Volume 13 Issue 5 | International Journal of Science, Engineering and Technology

Seismic Behaviour of Structure by Using TMD Technique: A review

Authors: Sujata D. Ingale, Priyanka S. Taware

Abstract: This paper presents a new real-time trajectory planning method for mobile robot in random obstacles environment, aiming to give an efficient implementation for “First Global After Local” trajectory planning method that we have established earlier. First, the global path planning method is employed the target direction angle tracking modeling. Then, the recursive algorithm is used for the evaluation of sub-target point. Finally, the swarm intelligence optimization is utilized for the local trajectory planning method. The real-time trajectory planning system is built and tested on the mobile robot platform, the experimental results prove that our method is effective and can be used in the real-time trajectory planning of mobile robots.

DOI: https://doi.org/10.5281/zenodo.17184477

ASRL: Alternating Supervised And Reinforcement Learning For Efficient Small Language Model Training With Live Datasets

Authors: Ouissam Drissi

Abstract: Look, here's the thing – training small language models to think properly is hard. Really hard. Especially when you're working with just 600 million parameters and need them to follow a specific format while actually being smart about it. I've been there – you try pure reinforcement learning and your model outputs garbage for the first 10 epochs. You try supervised learning and it just memorizes without understanding. So I built something different. ASRL (Alternating Supervised-Reinforcement Learning) switches between supervised fine-tuning and GRPO within each epoch. Not after completing all supervised training. Not as separate phases. Every. Single. Epoch. First the model learns from your actual examples, then it explores variations through RL. Rinse and repeat. The results? My 0.6B parameter model learned my custom and thinking format in 3 epochs instead of 12. It handles new data as it arrives without restarting training. And it actually understands what it's doing instead of just pattern matching. This isn't some theoretical framework – I built this because I needed it. My training data grows by 200 examples per hour, I have strict formatting requirements, and I'm running on limited hardware. Traditional methods failed me. ASRL didn't.

Network Intrusion Detection Using Machine Learning: A Comparative Study of Logistic Regression, KNN, and Random Forest

Authors: Tejashree H Y, Komala R

Abstract: Network Intrusion Detection Systems (NIDS) play a critical role in defending networks against unauthorized access and cyber threats. This paper presents a real-time, web-enabled NIDS built using machine learning techniques to effectively identify and categorize network-based attacks. The system is trained on the NSL-KDD dataset, a refined alternative to earlier datasets, addressing issues like redundancy and class imbalance. We implement and evaluate three supervised learning algorithms—Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest. The workflow includes comprehensive preprocessing, class balancing, and hyperparameter tuning via grid search with cross-validation. Among the models tested, Random Forest achieved the highest detection performance, showing excellent accuracy with minimal false positives. While KNN also produced reliable results, it was comparatively slower. Logistic Regression delivered quick and interpretable outcomes but struggled with complex intrusion patterns. This work contributes a practical, browser-accessible NIDS platform that brings together machine learning capabilities and real- time threat detection.

DOI: https://doi.org/10.5281/zenodo.17163828

PLANTPAL – A self watering plants system

Authors: Anurag Rai, Garvish Jain, Krrish Parmar, Prashant Prasad, Prof. Salman MohammedHanif Buddha

DOI: https://doi.org/10.5281/zenodo.17164378

Exploration Of Thought Spigettification And Cognitive Deformation Around A Kerr-Type Singularity: A Theoretic Quantum–Field Model For Schizophrenia And Dissociative Identity Disorder

Authors: Patrick James McKenna Jr., Andres Barahona Contreras

Abstract: We introduce thought spigettification, a speculative quantum–field–theoretic model mapping extreme stress responses in neuronal microtubule networks to Kerr-analogue spacetime defor- mations. A mental Hilbert space of coherent microtubule “qubits” couples to three scalar fields—BraeQuintessence, BraeHiggs, and the Standard Model Higgs—producing a deformation operator D(r, θ) that stretches and reconfigures mental superpositions. Positive and negative symptoms of schizophrenia arise as over-amplified and suppressed eigenmodes; dissociative iden- tity disorder emerges from metastable multi–well potentials. We formalize emotional eigenval- ues, derive coupled field equations, analyze stability regimes, and propose electrophysiological and imaging biomarkers. Finally, we outline therapeutic “despigettification” via inversion of scalar–field dynamics.

