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.17646731

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.17660589

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, Ogbonnaya Onu Polytechnic

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.