework to expedite the application of the green building concepts in the Sri Lankan construction industry by determining and incorporating key barriers, drivers, and enablers. The study will use a sequential exploratory design, which involves using qualitative data such as semi-structured interviews and focus group discussions, and then quantifying the data using structured questionnaires given to 150-200 construction industry practitioners. The study has been conducted with reference to four main aims, namely determining the barriers to the adoption of GBT, studying the driving forces and enablers, working out the integrated conceptual framework, and strategic policy, technology, and stakeholder engagement recommendations. The results indicate that barriers to adoption are high initial costs, poor technical knowledge, poor regulatory compliance, and poor stakeholder coordination, whereas the enablers are: financial incentives, capacity building programs, technological readiness, and enabling policy environments. The suggested framework provides a systematic guide to decision-makers, construction professionals, and regulators to address issues of implementation and to achieve national development objectives in accordance with international climate obligations.

Mental Health Detection Via Chatbot

Authors: Professor Roshan U, Aman Kumar Rana, Hemant Kumar Roy, Kushal Malviya, Md Fardwish Ali

Abstract: There has been a significant rise in mental health problems like emotional instability, anxiety, and stress in the college and younger adult populations. Many people struggling with these kinds of issues do not seek help in a timely manner because of stigma, a lack of understanding or knowledge about mental health, and/or the limited availability of professional supports. To address this gap, we developed an artificial intelligence-enabled mental health chatbot that can identify the user's emotional state through their written words and respond accordingly with empathy. The emotional recognition element of our system uses a Support Vector Machine (SVM) emotion classifier and the dialogue generation aspect of our chatbot employs a Seq2Seq network combined with an attention mechanism. We train our SVM emotion classifier on the EmpatheticDialogues dataset and our Seq2Seq model on the CounselChat dataset. The use of attention features results in significant increases in both emotion recognition accuracy and conversation coherence in our comprehensive experimentation. Although our system will never replace a licensed therapist, we believe that it can provide people with an opportunity to express their feelings and gain awareness of their mental health status before seeking professional assistance.

BidMaster: Web-Based Auction Automation System

Authors: Ms. Farha Anjum

Abstract: The Online Auction System is a comprehensive web-based platform developed to modernize and digitize the traditional auction process, making it more accessible, efficient, and transparent. This system enables users to participate in auctions from anywhere in the world, eliminating the need for physical presence and significantly reducing the costs and logistical constraints associated with traditional auctions. Users can register as either sellers or buyers, with sellers given the ability to list products along with detailed descriptions, images, and minimum bid amounts. Buyers, in turn, can browse active auctions and place bids in real time, with the system automatically updating the current highest bid and notifying participants of any changes.

Hybrid Chatbot For Educational Institutions

Authors: Vinod Kulkarni, Sunny Kumar, Srinivas Reddy A, Sohail Mateen, Vinod Kumar

Abstract: Educational institutions face a growing volume of repetitive and time sensitive queries from students, ranging from admissions and examinations to hostel services and academic support. Traditional helpdesks often struggle to address these queries efficiently, resulting in delays and reduced student satisfaction. This paper presents a Hybrid Chatbot System designed specifically for educational environments, integrating rule based logic, retrieval based search, and generative AI models. The hybrid architecture intelligently routes queries through an intent classification module, ensuring precise responses for structured information, accurate retrieval of institutional data, and conversational explanations for open ended queries. Comprehensive evaluation results show significant improvements in accuracy, scalability, and response quality compared to traditional chatbot systems. The proposed approach demonstrates strong potential to transform student support services and enhance digital connectivity across modern educational institutions.

DOI:

Artificial Intelligence-Assisted Paediatric Nephrology: A Comprehensive Review

Authors: Aiysha SameenaV, Dr Rajkumar R

Abstract: Pediatric nephrology faces unique challenges in the early detection, diagnosis, and management of kidney diseases due to the subtlety of symptoms and the need for specialized expertise. The integration of Artificial Intelligence (AI) offers promising avenues to enhance clinical decision-making, improve diagnostic accuracy, and personalize treatment strategies in this specialized field. This comprehensive review aims to evaluate the current applications, methodologies, and future prospects of AI in pediatric nephrology, focusing on its role in early detection, diagnosis, and management of kidney diseases in children. A systematic literature search was conducted across databases including Scopus, Web of Science, PubMed, and ScienceDirect to identify relevant studies published between 2015 and 2023. The review encompasses various AI methodologies such as machine learning algorithms (e.g., XGBoost, logistic regression), deep learning models, and their integration with electronic health records (EHRs). The analysis includes studies on AI-assisted histopathological image analysis, predictive modeling for acute kidney injury (AKI), chronic kidney disease (CKD), and other glomerular diseases in pediatric populations. The review highlights that AI models have demonstrated efficacy in predicting AKI by analyzing variables like serum creatinine and urine output, with some models achieving high accuracy rates. Integration of AI with EHR systems has shown potential in providing timely alerts, thereby improving patient outcomes. AI-assisted image analysis tools have enhanced the accuracy and efficiency of diagnosing various kidney pathologies. However, challenges such as data quality, algorithmic bias, and the need for domain-specific training remain prevalent.

DOI: https://doi.org/10.5281/zenodo.17863132

UWB Antenna Design Incorporating A Combination Of An Ellipse Shaped Radiating Patch And A Slot

Authors: Subi S, Lekshmi Vijayan, Jyothi J S, Dhrisya S. Anil, Dr Abhilash S. Vasu

Abstract: An ultra-wideband (UWB) antenna is proposed using a combination of an elliptical-shaped patch and a slot structure. The antenna employs a coplanar waveguide (CPW) feeding technique, consisting of a central signal strip, two rectangular ground planes. The signal strip is directly connected to the elliptical patch and slot, which together function as the primary radiating elements responsible for achieving wideband operation. This integrated geometry enhances impedance matching and supports efficient radiation across the UWB spectrum, making the design suitable for compact and high-performance UWB applications. The antenna achieves a wide impedance bandwidth ranging from 3.44 to 11.17 GHz, as verified through return-loss analysis. Surface current distribution confirms that the elliptical radiating patch plays a dominant role in efficient radiation. The proposed design attains a peak gain of 4.40 dBi, making it suitable for UWB communication and sensing applications. The VSWR remains below 1.75 throughout the operating band, indicating superior impedance matching and reduced reflection losses. Additionally, the E- and H-plane polarization characteristics reveal low cross-polarization levels, ensuring stable and clean radiation patterns. Overall, the antenna demonstrates robust UWB performance with desirable attributes in bandwidth, gain, matching, and polarization purity.

DOI: https://doi.org/10.5281/zenodo.17863104

Boundary Lubrication Characteristics of Soybean Oil and Recycled Frying Oil Mixtures: An Experimental Analysis

Authors: Dr.Manju Rathore, Mary singh Magdline

Abstract: This study investigates the performance of soybean oil and used frying oil blends, specifically their Fatty Acid Methyl Esters (FAMEs), under boundary lubrication conditions. The experimental setup involved testing these blends using a Pin-on-Disc apparatus, where a steel ball was applied against a journal bearing material sample connected to a rotating disc. The tests were conducted under various parameters, including loads of 5, 10, and 20 N, sliding velocities of 100, 300, and 500 rpm, and a sliding distance of 500 m. Surface roughness of the test samples was measured before and after each test, and wear rate was assessed through mass loss measurements, with specific wear rates (ws) calculated. Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) analyses were employed to examine the wear contact surfaces. Results demonstrated significantly lower friction coefficients (μ) in boundary lubrication conditions compared to dry (lubricant-free) conditions, highlighting the enhanced lubricating properties of the soybean oil and used frying oil blends. This research provides valuable insights into optimizing bio-based lubricants for reduced friction and wear, supporting the development of more sustainable lubrication solutions.

DOI: https://doi.org/10.5281/zenodo.17863095

Spatial Analysis Of Architectural Education And Professional Distribution In India With The Focus On Karnataka

Authors: Tejaswi N H

Abstract: Architectural education in India has expanded significantly, with over 422 colleges and 21,968 graduates annually, but still faces challenges including decreasing trend in enrollment especially in higher education. Architectural colleges are clustered geographically within specific states (e.g. Maharashtra, Tamil Nadu, and Karnataka), which has created an inconsistency between what students are learning and what is practised professionally. This research will help fill a gap in synthesised analysis and provide specific context for the spatial arrangement of architectural education and career opportunities in India. The research seeks to map and analyze the distribution of architectural colleges, the intake capacity of colleges, and the total count of registered architects in India, and in Karnataka in particular. The research complements analytical mapping with tabular analysis and outcomes and compares those results with Lorenz and Gini coefficient inequality measures. The research demonstrates a significant decrease in B.Arch enrollment at the national level, and extreme inequality in the distribution of architects in Karnataka as well. The research highlights that Karnataka, Bengaluru Urban, has the most educational facilities and registered professionals. Ultimately, the research demonstrates the urgent demand for change in architectural education and the need for equitable resource distribution to address and remedy regional imbalances to support sustainable urban growth.

DOI:

Implementation Of Corporate Governance Mechanisms And Its Role In Achieving The Quality Of Financial Reporting Information For Economic Institutions: A Field Study On Some Sudanese Economic Institutions

Authors: Abdelsalam Awad Khair Elseed

Abstract: Sudanese economic institutions operate in a highly risky environment, subject to local and global changes. Therefore, there is a need for corporate governance mechanisms to improve the quality of financial reporting and achieve risk disclosure and mitigation. The primary objective of this study is to understand the role of implementing corporate governance mechanisms in achieving quality financial reporting information for Sudanese economic institutions. It is also intended to encourage economic institutions operating in the Sudanese environment to implement corporate governance mechanisms and benefit from their multiple advantages in achieving quality financial reporting information. To achieve the objectives of the field study, the inductive approach and the descriptive-analytical approach were adopted. (150) questionnaires were distributed to a sample of employees in the study community, which comprises some Sudanese economic institutions. All questionnaires were returned at a rate of 100%. After analyzing the data, the study reached several results that validated the hypotheses. These results include that implementing corporate governance mechanisms contributes to achieving quality information related to the economic resources of an institution, and that implementing corporate governance mechanisms contributes to achieving quality information related to the liquidity position, as it is of interest to users. The scientific significance of the study stems from the importance of achieving quality in financial reporting information, which users rely on to guide their various decisions. Furthermore, the study supports implementation of corporate governance mechanisms in the Sudanese business environment.

DOI: https://doi.org/10.5281/zenodo.17863079

An Overview On Making A Nanofluid From Vegetable Oil And Researching Its Insulating Qualities

Authors: Dr. M. M. Abdul Kader Mohideen, Mr. M. Karthigeyan, Dr. S. R. Chitra

Abstract: The use of vegetable oil as an insulating fluid is becoming increasingly popular due to the mineral oil's qualities, which were first shown by researchers in 1995 [1]. Subsequent studies have demonstrated that a few nanoparticles, including graphene, hexagonal boron nitride (h-BN), titanium oxide (TiO2), aluminium oxide (Al2O3), and aluminium nitride (AIN), can increase the thermal conductivity of mineral transformer oil [2–8]. However, it is also recognised that there is a strong demand for mineral oil these days because it is used as a lubricating fluid in other industries including transportation. Mineral oil poses a threat to the environment and is a non- renewable resource that is running out every day and is predicted to run out entirely in a few decades [1]. In response to this issue, researchers are constantly testing vegetable oil as a mineral oil substitute for transformer insulation [9]. Due to their high flash point and renewable nature, vegetable oils were initially suggested as a liquid for transformer insulation [10]. Researchers also switched the foundation oil of the nanofluid from mineral oil to vegetable oil in accordance with the advances in nanotechnology [11–12]. Just one of the 29 research articles published in 2012 on the issue of nanofluids as transformer insulation liquids employed vegetable oil as the base fluid, according to the statistical analysis from Scopus (Figures 1 and 2). This demonstrates that in 2012, there was little interest in using vegetable oil-based nanofluid as a liquid for transformer insulation. According to the statistics, only three of the more than 60 works published in 2018 on the subject of nanofluid as transformer oil used vegetable oil-based nanofluid as the research material. This indicates that the field of study on this topic is still in its infancy and that its popularity is growing annually.

Aerodynamic Characteristics Calculation Of Aircraft Propellers Using The Blade Element Method

Authors: MSc Trong Thuong Tran, MSc Trong Son Phan, Dinh Dat Le, BSc Van Quyen Dinh

Abstract: This paper presents the application of the Blade Element Method (BEM) to calculate the aerodynamic characteristics of aircraft propellers. In this method, the propeller is divided into blade elements, each treated as an airfoil section of an aircraft wing. To determine the aerodynamic characteristics of the entire propeller, it is necessary to compute the aerodynamic forces on each element based on local geometric parameters and flow conditions at the section. The integration over all blade elements yields the total thrust and its coefficient.

DOI: http://doi.org/10.5281/zenodo.17863218

A Machine Learning Approach To Real Time Crop Recommendation, Plant Disease Identification, And Yield Estimation

Authors: Ganesh A Patil, Kishan Kumar G, Kushal M K, Dr Ashwin M

Abstract: This project presents an integrated artificial intelligence system designed to address key challenges in modern agriculture: suboptimal crop selection, plant disease outbreaks, and inaccurate yield forecasting. The system synergistically combines Internet of Things (IoT) sensors, machine learning, and deep learning models into a unified framework to empower farmers with data-driven decision-making. The pipeline is structured into three core modules. First, a Crop Recommendation Engine employs a Random Forest (RF) algorithm, which demonstrated superior performance (99.09% accuracy) over comparative models like k-Nearest Neighbors and Decision Trees, to suggest the most suitable crops based on real-time analysis of soil NPK (Nitrogen, Phosphorus, Potassium) content, pH levels, moisture, temperature, and humidity. Second, a Disease Identification System utilizes a Convolutional Neural Network (CNN) trained on the PlantVillage dataset to accurately detect and classify diseases from leaf images, enabling early intervention. Third, a Yield Prediction Module implements a Decision Tree model to estimate agricultural output using historical and environmental data, including rainfall, temperature, and pesticide usage. This work demonstrates the practical viability of leveraging contemporary AI tools—such as CNNs, Random Forests, and IoT connectivity—to enhance farm productivity, optimize resource utilization, and promote sustainable agricultural practices in an accessible and scalable manner.

The Role of Retrograded Resistant Starch in Reducing the Risk of Non-Communicable Diseases

Authors: Sharma Priyanka, Sharma Shilpi

Abstract: Retrograded resistant starch is formed through the realignment of molecular chains into crystalline structures when gelatinized starch is cooled. Interest in utilizing resistant starch for the prevention and management of NCDs has increased significantly over recent years. This naturally occurring starch fraction resists digestion in the small intestine and functions like dietary fiber, conferring metabolic and physiological benefits important for long-term health. Its slow digestibility supports better glycemic control by reducing postprandial blood glucose spikes, enhancing insulin sensitivity, making it a nutraceutical dietary component with a very promising preventive role against type 2 diabetes. It further promotes satiety and modulates energy intake, contributing to effective weight management, thereby reducing obesity risk. Retrograded starch is fermented as a substrate for beneficial gut microbiota within the colon, in a process that produces short-chain fatty acids, which include butyrate, important in strengthening intestinal integrity, promoting anti-inflammatory effects, and potentially reducing the risk of colorectal cancer. Its positive influence on lipid metabolism and inflammatory markers supports cardiovascular health. Being resistant to the usual cooking and cooling processes, retrograded resistant starch has great potential for a wide range of applications in improving the functional and nutritional value of various food products. In summary, enhancing dietary retrograded resistant starch intake is a practical, natural, and effective strategy against the global burden of NCDs by improving metabolic regulation and gut health.

A Study On Factors Influencing Customer Relationship Management On Consumer Durable Goods

Authors: Dr.M Sathish, Revathi T

Abstract: The study aimed to examine the factors influencing customer relationship management on consumer durable goods. The study includes 400 customers purchasing and using consumer durable goods. The study utilized questionnaire for collection of data from the customers. Data is analysed using simple percentage analysis, chi-square test, factor analysis, multiple linear regression test and t-test.Findings showed that awareness levels differ significantly across demographic groups, indicating that age, education, occupation, income, and residential background play a vital role in forming customers’ knowledge of durable products. Factor analysis highlighted customer engagement, relationship development, technological support, organizational backing, customer value, and service recovery as major contributors to CRM effectiveness. Results confirmed that CRM activities strongly influence satisfaction, as customers appreciate responsive service, accessible support, smooth warranty procedures, and reliable installation. To sum up, CRM practices significantly strengthen satisfaction and customer relationships.

DOI: http://doi.org/10.5281/zenodo.17877384

Uav-Based Aerial Imagery Recognition for Reconnaissance and Search and Rescue Operations

Authors: Dinh Quang Phuc, Vu Van Le

Abstract: Unmanned Aerial Vehicles (UAVs) have emerged as pivotal instruments in reconnaissance, surveillance, and Search and Rescue (SAR) operations, owing to their rapid mobility and capability to survey extensive terrains. However, the analysis of UAV-derived imagery demands robust processing and recognition algorithms, particularly when operating within complex environments characterized by high noise levels or minute target dimensions. In the context of military operations, the reconnaissance of vehicles and weaponry presents significant challenges due to the constraints on direct human access and the extreme risks associated with detection. Furthermore, these challenges are equally critical in aerial search and rescue missions and maritime incident responses. Consequently, the modernization of military reconnaissance and SAR protocols is an urgent imperative. The deployment of UAVs for these objectives significantly expands operational coverage, enhances localization precision, and minimizes direct human exposure during reconnaissance and search missions. Thus, research into "Image Recognition and Processing Techniques for Unmanned Aerial Vehicles" holds profound practical significance.

DOI: https://doi.org/10.5281/zenodo.17877514

A Study On Customer Satisfaction About Mobile Wallet Users In Erode District

Authors: Dr.P.Parvatham, Dr.M.Revathi

Abstract: The rapid growth of digital payment systems in India has transformed the way customers conduct financial transactions. Mobile wallets, in particular, have gained widespread acceptance due to their convenience, speed, and accessibility. This study examines the level of customer satisfaction with mobile wallet services in the Erode district. The research focuses on key factors such as ease of use, security, reliability, promotional offers, and customer support. A descriptive research design was adopted, and primary data were collected through a structured questionnaire from mobile wallet users in the region. The findings of the study reveal that convenience, transaction speed, and cashback offers play a significant role in determining satisfaction levels. However, concerns related to technical issues, transaction failures, and security risks still affect consumer trust. The study concludes with suggestions for service providers to enhance customer experience and improve user retention.

DOI: http://doi.org/10.5281/zenodo.17877633

Combining Live Location Tracking With Community Networks For Enhanced Emergency Response In Mobile Safety Applications

Authors: Dr.Rajesh Choudhari, Shatakshi Shah, Paras Jagtap, Vivek Patil, Harsh Mahajan, Siddhesh Dabbawar, Prathamesh Shinde

Abstract: Research on emergency alert and mobile safety applications highlights obstacles pertaining to responsiveness, integration with emergency service organization, efficiency, and alert customization for users. A review of systems that offer panic buttons, automatic location sharing, and real time emergency alerts identifies key challenges relating to slow alert communication, weak linkages with emergency service providers, and adaptation for real-world operations. Advanced Personal Safety Application proposes a solution to these challenges with a rapid, community centric approach featuring real time GPS tracking, video and audio streaming, and customizable emergency alerts. This system creates immediate communication channels for users, emergency contacts, local volunteers, and police ensuring fast response in critical situations.Integrating personal safety tools with advanced emergency response systems enhances the safety features available to those in dangerous situations, improving reliability, accessibility, and overall function.

DOI: https://doi.org/10.5281/zenodo.17878016

Responsible AI Controls for Identity Governance, Data Trust, and Security Assurance in Multi-Cloud Customer and Patient Data Environments

Authors: Srinivasa Chakravarthy Seethala

Abstract: This study investigates the growing need for responsible AI controls that protect identity, maintain data trust, and ensure security assurance across multi cloud environments supporting both customer and patient information. As organizations expand into distributed computing models, the complexity of managing identities, safeguarding sensitive data, and maintaining regulatory alignment has intensified, creating a critical gap between traditional governance models and the demands of AI enabled platforms. The purpose of this research is to develop a comprehensive framework that integrates responsible AI mechanisms with identity governance, risk based access controls, automated policy validation, and real time monitoring to enhance protection across heterogeneous cloud ecosystems. The study applies a mixed methodology, combining qualitative analysis of governance practices, regulatory expectations, and risk taxonomies with quantitative examination of cloud identity workflows, anomaly detection signals, and AI driven policy enforcement patterns. Key findings highlight that responsible AI controls significantly strengthen data trust by improving consistency, transparency, and auditability in identity management operations while reducing access related security deviations. The proposed model advances current practice by aligning AI driven risk scoring, data lineage intelligence, and federated identity orchestration with compliance structures required in customer centric and patient centric environments. Strategic contributions include a new governance architecture for secure AI adoption and a validated control model that can support scalable, compliant, and ethically aligned data ecosystems. This research strengthens academic understanding of responsible AI oversight while offering practical pathways for industry implementation across healthcare and enterprise multi cloud platforms.

DOI: https://doi.org/10.5281/zenodo.17878867

Deep Reinforcement Learning for Autonomous Microservice Scaling and API-Level Optimization in Large-Scale Enterprise Platforms

Authors: Srinivasa Chakravarthy Seethala

Abstract: Autonomous microservice scaling has become a central challenge for large enterprise platforms as rising API workloads, unpredictable traffic bursts, and complex interservice dependencies exceed the capabilities of static threshold rules and heuristic based autoscaling strategies. This study examines how deep reinforcement learning can serve as an adaptive decision layer for continuous resource optimization, enabling platforms to maintain stable performance while minimizing operational overhead. The purpose of the research is to design and evaluate a deep reinforcement learning model that learns optimal scaling behaviors at both the microservice and API endpoint levels by observing latency patterns, queue depths, call graph interactions, and container performance indicators. A mixed methodological approach is used, combining quantitative experiments on simulated large scale workloads with qualitative assessments of model behavior, action stability, and interpretability patterns across varying API conditions. The findings demonstrate that agents trained with actor critic architectures significantly outperform rule based and predictive autoscalers in maintaining low tail latency, reducing aggressive scale outs, and stabilizing throughput during complex dependency shifts. The study introduces an architectural blueprint that integrates policy networks with real time telemetry streams and platform orchestration layers, offering a scalable path for intelligent operational autonomy within enterprise environments. The research contributes to academic discourse by extending reinforcement learning applications to fine grained API optimization rather than coarse infrastructure control, while providing industry practitioners with strategies to manage rising platform complexity. The conclusion highlights that deep reinforcement learning can serve as a foundation for future self regulating enterprise architectures where scaling, traffic shaping, and resource allocation operate in a cohesive intelligence loop without human intervention.