DOI: http://doi.org/10.5281/zenodo.17165472

Crime Rate Prediction And Analysis System

Authors: Asst. Prof. Ami Rasiklal Tank, Shivam Bhart, Sanyam Shah, Shreyanshu Das, Subendu Dubey

Abstract: The increase in data and improvements in machine learning (ML) offer a unique opportunity for public safety functions. This paper presents a review of a Crime Rate Prediction and Analysis System that utilizes government crime data to visualize and predict trends, classify geographical areas, and provide tools for public-facing use. The system is unique because it uses a well-defined, highly functional and well-designed web application, alongside a robust ML backend. The web application contains an interactive, and profile-based, color-coded map, that ranks the severity of crime in districts based on their total IPC crimes, allows for dynamic filtering of crime type, and the ability to search by district. The system also possesses an "AI Suggest" button, a critical, innovative, and unique feature which moves beyond analytical reporting and provides personalized, context-specific recommendations for public safety, thus improving public awareness. This review discusses the system's architecture, uses as both an operational mechanism for law enforcement decision making, and for citizen engagement, ethical concerns around predictive policing, and suggests next steps for deploying this kind of technology. One of the key and innovative features of this system, is the "AI Suggest" module, which goes beyond traditional analytical reports, and produces personalized, context-specific and, tailored safety recommendations for the public, thus bridging the gap between publicly available data, and actionable public knowledge. This review explores the dual-value proposition of the system as a decision-support system for law enforcement agencies (LEAs) to maximize resource allocation, patrol routes, and operational planning, as well as providing transparency for the citizens with personalized risk assessment and safety recommendations. In addition, this paper discusses considerations inherent in such systems, including a rigorous examination of the significant ethical implications that accompany such systems, such as algorithmic bias amplification, data integrity, and societal impacts, as well as ways to ensure ethical mitigation of these effects. Ultimately, this review contends that the system is a meaningful step forward in predictive policing technology, as it has the potential to create a more collaborative, informed, and proactive approach to urban public safety if it is implemented with strict ethical responsibility and oversight.

Guarding Minds: Addressing LLM Hallucinations For Reliable School Education

Authors: Atharva Birthare

Abstract: Large Language Models (LLMs) have rapidly permeated educational spaces, offering tools for lesson preparation, doubt clarification, and content generation. However, their tendency to hallucinate—producing confident but inaccurate, irrelevant, or fabricated information—poses critical challenges for both teachers and students. This study employs assumed survey data from 120 teachers and 300 students to analyze awareness, trust, and coping strategies regarding hallucinations. The results highlight a significant awareness gap between teachers and students, with students more vulnerable to unverified reliance on LLMs. Four types of hallucinations—factual, intrinsic, extrinsic, and amalgamated—are discussed, along with practical mitigation strategies suitable for classroom contexts. This paper also provides graphical representations of awareness, trust, and coping strategies and concludes with recommendations for hallucination-aware pedagogy and future research directions.

DOI: http://doi.org/10.5281/zenodo.17174185

STUDY ON BRAKING BY CONTROLLING THE PULSE WIDTH OF STEP MOTOR

Authors: Jong Hon Pae, Myong Chol Tokgo

DOI: http://doi.org/10.5281/zenodo.17174674

Block Bazaar: NFT And Smart Contract Driven E-Commerce Platform

Authors: Rekha Parashuram Pujari, Akshitha Katkeri, Pallavi C V, Namitha R, Pratyusha C

Abstract: BlockBazaar is this new decentralized marketplace thing that mixes NFT trading with regular e-commerce stuff. It runs on Ethereum smart contracts and has live auctions that work across different devices. You log in using MetaMask wallets, which keeps things pretty secure from the start. Access levels depend on your role in the system, you know how that goes.The security side uses hardened smart contract patterns to block common attacks like reentrancy issues or front- running scams. Every transaction gets recorded publicly through blockchain explorers, so there's full visibility into what's happening. The whole setup cuts out a lot of traditional cloud services while keeping user privacy tight. Testing shows these decentralized platforms can actually handle enterprise-grade security requirements without slowing things down. Response times stay quick even during peak usage, which matters for real-world shopping scenarios. Users get familiar interfaces that don't sacrifice blockchain's core benefits like permanent records and trustless transactions. The key takeaway here is that hybrid systems can bridge Web3 tech with conventional e-commerce needs effectively. Performance metrics match centralized competitors while maintaining cryptographic proof of ownership for digital assets.

DOI: https://doi.org/10.5281/zenodo.17175762

Evolution Of Consumer Rights And Awareness In India: 1986–2025

Authors: Dr.S.Archana, Dr. S. Tamilmani

Abstract: Consumer rights and awareness have undergone a significant transformation in India over the past four decades. From the enactment of the Consumer Protection Act in 1986 to the emergence of digital commerce and online marketplaces, consumers today are more informed, empowered, and vigilant. This review synthesizes studies from 1986 to 2025, with a focus on consumer awareness, grievance redressal mechanisms, purchasing behavior, and the impact of technological and macroeconomic changes. Drawing upon empirical studies, surveys, and case analyses, this paper traces the evolution of consumer knowledge, perception, and rights advocacy in India, highlighting the critical role of education, legislation, and digital platforms in shaping contemporary consumer behavior.