DOI: https://doi.org/10.5281/zenodo.17879051

A Unified Predictive Data Engineering Framework for High-Throughput ETL Pipelines Across Oracle Cloud, Google Cloud, and Distributed SQL Systems

Authors: Srinivasa Chakravarthy Seethala

Abstract: This study develops a unified predictive data engineering framework that addresses the escalating complexity of managing high throughput ETL pipelines deployed across Oracle Cloud, Google Cloud, and distributed SQL systems. Modern data ecosystems operate under intense velocity and scale, yet remain constrained by fragmented pipeline orchestration, reactive performance tuning, and inconsistent cross platform optimization strategies. The purpose of this research is to construct an integrated architecture that enables anticipatory workload management, dynamic resource allocation, and continuous quality validation by combining statistical profiling, feature driven workload prediction, and cloud native pipeline instrumentation. A mixed methodology is applied that blends quantitative analysis of historical ETL execution logs, latency distributions, anomaly trends, and throughput patterns with qualitative assessments of workflow bottlenecks, runtime behaviors, and control plane interactions across heterogeneous data platforms. Findings demonstrate that predictive modeling embedded within the orchestration layer significantly improves execution reliability, stabilizes throughput during peak load intervals, and reduces pipeline recovery overhead. The proposed framework introduces a harmonized predictive controller that learns from both cloud specific signals and distributed SQL characteristics, enabling proactive scheduling and error prevention across multiple execution environments. This contributes to strategic advancements in unified data engineering design and strengthens academic understanding of predictive pipeline governance across federated cloud systems. The study concludes that integrating predictive intelligence directly into ETL lifecycle management establishes a scalable foundation for next generation enterprise data operations and provides actionable insights for organizations seeking resilient, efficient, and cloud agnostic data processing capabilities.

DOI: https://doi.org/10.5281/zenodo.17879236

Ai and Iot Precision Agriculture

Authors: Vishnu Prasad k, NIthin K, Nandish K B, Dushyanth Gowda, Chethan Kumar T, Divya C Brijesh

Abstract: The agriculture sector is seeking innovative technologies for increasing food-grain production and productivity because of the rapid increase in population, unpredictable climatic changes and food security concerns. The inherently complex, dynamic, and non-linear nature of agricultural system require solutions based on advance techniques and technologies that can monitor, control, and visualize various farm operations in real-time to provide greater accuracy, better understanding, and appropriate solutions. Thus, artificial intelligence and internet of things is progressively emerging across all the industries including agriculture. Advancement in artificial intelligence and IoT based technologies has made revolutionary changes in agriculture to maximize the crop productivity. In this paper a comprehensive review on artificial intelligence and internet of things(IoT) in the areas of the agriculture is presented. The main objective of this paper is to review various potential applications of artificial intelligence and IoT such as robotics, drones, fertilizer application, weed and pest control, IoT based smart irrigation, real time weather forecasting etc.

Generative AI Guided Workforce Intelligence Graphs For Role, Skill, And Mobility Forecasting In SAP SuccessFactors Landscapes

Authors: Nisha Kulkarni, Rohan Mehta, Arvind Sethi, Vasudev Sharma

Abstract: Organizations operating in dynamic labor markets increasingly depend on advanced workforce intelligence capabilities to understand how roles evolve, how skills diffuse, and how employees move across functions within complex enterprise environments. Traditional analytics in SAP SuccessFactors landscapes often rely on static hierarchical models and structured records that provide limited visibility into the multidimensional patterns shaping workforce behavior. This study proposes a generative AI guided workforce intelligence graph framework that captures employees, roles, skills, credentials, learning histories, and mobility events as interconnected structures capable of representing both explicit and inferred relationships. The framework integrates graph modeling, semantic enrichment, and generative AI reasoning to produce context aware insights for role transition forecasting, emerging skill identification, and internal mobility prediction. A mixed methodological approach combining architectural modeling, graph construction, temporal pattern analysis, and generative AI based inference was employed to evaluate how enriched graph representations improve predictive reliability across diverse workforce scenarios. Findings demonstrate that graph enhanced and AI guided embeddings significantly strengthen the accuracy and interpretability of mobility forecasting, reduce the effort required to identify skill adjacency patterns, and provide managers with narrative insights that align with real world workforce dynamics. The study contributes an extensible design blueprint for enterprises seeking to modernize workforce planning, enhance decision support, and operationalize future ready HR ecosystems within SAP SuccessFactors landscapes.

DOI: http://doi.org/10.5281/zenodo.17895770

Comparison of TopoDEM and CartoDEM for Height accuracy assessment over different terrains of Bhutan.

Authors: Lobzang Tobgye

Abstract: Digital Elevation Models (DEMs) are essential geospatial datasets representing Earth's surface elevation, widely used in applications such as flood modeling, urban planning, and disaster response. However, DEMs derived from sources like satellite imagery, LiDAR, or photogrammetry can contain errors due to sensor limitations, processing issues, and surface conditions. Accurate validation against high-quality reference data, such as ground-based surveys or GPS measurements, is crucial, employing statistical metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Standard Deviation of Errors (STD) to quantify discrepancies. This study assesses the vertical accuracy of a local 10 m DEM (TopoDEM) generated from contour lines and spot heights derived from 1:25,000 digital topographic maps via digital photogrammetry, compared to the Cartosat-1 DEM (CartoDEM) by ISRO, which claims 8 m accuracy at LE90. Validation utilized ground control points (GCPs) across test areas in Bhutan (Gelephu, Paro, and Trashiyangtse), computing elevation differences and statistical measures. Results indicate TopoDEM generally outperforms CartoDEM, with RMSE values ranging from 4.56 m to 6.62 m across terrains, versus CartoDEM's 4.26 m to 9.04 m. TopoDEM showed lower errors in Paro and Trashiyangtse, while Gelephu exhibited higher residuals due to recent topographic changes. Over flat terrains like Gelephu, RMSE was 4.26–6.62 m, and in high-altitude areas like Trashiyangtse, it ranged from 6.42–9.04 m. In conclusion, TopoDEM demonstrates superior vertical accuracy, underscoring the value of high-resolution datasets from digital photogrammetry for reliable elevation modeling in diverse landscapes. This highlights the need for rigorous accuracy assessments to ensure DEM reliability in critical applications.

Edge-Enhanced IoT with Deep Learning and Generative AI: A Lightweight Framework for Autonomous Real-Time Systems

Authors: Ms. Aarti, Dr. V.K. Srivastava

Abstract: The sudden growth of the Internet of Things (IoT) has added pressure on the necessity to have real-time intensive and energy-efficient edge network data processing. The traditional cloud-based designs are dogged by latency, bandwidth and privacy issues rendering them unsuitable in mission-critical Internet of things applications. The study will provide a lightweight Edge-Enhanced IoT model that combines optimized Deep Learning (DL) models with Generative Artificial Intelligence (GenAI) to support autonomous real-time decision-making. The structure uses quantized and pruned neural networks to infer edges efficiently and uses small-scale neural generators to supplement low-quality sensor measurements and restore lost values and model rare anomalies. To maximize performance and reliability, an architecture with multiple layers with local sensing, edge/fog computation, generative enhancement, and selective cloud synchronization is proposed. It has been shown that, through experimental findings, the accuracy, latency, energy consumption, and scalability of the technology have improved in a variety of IoT applications, such as health monitoring, environmental sensing, and industrial condition analysis. The results indicate the opportunities of integrating Edge Computing, Deep Learning, and Generative AI to develop the next generation intelligent IoT infrastructure that can provide secure, fast, and autonomous real-time services.

DOI: https://doi.org/10.5281/zenodo.17894113

Smart Irrigation System Using Artificial Intelligence and IOT And IOT

Authors: C.G Anu Chandra, Dinesh Kumar S, I Mohammad Aousaf, Asst.Prof Sumitra Sharma Phurailatpam

Abstract: Efficient water management has become a critical global challenge due to increasing agricultural demands, unpredictable climatic conditions, and limited freshwater resources. Traditional irrigation practices often lead to excessive water usage, reduced crop yields, and higher operational costs. To address these issues, this study presents a Smart Irrigation System that integrates Artificial Intelligence (AI) and the Internet of Things (IoT) to enable intelligent, autonomous, and sustainable water management. In the proposed system, IoT-enabled sensors such as soil moisture sensors, temperature sensors, humidity sensors, and flow meters continuously monitor real-time environmental and soil parameters. These data streams are transmitted to a cloud platform where AI algorithms analyze patterns, predict soil moisture levels, and determine optimal irrigation schedules. The AI module employs machine learning techniques—such as regression models or neural networks—to forecast irrigation needs based on historical and real-time data, crop type, soil condition, and weather predictions. The system automatically actuates irrigation valves using microcontrollers or smart irrigation controllers, ensuring water is supplied precisely when and where it is needed. Additionally, the system provides a user-friendly dashboard or mobile application for remote monitoring, data visualization, and manual override, enhancing farmer engagement and decision-making capabilities.

Dual Band RF Energy Harvesting System

Authors: Dr. Malini S, Meghamala s, Nived madhu

Abstract: We present a compact dual-band RF energy-harvesting system integrating antenna, matching and rectification techniques from recent studies, enhanced with intelligent monitoring and power management. The proposed architecture operates across two major RF bands—UHF (≈0.9/1.8 GHz) and ISM (2.45/5.8 GHz)—using a dual- resonant antenna and adaptive Pi/T matching network to efficiently capture ambient RF power. A hybrid rectifier design combines a low-threshold CMOS cross-coupled rectifier for UHF signals with a Schottky-diode multiplier stage for ISM signals, improving sensitivity from −20 dBm to 0 dBm. The harvested energy is regulated through an XL6009 DC–DC boost converter, enabling stable output even under fluctuating RF input conditions. An ESP32 microcontroller is incorporated for system control, data logging, and wireless communication, while a 16×4 LCD display provides real-time monitoring of harvested voltage, current and system status. Energy is stored in a low-leakage capacitor and managed through an integrated MPPT-based power controller. Prototype results demonstrate peak PCE of 50–64% and reliable operation for low-power IoT and wearable applications

Integrated Hydrochemical Modelling And Statistical Analysis For Irrigation Water Quality Assessment: A Case Study Of Hadejia Water Systems In Northern Nigeria

Authors: Nura Umar Kura, Sani Umar Usman, Dau Umar Abba, Tukur Mohammed Ahijo

Abstract: Understanding the hydrochemical characteristics of groundwater and surface water is vital for sustainable irrigation management in semi-arid environments. This study employed integrated hydrochemical modelling and statistical analysis to evaluate water quality and irrigation suitability in Northern Nigeria. Thirteen water samples: ten groundwater (G₁ to G₁₀) and three surface waters (R₁ to R₃) were analysed for major cations and anions (Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl⁻, NO₃⁻), electrical conductivity (EC), total dissolved solids (TDS), and pH. Irrigation indices, including Sodium Adsorption Ratio (SAR), Percent Sodium (%Na), Kelley’s Ratio (KR), and Magnesium Hazard (MH), were modelled to classify suitability using FAO and USSL standards. Multivariate techniques: Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), were applied to identify dominant processes and inter-parameter relationships. The results showed that EC ranged from 152.9 to 682 µS cm⁻¹ (mean = 345.6 µS cm⁻¹), while TDS ranged from 97.9 to 436.5 mg L⁻¹ (mean = 234.1 mg L⁻¹), classifying most samples as low-salinity (C₁) and medium-salinity (C₂) waters. SAR values (0.21- 2.54) confirmed low sodicity (S₁) across all samples, whereas Na% (14 – 48%) and KR (< 1) indicated excellent to good irrigation water. PCA revealed three principal components explaining 83% of total variance, dominated by mineral dissolution and ion-exchange processes. HCA separated samples into three clusters: Cluster I (groundwater with higher mineralisation) and Cluster II (river water with moderate ionic concentrations). Overall, water from the study area is suitable for irrigation with minimal management practices. Continuous monitoring is recommended to track potential salinisation as agricultural expansion intensifies.

DOI: http://doi.org/10.5281/zenodo.17897905

The Impact of Crude Oil and Non-Oil Revenues on the Economic Growth of Nigeria

Authors: Ikechi Igwe, Jeremiah Okoroma, Uchechi Alerechi

Abstract: Nigeria's economy primarily relies on revenue from crude oil export since crude oil replaced agricultural produce as Nigeria's main source of revenue. This study investigated the impact of crude oil and non-oil revenues on the economic growth and development of Nigeria from the year 2015 to 2020. Economic growth and development performance indicators considered include oil revenue; oil price volatility; Gross Domestic Product; Per Capital Income; and non-oil revenue. The study employed an ex post facto research design and the data used for the investigation were sourced from the Central Bank of Nigeria statistical bulletin, Nigeria National Bureau of Statistics, and the World Bank Fact Book. The research data were processed based on descriptive statistics, correlation, and the ordinary least square regression methods, respectively, using E-view 10 software. The results suggest that despite the huge revenue generated from crude oil, oil revenue had a weaker impact on the Nigerian economic growth, with a 3.10 % significant positive impact on the gross domestic product, than non-oil revenue, which had a 31.35 % significant positive impact on gross domestic product. It is therefore, recommended that the Nigeria government develop the non-oil sector simultaneously with the oil sector to reducing the country’s over dependence on the oil sector revenue.

DOI: https://doi.org/10.5281/zenodo.17906505

A Potent Leafy Medicinal Herb: Antioxidant Efficiency and Bioactive Composition of Amaranthus viridis”

Authors: Snehal S. Wadkar, D.K. Gaikwad

Abstract: A common leafy vegetable used in traditional medicine is Amaranthus viridis L. (family Amaranthaceae). In tropical and subtropical countries, A. viridis, sometimes known as green amaranth, is a popular leafy food and traditional medicinal herb. Using DPPH, ABTS, and reducing power tests, this study offers an expanded, repeatable methodology and data template for phytochemical screening, quantitative assessment of total phenolic and flavonoid contents, and antioxidant activity. Phytochemical and pharmacological studies have shown that its leaves are a rich source of antioxidant compounds, mainly phenolic acids, flavonoids, and tannins, which have radical-scavenging and reducing effects in vitro. A. viridis contains significant phytochemicals responsible for various biological activities.

DOI: https://doi.org/10.5281/zenodo.17906643

Exploring the Efficacy of Homoeopathic Medicine in the Treatment of Eating Disorders: A Comprehensive Review

Authors: Dr Shubhra Kumari, MD (Hom.)

Abstract: Eating disorders represent a complex cluster of psychiatric and nutritional conditions characterised by disordered eating behaviours, distorted body image, and severe physical consequences. Classified under DSM-5, major types include Anorexia Nervosa, Bulimia Nervosa, Binge Eating Disorder, and other specified or unspecified eating disorders. These conditions significantly compromise physical health, emotional well-being, family dynamics, and social functioning. Conventional treatment approaches include psychotherapy, pharmacotherapy, nutritional rehabilitation, and multidisciplinary support. Homoeopathy, a holistic therapeutic system based on individualised prescribing and the principle of similars, has recently gained attention as a complementary modality. This comprehensive review synthesises available literature, clinical observations, and case-based evidence on homeopathic interventions in eating disorders. Findings reveal the potential of individualised homoeopathic remedies to improve appetite regulation, emotional stability, metabolic balance, and overall quality of life. However, more rigorous scientific studies are required to validate efficacy.

DOI: https://doi.org/10.5281/zenodo.17908991

Gocart – A Modern E-Commerce Platform

Authors: Daksh Sharma, Rajiv Gandhi, Prof. Shailesh Gondal

Abstract: The exponential growth of electronic commerce has fundamentally transformed traditional retail paradigms, necessitating the development of robust, scalable, and user-centric digital platforms. This research paper presents GoCart, a comprehensive modern e-commerce platform engineered using the MERN (MongoDB, Express.js, React.js, Node.js) technology stack. The platform addresses contemporary challenges in online retail including seamless user experience, secure transaction processing, efficient inventory management, and responsive design across multiple device form factors. GoCart implements advanced architectural patterns including component-based frontend development, RESTful API design, and NoSQL database optimization to deliver a high-performance shopping experience. The system incorporates essential e- commerce functionalities such as user authentication and authorization, product catalog management, shopping cart operations, order processing workflows, and administrative dashboards. Through the implementation of modern web development best practices, including responsive design principles, state management optimization, and security protocols, GoCart demonstrates the viability of JavaScript-based full- stack solutions for enterprise-level e-commerce applications. This paper comprehensively documents the system architecture, implementation methodology, feature set, and technical considerations involved in developing a production-ready e-commerce platform. Performance evaluation and usability testing indicate that GoCart successfully meets the functional requirements of contemporary online retail while maintaining code maintainability and scalability. The findings contribute to the growing body of knowledge regarding modern web application development and provide practical insights for developers and organizations seeking to implement similar e-commerce solutions.

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Voice Assistant Using Gemini

Authors: Prof. Anamika Nandan, Suhothra Ks, Subhashith N, Shambu Gr, Rahul Hl

Abstract: The upgraded Voice Assistant built using Gemini is a compact and intelligent embedded device designed for smooth, voice- driven interaction. A Raspberry Pi acts as the main processor, managing speech recognition, AI communication, and audio playback. A USB microphone captures speech input, while an audio amplifier and mini speaker generate clear vocal responses. The Raspberry Pi interprets the user’s query, forwards it to the AI model, and outputs the response in both audio and text form. An ESP32 microcontroller receives the text and displays it on an LCD screen for visual feedback. Powered through standard USB sources, the system offers hands-free assistance capable of answering questions, controlling IoT devices, and enabling real-time AI interactions. With its combination of AI processing, enhanced audio output, and microcontroller-based display, the system fits applications in home automation, education, accessibility, and personal assistance.

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Dark Matter Detection through Gravitational Lensing — analyzing how invisible matter bends light

Authors: C. Jayavant Kamesh

Abstract: Dark matter, though invisible to electromagnetic observations, reveals its presence through gravitational effects on visible matter and radiation. One of the most powerful methods for detecting and mapping dark matter is gravitational lensing—the bending of light from distant sources by massive foreground structures. This phenomenon, predicted by General Relativity, allows astronomers to infer the distribution and abundance of dark matter independent of its particle properties. Both strong lensing, which produces arcs and multiple images, and weak lensing, which induces subtle distortions in galaxy shapes, provide critical insights into dark matter halos on galactic and cosmological scales. By analyzing lensing signals across large surveys, researchers can reconstruct mass distributions, test cosmological models, and constrain the role of dark matter in structure formation. Gravitational lensing thus serves as a cornerstone technique for probing the nature and behavior of dark matter in the universe.

DOI: https://doi.org/10.5281/zenodo.17918903

IoT-Based Autonomous Delivery Vehicle for Smart School Logistics and STEM EducationS

Authors: Nguyen Tai Tuyen, Mai Xuan Dat, Vu Hoang Viet

Abstract: The research describes how to build an autonomous delivery system which operates in educational environments with their structured and predictable environments. The system operates through two navigation systems which allow users to control it manually using Bluetooth and enable the vehicle to follow lines for turning within school environments. The system depends on basic ultrasonic sensors which work with servo-controlled scanning to detect obstacles automatically. The ESP32-CAM module allows video streaming which enables the vehicle mission supervisor to monitor operational time for maintaining continuous safety. The system design follows Internet of Things (IoT) principles through embedded controllers (Arduino UNO and ESP32-CAM) which enable sensor integration and mobile app development using MIT AppInventor. The system consists of easy-to-maintain and affordable hardware and software components which operate as separate modules. The system enables unrestricted movement through standard Vietnamese school buildings. The system operates reliably at Uông Bí secondary school (Quảng Ninh, Vietnam) during testing experiments which demonstrate its ability to detect obstacles and change operation modes on the concourse. The solution demonstrates excellent potential for educational logistics and internal document delivery and STEM and hands-on STEM and IoT education programs.

DOI: https://doi.org/10.5281/zenodo.17919008

Effectiveness Of Concept Mapping In Science On Academic Achievement Of 7th Class CBSE Students In Indore City

Authors: Dr. Sarita Sharma, Ms. Mahima Hardiya

Abstract: This quasi-experimental study investigates the effectiveness of concept mapping versus traditional teaching on academic achievement in science among 60 7th class students from two CBSE schools in Indore. Data analysis using Mann-Whitney U test revealed significant superiority of concept mapping group post-test scores (U=727, p<0.05). Findings support concept mapping for improved science learning outcomes.edu.dauniv+1

Fundamentals of Quantum Mechanics

Authors: Arpitha Vasudev, Anusha G S, Jayalakshmi B, Laxmi S Sunagar, Kanishka T

Abstract: Quantum Mechanics is one of the most important scientific developments of the twentieth century. It serves as the basis for understanding how matter and energy behave on very small scales. Unlike classical mechanics, quantum mechanics explains physical phenomena through probabilistic models, wave functions, operators, and discrete quantized states. This paper offers a thorough study of the fundamentals of quantum mechanics, covering its historical background, postulates, mathematical framework, wave-particle duality, Schrödinger equation, uncertainty principle, and operator theory. The paper also looks at how quantum mechanics is used in modern technologies such as quantum computing, lasers, semiconductors, and medical imaging. The goal of this paper is to provide a clear and detailed foundation for students and researchers who want to grasp the main principles of quantum theory. General Terms Quantum Mechanics, Physics, Wave Function, Uncertainty, Quantum Theory, Measurement, Operators.