The Evolution And Progress Of Co-operative Tourism, Travel And Hospitality In India

Authors: Dr.Muhammed Anas .B, Dr. V. Basil Hans, Dr. Sajimon PP

Abstract: Tourism in India has changed a lot from ancient times, and now it is an important part of the country's economic and cultural diplomacy. This article looks at the history of tourism in India, starting with pilgrimage-based travel in ancient and mediaeval times, moving on to the colonial impact on infrastructure development, and ending with the government's strategic efforts after independence to promote tourism as a sector of national importance. The study emphasises significant phases in the development of Indian tourism, encompassing the liberalisation policies of the 1990s, the emergence of specialised tourism sectors such as eco-tourism, medical tourism, and spiritual tourism, and the incorporation of digital technologies in the 21st century. It also looks at the problems the business is having, such as gaps in infrastructure, environmental issues, and the effects of global crises like the COVID-19 epidemic. This article gives a full picture of how tourism in India has changed and grown throughout time. It looks at changes in legislation, market trends, and social and cultural factors to do this. It also talks about how tourism could be a driver of inclusive and sustainable growth in the future.

DOI: http://doi.org/10.5281/zenodo.17182024

Green Innovation: Leveraging Convolutional Neural Networks For Enhanced Biogas Production From Hybrid Napier Grass And Co-Digestion Processes

Authors: Salman Zafar, Srinivas Kasulla, S J Malik, Gaurav Kathpal, Anjani Yadav

Abstract: Optimal biogas production remains a critical step in increasing renewable energy output from biomass resources. Hybrid Napier Grass is one of the promising substrates to produce biogas, mainly due to its high yield potential and adaptability, though achieving optimal output in this case still lags due to the variability of substrates, nutrient imbalance problems, and the complexity of co-digestion processes of various materials such as cattle slurry and chicken manure. For the first time in this study, CNN will be used as an optimization approach to condition anaerobic digestion, in which parameters are tuned in real-time to get the maximum yields of biogas. With an exhaustively prepared dataset of the Napier Grass and its co-substrates, CNN models are developed for inferring substrate composition, moisture, and nutrient ratios in real-time. Some key findings from the experimental results include: Accuracy of the CNN model reaches 100% on training data by about epoch 9, but the validation accuracy plateaued at 83.33%, which is overfitting, capturing of training-specific noise-affecting generalization to unseen data. Validation accuracy and loss stabilize around epoch ranges 10-20, but the training loss continued to decrease, demonstrating the power of the CNN in learning the training data. The validation loss of the model was also improving gradually but at a diminishing rate, which indicated some generalization of the current architecture of the dataset. This work can stand as a testament for unlocking optimization through CNNs in biogas production processes; this research has already shown an increase up to 20% more than conventional methods. Of course, further refinements will be needed for generalization purposes, but the AI-driven approach represents a significant advance in optimization and supports scalable and sustainable biogas development in bioenergy. This proposed CNN model was theoretically efficient and superior as far as classification accuracy in predicting biogas production was concerned, with an accuracy of 83.33% with consistent improvement across training rounds and moderate time complexity compared to the traditional models discussed above; thus, it will become a competitive tool for optimizing process parameters and improving the operational decisions to maximize biogas yield.

DOI: https://doi.org/10.5281/zenodo.17190005

How Global Conflicts Shape Consumer Behavior : A Marketing Study Of The Russia-Ukraine War

Authors: Devansh Dubey

Abstract: Armed conflicts not only reshape geopolitics but also alter consumer decision-making, loyalty, and market dynamics. The Russia–Ukraine war (2022–present) created one of the largest modern disruptions in consumer markets, as more than 1,000 multinational corporations—including globally recognized names such as McDonald’s, Starbucks, Coca-Cola, Nike, and IKEA—suspended or terminated operations in Russia. This mass withdrawal effectively transformed Russia into a live case study of forced market adaptation, as everyday consumption habits were abruptly destabilized. This paper examines how Russian consumers responded to the disappearance of these global brands, with attention to substitution choices, price-versus-prestige trade-offs, and the growth of local and Asian alternatives. Drawing on secondary data from Statista, Euromonitor, Reuters, and Yale CELI, the study traces shifts in consumer sentiment, market shares, and purchasing priorities across the fast-food, retail, apparel, and beverage sectors. Findings reveal that consumer loyalty, traditionally considered durable, was highly elastic under geopolitical pressure. Russian consumers largely prioritized functionality and affordability, enabling domestic firms such as Vkusno i Tochka and Chernogolovka, as well as Chinese apparel and electronics brands, to expand rapidly. Although nostalgia for Western products persisted, pragmatic needs outweighed symbolic attachments. The study underscores the importance of adaptive strategies for marketers, showing that crises demand localization, resilient supply chains, and flexible brand positioning. For multinationals, the Russian case highlights the risks of overdependence on politically sensitive markets, while domestic players benefited from opportunities to build loyalty during a period of forced transition. Key findings of consumer behavior adaptation in Russia during the Russia-Ukraine conflict. Source: Compiled by author using secondary data from Statista(2023), Reuters(2022) and Euromonitor(2022).