Smart Attendance System Using Facial Recognition

Authors: Suraj P Madiwal, Vittal S Goudra, Shreepad Gundupkar, Uday Kushalapur, Professor Deepak Sharma

Abstract: Accurate and efficient attendance tracking plays a key role in educational institutions. It helps maintain discipline, ensures transparency, and supports academic evaluation in various ways. Conventional methods for taking attendance tend to be slow and full of errors. They are also easy to manipulate with proxy marking, which makes them unfit for today's academic settings. This paper presents a fully automated Smart Face Recognition Attendance System. The system handles student registration, verifies at gates, monitors entry and exit in classrooms, and offers centralized real-time reporting. It combines face encoding algorithms with computer vision techniques. A MongoDB database backs it up for reliable storage and quick retrieval of attendance events. Multiple cameras allow for ongoing monitoring of student movements across campus locations. This setup relies on stable facial detection and records events with time stamps. Experiments show the system cuts down on manual work a lot. It stops proxy attendance effectively and delivers precise summaries for daily, weekly, and course-wise attendance. The overall solution scales well, stays cost-effective, and proves dependable. That makes it ideal for use in academic institutions.

Civic Complaint Tracker

Authors: Gaddam Tejas Reddy, Galeti Indravardhan, Harsha vardan V Reddy, Kshitij B Bhagawati, Mrs. Dasari Bhulakshmi

Abstract: Rapid urbanization has significantly increased the demand for efficient and transparent civic service management systems. Conventional methods of registering civic complaints, such as visiting municipal offices or making phone calls, are often inefficient, time- consuming, and lack proper tracking mechanisms. To overcome these limitations, this paper presents a Civic Complaint Tracker, an Android-based mobile application designed to streamline the process of reporting, monitoring, and resolving civic issues. The application is developed using Kotlin and Java, ensuring reliability, scalability, and compatibility with modern Android platforms. The proposed system consists of two primary modules: a Citizen module and an Administrator module. Citizens can report civic issues such as potholeswaste accumulation, water leakage, or streetlight failures by submitting structured details including a complaint title, detailed description, address, and photographic evidence. This structured input improves accuracy and helps authorities understand the issue clearly. The Administrator module allows authorized personnel to securely log in, review complaints, and update their status as Pending, In Progress, or Resolved, enabling effective monitoring and accountability. The system enhances transparency by allowing citizens to track the real-time status of their complaints, thereby reducing uncertainty and repeated follow-ups. Digitization of the complaint lifecycle minimizes paperwork, improves response time, and supports data-driven decision-making for municipal authorities. The Civic Complaint Tracker promotes citizen participation, strengthens communication between the public and local governance bodies, and aligns with smart city initiatives. Overall, the proposed solution demonstrates how mobile technology can significantly improve civic grievance redressal mechanisms and public service delivery.

Microbial Stability and Sensory Persistence of an Enriched Cereal-Legume-Plantain Flour Blend During Ambient Storage: Implications for Food Security

Authors: Alilu Jabiru Daddy, Usman, G.O.

Abstract: This study investigated the ambient storage stability of a novel composite flour blend formulated from toasted yellow maize (Zea mays), Bambara groundnut (Vigna subterranea), and firm ripe plantain (Musa paradisiaca). The research aimed to determine the safe shelf-life and consumer acceptability persistence of the product under conditions typical of resource-constrained settings. Six flour blends, ranging from 100% maize (control) to a 50:50 ratio of maize and a Bambara/plantain composite, were packaged in polyethylene bags and stored at 28±2°C for eight weeks. Microbiological quality (total mold and coliform counts) and sensory attributes (appearance, aroma, mouth feel, consistency, and overall acceptability) were evaluated bi-weekly. Results indicated that coliform counts remained negligible (<10² cfu/g) throughout the storage period, confirming good hygienic production. Total mold counts, however, showed a time-dependent increase, remaining within the acceptable safety limit of ≤1×10⁴ cfu/g for up to six weeks before exceeding this threshold in subsequent weeks. Sensory evaluation revealed a gradual decline in all hedonic scores over time. Despite this, the fortified blends, particularly those with 40% and 50% composite flour (AJD5 and AJD6), maintained significantly higher acceptability scores, ending the storage period at "liked slightly" to "liked moderately" levels (5.90-6.50 on a 7-point scale). The control sample exhibited a more rapid decline in sensory quality. The study concludes that the enriched cereal-legume-plantain flour blend maintains microbiological safety for six weeks and acceptable sensory quality for at least eight weeks under ambient storage. This extended shelf-life, coupled with sustained consumer preference for fortified blends, underscores the product's practical utility and potential to enhance food security by providing a stable, nutritious, and acceptable food base in environments without refrigeration.

A Survey of Natural-Language Driven Command-Line Assistants and Intelligent Shell Systems

Authors: Prof. Nikhil Agrawal, Anushka Patil, Nikhil Kurkure, Dheesh Medekar, Renesh Sharma, Kuntal Thakur

Abstract: – For decades, the command-line interface has been fundamental to system administration, software development, and automation, supporting both professionals and advanced users.Its usability however has been restricted and intimidating to say the very least as a user is required to memorize the commands and syntax. With recent breakthroughs in NLP and LLM based architectures in quite literally everything the traditional CLIs are being integrated with them these as well. This survey brings together the latest research and practical system developments in natural language powered command line assistants and automated shell systems. We start by looking at how CLIs have evolved historically and examining early automation technologies, tracing the journey from manually crafted scripts to neural network approaches that harness the capabilities of modern transformer models. The paper presents a systematic way to classify intelligent shell systems based on their underlying model architecture, how they decide whether to execute commands, their safety measures, and their strategies for handling errors. We evaluate several representative systems including ShellGPT, Warp AI, Copilot CLI, and our own hybrid assistant that combines both local and cloud-based language models using detailed comparison matrices and real world use case analysis. We dive deep into the major challenges these systems face: hallucinations where the AI generates incorrect commands, ambiguous error messages that are hard to interpret, limited training data, and serious security concerns. Finally, we explore where future research should head, imagining the next wave of autonomous system administration agents, secure on-device AI inference, and voice controlled CLI automation. This survey contributes a unified classification system, an extensive literature review, empirically grounded comparisons between systems, and practical recommendations for researchers and developers who want to build robust, intelligent command line automation tools, to say the very least the final target is to help create something at the kernel level which will be an assisted mechanism for the entire os and all applications within it.

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The Automated Auteur: A Novel Framework For AI-Powered Intelligent Video Editing

Authors: Ms. Pallavi D G, Smt. Preethi H U

Abstract: The proliferation of video content across social media, marketing, and entertainment has created an unprecedented demand for efficient, high-quality video editing. Traditional editing remains a labour-intensive, skill-dependent process, creating a significant bottleneck. This paper introduces a comprehensive AI-driven video editing framework that automates and enhances key aspects of post-production. Our proposed methodology integrates computer vision, natural language processing, and reinforcement learning to create a system capable of understanding narrative intent, analysing raw footage, and producing edited sequences according to dynamic stylistic and technical rules. We detail a multi-stage pipeline comprising: 1) Content Analysis (scene detection, shot classification, emotion/object recognition), 2) Narrative Structuring (based on a learned or user-provided "beat sheet"), and 3) Automated Editing (shot selection, sequencing, and basic transitions). We conducted experiments comparing AI-edited sequences against human-edited baselines for tasks like highlight reel generation, documentary-style assembly, and social media clip creation. Quantitative metrics (continuity preservation, pacing consistency, aesthetic composition score) and qualitative user evaluations demonstrate that our framework achieves 88% user satisfaction for specific, well-defined editing tasks and reduces editing time by approximately 70%. However, for complex narrative work, human oversight remains crucial. The findings indicate that AI is best positioned as a collaborative tool—an "automated assistant"—that handles technical and repetitive tasks, freeing human editors to focus on creative direction and emotional nuance.

Bridging The Gap: A Model Context Protocol (Mcp) Framework For Ai Agents In Media Supply Chains

Authors: Partha Sarathi Samal, Suresh Kumar Palus, Sai Kiran Padmam, Bhavan Kumar B R

Abstract: Media supply chains currently suffer from severe fragmentation. Tools for transcoding, quality control (QC), and media asset management (MAM) exist in isolated silos. Connecting these systems requires custom, brittle API integrations that fail when vendor specifications change. AI Agents offer a solution through autonomous planning and execution. However, agents struggle to connect to diverse legacy systems without a standardized interface. This paper proposes a unified framework based on the Model Context Protocol (MCP). MCP acts as a universal transport layer. It allows AI agents to discover, query, and manipulate media assets securely across heterogeneous environments. We outline a "Media-MCP-Agent" architecture that enables autonomous agents to parse technical metadata, trigger transcode jobs and rectify QC failures without hard-coded scripts. We provide a rigorous analysis of the security implications, specifically regarding prompt injection, and discuss the future of hierarchical agent swarms in broadcast operations.

DOI: http://doi.org/10.5281/zenodo.18104044

Adoption of Circular Economy in India’s Passenger Car Sector: A Systematic Literature Review

Authors: Ms. Sammi kumari, Dr. Akshat Aditya Rao

Abstract: This paper provides an in-depth SLR on the topic of implementing CE among passenger car clients in India, including the synthesis of 65 articles published in 2014-25. The study will seek to determine the major drivers, barriers to and consumer behaviour pattern in adopting CE, and the implication to the policymakers and industry stakeholders. Relevant articles were obtained based on PRISMA framework within databases. The theoretical lenses that have been incorporated in the review include the TPB, TAM, and VBN, which demonstrate the high gaps in consumer-oriented CE studies, especially in the Indian case. Descriptive and thematic synthesis revealed that the key enablers of CE adoption are environmental awareness, economic incentives, brand influence and demographics. Perceived product risk, lack of awareness, cultural resistance on reused products, and limit on policy/infrastructure are crucial obstacles. The paper further classifies the existing condition of CE practices, like remanufactured products, recycling, shared mobility, and product-service systems, and addresses the attitudes, perception, and dynamics of trust among the consumers. Comparative knowledge to suggest that global CE adoption is facilitated by a strong policy and consumer trust, India is confronted with some contextual issues such as social-cultural norms, ineffective implementation of regulations and low consumer literacy. The review has a theoretical contribution because it demands additional studies on behaviour and longitudinal ones as well as practical benefit because it provides policy and industry-specific recommendations. Lastly, it is suggested that further studies should be conducted on empirical knowledge of CE adoption, especially in emergent electric vehicle market of India. The study provides a strategic base to the creation of consumer-based inclusive CE solutions in the Indian automobile industry.

DOI: https://doi.org/10.5281/zenodo.17949225

Effects of Technological Orientation and Sustainable Orientation on Financial Accessibility of Jos Water Services Corporation (Jwsc) Plateau State; The Moderating Role of Management Support

Authors: Longzem Nda Henry, Dr. Danjuma Nimfa Tali

Abstract: This study investigated the effects of Technological Orientation and Sustainable Orientation on Financial Accessibility within the Jos Water Services Corporation (JWSC), emphasizing the moderating role of Management Support. Employing a crosssectional research design, the study surveyed 120 employees using structured questionnaires. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLSSEM). The findings reveal that Technological Orientation significantly enhances Financial Accessibility (STD = 0.52, PValue = 0.000), while Sustainable Orientation also shows a positive relationship (STD = 0.40, PValue = 0.000). Management Support emerges as a crucial moderator, amplifying the effects of both orientations on Financial Accessibility (STD = 0.32 for Technological Orientation and STD = 0.28 for Sustainable Orientation, with PValues of 0.034 and 0.020, respectively). The overall model explains 65% of the variance in Financial Accessibility, underscoring the interconnectedness of these constructs. This research contributes to the literature on strategic orientation and financial accessibility, offering actionable insights for policymakers and organizational leaders seeking to improve service delivery in the water sector. Future studies should explore the longterm impacts of these orientations and the role of digital transformation in enhancing financial accessibility.

DOI: https://doi.org/10.5281/zenodo.17950358

Plant And Soil Nutrient Monitoring In Precision Agriculture: A Review Of IoT And Machine Learning

Authors: Priyanka Mehta, Dr. Mona Shah

Abstract: The paper provides a comprehensive review of modern technologies in Agriculture —particularly the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) and how they are transforming the agricultural sector. It also focuses on the issues faced in the current traditional farming methods and technologies like climate variations, reduced soil fertility, water scarcity, and the lack of real-time data for decision-making. The review explains the use of technology like IoT devices such as Sensors for soil, drones, remote-sensing satellites, and weather stations, which are used for continuous monitoring and data collection for soil nutrients, pH, moisture, temperature, and crop health. When the data is processed through ML algorithms like Random Forest, SVM, XGBoost, and ANN, it enables accurate crop prediction, fertilizer recommendation, yield forecasting, disease detection, and soil quality assessment, etc. The paper also intends to provide brief about the important parameters of soil like NPK, Texture, PH level, Moisture level, CEC, organic carbon and micronutrients. It also explains previous research which mentioned use of IoT based sensing, spectroscopy, image processing, etc. how they are useful to monitor these indicators. By a detailed literature review, this paper shows intends to explain how multisensory data fusion and ML-driven decision systems can result in providing faster cheaper and accurate analysis for precision agriculture in comparison to conventional laboratory methods.

DOI: https://doi.org/10.5281/zenodo.17950545

FarmIQ: An Intelligent Agricultural Solution

Authors: Kukku Anna Mathew, Lakshmi P P, Mala H S, Jasira B

Abstract: Agriculture is becoming increasingly vulnerable to changing weather conditions, soil nutrient imbalance, and the rapid spread of crop diseases, all of which place considerable pressure on farmers to make proper decisions with narrowed resources. In many cases, farmers continue to depend on intuition or general advice rather than structured analysis, resulting in poor crop selection, inefficient fertilizer application, and delayed recognition of plant infections. These challenges indicate a rising requirement for smart solution that simplify field-level decision- making while improving precision. This study presents FarmIQ, an integrated Agricultural Decision Support System designed to provide real-time, data-driven guidance for sustainable crop management. The system brings together multiple computational techniques, applying machine learning for crop recommendation, fertilizer optimization, and yield forecasting, along with a deep-learning-based method for automated leaf disease classification. FarmIQ evaluates essential soil and environmental parameters, including npk, pH, temperature, humidity, and rainfall, to find crops that are suitable with current field conditions. A fertilizer module analyzes nutrient deficiencies and generates targeted recommendations to maintain soil health, while yield prediction is done using regression models trained on historical production data. For disease detection, the system uses a fine-tuned VGG16 convolutional neural network capable of identifying common infections in tomato, potato, grape, and corn plants and providing treatment suggestions immediately after classification. All functionalities are delivered through a lightweight Flask based user facing platform to input soil details, upload leaf images, and obtain results instantly and clearly. Experimental evaluation shows that FarmIQ performs reliably across varied agricultural scenarios, demonstrating strong potential for practical use in field environments. The system contributes to improved decision accuracy, reduced dependency on agricultural experts, and more efficient resource utilization. By integrating predictive analytics and automated diagnosis into a unified platform, FarmIQ supports the transition toward data-driven and sustainable agriculture for farmers, students, and researchers.

Car Rental Management System A Modern Digital Platform for Automated Rental Operations

Authors: Nitesh Rajput, Prof. Dr. Nidhi Dehale

Abstract: Increased demand for efficient car rental due to urban growth and mobility services highlights the limitations of traditional, manual systems (paper records, phone calls, human billing), which cause inconsistency, conflicts, errors, and poor satisfaction. This paper introduces a comprehensive Car Rental Management System automating the entire workflow: registration, customer management, booking, billing, payment, and reporting. Utilizing modern web technologies, the system offers real-time availability, secure multi-level authentication, automated dynamic pricing, centralized data, and reporting dashboards. Built on a scalable three-tier architecture, it solves operational issues, improves user experience, and shows measurable gains in efficiency, reduced errors, transparency, and scalability for future features like mobile apps and AI. Performance evaluations confirm faster booking, greater billing accuracy, and higher customer satisfaction than manual methods.

DeepFake Video Detection

Authors: Sakshi K, Rakshitha N, Thejashwini, Varshitha P

Abstract: The swift progress in deepfake creation methods has triggered significant worries about the genuineness and dependability of online video material. Manipulated facial appearances in deepfake videos and expressions present considerable dangers in areas like social media, journalism, and digital forensics.While current deep learning-based detection techniques have shown encouraging outcomes, numerous models demonstrate restricted generalization when utilized on unfamiliar datasets or live video feeds. This study introduces a deepfake video detection system that incorporates ResNeXt for spatial characteristics.extraction using an Attention-Driven Bidirectional Long Short-Term Memory (Bi- LSTM) model for temporal examination. ResNeXt adeptly extracts distinguishing facial characteristics from separate frames, whereas the attention-boosted Bi-LSTM selectively emphasizes significant temporal segments throughout video sequences.This integrated structure enhances the understanding of both spatial discrepancies and temporal dynamics.dependencies related to deepfake modification. Experimental findings indicate that the suggested method demonstrates excellent results on benchmark datasets. In spite of its effectiveness, issues connected to dataset reliance and immediate implementation persist, which are examined alongside possible future research pathways

ExerciseBuddy: An AI-based exercise Tracking and Feedback System

Authors: Riddhi J Deore, Nikhil V Kasbe, Rohit K Suryawanshi

Abstract: Exercise Buddy is an AI-driven fitness solution that leverages Media Pipe and Open CV for real-time pose detection and repetition counting, enabling users to track their workouts accurately. The system provides personalized workout recommendations by analyzing user performance data, ensuring effective and adaptive fitness routines. Users can choose between live monitoring or video-based analysis for detailed insights into their exercise form and progress. Exercise Buddy aims to address gaps in existing fitness solutions by offering advanced features like emergency monitoring and tailored guidance.

DOI: https://doi.org/10.5281/zenodo.17961724

MANAS: Personalized AI Mental Health Chatbot With Crisis Detection And Reports

Authors: Preksha K, Pragna Y S, Raksha H M, Smriti Ravichandra, Dr. Rajashekar M B

Abstract: Traditional mental health interventions struggle with accessibility, continuous monitoring, and personalized care, while current digital systems lack real-time crisis detection. The MANAS (Mental Health Assistant System) platform is an AI-supported ecosystem that addresses this gap, providing personalized psychological guidance and continuous emotional tracking. MANAS utilizes a large language model (LLM) for empathetic therapeutic dialogue and incorporates a Trait Detector to analyse user input for high-risk indicators, such as explicit suicidal intent. Safety is ensured via a "human-in-the-loop" Alert Management workflow, which automatically dispatches urgent, encrypted email notifications to registered guardians upon crisis detection. The platform also integrates personality-based therapist matching and a Report Generator for downloadable PDF session summaries. Successfully implemented and tested, MANAS is a reliable, secure, and impactful solution that significantly enhances the accessibility and safety of digital mental healthcare.

DOI: http://doi.org/10.5281/zenodo.17961851

Predictive Techniques for Stock Price Movement Using Quantum-Classical Approach: A Comprehensive Review

Authors: Manoj B. Bhatkar, Prashant M. Yawalkar, Vijay B. More

Abstract: This review explores the emerging field of quantum-classical hybrid approaches for predicting stock price movement. Predicting the stock price movement has been a popular topic amongst machine learning (ML) enthusiasts. However, predicting how a particular stock or stock market will perform is a difficult task, due to market dynamics, company (stock) specific dynamics, volatility, and other environmental factors. Using advanced classic machine learning techniques, such predictions have become possible, but also computationally complex. There is a need to develop a non-linear prediction model for predicting the movement of the price of a stock, in a more accurate and faster manner. We examine recent advancements in quantum computing algorithms, focusing on the potential to enhance the efficiency and accuracy of traditional prediction models. This review delves into key concepts such as quantum feature maps, variational quantum circuits, and hybrid architectures that integrate quantum and classical components. We discuss the potential advantages of quantum-enhanced techniques, for their ability to process complex financial data efficiently and unlock hidden patterns. Furthermore, we analyze the current challenges and limitations. This review aims to provide an in-depth overview of the latest quantum-classical stock price prediction, highlighting promising avenues for further research and development in this exciting field.

DOI: https://doi.org/10.5281/zenodo.17961944

Dynamic ML: A Novel Automated Machine Learning Tool for Streamlined Model Development

Authors: Ishita Chincholkar, Siddhesh Rahane, Atharva Kawale, Sudarshan Jadha

Abstract: In the quickly developing field of AI, effectiveness and convenience are urgent for engineers trying to bridle progressed calculations without the intricacies of manual coding. This venture presents Dynamic ML: A Mechanized Designer's AI Instrument for Continuous Dataset Transformation and Preparing, a cutting edge arrangement intended to upgrade AI work processes through unique variation and robotization. DynamicML use dynamic AI strategies and ongoing dataset age to smooth out the advancement cycle. It mechanizes the making of dynamic datasets and the preparation and testing of models, taking out the requirement for monotonous coding errands. By incorporating versatile AI with consistently advancing information, Dynamic ML improves on model turn of events and speeds up the streamlining cycle. Including an instinctive connection point, Dynamic ML permits engineers to collaborate with the framework easily, zeroing in on undeniable level plan and application as opposed to on coding. This robotization of dataset the board and model preparation altogether lessens time-to-sending and lifts generally efficiency. DynamicML addresses a huge headway in AI toolsets, offering a powerful answer for ongoing information dealing with and computerized model preparation in a smoothed out, sans code climate.

DOI: https://doi.org/10.5281/zenodo.17962137

Salinity Gradient Power: An Unique Renewable Energy Source

Authors: Dr. Rishu Agarwal, Kavita Singh

Abstract: The growing global demand for clean and sustainable energy has led to the exploration of unconventional renewable energy sources. One such emerging technology is salinity gradient power, also known as blue energy, which harnesses the chemical potential difference between freshwater and seawater. This paper discusses the principles, methods, efficiency, applications, and future prospects of salinity gradient power. The study highlights pressure retarded osmosis (PRO) and reverse electro dialysis (RED) as primary conversion techniques and evaluates their potential in addressing global energy needs.

Social Media Fake Account Identification Using Machine Learning Approach

Authors: Rohini Ashok Gamane, Vaibhav Dabhade

Abstract: The widespread use of social media has resulted in a surge of fake accounts, posing serious risks to individuals, organizations, and society at large. Identifying fake accounts effectively is essential to preserving the integrity and credibility of social media platforms. This study introduces a machine learning-based approach to detect fake social media accounts. We employed five machine learning algorithms—Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Artificial Neural Networks (ANN)—to classify accounts as fake or genuine. The dataset used in this study consisted of features extracted from social media profiles, such as user behavior, profile details, and network characteristics. Experimental results revealed that the ANN algorithm outperformed the others, achieving a high accuracy of 95.6% in detecting fake accounts. The proposed approach offers significant benefits for social media platforms by enabling more efficient detection and prevention of fake accounts. Furthermore, the findings of this study can guide the development of advanced fake account detection systems, contributing to a safer and more reliable online environment.

DOI: https://doi.org/10.5281/zenodo.17962377

Brand Trust Research Paper

Authors: Krish Bharti, Dr. Ankur Sharma

Abstract: The present study explores how one may distinguish between brand trust and quality perception when evaluating local and branded products in India. It is based on a primary research survey that gathers information from 60 respondents by means of structured Likert-scale questionnaires. The research tests hypotheses about the relationships of brand perception to quality expectation and purchase preference. It also looks into the changes in brand trust and quality perception that are related to local brands or to national ones. Such a comparison between the consumer trust in local versus national brands helps in deeply understanding the consumers' buying decision behavior, which is of utmost interest to the business organizations, policymakers, and the researchers as well.The results illustrate the existence of a significant quality perception gap in favor of branded products (72.2% associate high quality) and that a significant trust differential (56.7%)indicate trusting branded products more) exists. Despite this, local products continue to have price perception advantages (56.7% perceive as reasonably priced) as well as a community support recognition (61.6% acknowledge local economic benefits).

DOI: https://doi.org/10.5281/zenodo.17962487

AI Based Smart Policing System Using Large Language Models

Authors: Bhagyashri R. Kasar, Prashant M. Yawalkar, R. P. Dahake

Abstract: The need for more sophisticated policing methods is growing as crime rates rise and crime data processing becomes increasingly difficult. In order to increase law enforcement effectiveness, this has prompted the adoption of smart policing systems and predictive policing techniques that make use of artificial intelligence (AI). Among the many AI technologies used, machine learning (ML) is regarded as a powerful instrument for identifying patterns, analyzing crime trends, and predicting criminal activity. LLMs generative AI has recently gained widespread recognition in sectors like agriculture, healthcare, law, and finance. However, the LLM's potential in smart policing and crime prediction is still mostly unrealized. By creating a framework that integrates the most advanced LLMs—BART, GPT-3, and GPT-4—this study seeks to bridge this gap and improve analysis and crime prediction. Using actual crime datasets from San Francisco and Los Angeles, the framework's performance is evaluated using zero-shot prompting, few-shot prompting, and fine-tuning techniques. In the majority of the experimental setups, a comparative analysis between the LLM-based methods and the conventional ML models revealed that GPT models performed better in terms of crime classification. The study offers future paths for AI-driven police and demonstrates how LLMs have the ability to transform contemporary crime analysis.

DOI: https://doi.org/10.5281/zenodo.17962534

Impact Of HR Digitalization On Organizational Performance: A Study Of SMEs In The Western Province Of Sri Lanka

Authors: Buddhika YPAS

Abstract: This study investigates the impact of Human Resource (HR) digitalization on organizational performance within Small and Medium Enterprises (SMEs) in Sri Lanka’s Western Province. Despite the global digital transformation trend, empirical evidence within developing economy SMEs remains scarce. Utilizing a cross-sectional quantitative survey of 384 SME decision-makers, data were analysed using Structural Equation Modelling (SEM), regression, and moderation analysis. Findings reveal that HR digitalization significantly explains organizational performance (R² = 0.747). Specifically, digital performance management most substantially enhances employee satisfaction, while HR analytics most profoundly improves strategic decision-making capability. These effects are moderated by firm size, industry sector, and duration of digital adoption. As the first major empirical study in this context integrating the Technology-Organization-Environment (TOE) and Resource-Based View (RBV) frameworks, it addresses a critical gap in the literature (Zavyalova et al., 2022; Rajapakshe, 2018). The research provides actionable, evidence-based guidance for SME managers prioritizing digital investments, technology vendors developing localized solutions, and policymakers crafting supportive national digital strategies for economic resilience.

Taguchi-based Experimental Study On Surface Roughness Of EN31 Steel In Turning Operations

Authors: Vishal J Dhore, Gaurav P Demse, Junaid M Shaikh, Khushi S Dikkar, Praveer Agrawal

Abstract: This work investigates the influence of turning process parameters feed rate, cutting speed, and depth of cut on the surface roughness of EN31 steel, a high-carbon alloy commonly used in automotive components and precision bearings. Hard-turning experiments, designed using the Taguchi L16 orthogonal array, were conducted with a Cubic Boron Nitride tool to evaluate process parameter variations. Analysis revealed that feed rate had the greatest influence on surface roughness (55.22%), followed by cutting speed (22.72%) and depth of cut (22.06%). Increased feed rates and cutting depths resulted in higher surface roughness, emphasizing the importance of parameter optimization. The optimal conditions produced a smoother surface, reduced variability, and minimized tool wear. The study highlights the combined effects of machining parameters and valuable guidance for improving hard-turning processes, supporting the development of predictive models for precise and economical manufacturing in industries such as aerospace and automotive.

DOI: https://doi.org/10.5281/zenodo.17963068

Mechanical Behavior Of Tungsten Carbide Under The High Impact Velocity Application: A Numerical-Based Study

Authors: N. S. Gaikwad, D. D. Deshmukh, A. Y. Chaudhari, S. B. Patil

Abstract: This study examines the mechanical response of tungsten carbide under high-velocity impact conditions. Finite element analysis (FEA) simulations were conducted on a rectangular plate, both with and without the incorporation of tungsten carbide. The explicit dynamic simulations were executed using the Ansys LS-DYNA solver. A high-velocity projectile was simulated to impact the rectangular plate, and the resulting deformation behavior and induced stresses for both scenarios were analyzed and compared. The results demonstrate that the plate reinforced with tungsten carbide exhibits significantly higher resistance to deformation under high-velocity impact. Additionally, the stress levels induced in the tungsten carbide-reinforced plate were notably lower compared to the plate without tungsten carbide. These findings highlight the potential of tungsten carbide as a promising material for applications involving high-velocity impacts.

DOI: https://doi.org/10.5281/zenodo.17963458

Analysis aspects of pressure tank using Ansys

Authors: Shailendra Ramchandra Gawand, Santosh Rama Shekokar, N. A. Kharche

Abstract: This review paper presents a detailed analysis of pressure vessels, focusing on finite element analysis (FEA) to evaluate structural integrity. Pressure vessels are widely used in industries such as chemical processing, power plants, and oil refineries, where they are subjected to extreme temperature and pressure conditions. Ensuring their safe and efficient operation requires a thorough understanding of stress distribution, deformation characteristics, and failure mechanisms. This study reviews existing research on the thermal stresses and deformations that develop in pressure vessels under various loading conditions. The analysis compares results from FEA with analytical methods to assess the accuracy and reliability of computational approaches. The pressure vessel is designed according to ASME Section VIII Division 2 standards, and key parameters such as shell thickness, head dimensions, and structural stability are evaluated using simulation tools. To gain deeper insights into structural performance, static and thermal analyses are conducted using ANSYS, incorporating multi-physics simulations that consider combined loading effects. Additionally, modal analysis is reviewed to determine the natural frequency of pressure vessels, which is crucial for understanding vibration characteristics and avoiding resonance-related failures. A specific focus is given to the effects of different constraint conditions, particularly those related to saddle support configurations. The study reviews two different constraint conditions of the right-hand saddle, examining their influence on stress distribution and deformation patterns under constant internal design temperature and pressure. This comparative assessment provides valuable insights into optimizing support structures to enhance pressure vessel performance and longevity. Overall, this review consolidates key findings from previous studies on pressure vessel analysis, highlighting advancements in FEA methodologies and their application in structural assessment. The insights presented in this paper aim to aid engineers and researchers in designing more reliable pressure vessels with improved safety margins and operational efficiency.

DOI: https://doi.org/10.5281/zenodo.17963645

Article Uploader :- A Website For Upload Article

Authors: Alfez Mansuri, Dr. Pushpa Pathak

Abstract: Online Scholarly Publishing Increasingly Depends On Web Platforms That Accept Manuscripts, Route Them Through Editorial And Peer-Review Steps, And Support Publication Readiness Through Structured Metadata And Workflow Controls. This Paper Proposes An Article Uploader Platform Architecture And Evaluates It Using Usability- And Process-Oriented Criteria, Emphasizing Role-Based Actions (Author, Editor, Reviewer), Traceable Decisions, And Integrity Controls Such As Similarity Checking. Results Indicate That An End-To-End Workflow Model Reduces Turnaround Friction, Improves Submission Completeness, And Strengthens Editorial Oversight When Combined With Notifications, Audit Trails, And Screening Mechanisms.

Heart Disease Prediction Using Ecg

Authors: Hemanth R, Manoj H, Chiranth BS, Kushith S, Dr Ashwin M

Abstract: Heart disease continues to be one of the most serious health concerns worldwide, often going undetected until the condition becomes life- threatening. Early diagnosis can save lives, but interpreting Electrocardiogram (ECG) readings requires clinical expertise and can be time- consuming in busy or underserved healthcare environments. To address this challenge, this project presents an AI-powered web application that analyzes ECG reports and predicts the risk of heart disease in real time. The system allows users to upload ECG images or files, which are then processed through advanced signal analysis and machine learning techniques. Important cardiac features such as heart rate, waveform patterns, and irregular signals are extracted to assess potential heart abnormalities. The trained model classifies the user’s condition into different risk levels and provides helpful recommendations based on the results. This solution focuses on accessibility, accuracy, and rapid assessment, making it useful for both patients and healthcare professionals. By combining intelligent prediction with a simple and interactive interface, the system supports early screening and encourages timely medical consultation, ultimately contributing to better cardiac health outcomes and improved quality of life.

Modeling and analysis of hoist in workshop in CAE tool A Review

Authors: Tilak Bohra, D. P. Kharat

Abstract: Heavy duty tasks in any industry or workshop are managed by several mechanical equipment’s like cranes, hoists, lifts etc; it involves loading/unloading of goods, shipping of heavy materials, lifting and dropping heavy equipment’s. Heavy duty tasks such as engine removal from vehicle for repairing/rebuilding and restoring, likewise complex operations that involves the movement of heavy engine parts in the workshop from one place to another. The task usually requires time depending on the hoisting device employed. Chain hoist requires more time as it is operated manually by human efforts and has certain limitations, like it is fixed with some constructed beam or tripod on other hand, hydraulic hoist has more capacity to lift heavier engine than chain hoist and electrical hoist. However, Electrical hoist has lifting capacity up to weight of about 200-250Kg but has adequate speed than other lifting devices like chain hoist and hydraulic hoist. In this servey, several design concepts are considered for hoisting/lifting heavy engine assembly more than 250kg and we planned to test the portable lifting hoist by modeling the assembly in Creo Parametric and analyze it in Ansys Workbench with prescribed engineering data and different loading conditions to optimize various failure stages, equivalents stresses and other optimistic findings.

DOI: https://doi.org/10.5281/zenodo.17974403

Design Of Smart Dining Table Using Arduino Nano And Low Induction Heating

Authors: Harshal Mahajan, Manish P. Aachliya, Achal R. Gole

Abstract: This paper introduces the design and validation of a Smart Dining Table utilizing an Arduino Nano microcontroller and Low Induction Heating (LIH) technology to automatically keep food at its original serving temperature, solving inefficiencies in typical temperature preservation methods. The classic methods tend to involve manual input of temperature, resulting in human errors and power wastage. Conversely, this work presents a new closed-loop temperature control system through LM-series analog temperature sensors for sensing and maintaining the temperature at which food is first served without the intervention of the user. The system structure combines Arduino Nano with LM35 temperature sensors, low-power LIH coils, and a touch interface. After placing food, the LM35 sensor immediately detects its surface temperature, which is set by Arduino Nano as the target reference. The PID control algorithm constantly modulates the induction coils' output to keep the measured temperature at ±2°C, minimizing variance from the original serving condition. LIH coils, which have been designed to work optimally in low frequencies, achieve 89% thermal efficiency with localized heating. Experimental tests confirmed the system to maintain food temperatures with 95% accuracy during 45-minute periods, having a 35% energy reduction compared to standard warmers. Users tested the system with a satisfaction rate of 95%, highlighting the system's smooth automation and consistency. This work makes a contribution to smart appliance design by illustrating an energy-efficient, autonomous, and low-cost solution that prioritizes user convenience. Potential uses include residential kitchens, healthcare settings, and smart restaurants. Future directions will investigate multi-sensor fusion for heterogeneous food loads and edge-computing optimization.

DOI: https://doi.org/10.5281/zenodo.17974700

Zero Wait Time: Smart Automated System For Seamless CNG Gas Booking

Authors: Pallavi Thoke, Aditya Sonje

Abstract: The increasing demand for Compressed Natural Gas (CNG) has led to long queues at fueling stations, causing significant time wastage, congestion, and inefficiencies. This paper proposes an automated appointment-based CNG gas booking system to streamline operations, minimize waiting times, and improve customer convenience. The system integrates real-time slot booking, secure authentication, and efficient resource management, ensuring a smoother refueling experience. By implementing this solution, fuel stations can optimize service delivery, enhance operational efficiency, and maintain a structured approach to managing fuel distribution. Additionally, this system can reduce traffic congestion around fueling stations, promoting a more organized and eco-friendly refueling process.

DOI: https://doi.org/10.5281/zenodo.17974906

A Review Of Sustainable Multi-Utility E-Farm Vehicle

Authors: Chandrashekhar D. Mohod, Sumeet Patil, Sujal Ambekar, Pranav Barhate

Abstract: This abstract presents the development of a multipurpose electric farm vehicle designed to address key challenges in Indian agriculture, including low mechanization, labour shortages, and high operational costs. The vehicle integrates multiple functions such as seed sowing, ploughing, and water sprinkling to enhance efficiency, reduce manual labour, and lower overall costs. The proposed solution uses electric power to minimize environmental impact and operational costs, offering an eco-friendly alternative to traditional fossil-fuel machinery. It aims to improve productivity for small-scale farmers, making advanced agricultural technology more accessible, sustainable, and cost-effective. This innovation also holds potential for future automation and further mechanization in farming practices. The multi-purpose agricultural vehicle is designed for small farmers in India, powered by solar energy. It performs tasks like seed sowing, water spraying, and ploughing, using a DC motor, and efficient gear mechanisms. The vehicle reduces labour, increases productivity, and is cost-effective for small-scale farming. A multipurpose agricultural vehicle has been designed to perform tasks like pesticide spraying, ploughing, and goods carrying.

DOI: https://doi.org/10.5281/zenodo.17975060

Systematic And Structure Framework For Indian Knowledge System

Authors: Mayuri Ashok Chavan, Shubhangi Waman Halkunde, Mangla Prakash Dahake

Abstract: Indian Knowledge System (IKS) is a systematic and structured approach to transmitting knowledge from one generation to the next. It distinguishes itself as a process of knowledge transfer rather than merely a tradition. Rooted in the Vedic literature, the Upanishads, Vedas, and Upnishidhas. Indian education system believes in the existence of life in all the things of the universe. Our Vedas treated nature as God where even plants like Neem, Tulsi, Peepel, and so on are worshipped and promote plantation. India is always been a hub of knowledge where the world’s top universities like Nalanda Thachhshila, and Magadh University were set up and all the disciplines were taught here.

DOI: https://doi.org/10.5281/zenodo.17975350

Fallacious Reasoning (Emphasizes The Flawed Or Misleading Nature Of The Reasoning)

Authors: R. D. Rajkuvar, D. V. Chavan

Abstract: – It is essential to recognize that "faulty reasoning" and "faulty logic" denote the same principle, both indicating a deficiency in reasoning. The term "faulty" denotes the presence of errors or imperfections, while "logic" pertains to sound reasoning or judgment. Faulty logic can also be seen as a rhetorical technique. This paper demonstrates how incorrect or misleading answers can ultimately guide us toward accurate conclusions. By honing the ability to identify faulty logic, individuals can become more discerning consumers of both enlightenment and outcome. When an argument or reasoning is described as faulty, it suggests that the argument is flawed or incorrect, often due to a failure in logical reasoning.

DOI: https://doi.org/10.5281/zenodo.17975564

NBA And NAAC Accreditation: A Roadmap For UG Engineering Institutions In India

Authors: Shambhu L. Prasad, Vinod S Khairnar

Abstract: Accreditation plays a crucial role in maintaining and enhancing the quality of higher education institutions. This paper explores study of two major accreditation systems in India—the National Assessment and Accreditation Council (NAAC) and the National Board of Accreditation (NBA). The study examines their objectives, assessment criteria, processes and impact on institutional holistic development. It also highlights the challenges faced during accreditation process and provides adjuration for improving quality assurance in Indian higher education. By integrating insights from multiple studies, this paper offers a detailed exploration of how accreditation influences academic quality, faculty development, industry collaborations, and overall institutional growth.

DOI: https://doi.org/10.5281/zenodo.17975972

Responses Of Plants To Silicon Supplementation And Mycorrhizal Inoculation: An Integrative Review

Authors: Kumawat Komal, Chandrakala, Y

Abstract: Silicon (Si) supplementation and arbuscular mycorrhizal fungi (AMF) inoculation have each been widely reported to ameliorate abiotic stresses and alter plant biochemical pathways. Recent literature demonstrates both complementary and synergistic effects when Si and AMF are applied together: improved ionic homeostasis, enhanced antioxidant enzyme activities, increased compatible solutes (proline,soluble sugars),changes in lignin/structural compounds, and altered phytohormone signalling. This integrative review synthesises mechanistic evidence from physiological, biochemical and molecular studies (2018–2025), proposes an experimental methodology to evaluate combined Si+AMF effects, and presents a set of relatable (simulated but literature-informed) results to illustrate typical biochemical changes. Finally, we discuss knowledge gaps and practical recommendations for agronomy and future research. Key literature is cited throughout.

Agricultural crop disease recommendation and leaf disease prediction

Authors: Prof. Nandini Gowda P, Spoorthi S, V. Bindu Shree

Abstract: Agriculture is the backbone of many countries, including India, and provides livelihoods to millions of people facing challenges such as climate change and plant disease outbreaks. Through research, a web application has been developed that provides real-time recommendations for crop selection based on various factors such as soil nutrients, temperature, humidity, pH levels, and rainfall. Recent advances in machine learning and artificial intelligence offer promising solutions to these problems, enabling accurate, data-driven decision-making in agriculture. These technologies have the potential to transform how we predict crop yields and detect plant diseases, thus improving agricultural practices. To achieve this, we trained and examined seven machine learning models, Decision tree, Naive Bayes, SVM, Logistic Regression, Random Forest, XGBoost, and KNN. Among these, Random Forest gives the highest accuracy, making it the best choice for crop forecasting. In addition to crop recommendation, the web application also integrates a Plant Disease Identification system using Convolutional Neural Network (CNN). By analysing leaf images, CNN detects and accurately classifies plant diseases, allowing farmers to intervene early to prevent crop losses. This study aims to empower farmers with accessible technology to make informed decisions, improve crop selection, and effectively cure plant diseases. Combining crop recommendation with disease detection, intelligent crop recommendation systems with plant disease detection contribute to sustainable agriculture, economic stability, and food security in India and beyond.

DOI: https://doi.org/10.5281/zenodo.17984980

IOT Based Egg Sorting System Using Load Sensor – A Review

Authors: Adriene, Dharani D, Karnika M U, Sharmeela R

Abstract: One of the essential operations in the poultry supply chain, egg sorting ensures uniform grading and quality before packaging. The limitations of manual sorting—such as slow speed, inconsistent accuracy, and higher breakage rates—remain a major challenge for small and medium-scale farms. Automated systems used in large industries provide high precision but are often expensive, bulky, and inaccessible to smaller producers. Integrating load cell–based weighing, microcontroller processing, and conveyor-driven movement enables accurate, continuous, and non-destructive sorting suitable for low-cost applications. By combining mechanical components, sensor-based detection, and automated diverter mechanisms, the proposed system enhances productivity, reduces labour dependency, and offers a compact and scalable solution for efficient egg grading. This review summarizes the working principles, operational benefits, and application potential of a multiple egg sorting system, while highlighting opportunities for future improvements and technological integration.

DOI: https://doi.org/10.5281/zenodo.17985105

Angiospermic Plants of Tulsi Amrit Sansthan Campus , Kanore Udaipur (Rajasthan)

Authors: Pavan Kumar harsana

Abstract: Plant taxonomy is the branch of sciences which is concerned with the identification, classification and nomenclature of the plants of particular area or region.It is one of the most important branches of plant sciences, plant taxonomy and plant systematics are related to each other, there are no clear difference between the two branches, Plant systematics is the evolutionary relationship between the pants, whereas plant Taxonomy is the actual handling of the plants in nature. However both the branches are intricately related to each other.In plant taxonomy basically three systems of classification are used, they are artificial, natural and phylogenetic classification. Plants are very important not only for humans but also for other lifes on earth, life on earth is can not possible without plants. Plants supply food to all humans as well as many other living organisms on the earth, plants maintain the atmosphere, by the process of photosynthesis on the earth's gaseous balance occurs in nature. In nature biogeochemical cycles complete by the plants and their metabolism. Plants are a habitat for many organisms on earth .Plants provides humans timber, firewood, fiber, medicines, other substances. A Flora is the systematic enumeration of the plant species occurring in a region. A flora can cover a large geographical region, a district, state, country even a continent. A flora may contain a simple description of the plants of that region to the detailed account of the plants. Deforestation is the big reason for the degradation of the floristic wealth of any area. Increasing anthropogenic activity are the again major causes for the degradation of the flora’s of any region. Conservation efforts are necessary for protection of the plants species in relevant Here in this research article we are presenting preliminary studies on the flora of the Tulsi Amrit Sansthan, Kanore Udaipur in rainy season a number of the herbs, shrubs, trees grown in campus and enhances the beauty of the area.

Predictive Analytics For Disaster-Resilient Building Design Using Machine Learning

Authors: Dr. Pankaj Malik, Vanshika Soni, Ishaan Nalge, Jatin Bheniya, Ekanki Shrivastava

Abstract: Natural disasters such as earthquakes, floods, and cyclones pose significant risks to buildings, especially in rapidly urbanizing regions. Traditional design methods often fail to capture the complex, multi-factor relationships between environmental hazards and structural vulnerabilities. This study presents a machine learning–driven predictive analytics framework for disaster-resilient building design. The proposed system integrates geospatial information, material properties, structural parameters, soil conditions, and historical disaster data to assess building vulnerability across multiple hazard scenarios. A dataset of 25,000 building samples was used to train and evaluate several machine learning models, including Random Forest, Gradient Boosting (XGBoost), Support Vector Machines, and Artificial Neural Networks. Experimental results demonstrate that XGBoost achieved the highest predictive performance, with 94.5% accuracy, 0.93 precision, 0.94 recall, and an AUC of 0.95, outperforming all other models. Feature importance analysis revealed that soil type, building height, foundation depth, elevation, and reinforcement quality were the top contributors to disaster vulnerability. Model predictions enabled the generation of optimized, hazard-specific design recommendations, improving structural resilience by up to 38% compared to baseline building designs. The findings confirm that machine learning–based predictive analytics can significantly enhance early-stage building safety assessment, making it a powerful tool for architects, engineers, and policymakers in developing disaster-resilient infrastructure

Smart Refrigerator With RFID-Based Expiry Alert System Using ESP32

Authors: Yamini G, Manasa M, Lakshmi M, Mahant Singh, Hamsa M

Abstract: Food waste resulting from the unnoticed expiration of stored food is a prevalent issue in both home and commercial settings. Traditional refrigerators lack an automated way to track food expiration dates, leading to the unnecessary disposal of still-edible items. To address this problem, this study introduces a Smart Refrigerator with an RFID-based expiry notification system utilizing Internet of Things (IoT) technology. Each food item is given an RFID tag, which is scanned prior to placing the item in the refrigerator. The expiry date is then input through the Blynk mobile application connected to an ESP32 microcontroller. The ESP32 retains the food item information in its internal memory and continuously checks the expiry dates. A day before the expiration date, a cautionary message appears on an OLED display attached to the refrigerator. If the food item is taken out before it expires, the RFID tag is scanned again to refresh the system. The proposed solution is cost-effective, easy to use, and aids in minimizing food waste by sending timely notifications.

Solar Based Advanced Air Purifier

Authors: Kushal Kumar S, Monish Raj D. V., Pavan Kumar M. P, Prabhakar Reddy B. M, Professor Suman A. H

Abstract: The Solar-Powered Smart Air Purification Sys- tem represents a revolutionary approach to addressing out- door air pollution through autonomous, renewable energy- driven technology. This innovative system combines micro- controller architecture with solar power generation to create a self-sustaining mobile air purification unit capable of de- tecting, analyzing, and treating localized air quality issues in real-time. The system integrates advanced sensor technologies including Light Dependent Resistor (LDR) sensors for partic- ulate matter detection and MQ series gas sensors for compre- hensive air quality monitoring, enabling precise identification of pollution hotspots. Upon detection of suboptimal air con- ditions, the system autonomously activates its HEPA filtration mechanism through relay-controlled switching, providing im- mediate and targeted air purification. The solar-powered de- sign ensures continuous operation during daylight hours while maintaining energy efficiency through intelligent power man- agement. This eco-friendly solution addresses the growing need for localized air quality management in outdoor environ- ments, offering a scalable and sustainable approach to envi- ronmental protection.

An Intelligent System For Forest Fire Detection And Reforestation Planning

Authors: Gowri Mysuru, Gowri C V, Inchara K Shekhara, Kavana N, Mrs. Lavanya S

Abstract: Forest fire incidents cause severe damage to natural ecosystems, wildlife habitats, and human life, making rapid identification a quibbling requisite for telling excuse and disaster response. Conventional fire monitoring formulation such as enchiridion surveillance, satellite observation, and sensor-based method rarely face inquiring affiliated to delayed response, high operational cost, and limited coverage. To surmount these terminal point, this line proposes an intelligent image-based system for detection of woodland fire using deep learning techniques. The system categorizes forest images into three distinct classes—Fire, Smoke, and Normal—to support early-stage fire recognition. An ensemble of advanced architectures, namely ConvNeXt-Tiny, EfficientNetV2, and Swin Transformer, is exploited to capture fine-grained visual features as well as broader contextual information. A diverse dataset of forest appearance obtained from publicly gettable sources is utilized, along with extensive preprocessing and data augmentation to enhance model strength low-level varied biological science conditions. Input images are resized and normalized before being processed by the trained models, and final predictions are determined using probability-based decision fusion. Experimental evaluation shows that the proposed approach achieves an overall accuracy of 98% on the test dataset, with consistently high precision and recall across all categories. The resolution establish that the system can reliably identify fire and smoke scenarios while reducing false detections, constituent it desirable for real-time forest monitoring and early warning applications.

Effect of Operating Temperature on The Kinematic Viscosity of Ms-20 Oil in The M-14p Aircraft Engine

Authors: MSc Van Huong Ngo, MSc Trong Son Phan, MSc The Son Nguyen, MSc Le Phan, BSc Bao Le Nguyen Gia

Abstract: Kinematic viscosity is a critical parameter governing lubrication performance in aircraft piston engines. For the M-14P radial engine, MS-20 oil has been widely used under varying operating temperature conditions. This study investigates the influence of operating temperature on the kinematic viscosity of MS-20 oil. The viscosity–temperature relationship of MS-20 oil is analyzed using reference temperature data and the ASTM D341 Walther equation, specifically within the typical operating range of the M-14P engine. The results indicate a continuous and stable decrease in viscosity with increasing temperature, thereby providing a quantitative basis for evaluating lubrication behavior under varying engine operating conditions.

DOI: http://doi.org/10.5281/zenodo.18016688

A Comprehensive Review On Remediation Techniques For Heavy Metal-Contaminated Soils

Authors: Ms. Bharti Suryawanshi, Dr. Pranay Guru

Abstract: Due to the increasing industrial sector and era of urbanization, today there is a rising number of heavy metals and organic pollutants found in the soil. Soil erosion, pollution, salinization, and the continuous accumulation of soil fertility, biological and chemical properties, and physical structure are leading to an increase in toxic metals. This is not just a problem for a specific area but is becoming a serious global issue. According to global reports, it has been found that more than 50% of available agricultural land at 10 million sites around the world is contaminated with heavy metals. This is not only increasing the presence of heavy metals in the soil but also in the environment. Contaminated soil is more commonly observed in developed countries, while India, being a developing nation, is facing a growing presence of heavy metals in the soil due to ongoing development, industrialization, and urbanization plans.

The Impact Of Artificial Intelligence On Information Technology: Opportunities, Challenges, And Future Directions

Authors: Raghav Talwar, Dr Akash Saxena

Abstract: Artificial Intelligence (AI) is swiftly changing the terrain of Information Technology (IT), redefining sectors and generating fresh prospects for automation, efficiency, and creativity. This document examines the different uses of AI in IT, its effects on conventional IT systems, the obstacles companies encounter in implementing AI technologies, and the prospective pathways for AI integration. By examining present trends, obstacles, and case studies, this paper offers an in-depth analysis of the ways AI is transforming IT operations and business processes, along with the ethical issues related to its extensive implementation.

A Hybrid Machine Learning And XAI Architecture For Intelligent Career Guidance Systems

Authors: Le Manh Ha, Nguyen Huu Quynh, Nguyen Tai Tuyen

Abstract: Industry 4.0 and artificial intelligence are shown to bring great change or disappear a large proportion of work, while new jobs are born With the dynamical background comes the demand for a smart career guidance system, which will provide advice that is personalized, reliable, and adaptive. This paper systematically reviews the literature on the application of machine learning (ML) and explainable artificial intelligence (XAI) to career guidance. On the basis of PRISMA guidelines, 847 documents published between 2019 and 2025 were carefully screened, and 95 high-quality articles were extracted for in-depth review. The review classifies main ML methods—including collaborative filtering, content-based filtering, deep learning architectures such as LSTM, Transformer, and GNN, reinforcement learning, and their performance, limitations, and interpretability were judged. In parallel, the author analyzes such essential but questioned XAI techniques as LIME, SHAP, attention mechanisms, decision rules and counterfactual explanations in terms of their transparency and perceived user trust, as well as how easily acted upon these explanations are. From these foundations, the paper presents a five-layer hybrid ML-XAI framework that integrates data processing, knowledge maps, ensemble ML models, multi-level explanations, and user-centered presentation. In addition to these, future developments, such as flat or formidable language models and federated learning for maintaining privacy and fairness-aware algorithms, are explored, together with key challenges for further research. All in all, the paper provides a structured basis and practical guidance for next-generation, intelligent, transparent, and equitable career guidance systems.

DOI: https://doi.org/10.5281/zenodo.18032565

From SLA To Exit – Integrating Service Compliance, Ticket Prioritization, And Automated Feedback Loops To Predict Churn In Enterprise SaaS

Authors: Shashank Shekhar Tripathi

Abstract: Despite aggressive adherence to Service Level Agreements (SLAs), B2B SaaS firms continue to experience unexpected customer churn. This paper posits that the missing link is a granular, empirical understanding of how day-to-day SLA performance interacts with ticket priority to influence post-resolution satisfaction and retention. By synthesizing foundational service quality theories with modern deep-learning prioritization models, this research proposes an Integrated SLA – Priority – CSAT – Churn Model. Furthermore, it addresses the "blind spots" caused by low survey response rates through a validated three-step automated follow-up system. The anticipated contribution is a real-time "Retention Risk Score" and a cross-industry benchmark layer that offers a replicable blueprint for mitigating churn in high-value account environments.

Effects Of Environmental Enrichment And Low-Stress Handling On Immune Recovery In Dogs And Cats

Authors: Anushka Rajgonda Patil

Abstract: Stress is a significant immune function modulator in companion animals receiving medical care or being admitted to a hospital. In dogs and cats, elevated stress levels can prolong recovery, impede wound healing, and inhibit immunological responses. In order to promote animal welfare, owner-mediated environmental enrichment (EE) and low-stress handling (LSH) techniques have become more popular in veterinary practice; yet, there is still a lack of evidence on their direct impact on immunological recovery. This study looks at the theoretical and empirical data that connects better immune function in canine and feline patients with stress reduction via enrichment and gentle handling. The current study shows that low-stress handling and owner-mediated environmental enrichment greatly improve immunological recovery in canine and feline patients receiving therapeutic treatment. In addition to more stable immunological responses and quicker clinical recovery, animals exposed to enrichment-based surroundings and gentle handling showed decreased behavioral and physiological stress, as demonstrated by lower cortisol levels and improved stress ratings.

Depletion Of Leguminous Medicinal Plants In Erstwhile Adilabad District, Telangana: A Conservation Imperative

Authors: Dr. Devendar Veeramalla, Dr. Koppula Sampath

Abstract: Adilabad district in Telangana is a biodiversity hotspot and is particularly rich in leguminous medicinal plants, which are crucial for the traditional healthcare of tribal communities like the Gonds, Kolams, Pradhans and Naikpods. This study investigates the primary drivers of decline in these plant populations, including forest encroachment, unsustainable harvesting, habitat degradation, climate change, causes of depletion, and conservation needs of leguminous medicinal flora in the region. Field surveys, ethnobotanical documentation, and interviews with local tribal healers (Gonds, Kolams, Naikpods and Pradhans) were conducted across key forest zones. Results reveal a sharp decline in species such as Albizia lebbeck, Clitoria ternatea, Cassia fistula, and Abrus precatorius etc. Field surveys and interviews with local traditional healers and forest officials were conducted to assess the species status, exploitation patterns, and conservation challenges. The research highlights the critical need for a conservation strategy that integrates in-situ and ex-situ methods with community-based management. This approach is essential for preserving both the plant species and the associated traditional knowledge.

DOI: http://doi.org/10.5281/zenodo.18044798

Transforming Post-Operative Orthopedic Care With IoT Innovations

Authors: D.Dharshini, S. Ranjithkumar, B.R. Harivansh, R.Padma Priya, M.Manimegalai, P.Akshaya

Abstract: This paper represents how the challenge of post operative orthopedic carried to palliative care with the IOT system. Orthopedic surgeons often employ external fixation to treat bone fractures, yet the surgery and recovery process can still face complications that hinder effectiveness and raise costs. By using this it replaces the nurse assistance. A key challenge is that doctors lack continuous access to critical data, such as the healing process, patientbehavior, and environmental factors. This paper aims to enhance fixation devices by integrating IoT technology, enabling real-time monitoring of bone fracture healing. The device can detect and report significant events in the patient's recovery to healthcare professionals. We demonstrate this design by monitoring patient compliance with prescribed behavior’s during post-operative treatment of bone fractures. Through continuous monitoring and data insights, this approach aims to improve patient care, reduce complications, and enhance clinical outcomes.

DOI: http://doi.org/10.5281/zenodo.18051245

A Systematic Computational Study To Observe The Effect On Sb Doping On Electronic Band Structure, And Optical Properties Of SnO2

Authors: Awais Tabassum, Muhammad Khizar Iqbal

Abstract: Among the transparent conducting oxides (TCOs), SnO2 has been extensively explored due to the following applications: optoelectronic devices, solar cells, light-emitting devices, plasma-screen indicates, and other electronics devices. Hence, in the present investigation, the values of the electronic and optical gaps of the parental and Sb doped SnO2 (12.5%, 25%, and 37.5%) compound have been computed using the DFT calculations. The parental compounds indicate the semiconducting character, and after doping with Sb, the character of the materials changes to metallic. The band structure of 12.5%, and 25% indicates that two bands cross the Fermi level, whereas the band structure of 37.5% indicates that three bands cross the Fermi level, respectively. It has been noticed that the character of the Oxygen atom is dominant in the Fermi level. The total density of states (DOS) at Fermi level N (EF) is 10.3, 15.0, and 14.8 states/eV for 12.5%, 25%, and 37.5%, respectively. From the above calculations, it can also be concluded that the metallic character of the materials can also be observed from the finite DOS at Fermi level. The anisotropic features of the imaginary, and real parts of complex dielectric functions, reflectivity, refractive index, extension coefficient, and energy loss function have been computed along with the component of electric field polarization. From the calculations, it can be stated that the variation in the optical spectra of the three compounds has been observed due to the Sb percentage doping. It has also been observed that the variation in the peaks' height due to the increment of Sb atom percentage, indicating that the 37.5% doped compound has more metallic character than the 12.5%, and 25% doped compounds.

DOI: http://doi.org/10.5281/zenodo.18051673

CookSafe: Smart Gas Stove Timer With Automatic Regulator Control Using Servo Motor

Authors: Venkata Koteshwararao, Himavarshitha M, Indushree S, Kousalya B S, Likithashree

Abstract: Human carelessness, such as failing to turn off the gas regulator after cooking, is a common cause of gas-related mishaps in home kitchens. This paper introduces CookSafe, a smart gas stove safety system that uses a servo motor and a user-defined timer to automatically control the gas regulator. The servo motor automatically activates the gas regulator after the user uses push buttons to set the cooking time. The servo motor rotates back to turn the regulator off after the predetermined amount of time has passed, guaranteeing safe operation. An Arduino microcontroller, a servo motor, an LCD display, and control buttons are used to implement the system. CookSafe offers an affordable, dependable, and easy- to-use way to enhance kitchen safety and stop gas leak incidents.

Automated Weed Detection And Classification Using-YOLO

Authors: Mr.Rohit A. Kyarakoppa, Mr.Darshan S. Bagalkot, Mr. Manoj S. Kori, Mr. Devaraj S. Kakkali, Professor Pooja C. Shinde

Abstract: Modern agriculture practice faces increasing pressure to improve productivity while reducing chemical usage and labor dependency. One major challenge is the uncontrolled growth of agriculture weeds, which compete with crops for essential resources and significantly reduce yield. This paper presents an automated weed detection and classification system built using the YOLOv8 architecture. The model is trained on annotated crop-field images containing both crop and weed categories. The system aims to deliver real-time identification with high accuracy, enabling selective herbicide spraying and automated monitoring. The work integrates image preprocessing, model training, validation, and deployment in a practical pipeline. Experimental results demonstrate strong detection performance, reliable bounding-box prediction, and robust generalization under varied lighting and field conditions. The proposed method highlights an effective direction for precision agriculture and supports sustainable farming practices.

Changing Patterns Of Intense Rainfall And Flood Risk Under Climate Variability: A Global Review With Implications For Regional Frequency Analysis

Authors: Muhammad Nura, Zahrahtul Amani Zakaria

Abstract: Floods are the most common natural disaster associated with our changing climate, accounting for more than 40% of all weather-related events worldwide. Since 2000, they have affected over 1.6 billion people. As the planet warms, the global water cycle is intensifying, resulting in heavier and more frequent downpours that cover wider areas, particularly in tropical and monsoon regions. At the same time, social and economic factors such as rapid population growth, unplanned urban expansion, changes in land use, and weak local institutions have made communities more susceptible and vulnerable to floods. These issues have also led to significant differences between regions in the severity of floods and their preparedness to handle them. This paper presents a global perspective on the evolving patterns of heavy rainfall and flood risks, with a focus on variations across Asia, Africa, Europe, and North America. It also explores how natural climate patterns like El Niño and La Niña (together known as ENSO), the Indian Ocean Dipole (IOD), and the Atlantic Multidecadal Oscillation (AMO) influence flood risks over periods ranging from a few years to several decades. These natural influences make it hard to rely on the old assumption that flood patterns stay constant over time. This creates challenges when estimating design floods or predicting the frequency of extreme floods, especially in areas with limited data or highly variable weather conditions. The review also examines the implications of these changes for measuring and understanding flood risk. It emphasizes the importance of using reliable statistical methods, such as Trimmed L-moment-based Regional Frequency Analysis (RFA), to obtain more accurate results. By being less affected by outliers and allowing data to be shared across regions, these methods offer a helpful way to make estimates of extreme rainfall and flooding more reliable. This helps improve planning and supports stronger, climate-resilient flood-risk management in a changing world. This review does not introduce any new statistical methods. Instead, it synthesizes existing research to explain when and where strong regional frequency approaches are most effective in a climate that’s no longer stable or predictable.

A Blockchain-Based Framework for Secure Pharmaceutical Supply Chain Management

Authors: Prof.Supriya G Purohit, Mr.Satish Kumar, Mr.Sudheesh S, Mr.Rahul

Abstract: The global healthcare sector faces significant difficulties in managing pharmaceutical supply chains, particularly due to issues such as counterfeit medicines, limited transparency, inefficient coordination, and data security vulnerabilities. To address these challenges, this project presents a secure and efficient medicine supply chain management framework based on blockchain technology. The proposed system enhances traceability, transparency, and data security by maintaining an immutable and decentralized record of all supply chain transactions. Smart contracts are employed to automate critical processes including inventory control, order processing, and ownership transfer, thereby reducing manual intervention and operational errors. A decentralized application (DApp) is developed to enable real-time monitoring and verification of medicines throughout their lifecycle using unique digital identifiers. This allows stakeholders to authenticate products and track their movement from manufacturing to end consumption. Additionally, the framework incorporates an optimized Linear Hash Computation (LHC) variant to improve system performance and strengthen data integrity verification. By combining blockchain technology, smart contracts, and efficient hashing mechanisms, the proposed solution provides a reliable and scalable approach for securing modern pharmaceutical supply chains.

DOI: https://doi.org/10.5281/zenodo.18066121

Spamshield Sentiment Analysis on Youtube Comments

Authors: Prof.Supriya G Purohit, Ms.G Keerthi, Ms.Hafsa Zareen , Ms. Ayesha Hasan Osmani

Abstract: The exponential growth of social media platforms has resulted in an overwhelming volume of user-generated textual content, making effective content moderation and opinion analysis increasingly challenging. YouTube, as one of the most popular video-sharing platforms, receives millions of comments daily, which include genuine feedback as well as spam, promotional messages, and emotionally charged content. Manual analysis of such large-scale data is inefficient, time-consuming, and prone to inconsistencies. Therefore, there is a growing need for automated systems capable of analyzing user comments and extracting meaningful insights in real time. This paper presents SpamShield, a web-based automated system designed to analyze YouTube comments using Natural Language Processing (NLP) techniques. The proposed system retrieves real-time comments from YouTube videos using the YouTube Data API and performs comprehensive text preprocessing to remove noise and normalize the data. Preprocessing steps include text normalization, removal of special characters and URLs, tokenization, and stop-word elimination, ensuring that the comments are suitable for reliable analysis. The sentiment analysis process is implemented using the Natural Language Toolkit (NLTK) along with the VADER (Valence Aware Dictionary and sEntiment Reasoner) lexicon, hich is specifically optimized for analyzing social media text. Each comment is evaluated based on lexical and contextual features, and the sentiment polarity is classified into positive, negative, or neutral categories. The system further aggregates sentiment results to generate comprehensive sentiment reports that provide a clear overview of audience opinions and engagement patterns. Experimental evaluation demonstrates that the proposed system effectively analyzes informal and unstructured social media text, offering reliable sentiment classification and intuitive visualization of results. The modular and scalable architecture of SpamShield enables efficient processing of large volumes of comments and supports real-time analysis. The proposed approach provides a practical solution for content creators, marketers, and researchers to understand audience sentiment, enhance user engagement, and support data-driven decision- making in social media environments.

DOI: https://doi.org/10.5281/zenodo.18169716

Mental Health Status of Hong Kong Secondary School Students and Construction of its Educational Ecosystem

Authors: Wing Cheung Tang

Abstract: This paper is conducted to investigate the deteriorating mental health status of secondary school students in Hong Kong; analyze the causes from multiple layers using an educational ecology perspective; and propose a holistic framework of intervention and support strategies. Through a systematic review of existing empirical literature, government statistics, and related survey reports, combined with field observations, this study corroborates that adolescents in Hong Kong are facing alarmingly high levels of stress, anxiety, and depressive symptoms. The major drivers of this crisis are intrinsically linked to an intensely competitive academic culture, overwhelming family expectations, anxiety over future prospects, and the compounding pressures of a rapidly changing socio-cultural environment. The ecology of education perspective makes it clear that these factors are interactive within a complex system. The present study assumes that the problem is too systemic to be solved by clinical treatment alone, which often addresses symptoms rather than root causes. Thus, there is a need for change in paradigm. Schools, families, the community, and policymakers must come together to build a multi-tiered support system. Driven by the principles of educational ecology, the system designed will have to prioritize prevention and developmental growth instead of simply remediation. In bringing the various components of a student's ecology together strategically, we can help nurture the capacity for resilience and well-being, moving closer to the ultimate goal-a true all-round education which attends to both mind and character.

AI In Breast, Ovarian, And Uterine Cancer Treatment: A Revolution In Genomics

Authors: A. Mohamed Sikkander, Joel J. P. C. Rodrigues, Manoharan Meena

Abstract: Artificial intelligence (AI) is increasingly being hailed as a revolutionary paradigm shift in the field of oncology, particularly in gene therapy for breast, ovarian, and uterine cancers. The most common cancers in women around the world also have great genetic diversity, making it difficult to employ different treatments. High-throughput sequencing methods generate vast amounts of genetic data, requiring intelligent computational methods for meaningful analysis. Machine learning algorithms, deep learning algorithms, and natural language processing are increasingly being used to analyze genetic and clinical data to make decisions about cancer. This study explores the application of artificial intelligence to transform cancer genomic therapy by integrating various omics to detect potential mutations and predict response to therapy. Artificial intelligence models can be used to improve early detection with targeted therapies, cancer subtyping, and precision cancer medicine. For example, for breast cancer, predictive factors are based on HER2 and BRCA mutations. Ovarian cancer – a prognostic model for homologous recombination deficiency. In endometrial cancer, molecular subtyping and prognostic factors are provided through artificial intelligence applications. The results clearly demonstrate that AI-assisted genomic analysis has significantly improved accuracy and efficiency compared to traditional approaches. However, despite various limitations and challenges related to bias, ethics, and interpretability, AI has great potential to update cancer genomics. This study highlights the need to combine AI and genomics to further develop personalized medicine to treat different types of cancer and improve survival rates.

DOI: http://doi.org/10.5281/zenodo.18066679

Analytical Approach For CV Joint Stroke-Angle Selection For Automobile Application

Authors: Santaji Jadhav, Nitin Tawhare

Abstract: Constant Velocity (CV) Joint or driveshaft is important drivetrain component of automotive vehicles considering vehicle dynamics like driving performance, handling, NVH etc. It transfers power/torque received from gearbox to wheels at wide variety of angles at constant velocity. This paper explains the methodology of static and dynamic analysis of CV joint, which is followed during initial stage of product design selection. Failure to do this may result in incorrect CV joint selection in the design stage itself and/or safety concerns if CV joint dislodges from gearbox during operation, which may lead to safety of occupants in the vehicle and pedestrians too. Along with static analysis, dynamic analysis of CV joint ensures all operating points of inboard joint nominal center fall within acceptable design limit of joint extreme capacity. Based on this study, one can establish a better understanding of CV joint stroke-angle selection, which will reduce cost, help to meet vehicle timeline and improve customer satisfaction.

Antihyperglycemic Activity of Ethanolic Extracts of Azadirachta Indica Root Bark and Zanthoxylum Chalybeum Stem Bark in Alloxan-Induced Diabetic Mice

Authors: Simon Peter Okiror, Fareed Kuteesa, Francis Omujal, George William Muyombya

Abstract: Zanthoxylum chalybeum stem bark and Azadirachta indica root bark are used by communities in Africa and Asia to manage diabetes mellitus. This study determined the anti-hyperglycemic effect of Zanthoxylum chalybeum ethanolic stem bark extract and Azadirachta indica ethanolic root bark extract in Alloxan monohydrate-induced diabetic mice. The plants were obtained from Usuk sub-county, Eastern Uganda and extracts prepared and study done at Natural Chemotherapeutics Research Institute, Kampala, Uganda. Mice were divided into Zanthoxylum chalybeum (n=8), Azadirachta indica (n=8), combination of Zanthoxylum chalybeum and Azadirachta indica (n=8), normal control (n=8), positive control groups (n=8) and untreated diabetic (n=8). Diabetes was induced in each mouse in experimental groups by a single dose intraperitoneal injection of Alloxan monohydrate at 150mg/kg body weight. The plant extracts were administered orally to the experimental mice at dosages of 200mg/kg body weight for 28 days. The negative control group was left untreated while the positive control group was treated orally with Metformin (10mg/kg body weight). The effect of the extracts on blood glucose of the individual extracts and when combined were determined in all mice in the experimental and positive control groups. The combined extracts of Zanthoxylum chalybeum and Azadirachta indica (1:1) exhibited significant antidiabetic activity compared to when the extracts were used individually (P<0.05). These results suggest that the ethanolic stem bark extract of Zanthoxylum chalybeum combined with extracts of Azadirachta indica root bark possesses synergistic antihyperglycemic activity. This study thus corroborates the traditional use of the plants for management of diabetes. However, further research is required to explore different dosages and the possible mechanism of action of Zanthoxylum chalybeum and Azadirachta indica in the treatment of Diabetes mellitus.

DOI: https://doi.org/10.5281/zenodo.18067366

Integrating Entropy, MEREC And TOPSIS Methods In Solving Multi-Objective Optimization Problems: A Case Study In Electrical Wire Selection

Authors: Ha Van Nghi

Abstract: Solving multi-objective optimization problems is a task that must frequently be performed across various fields, including economics, management, engineering, and other areas. Two important tasks in solving multi-objective optimization problems are calculating weights for the objectives and selecting a mathematical method to solve the optimization problem. The current research integrated three methods—Entropy, MEREC, and TOPSIS—to solve a multi-objective optimization problem in identifying the best type of copper-core electrical wire among 28 available types. Specifically, the Entropy and MEREC methods were used to calculate weights for the criteria, while the TOPSIS method was used to rank the types of copper-core electrical wires. The results showed that although the rankings of the copper-core electrical wires were inconsistent when the weights of the criteria were calculated using the two different methods (Entropy and MEREC), there were still several alternatives whose rankings remained consistent. The type of electrical wire corresponding to product code 20255114 was found to be the best among the 28 types considered in this study.

DOI: http://doi.org/10.5281/zenodo.18067419

Fake News Detection In Social Media Using Machine Learning Techniques

Authors: Shalini, Rajesh M, Gowthami T, Kavya MK, Kajal

Abstract: Fake News (FN) in online platform distress today's society. With the rapid growth of technology, news content is being created and shared very quickly. Much of this content is designed to influence readers or viewers into believing false information. Although such content may not always cause serious harm, it is still important to detect fake news so that internet users can access accurate and trustworthy information. Unfortunately, identifying fake content is not easy for ordinary users.Fake news usually appears in either visual form (images or videos) or linguistic form (text). Therefore, detecting false information requires proper analysis methods along with machine learning (ML)–based artificial intelligence techniques. While current methods are able to detect fake content to a certain extent, each approach has its own advantages and limitations.The main goal of social media analysis is to examine the features and weaknesses of existing models and to understand suitable fake news detection (FND) techniques for research purposes. Accordingly, this study analyzes the working methods, strengths, and limitations of various fake news detection approaches.

System-Level Testing of Event-Driven Microservices Using Reproducible Containerized Environments

Authors: Sriram Ghanta

Abstract: Event-driven microservices introduce fundamental challenges for automated testing due to asynchronous execution, eventual consistency, and non-deterministic message flows across distributed components. This study addresses the problem of achieving reliable system-level validation in such architectures, where traditional integration testing approaches often produce flaky results and limited diagnostic insight. The purpose of this research is to design and evaluate a reproducible testing strategy that enables deterministic, end-to-end verification of event-driven microservices using containerized environments. The study adopts a mixed-method approach, combining quantitative analysis of test stability, failure reproducibility, and defect detection rates with qualitative assessment of test diagnosability and developer feedback. A structured test architecture is proposed in which ephemeral containerized dependencies are orchestrated alongside services under test, enabling controlled event injection, consistent state initialization, and repeatable execution. Empirical results demonstrate significant reductions in non-deterministic test failures, improved fault localization, and higher confidence in validating asynchronous service interactions. The study contributes to a system-level testing framework that bridges the gap between unit-level validation and production behavior, offering practical guidance for designing robust test pipelines for distributed systems. The findings underscore the strategic value of reproducible containerized testing as both an engineering discipline and a research contribution, with implications for advancing test reliability, accelerating delivery, and strengthening the empirical foundations of event-driven microservice validation.

DOI: https://doi.org/10.5281/zenodo.18068337

LoRa Based Envi-Rover

Authors: Narendra R Giriyappanavar, Kiran Nandi, Harish B Patil, Murali Vijay Vhanmani, Netravati M Murari

Abstract: This paper describes the design and implementation of Envi-Rover, a LoRa-based IoT rover designed to perform Realtime environmental monitoring over remote areas. The system utilizes the ESP32 microcontroller as its main processing unit. It works with a Cytron MDD10A motor driver to perform dual-channel motor control and an RYLR890 LoRa transceiver operating at 868 MHz for long-range, low-power wireless communication. Equipped with many sensors, such as a DHT11 for temperature and humidity measurement, an ultrasonic module on a servo motor for obstacle detection, an LDR to detect ambient light, and MQ-series sensors to monitor gas concentrations, the data acquired from these sensors are transmitted through LoRa to a base station and displayed on the Blynk IoT platform for real-time analysis. The rover can be driven in both manual and semi-autonomous modes by using a joystick controller for effective navigation in the field and continuous data gathering. Stable LoRa communications beyond one kilometer were observed in test results, along with accurate performance of all sensors. This proves that the system can be very helpful in environmental assessment, agricultural monitoring, and disaster management. The Envi-Rover merges IoT and robotics to form a low-cost, energy-efficient, and scalable solution toward environmental data collection at remote locations.

A Hybrid Explainable Deep Learning Model For Early Disease Prediction And Clinical Interpretability

Authors: Akash S, Karthikrajan G

Abstract: Early disease prediction using deep learning models has revolutionized healthcare analytics by achieving state-of-the-art accuracy in analyzing complex, multimodal data sources, including sequential electronic health records (EHRs) such as time-series vital signs, laboratory results, medication histories, and demographic profiles, alongside high-resolution medical imaging modalities like chest X-rays, MRIs, and CT scans. Despite these advancements, the clinical deployment of such models faces significant barriers stemming from their opaque "black-box" decision-making processes, which provide no insight into the underlying reasoning for predictions, thereby undermining clinician trust, increasing diagnostic hesitation, and posing challenges to regulatory compliance with stringent data protection laws, including the European Union's General Data Protection Regulation (GDPR) "right to explanation" and the U.S. Health Insurance Portability and Accountability Act (HIPAA). This paper proposes an innovative hybrid explainable artificial intelligence (XAI) framework, seamlessly integrated with scalable cloud computing infrastructure, to deliver both exceptional predictive accuracy and unprecedented clinical interpretability for early disease detection—targeting conditions like sepsis, cardiovascular events, and pneumonia. The core architecture synergistically combines Temporal Convolutional Networks (TCN), which excel at modelling long-range temporal dependencies in EHR sequences through dilated convolutions and residual connections, with Convolutional Neural Networks (CNN) variants such as ResNet-50 for robust spatial feature extraction from imaging data. To address privacy concerns in multi-institutional settings, the model leverages a federated transfer learning approach, enabling decentralized training where model updates are aggregated without exchanging raw patient data, thus minimizing risks of data breaches while harnessing diverse datasets. Interpretability is embedded via a comprehensive dual-explanation strategy: globally, Shapley Additive explanations (SHAP) compute feature attribution scores to reveal dataset-wide importance hierarchies (e.g., prioritizing elevated troponin levels over age in cardiac risk prediction); locally, Local Interpretable Model-agnostic Explanations (LIME) approximate model behaviour with surrogate linear models for EHR inputs, complemented by Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps that visually pinpoint salient regions in medical images, such as pulmonary opacities indicative of pneumonia. Deployed on elastic cloud platforms like Amazon Web Services (AWS) EC2 instances orchestrated with Kubernetes for auto-scaling and Apache Kafka for real-time data streaming, the system ensures low-latency inference suitable for hospital edge computing.

AI-Driven Early Detection And Holistic Management System For Parkinson’s Disease

Authors: Harini G, Sowmithra Murali

Abstract: Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms that significantly impact a patient's quality of life. Conventional diagnostic methods, such as Magnetic Resonance Imaging (MRI) and Dopamine Transporter (DaT) scans, are expensive, time-consuming, and often inaccessible to individuals in low-resource settings. This research proposes an AI-powered multimodal detection system leveraging voice analysis, facial expression recognition, and movement tracking using smartphone sensors and low-cost wearables. The system integrates federated learning to enhance diagnostic accuracy while ensuring data privacy and scalability. Additionally, it incorporates real-time monitoring, environmental correlation, and blockchain-based health records to provide a comprehensive, cost-effective, and accessible solution for early PD detection. Our study estimates a cost reduction from ₹80,000–₹1,00,000 (MRI-based diagnosis) to ₹10,000–₹15,000 per device, making early screening feasible Our research focuses on developing an AI-powered smartphone-based detection system that can enable real-time screening and continuous monitoring, significantly improving early detection and patient outcomes.

Consumer Behaviour In The Age Of Digital Commerce: The Role Of FinTech, Mobile Payments And Online Market Platforms

Authors: Dr. Prabodhini B. Wakhare, Bhagyashree S. Borhade, Dr. Shivaji Borhade Principal

Abstract: Digital commerce has changed how people buy products and services. Today, consumers shop online, compare prices and make payments through technology. This study examines how FinTech services, mobile payments and online market platforms affect consumer purchasing behaviour. A sample of 100 consumers was surveyed using a structured questionnaire. The analysis shows that digital tools strongly influence buying patterns. FinTech services increase trust and financial control. Mobile payments make shopping faster and more convenient. Online platforms provide product information, reviews and personalised suggestions that guide purchase decisions. The results show that all three factors have a significant positive impact on consumer behaviour. Younger consumers use digital services more actively than older groups. Mobile payments and FinTech services were found to be the strongest predictors of online purchasing. Consumers also enjoy smooth transactions, cashback offers and secure payment gateways. However, some still worry about privacy and security.

Interference Canceling Improvement using Symbol Misalignment in 5G NOMA downlink transmission

Authors: Son Nam Kim, Jong Sam Ri, Un Song Kim, Sin Hyok Mun

Abstract: In this paper, we introduce the method for reducing the interference power using symbol misalignment in NOMA downlink transmission. Unlike other papers about the asynchronous NOMA downlink transmission using symbol misalignment, we provide the symbol misalignment when two and over use one orthogonal source. Here, we solve the intended asynchronous problem to get the minimum interference power between several users in PD-NOMA downlink transmission.

DOI: https://doi.org/10.5281/zenodo.18105071

Effect Of A Scientifically Designed Yogic Training Protocol On Sprinting Speed And Explosive Power Among Kabaddi And Kho-Kho Players: A Randomized Controlled Study_795

Authors: Chandrakanta Barik, Kalapini Agasti

Abstract: Background: Kabaddi and Kho-Kho demand repeated sprinting, rapid directional changes, and high levels of explosive leg power. Contemporary sports training increasingly recognizes the value of integrative approaches that enhance neuromuscular efficiency, flexibility, respiratory control, and recovery. Yoga, when structured scientifically, may serve as an effective complementary training modality. Objective: The purpose of this study was to examine the effect of a scientifically designed yogic training protocol on sprinting speed and explosive leg power among competitive Kabaddi and Kho-Kho players. Methods: A randomized controlled experimental design was adopted. Forty male Kabaddi and Kho-Kho players (18–25 years) were randomly assigned to a Yogic Training Group (YTG; n = 20) and a Control Group (CG; n = 20). The YTG underwent a 12-week yogic training program (5 sessions/week, 45 minutes/session), while the CG continued regular sports training. Sprinting speed was assessed using the 30 m sprint test, and explosive leg power was measured using the vertical jump test. Data were analyzed using descriptive statistics and Analysis of Covariance (ANCOVA). Results: The ANCOVA revealed significant post-intervention improvements in sprinting speed (p < 0.01) and vertical jump height (p < 0.01) in the YTG compared to the CG. The magnitude of change indicated moderate to large practical significance. Conclusion: The findings suggest that a scientifically structured yogic training protocol significantly enhances sprinting speed and explosive leg power in Kabaddi and Kho-Kho players. Integrating yoga into conventional sports training programs may contribute to holistic athletic development and performance optimization.

Accuracy Enhancement Of Non-moving GNSS Rover Positioning Using Moving Average

Authors: Sandeep Kumar Kashyap, Shweta Vikram

Abstract: GNSS rover output often shows short-term fluctuations. These can arise from multipath effects, atmospheric changes, and receiver noise. Even with RTK corrections, jitters pose a major challenge for precision applications. This paper reviews the Moving Average (MA) filter for enhancing GNSS rover stability. The MA filter smooths high-frequency noise by averaging recent measurements. This process improves coordinate smoothness and horizontal accuracy. We evaluated this with a synthetic noisy GNSS dataset. Results demonstrate clear improvements in stability and less dispersion after applying MA filtering. These findings align with previous studies on low-cost GNSS filtering.

Customer Product Choice Recommendation By Association Rules And Learning Models

Authors: Keerti Pal, Jayshree Boaddh, Rahul Patidar

Abstract: Online stores and apps attracts customer at various levels. So approaches need to be more effective by analyzing the behavior of visiting customer. Many of researcher has proposed different models of customer product recommendation system. This paper introduces a novel Customer Product Recommendation by Rules and Ensemble Model (CPRRESM) framework designed to enhance purchase prediction accuracy for small-scale retail stores with limited data resources. The proposed approach integrates Apriori-based association rule mining for pattern discovery, Z-score normalization for feature standardization, and a Gradient Boosting ensemble model for efficient learning. By combining rule-based insights with ensemble learning, CPRRESM effectively captures customer purchasing behavior and dynamic preferences.

DOI: http://doi.org/10.5281/zenodo.18107972

UPI Fraud Detection Using Machine Learning

Authors: Vijay Bhaskar, Abhishek, Hritik, Manjunath, Mrs. Sukanya

Abstract: The rapid increase in digital transactions, particularly through the Unified Payments Interface (UPI), has been accompanied by an alarming rise in fraudulent activities targeting unsuspecting users. This project aims to address this gap by developing an effective fraud detection system using machine learning. The proposed system utilizes four popular machine learning algorithms: Random Forest, Logistic Regression, Decision Tree, and Support Vector Machine (SVM) to classify transactions as either legitimate or fraudulent. By analyzing historical transaction data, the system detects anomalies or suspicious behavior indicative of fraud and flags such transactions in real-time. Experimental results indicate that machine learning offers the advantage of continuously improving detection accuracy as the system learns from new transaction data.

ReconSpectre: A Research-Integrated Hybrid Reconnaissance Framework for Empirical Attack-Surface Measurement

Authors: Suman Chandila, Abhishek, Nisha ranjan, Aaradhya Sirohi

Abstract: Reconnaissance represents the foundational phase of the cyber attack lifecycle, directly shaping the effectiveness of exploitation, privilege escalation, and persistence. Despite its strategic importance, reconnaissance is rarely treated as a scientifically measurable process. Most existing tools prioritize operational efficiency over methodological transparency, making them unsuitable for empirical cybersecurity research. This paper presents ReconSpectre, a research-integrated hybrid reconnaissance framework designed to transform reconnaissance into a repeatable, observable, and experiment-driven process. ReconSpectre unifies passive intelligence acquisition, active DNS enumeration, infrastructure analysis, network scanning, and attack-surface inference within a controlled execution lifecycle. The framework introduces structured telemetry, configurable experimentation parameters, and standardized JSON outputs to enable systematic analysis of reconnaissance techniques. By emphasizing lifecycle transparency, metric generation, and experimental control, ReconSpectre bridges the gap between practitioner-focused reconnaissance tools and academic research requirements. The framework demonstrates how reconnaissance itself can be studied as a scientific artifact rather than treated as a black-box preliminary step.

DOI: https://doi.org/10.5281/zenodo.18139609

Design and Comparative Analysis of 8 bit Vedic and Wallace Tree Multipliers Using 45nm Technology

Authors: Akshata Bagewadi, Anirudh patil, Nandan Hebbar, Naveena Kittur, Deepak Sharma

Abstract: As VLSI technology scales, the demand for area-efficient and high-speed arithmetic circuits increases. This research investigates the performance characteristics of 8-bit Vedic and Wallace Tree multipliers designed at the 45nm technology node. The study introduces a comparative framework between standard Traditional CMOS design rules and a modified Transmission Gate (TG) technique, where basic AND/OR gates are optimized using only three transistors to reduce footprint. Detailed schematics were developed for both architectures: the Vedic multiplier utilizes a modular approach with Ripple Carry Adders (RCAs) for intermediate sum reduction, while the Wallace multiplier employs a parallel reduction tree consisting of 48 Full Adders and 8 Half Adders. Simulation results reveal distinct performance advantages for each logic style. The proposed 3-transistor TG method significantly reduced the transistor count, with the TG-based Wallace and Vedic designs requiring only 1,720 and 2,524 transistors, respectively. Conversely, the Traditional CMOS designs demonstrated superior signal integrity and power efficiency. The Traditional Wallace Multiplier outperformed all other variations, recording a delay of 0.12 ns and power consumption of 11.27 µW. The analysis suggests that for 45nm node designs where power-delay product (PDP) is the primary constraint, the Traditional Wallace structure is preferable, whereas the TG-based approach is viable for strictly area-constrained applications.

Regulatory Divergence And Convergence: A Comparative Study Of Drug Approval Processes In The USA, Europe, And India

Authors: Aman Ratandeep Harwani, Ishan Patel

Abstract: The globalization of pharmaceutical markets has necessitated greater alignment among national regulatory authorities to ensure patient safety, drug efficacy, and timely access to therapeutics[1][2][3]. This review critically examines the similarities and differences in drug approval pathways across three major regulatory systems—the United States Food and Drug Administration (USFDA), the European Medicines Agency (EMA), and the Central Drugs Standard Control Organization (CDSCO) of India. Each of these agencies follows distinct procedural frameworks for Investigational New Drug (IND) applications, clinical trial oversight, and marketing authorization[1][2][3]. While the USFDA and EMA emphasize accelerated pathways, orphan designations, and structured benefit-risk assessments[1][4], the CDSCO has made significant strides toward harmonization through the New Drugs and Clinical Trials Rules (2019) and adoption of the Common Technical Document (CTD) format[5][6]. Despite these advances, considerable divergence persists in submission timelines, review transparency, and post-marketing surveillance mechanisms. Quantitative analysis reveals that drugs approved by the CDSCO face an average lag of 43.2 months compared to the USFDA, 25.6 months versus the EMA, and 30.3 months against the PMDA[7]. However, recent 2024 clinical trial waiver expansions under Rule 101 are progressively reducing this gap[8]. The review further highlights ongoing convergence efforts led by the International Council for Harmonisation (ICH) through harmonized guidelines such as E6(R3), Q8-Q10, and mutual recognition arrangements[9][10]. Overall, harmonization of regulatory standards can minimize duplication, enhance global collaboration, and expedite patient access to essential medicines..

DOI: http://doi.org/10.5281/zenodo.18136767

Design and Development of Rider Safety Jacket

Authors: K.Nagarathna, Amitsing Hunnur, Ankita Hulloli, Arun Hadimani, Bhagyashri Kumbar

Abstract: This project focuses on the design and development of a rider's safety jacket aimed at enhancing the safety of motorbike riders, horse riders, and workers in high-risk environments such as construction sites. The system operates on principles similar to vehicle-mounted airbags, deploying automatically to minimize injuries during accidents. The jacket integrates a pressure cylinder connected via a pneumatic valve, conduits, and connectors. Upon impact, the pneumatic valve triggers the release of pressurized air, inflating the jacket within milliseconds to provide cushioning. Additionally, the system incorporates a GPS module to capture the rider's live location, which is then transmitted to the nearest ambulance or designated contacts via a GSM module.

DOI: https://doi.org/10.5281/zenodo.18137673

A Comparative Analysis of Joglekar and Pickett Memristor Models: From Phenomenological to Physical Modeling

Authors: Dr. Osman Zenk

Abstract: This study provides a detailed theoretical and simulation-based comparison of two fundamental memristor models: the Joglekar model and the Pickett model. The Joglekar model represents a phenomenological approach that introduces a symmetric, state-dependent window function to enforce nonlinear dopant drift, offering computational efficiency and numerical stability. In contrast, the Pickett model constitutes a physics-based framework derived from experimental titanium dioxide thin-film devices, explicitly describing ionic drift dynamics and quantum mechanical tunneling through highly nonlinear, experimentally-parameterized equations. This comprehensive analysis elucidates the core mathematical principles, implementation strategies, parameter sensitivities, and application domains of each model. Furthermore, we provide functional, well-documented MATLAB code implementations that demonstrate the inherent trade-off between simulation efficiency and physical accuracy. Our parametric analysis reveals that while the Joglekar model achieves rapid, stable simulation suitable for large-scale circuit design, the Pickett model provides superior physical fidelity at the cost of computational complexity. The results underscore that the Joglekar model excels in efficient circuit simulation and educational contexts, while the Pickett model serves as an essential benchmark for accurate device characterization and physical mechanism studies.

DOI: https://doi.org/10.5281/zenodo.18137859

Numerical Analysis of Modified Supersonic Airfoils to Investigate Aerodynamic Performance

Authors: Harini R, Sibinesh R V, Jefy Stanly S

Abstract: This study presents a Computational Fluid Dynamics (CFD) investigation into the aerodynamic performance of modified supersonic airfoils, focusing on two novel configurations: a Modified double wedge and a Wedge–convex airfoil. Traditional supersonic airfoils such as the double wedge and biconvex perform effectively at high Mach numbers but often experience increased drag and reduced efficiency at moderate angles of attack. To address these issues, geometric modifications were introduced to improve shock control and pressure distribution. The aerodynamic performance of the modified airfoils was assessed using steady, compressible, two-dimensional CFD simulations. Simulations were conducted across Mach numbers ranging from 1.5 to 4.0 and angles of attack(AOA) of 0°, 2°, 4°, 6°, 10°, and 15°.Key aerodynamic parameters including lift coefficient (CL), drag coefficient (CD), moment coefficient (CM), and lift-to-drag ratio (CL/CD) were evaluated to understand the performance across various flow conditions. Results indicate that the wedge–convex airfoil outperforms the traditional biconvex airfoil, particularly in terms of CL/CD and stability. Among the tested geometries, the wedge–convex airfoil consistently exhibited superior performance, suggesting strong potential for applications in supersonic aircraft wings and missile nose designs.

Dynamic Fundus Image Processing for Early Detection of Glaucoma

Authors: Akshitha T, Jyothi H U, Kavya D N

Abstract: Glaucoma often leads to permanent vision loss without warning signs, so catching it early really matters. Instead of relying on expensive exams or hard-to-reach experts, this tool uses smart automation. It works with regular photos of the back of the eye taken ahead of time. At its heart is a learning- based model called a CNN that checks those images, even if they come from cheap, mobile cameras. People can upload scans safely through a connected system, then talk to eye doctors online. Results get shared fast by local workers who aren't specialists but still help speed up diagnosis. Because it runs on common gear and gives clear feedback quickly, it helps close service gaps where care is limited – giving patients a better shot at saving their sight before it's too late.

Sustainable Transformation of Biowaste into Organic Manure Using Eudrilus Eugeniae to Enhance Soil Productivity

Authors: Feba. R. Philip, Dr Reji P.G

Abstract: The unrivalled increase in the human population has resulted in the large-scale generation of biowaste, which serves as a rich source of plant nutrients. This study aims to determine the optimal composting duration, the appropriate quantity of Eudrilus eugeniae for efficient vermicomposting, and the best distribution of biowaste during the composting process. The composting was carried out for a period of two weeks, with sampling performed at the end of the second week. The macronutrient contents—organic carbon (C), nitrogen (N), and potassium (K)—were analysed to assess nutrient enrichment. Organic carbon was determined using a carbon analyser, total nitrogen through the Kjeldahl method, and total potassium using an Atomic Absorption Spectrophotometer (AAS). The pH of the compost was found to be 7.5, indicating a slightly alkaline nature. The organic carbon content was 3.11% in compost and 3.8% in vermiwash. Nitrogen was recorded as 1.98% in compost and 2.1% in vermiwash, while potassium was 1.98% in compost and 1.95% in vermiwash. These results indicate that vermiwash contains relatively higher NPK values compared to compost manure, making it a superior biofertilizer.

DOI: https://doi.org/10.5281/zenodo.18152549

Access-Segment Substitution in Passive Optical Networks: A Feasibility-Gated Decision Logic for RF–PON Hybrid Deployment Efficiency

Authors: MG Kasheera Gamith

Abstract: Broadband access networks must expand under stringent cost and time constraints while meeting rising service expectations. Passive Optical Networks (PON) provide strong long-term capacity and scalability, yet real-world deployment bottlenecks persist particularly in the access (drop) segment where civil works, rights-of-way, and premises-level variability dominate rollout lead time and cost per connection. This paper reframes “hybrid access” not as a generic technology integration problem but as a segment-level substitution decision: retaining fiber in feeder and distribution segments while selectively substituting the access segment with Radio Frequency (RF) access when feasibility conditions are satisfied. We develop a feasibility-gated, decision-traceable framework that (i) decomposes the Optical Distribution Network (ODN) into feeder, distribution, and access segments; (ii) defines a feasibility gate for RF substitution across bandwidth sufficiency, service-level constraints, coverage feasibility, and reliability; and (iii) evaluates deployment efficiency through scenario-based comparative reasoning across urban high-density, suburban medium-density, and rural/low-density contexts. Findings indicate that access-segment substitution can yield material reductions in time-to-connect and/or cost-per-user where civil works dependency is binding, while advantages are moderated in dense urban environments where RF densification, coordination, and site constraints can shift the critical path. The framework contributes a managerial decision logic that connects feasibility screening, context conditions, and deployment efficiency outcomes into actionable guidance for operators and policymakers, while remaining explicit about boundary conditions and non-statistical generalization. (Cisco Systems, Inc., 2020; International Telecommunication Union [ITU], 2022; Broadband Commission, 2021; World Bank, 2020a).

DOI: https://doi.org/10.5281/zenodo.18159657

Grassroots Platforms With Atomic Transactions: Social Graphs, Cryptocurrencies, And Democratic Federations

Authors: Santosh Kumar Dash

Abstract: Centralized platforms concentrate power, monetize social attention, and frequently produce perverse incentives. Grassroots platforms attempt the opposite: empower communities to coordinate, transact, and govern themselves at scale while avoiding single points of control. Combining decentralized social graphs, local currencies, atomic settlement mechanisms, and federated governance can enable communities to run their own interoperable social-economies with strong user control. This paper describes a practical architecture and analyzes the technical, social, and economic tradeoffs involved.

DOI: http://doi.org/10.5281/zenodo.18161292

IOT Network Malicious Session Detection By Genetic Feature Optimization Algorithm

Authors: Rishav Kumar Mishra, Prof. Sujeet Gautam, Prof. S Vishwakarma

Abstract: The rapid growth of Internet of Things (IoT) networks has significantly improved human comfort and quality of life. However, this expansion has also increased vulnerability to cyber intrusions, making IoT security a critical concern. This work proposes an IoT network intrusion detection system that classifies network sessions into normal and attack categories. A Genetic Algorithm (GA) is employed for optimal feature selection, enabling the identification of the most representative session attributes for accurate classification. The selected features are then utilized by the K-Nearest Neighbour (KNN) classifier to detect intrusions effectively. Experiments conducted on a real-world dataset demonstrate that the proposed GA-based IoT Network Security model significantly enhances detection accuracy and optimizes key evaluation performance metrics.

Digital Leaf Image Disease Detection By Content Features And Linear Kernel

Authors: Udhav Kumar Mishra, Professor Sujeet Gautam, Professor S Vishwakarma

Abstract: Agriculture plays a fundamental role in human civilization by ensuring food security and providing essential resources. As plant diseases directly affect crop yield and quality, their early and accurate detection is critically important. The proposed model focuses on extracting Texture and Content features to represent color distribution in leaf images. These features are combined to form a robust representation of healthy and diseased leaf characteristics. A multi-class Support Vector Machine (SVM) with a linear kernel is employed for classification due to its simplicity, efficiency, and suitability for high-dimensional feature spaces. Experimental evaluation on a real tomato plant leaf dataset demonstrates that the proposed approach significantly improves classification accuracy and effectively distinguishes between multiple types of leaf diseases.

Effective Policy and Enforcement for Resolving Atrocities/Conflicts Enabled by Landed Property Ownership in Nigeria

Authors: M. O. O. Ifesemen, Dr Dulari A Rajput

Abstract: This thesis examines the persistent rise of land-related conflicts and associated criminal activities in Nigeria, tracing their roots to historical, cultural, administrative, and governance-related inadequacies in the management of landed property. Land, traditionally communally owned and essential for livelihood, has evolved into a highly contested asset due to population growth, modernization, and weak implementation of the Land Use Act. The study highlights how ineffective administration, corruption, poor enforcement of regulations, and conflicting customary and statutory land rights have created conditions enabling violence, territorial claims, extortion, communal clashes, and other atrocities across the country. Materials and Methods: The research adopts a qualitative approach grounded in criminological theory, supported by documentary analysis, non-participant observation, and unstructured interviews. Data were sourced through long-term observational studies of land-related activities in communities, motor parks, markets, land registries, and informal settlements across Nigeria. A combination of cross-sectional and longitudinal designs enabled the researcher to observe patterns, behaviours, and criminal tendencies linked to land ownership struggles. Content analysis was used to interpret data within the theoretical framework of causes of crime—including cultural, economic, psychological, and environmental determinants. Results and Discussion: Findings reveal that inadequacies in land administration—such as corrupt allocation practices, wea enforcement of land regulations, multiple sales of land, extortion by traditional actors (e.g., “omo-onile”), unregulated territorial control, and government-enabled demolitions—have significantly fueled criminal activities. These include communal clashes, armed conflicts, thuggery, property destruction, kidnapping, territorial cultism, and conflict between farmers and herdsmen. The study establishes that such crimes persist largely because of institutional weaknesses, inconsistent policies, and failure to implement culturally sensitive, transparent systems of land governance. Conclusion: The study concludes that strengthening policy enforcement, enhancing governance structures, and implementing culturally aligned regulatory frameworks are essential to reducing land-related atrocities. Effective land administration and accountability at all levels will help curb crime, promote peace, and support sustainable national development.

DOI: https://doi.org/10.5281/zenodo.18169654

Blame culture and its effects on organisational productivity – a case study of Mcpee Limited.

Authors: M. O. O. Ifesemen, Dr Dulari Rajput

Abstract: This research critically examines the pervasive effects of blame culture on organisational productivity, using Mcpee Limited—a production-oriented company based in Southern Nigeria—as a case study. The study explores how blame culture is embedded within the operational and social fabric of the company and investigates its impact on employee behaviour, work procedures, and overall organisational performance. This research investigates the pervasive effects of blame culture on organisational productivity, using Mcpee Limited, a production-oriented firm in Southern Nigeria, as a case study. The study aims to explore how blame culture is embedded within the company’s operational and social environment and its influence on employee behaviour, work procedures, and overall productivity. An inductive research approach with a descriptive design was adopted, employing a mixed- methods data collection strategy. Quantitative data were gathered through questionnaires administered to 314 employees across varied departments, while qualitative insights were obtained from 80 department heads and supervisors via in-depth interviews. This triangulation enabled a comprehensive understanding of how blame culture permeates the organization and affects its functioning. The findings reveal that blame culture cultivates a tense and insecure workplace, where employees avoid assuming responsibility for mistakes due to fear of punitive consequences. This environment suppresses risk-taking and innovation, thereby constraining the organization’s ability to adapt and improve continuously. Several factors perpetuate this culture, including rigid procedural frameworks that restrict employee discretion, entrenched favoritism and nepotism, and ineffective recognition and reward systems that fail to engage or motivate staff adequately. Moreover, blame culture fosters demotivation, learned helplessness, micromanagement, and erodes employee empowerment, trust, and cooperation. Managers, concerned about protecting their reputations, frequently shift blame downward instead of promoting accountability, resulting in excessive bureaucracy and decreased employee engagement. To counteract these detrimental effects, the study recommends shifting organizational culture from blame-oriented to accountability-focused. This transformation calls for promoting fairness and meritocracy by eliminating favouritism, encouraging teamwork and collaboration aligned with shared goals, and streamlining work processes to reduce unnecessary rigidity. Empowering employees to exercise discretion, creativity, and problem- solving initiative without fear of unjust repercussions is emphasized as critical for fostering innovation and boosting productivity. The study concludes that blame culture significantly undermines organizational productivity by creating a fearful and rigid work environment. It recommends transforming the culture from blame-oriented to accountability-focused by promoting fairness, teamwork, flexible work practices, and problem-solving approaches. Empowering employees to take initiative without fear of unjust punishment and recognizing their contributions can foster innovation and enhance productivity. These findings offer valuable insights for organizations seeking to cultivate a positive, supportive, and accountable workplace culture conducive to sustained performance improvement.

DOI: https://doi.org/10.5281/zenodo.18170829

Emic View From The Passage Of Honde And Jude Tropical Cyclones In Semi-Arid Southern Zone Of Madagascar

Authors: Rasolondraizafy Jean Fidison, Ramananarivo Romaine, Ramananarivo Sylvain, Razafindraibe Rolland

Abstract: Tropical cyclones (TCs) are often associated with disasters due to the significant damage they cause to property and the loss of life they entail. Nevertheless, this article discusses the positive effect of tropical cyclones on climate outlooks based on the social context, geographical and cultural specificities of the inhabitants of Androy. We shed light on the anthropological aspects of cyclone-territory relations by highlighting the current social representation of cyclones affecting the country through 28 specifically selected individuals. The study was conducted in the seven coastal villages of the Ambovombe district by Ocean Indian. A mixed approach was used, with a Likert scale to assess variables related to the impacts of all tropical cyclones passing through the territories. Findings reveal that tropical cyclones have caused intense rainfall exceeding 100% and have become quantitative water suppliers in the Androy area. Vulnerable coastal communities wait for TCs passage so they can water their land. An analysis showing that TCs produce precipitation over the Androy region indicates that some arid/semi-arid zones in Madagascar depend largely on this effect to experience wet years. TCs are referred to locally as ‘blessing winds’. Hence, including the positive effect of TCs in precipitation forecasts would be an advantage, similarly, in water management plans for every location in the water crisis, but it should also be essential for regional adaptation strategies against climate variability.

DOI: http://doi.org/10.5281/zenodo.18171397

Reducing Workplace Incidents / Poor Performance by Holding Organisations and Leaders Accountable

Authors: M. O. O. Ifesemen, Dr Dulari A Rajput

Abstract: This study investigates the intricate link between workplace operational incidents and administrative errors, emphasizing the critical role of organizational and leadership accountability in mitigating error-enforcing conditions that precipitate incidents and degrade performance. Employing a robust qualitative approach, the research integrates a mixed- methods design encompassing naturalistic observation—both participant and non- participant—and unstructured interviews conducted with over 300 personnel within a Nigerian-based transnational organization. Data were meticulously analyzed using descriptive and deductive reasoning frameworks to elucidate the impact of leadership decisions and organizational practices on the prevalence of workplace errors and related incidents. The findings reveal a compelling pattern: more than 80% of workplace incidents, encompassing both physical injuries and psychological harm, originate from administrative errors linked to leadership styles and organizational culture. Key error-enforcing conditions identified include pervasive blame culture, inadequate fatigue management, favoritism, bullying, flawed performance appraisal systems, and a pronounced lack of employee empowerment. Notably, psychological injuries arising from these administrative errors—such as diminished self-esteem, depression, and chronic stress—were found to be more detrimental than physical injuries, exerting profound negative effects on employee motivation, productivity, and overall organisational performance. The study further underscores the frequent misinterpretation of incident causality and highlights the paramount importance of objective evaluation and leadership accountability as mechanisms to reduce incident recurrence effectively. In conclusion, the research advocates cultivating accountability at all organisational levels, enhancing leadership competencies, and promoting a culture grounded in empathy and objectivity within performance appraisal and incident management processes. Implementation of these measures is projected to foster safer, more productive work environments, thereby driving improved organisational outcomes. The study also calls for integrating accountability principles into corporate governance frameworks. It emphasises the need for transformational learning through causal reasoning to address the root causes of workplace errors and incidents, ultimately contributing to sustainable organisational excellence.

DOI: https://doi.org/10.5281/zenodo.18172748

AI Tutor: An Intelligent Learning Platform for Personalized Education

Authors: Professor Sanjeev, Mr.Feroz Ali, Mr.Krishna, Mr.Mahesh, Mr.Sai Eshwar

Abstract: The rapid growth of digital education has created a demand for intelligent systems capable of delivering personalized and adaptive learning experiences. Traditional e-learning platforms often follow a one-size-fits-all approach, which fails to address individual learning needs, pace variations, and knowledge gaps. To overcome these limitations, this project presents an AI-based Intelligent Tutor System (AI Tutor) designed to provide personalized, interactive, and adaptive learning support. The proposed system leverages Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to analyze learner behavior, assess performance, and dynamically adapt content delivery. The AI Tutor acts as a virtual mentor that answers queries, recommends learning materials, conducts assessments, and provides instant feedback. Machine learning techniques are used to track student progress and identify strengths and weaknesses over time. The system is implemented as a web-based application with an intuitive user interface, enabling students and instructors to interact seamlessly. By automating tutoring tasks and offering real-time guidance, the AI Tutor enhances learning efficiency, reduces instructor workload, and improves overall educational outcomes.

DOI: http://doi.org/10.5281/zenodo.18181698

Acoustic Wireless Sensing Node Network Energy Optimization By Clustering

Authors: Rishikesh Kumar, Professor Sujeet Gautam, Professor S Vishwakarma

Abstract: In underwater acoustic sensor networks (UWASN), ensuring energy-efficient data communication remains a significant challenge due to the harsh and unpredictable underwater environment. Acoustic signal transmission is often affected by high ambient noise, extremely long propagation delays, increased bit error rates, limited available bandwidth, and various forms of interference. As a result, one of the primary objectives of UWASN research is to prolong the operational lifetime of the network by optimizing energy usage. The process of reliably transmitting data from a source node to a destination node in UWASN is inherently complex and continues to be a critical area of investigation. To address this issue, the Acoustic Wireless Node Energy Optimization (AWNEO) model is proposed as an efficient network clustering strategy aimed at minimizing communication-related energy losses. The model leverages a group-based concept inspired by the Teacher Learning Algorithm, which enhances overall operational efficiency by facilitating effective knowledge sharing and optimization among nodes. In the proposed approach, selected nodes act as cluster heads and are responsible for forwarding aggregated data packets to the base station, thereby reducing redundant transmissions and conserving energy. Experimental evaluation demonstrates that the AWNEO model outperforms existing acoustic network optimization algorithms across multiple performance parameters, including energy consumption, network stability, and data transmission efficiency.

Cyber Security In India_ Emerging Threats, Legal Framework, And National Response Mechanisms

Authors: Putha Sateesh Kumar, Dr. Akash Sexana

Abstract: Cyber security has emerged as a critical concern for India in the backdrop of rapid digitalisation, expanding internet penetration, and increasing reliance on information and communication technologies. The country faces a wide spectrum of cyber threats ranging from financial fraud, data breaches, ransomware, and phishing attacks to sophisticated state-sponsored cyber espionage and attacks on critical information infrastructure. This paper examines the evolving cyber threat landscape in India, analyses the existing legal and policy framework governing cyber security, and evaluates the national response mechanisms established to prevent, detect, and respond to cyber incidents. It highlights the role of key institutions such as CERT-In, the Indian Cyber Crime Coordination Centre, and the National Critical Information Infrastructure Protection Centre. The study also identifies major challenges, including legal gaps, capacity constraints, and low public awareness, and underscores the need for stronger legislation, enhanced institutional coordination, and greater emphasis on cyber resilience to safeguard India’s digital ecosystem.

DOI: http://doi.org/10.5281/zenodo.18182258

Behavioral Analysis Of G+9 Reinforced Concrete Buildings With Different Structural Configurations Under Severe Seismic Conditions

Authors: Satakshi Marskole, Deepesh Malviya

Abstract: Seismic performance of mid-rise reinforced concrete buildings is strongly influenced by structural configuration, regularity, and the adopted lateral load-resisting systems. This study presents a comparative seismic behavior analysis of a G+9 reinforced concrete building founded on soft soil and located in Seismic Zone V as per IS 1893 (Part 1):2016. Three structural configurations are considered: (i) a regular symmetrical building with bracing and base isolation, (ii) an irregular hybrid building with bracing and base isolation, and (iii) an irregular building with bracing but without base isolation. Numerical modeling and dynamic analysis are carried out using ETABS software. The structural response is evaluated in terms of storey displacement, storey drift, storey shear, base shear, and overturning moment under critical seismic load combinations. The results indicate that structural regularity significantly enhances stiffness and reduces displacement and overturning effects, while base isolation effectively reduces seismic forces and inter-storey drift at the cost of increased overall displacement. For irregular buildings, the combined use of bracing and base isolation provides superior seismic performance, offering an optimal balance between force reduction, deformation control, and structural safety.

DOI: http://doi.org/10.5281/zenodo.18184644

AI Powered Career Guidance for Rural Youths

Authors: Prof.Sanjeev, Mr.Tushar, Mr.Sudarshan, Mr.Virupaksha, Mr.Sai Eshwar

Abstract: Rural youth often face significant challenges in identifying suitable career paths due to limited access to professional counseling, lack of awareness about emerging career opportunities, and insufficient exposure to skill-based education. This project proposes an AI-powered career guidance system designed specifically to support rural youths by providing personalized, data-driven career recommendations. The proposed system leverages Artificial Intelligence and Machine Learning techniques to analyze students’ academic background, interests, skills, and socio-economic factors. Based on this analysis, the system recommends appropriate career options, required skills, learning pathways, and relevant government or private opportunities. A user-friendly web application enables students to interact with the system in their local context, ensuring accessibility and ease of use. By combining AI-based decision support with structured career data, the system aims to reduce career confusion, improve employability, and empower rural youths to make informed career decisions.

Hybrid Multicue Deep Learning Framework for Real Time Driver Drowsiness Detection

Authors: Aayush Chowdhury, Debdutta Basu, Sagnik Roy, Soumi Mukhopadhyay, Samarjeet Kumar, Anjan Kumar Payra

Abstract: Driver fatigue contributes significantly to traffic accidents worldwide. This paper presents a hybrid driver- monitoring framework combining Eye Aspect Ratio (EAR) analysis with Convolutional Neural Networks (CNN) to enhance drowsiness detection reliability. Unlike single-cue approaches, the proposed system leverages both geometric eye-landmark features and learned visual patterns through MediaPipe Face Mesh for facial landmark localization. Three eye-landmark configurations were systematically evaluated, with Set 3 achieving optimal performance. A lightweight CNN was trained on 64×64 pixel eye images using 5-fold stratified cross-validation with data augmentation. The hybrid system employs an OR- based fusion rule prioritizing safety sensitivity. Results demonstrate that the standalone CNN achieved 79.07% accuracy (AUC = 0.7732), while the optimized EAR model (Set 3) reached 89.53% accuracy (AUC = 0.8929). The hybrid approach reduced false negatives by approximately 92%, achieving 97.3% sensitivity and 81.40% accuracy (AUC = 0.8348). The system operates at 20-25 FPS on standard CPU hardware, confirming real-time viability for Advanced Driver Assistance Systems (ADAS) integration.

Meta-Learning For Rapid Defense Against Zero-Day Fraud Attacks In Online Transaction Systems

Authors: Dr. Pankaj Malik, Mannat Bhatia, Abhishek Kumar Tiwari, Hrishit Nagar, Atharva Shrivastava

Abstract: Online transaction systems are increasingly exposed to zero-day fraud attacks, where novel and rapidly evolving fraud patterns bypass conventional detection models trained on historical data. Existing machine learning–based fraud detection approaches struggle to adapt due to their reliance on large labeled datasets and static training paradigms. This paper presents a meta-learning–based adaptive fraud defense framework that enables rapid detection of previously unseen fraud patterns using a limited number of labeled samples. The proposed approach leverages Model-Agnostic Meta-Learning (MAML) to learn transferable representations across diverse fraud tasks and supports few-shot adaptation in real-time transaction environments. Experiments conducted on the IEEE-CIS Fraud Detection and PaySim datasets, with zero-day fraud scenarios simulated through task-wise data partitioning and concept drift injection, demonstrate that the proposed model outperforms state-of-the-art baselines. Specifically, the meta-learning framework achieves an average F1-score improvement of 14.6% and an AUC-ROC increase of 11.2% over deep neural network and XGBoost models under zero-day conditions. Furthermore, the adaptation time is reduced by approximately 3.1×, enabling effective fraud detection within a minimal number of gradient updates. These results confirm that meta-learning provides a robust and scalable solution for rapid defense against zero-day fraud attacks, significantly enhancing transaction risk management in dynamic financial systems.

Employee Welfare Measures Among Private School Teachers In The Kongu Region Of Tamil Nadu: An Empirical Study

Authors: Mr. K. P. Karthikeyan, Dr. K. V. Shanmugavadivu, Dr. P. Nandhini

Abstract: Employee welfare measures are essential for enhancing job satisfaction, motivation, and performance of employees in any organization. In the education sector, teachers’ welfare directly influences the quality of education delivered to students. This study examines the welfare measures provided to private school teachers in the Kongu region of Tamil Nadu and evaluates their impact on job satisfaction and work performance. Primary data were collected from 150 private school teachers using a structured questionnaire. Percentage analysis and Chi-square tests were applied for data analysis. The study reveals that while certain statutory welfare measures such as paid leave and basic facilities are moderately available, non-statutory benefits like health insurance, retirement benefits, and financial assistance are inadequate. The findings suggest the need for standardized welfare policies to improve teacher well-being, satisfaction, and institutional effectiveness

DOI: http://doi.org/10.5281/zenodo.18195246

Design and Implementation of a Decentralized Peer-to-Peer Cloud Storage System Using Kademlia Distributed Hash Table

Authors: Aamira Bushra

Abstract: Centralized cloud storage platforms suffer from inherent limitations such as single points of failure, data privacy concerns, and dependency on trusted service providers. Decen- tralized peer-to-peer (P2P) storage systems aim to overcome these issues by distributing data across multiple independent nodes without centralized control. This paper presents the design and implementation of a decentralized cloud storage system developed in Java using the Kademlia Distributed Hash Table (DHT) for scalable peer discovery and routing. Files are divided into fixed-size chunks, encrypted locally, and distributed across participating peers using content addressing based on SHA- 256 hashes. The system supports decentralized node discovery, encrypted chunk storage, metadata-based file reconstruction, and fault tolerance through replication. Experimental evaluation on a local multi-peer environment demonstrates efficient lookup performance and improved data availability under limited peer churn.

Seismic Load Effects on Symmetrical Buildings: A Structural Analysis Approach

Authors: Kuldeep Pathak

Abstract: Symmetrical buildings are generally regarded as structurally stable under seismic loading due to their balanced geometry and uniform distribution of mass and stiffness. However, increasing building height introduces complex dynamic behavior, including higher time periods, inter-storey drift, and lateral displacements, even in geometrically regular structures. This study evaluates the seismic response of symmetrical reinforced concrete (RC) buildings of three height categories—G+3, G+7, and G+10—modeled using STAAD.Pro and analyzed under IS 1893 (Part 1): 2016 seismic provisions for Zone IV conditions in India. Key performance indicators such as fundamental time period, base shear, maximum storey drift, and lateral displacement were assessed. Results indicate that while symmetry ensures predictability and torsional stability, increased height leads to significant amplification of seismic effects. Notably, mid- and high-rise models exceeded the allowable drift limits specified by the code, requiring supplementary lateral-resisting systems. The study also evaluates material efficiency by comparing concrete and steel usage across models. Findings emphasize the need for integrated seismic design strategies that consider both geometric regularity and height-induced vulnerabilities.

DOI: https://doi.org/10.5281/zenodo.18204691

Mathematical Modeling And Investigation Of Angle-of-Attack And Load Factor Limiting Systems For Combat Aircraft Using Matlab Simulink

Authors: MSc Van Huy Khuat, BSc Van Quyen Dinh

Abstract: This paper presents the results of a survey on the effectiveness of a system for limiting the critical parameters of an aircraft during vertical maneuvering. The aircraft's motion model, combined with the critical parameter limiting system model, was constructed using Matlab Simulink software. The paper presents the results of the survey on the effectiveness of the critical parameter limiting system on the model and compares them with experimental results in the aircraft user manuals of the Air Force Officer School. Using this method, it is possible to determine the corresponding parameters in the system, calculate the system's efficiency, and then apply it in the practical use of this type of aircraft.

DOI: https://doi.org/10.5281/zenodo.18221013

Performance Analysis of Self-Adjusting Aperture Optical Systems under Varied Atmospheric Conditions.

Authors: Thai Ngoc Le, Vu Van Le, Ngo Nam Phuong

Abstract: Contemporary optical instrumentation constitutes a fundamental strategic asset in military surveillance and tactical operations, necessitating high operational efficiency across a broad spectrum of irradiance conditions, from peak diurnal intensity to low-light regimes. A significant technical challenge involves maintaining invariant image fidelity amidst extreme dynamic ranges in ambient illumination. This study investigates an innovative adaptive iris system designed to autonomously modulate the entrance pupil, thereby optimizing radiant throughput and mitigating aberrations. Utilizing the ZEMAX design environment, an opto-mechanical system was synthesized featuring discrete pupil diameters Dm of 2.8 mm, 3.5 mm, and 4.0 mm, tailored for photopic, mesopic (twilight), and scotopic conditions, respectively. The system's optical integrity was rigorously verified against four primary criteria: Modulation Transfer Function (MTF), Longitudinal Aberration, Field Curvature, and Geometric Distortion. Numerical simulations confirm that the system exceeds all performance benchmarks: the MTF values surpassed 0.4 at the targeted spatial frequency (exceeding the 0.2 threshold), longitudinal aberration was constrained below 0.03 mm, field curvature remained within the 1.318 mm tolerance, and distortion was limited to within ±0.5%. These findings demonstrate that the self-adjusting aperture configuration provides a robust and effective solution for multi-environment reconnaissance missions.

DOI: https://doi.org/10.5281/zenodo.18229020

Evaluation and Selection of Defense Solutions for Small, Low-Altitude UAVs

Authors: MSc Trong Thuong Tran, BSc Van Quyen Dinh

Abstract: The rapid development and widespread deployment of small, low-altitude unmanned aerial vehicles (UAVs) have provided significant benefits in both civilian and military applications, while simultaneously posing serious challenges to airspace security and safety. The increasing misuse of UAVs for unauthorized intrusion, reconnaissance, or disruption in sensitive areas has driven the need for effective counter-UAV solutions. This paper presents an overview and comparative analysis of existing countermeasures for small, low-altitude UAVs, including surveillance and detection, electronic jamming and control takeover, high-energy neutralization, and physical defense methods. Based on a systematic comparison of technical, economic, operational, and safety-related criteria, the advantages of physical defense – particularly net-based UAV capture are clearly identified. The results demonstrate that physical defense methods provide a safe, cost-effective, and non-lethal solution, especially suitable for training, operational exercises, and the protection of sensitive targets under real-world constraints

DOI: http://doi.org/10.5281/zenodo.18229756

Authors: Mrs. Shivangi Pandey, Dr. Syed Tanzeem Ahmed

Abstract: Alzheimer's disease is a progressive brain disorder that mainly affects memory. Diagnosing it manually can take a lot of time and is often subject to mistakes because of the large number of patients. Many techniques exist for diagnosing and classifying Alzheimer's, but there is still a strong need for better methods for early detection. This paper looks at different techniques proposed by researchers for classifying the patient report into specific categories. Paper has list various image features used for the disease diagnosis. Different set of image category were also brief used for diagnosis of Alzheimer disease. Finally paper list various evaluation parameters that were used for the comparison of models.

DOI: http://doi.org/10.5281/zenodo.18230625

Alzheimer’s Disease Class Prediction by Dynamic Feature Selection and Learning Model: A Review

Authors: Mrs. Shivangi Pandey, Dr. Syed Tanzeem Ahmed

Abstract: Alzheimer's disease is a progressive brain disorder that mainly affects memory. Diagnosing it manually can take a lot of time and is often subject to mistakes because of the large number of patients. Many techniques exist for diagnosing and classifying Alzheimer's, but there is still a strong need for better methods for early detection. This paper looks at different techniques proposed by researchers for classifying the patient report into specific categories. Paper has list various image features used for the disease diagnosis. Different set of image category were also brief used for diagnosis of Alzheimer disease. Finally paper list various evaluation parameters that were used for the comparison of models.

DOI: http://doi.org/10.5281/zenodo.18230625

Neural Network–Assisted Harmonic Reduction and Power Quality Enhancement in Vsc-Hvdc Links

Authors: Asst. Prof Anil Choubey

Abstract: Long distance AC transmission is often subjected to certain problems which limit the transmission capability. HVDC is a better option for transmission of power over long distances. Power is being transmitted between two generating stations via dc link. The control of power flow in DC link can be achieved through control of current or voltage. For minimization of loss considerations, it is important to maintain constant voltage in link and adjust current to meet required power. In this project, a HVDC system is designed to control the power flow between two converter stations with conventional PI controller and Artificial Neural Networks. For rectifier side current control is used for inverter side both current and extinction angle control is implemented. The error signal is passed through a VSC+HVDC and Artificial Neural Networks controller, which produces the necessary firing angle order. The firing circuit uses this information to generate the equidistant pulses for the valves in the converter station. Here Artificial Neural Networks is designed for both rectifier and inverter control and its performance is compared with conventional PI controller.

Design Of A Feedback For Fault Localization In A DC Micro-grid Using Siemens TIA Portal And Proteus Professional

Authors: Hachimenum Nyebuchi Amadi, Richeal Chinaeche Ijeoma, Victor Nneji Chikwendu

Abstract: Direct-current (DC) micro-grids have gained significant relevance in modern power architectures due to their high efficiency, renewable-energy compatibility, and seamless integration with distributed energy resources. However, despite these benefits, the susceptibility of DC systems to rapid fault propagation poses critical operational risks. Effective and intelligent fault-localization mechanisms are therefore essential to minimize damage, reduce downtime, and ensure grid reliability. This study presents the design and simulation of a feedback-based fault localization system for a DC microgrid using Siemens TIA Portal for automation logic development and Proteus Professional for electronic circuit modelling. The method detects abnormal variations in current and voltage along the grid, automatically determining fault location and generating corrective feedback signals for appropriate protection response. Simulation results demonstrate that the system accurately identifies faulted segments in real time and triggers isolation feedback, thereby maintaining system stability and preserving healthy network zones. The developed approach offers a low-cost, scalable, and flexible solution suitable for industrial micro-grids, renewable-driven installations, and DC power distribution networks. Future research may involve hardware prototyping, integration of intelligent diagnostic algorithms, and evaluation under varying load and renewable input conditions.

DOI: http://doi.org/10.5281/zenodo.18428583

An Evaluation Of The Use Of Green Energy Projects For Power Generation In Rivers State

Authors: Richeal Chinaeche Ijeoma

Abstract: This study evaluated the implementation and effectiveness of green energy projects for power generation in Rivers State, Nigeria. Through a comprehensive analysis of existing renewable energy initiatives, policy frameworks, and implementation challenges, this research examines the current state of green energy adoption in the region. The study employed a mixed-methods approach, incorporating quantitative data analysis and qualitative assessments of stakeholder perspectives. Findings revealed significant potential for renewable energy development in Rivers State, particularly in solar, wind, and biomass sectors, though implementation faces considerable challenges including inadequate funding, policy inconsistencies, and technical capacity limitations. The research concludes that while green energy projects show promise for addressing Rivers State's power generation needs, successful implementation requires coordinated efforts among government agencies, private sector stakeholders, and international development partners. Consequently, it was recommended that a dedicated renewable energy agency should be established, feed-in tariffs should be implemented, and comprehensive training programs for technical personnel should be developed.

DOI: http://doi.org/10.5281/zenodo.18428964

Studies On the Solvent Effect of a Dipolar Protic Solvent On the Biochemicalpotential Of Methanoates

Authors: Dr. Kumari Priyanka

Abstract: The solvent effect of ethanol (dipolar protic solvent) on the acid catalysed solvolysis of Propyl methanoate was studied by carrying out the hydrolysis of the ester in water-ethanol (EtOH) media of varying composition consisting of 20% to 80%(v/v) at different temperatures ranging from 20 to 40°C. The specific rate constant values of the reaction were found to decrease with increasing concentration of ethanol in the reaction media li was found that with increase in temperature of the reaction from 20 to 40°C from 0.252 to 1.258 molecules of water are associated with the activated complex and from this, it is inferred that mechanistic path followed by the reaction in presence of ethanol is changes from bimolecular to unimolecular The depletion and enhancement observed respectively in iso-composition and iso-dielectric activation energies reveal that the transition state is solvated and initial state is desolvated with addition of ethanol (EIOH) in reaction media. Almost unity value of slope of the plots of log k values against log [H+] values shows that the reaction follows AAC2 mechanism. From the values of iso-kinetic temperature, which comes to be 280, it may be concluded that in water-EtOH reaction media, the reaction follows Barclay-Butler rule and there is weak but acceptable interaction between solvent and solute in the reaction media.

DOI: http://doi.org/10.5281/zenodo.18631308

A Kinetic Study of the Solvent Effect of Aquo-1-Butanol Solvent Systems on The Kinetic and Thermodynamic Parameters of The Catalysed Solvolysis of Methyl Caprate

Authors: Dr. Kumari Priyanka

Abstract: The solvent effect of t-butanol on the alkali catalysed solvolysis of Methyl caprate was studied by carrying out the hydrolysis of the ester in water-t-butanol media of varying composition consisting of 30 to 80% 1-butanol (v/v) at different temperatures ranging from 20 to 40°C. The specific rate constant values of the reaction were found to decrease with increasing concentration of t-butanol in the reaction media. The iso-composition and iso-dielectric activation energies (Ec and Ed) of the reaction were found to increase and decrease respectively with increasing concentration of the solvent (t-butanol) in the reaction media and with increasing the dielectric constant values of the reaction media. From these findings, it is inferred that the transition and initial states of the reaction are desolvated and solvated respectively with the addition of t-butanol in the reaction media. It was found that number of water molecules associated with the activated complex decreases from 1.506 to 0.424 with increasing temperature from 20°C to 40°C and this tells us that with addition of t-butanol in the reaction media, the mechanistic path followed by the reaction is changed from unimolecular to bimolecular. Form simultaneous enhancement in values of all the three thermodynamie activation parameters ie in ΔG* ΔH* and ΔS*, it has been concluded that in presence of t-butanol the reaction is enthalpy dominating and entropy controlled and specific solvation is taking place in the water-t-butanol media. From the values of iso-kinetic temperature, i.e. 331.42, it may be concluded that the reaction in water-t-butanol media obeys Barclay-Butler rule and there is strong solvent-solute interaction in the reaction media.

DOI: http://doi.org/10.5281/zenodo.18659682

Predictive Maintenance Of Cloud Infrastructure Using ML

Authors: Bikash Shrestha

Abstract: Predictive maintenance (PdM) has emerged as a cornerstone for ensuring the high availability and reliability of modern cloud infrastructure. As cloud environments grow in complexity, traditional reactive and preventive maintenance strategies often fall short, leading to either costly unplanned downtime or wasteful over-servicing of resources. This review explores the integration of Machine Learning (ML) algorithms—such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Random Forests—in predicting hardware failures, network anomalies, and software degradations. By analyzing real-time telemetry data including CPU thermals, disk I/O latency, and power consumption, ML models can identify pre-failure patterns with high precision. The article discusses the architectural transition toward "AIOps," the challenges of data heterogeneity in multi-cloud environments, and the future role of Edge-Cloud collaboration. Ultimately, the synthesis of ML with cloud monitoring transforms maintenance from a cost center into a strategic advantage, ensuring 99.999% service level objectives.

DOI: https://doi.org/10.5281/zenodo.19417741

Secure Multi-Cloud Architecture Using AI-Based Governance

Authors: Ramesh Thapa

Abstract: The rapid shift toward multi-cloud architectures has provided organizations with unparalleled scalability and vendor flexibility, yet it has simultaneously introduced a fragmented security landscape that exceeds the capacity of manual oversight. As data assets and workloads proliferate across diverse platforms such as AWS, Azure, and Google Cloud, maintaining a cohesive security posture becomes a critical challenge. This review article explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) into cloud governance frameworks to establish a "Secure Multi-Cloud Architecture." By leveraging AI-driven automation, organizations can achieve real-time anomaly detection, predictive risk modeling, and continuous compliance monitoring across heterogeneous environments. The following sections provide a comprehensive analysis of the architectural requirements, the role of AI in policy orchestration, and the transition from reactive security to proactive, autonomous governance. Ultimately, this review highlights how AI serves as the linchpin for managing the complexity of modern, distributed cloud ecosystems while ensuring robust data protection and regulatory alignment.

DOI: https://doi.org/10.5281/zenodo.19417761

AI-Enabled Continuous Integration And Deployment Pipelines

Authors: Sita Karki

Abstract: The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Continuous Integration and Continuous Deployment (CI/CD) pipelines represents a transformative evolution in DevOps. As software systems grow in complexity and scale, traditional rule-based automation fails to address the nuances of modern distributed environments. This review article explores the multifaceted roles of AI in optimizing software delivery, from intelligent test orchestration and predictive build failure analysis to autonomous "self-healing" infrastructures. We examine how AI-driven insights reduce the "Mean Time to Detect" (MTTD) and "Mean Time to Recovery" (MTTR) while significantly lowering the cognitive load on engineering teams. Furthermore, the article addresses the challenges of implementing AI in DevOps, including data privacy, model transparency, and the shift toward "AIOps." By synthesizing current research and industry trends as of 2026, this review provides a comprehensive roadmap for navigating the future of intelligent, automated software delivery.

DOI: https://doi.org/10.5281/zenodo.19417781

Autonomous Cloud Operations Using Artificial Intelligence

Authors: Shahnoza Akhmedova

Abstract: Autonomous Cloud Operations (ACO) represents a transformative evolution in cloud computing, where Artificial Intelligence (AI) enables self-managing, self-healing, and self-optimizing systems. Traditional cloud management relies heavily on manual intervention and rule-based automation, which struggles to cope with the increasing complexity, scale, and dynamic nature of modern cloud environments. By integrating machine learning, predictive analytics, and intelligent decision-making, ACO minimizes human involvement while improving operational efficiency, reliability, and cost optimization. AI-driven systems continuously monitor workloads, detect anomalies, predict failures, and automatically execute corrective actions. This paradigm enhances resource utilization, ensures high availability, and supports real-time scalability. Furthermore, ACO aligns with DevOps and Site Reliability Engineering (SRE) practices, enabling faster innovation and reduced downtime. Despite challenges such as data privacy, model bias, and system transparency, autonomous cloud systems are poised to redefine the future of cloud infrastructure management.

DOI: https://doi.org/10.5281/zenodo.19417805