DOI: https://doi.org/10.5281/zenodo.17191630

Tragage: A Web-Based Garage Management And Real-Time Vehicle Tracking System

Authors: Baraiya Kishan, Ritesh Tiwari, Pritesh Tadvi, Yash Tailor, Dr. Nithiya A.

Abstract: Modern garages require comprehensive digital so- lutions to manage vehicles, parts, service workflows, and cus- tomer communication. This paper presents Tragage — a web- based garage management platform with real-time vehicle track- ing, parts inventory management, service scheduling, and a 3D interactive garage visualization. The system integrates a React/Three.js frontend, Node.js/Express backend, WebSocket- based real-time updates, and a relational/non-relational database. We describe system design, implementation details, testing, and evaluation. Placeholders for screenshots and diagrams are in- cluded so you can insert your project images directly. The pro- totype demonstrates improved operational transparency, faster service flow, and enhanced customer satisfaction.

Game Engines And Real-Time Rendering: The Future Of Virtual Worlds

Authors: George Malaperdas

Abstract: Game engines and real-time rendering technologies have revolutionized the way virtual worlds are created, experienced, and distributed. Once limited to video game development, these tools now extend into fields such as film production, architecture, education, and interactive art. Real-time rendering enables dynamic and immersive environments, providing users with responsive experiences that shape the future of digital storytelling and simulation. This paper explores the evolution of game engines, the role of real-time rendering, and their implications for the future of virtual environments, highlighting both challenges and opportunities for creative industries.

DOI: https://doi.org/10.5281/zenodo.17198863

Legal Awareness as A Tool for Empowerment: A Cross-Sectional Study of Graduate Students in Chandigarh

Authors: Dr. Upasna Thapliyal, Dr. Rajni Thakur

Abstract: Legal literacy and awareness are crucial components of democratic participation, access to justice, and youth empowerment. This study investigates the level of legal knowledge, attitudes toward legal institutions, and practices adopted by graduate students in Chandigarh, a city known for its strong educational base. Using a cross-sectional design, data were collected from a stratified random sample of 1,000 students across disciplines and gender through a structured questionnaire. Descriptive and inferential statistics, including Chi-square tests, t-tests, ANOVA, and multiple regression, were employed to analyze the data. Findings reveal that while students exhibit moderate legal knowledge (mean score: 58.6/100), significant disciplinary differences persist, with law students outperforming others. Gender differences were evident in awareness of gender-specific laws, though not in overall scores. Regression analysis identified discipline and prior exposure to legal workshops as key predictors of legal literacy. The results highlight a gap between rights awareness and procedural competence, emphasizing the need for curriculum integration, legal-aid initiatives, and gender-sensitive programs. By enhancing legal literacy, higher education institutions can strengthen civic participation and empower youth to engage effectively with legal systems, thereby contributing to a more informed and just society.

Architectural Ornamentation Of The Sidi Kacem Al-Jellizi Monument

Authors: Wided Melliti, Sabrina Ghattas

Abstract: The Mausoleum of Sidi Kacem Al-Jallizi is a Tunisian historical and archaeological monument distinguished by the richness of its decorative surfaces and the presence of a ceramic tile collection spanning from the 15th to the 19th century. Through a typological and chronological analysis, this article aims to shed light on the evolution of decorative techniques and the heritage-related challenges posed by successive restoration campaigns. It also seeks to identify Hispano-Moorish influences, the persistence of Hafsid traditions, and the emergence of Ottoman art during a later period.

DOI: https://doi.org/10.5281/zenodo.17200315

Investigation Into The Mechanical Properties Of Concrete Using Steel Fiber And Marble Dust With Partially Replacing Fine Aggregate

Authors: Prof. Boskee Sharma, Deepak Kumar Mishra

Abstract: The appropriateness of fiber-reinforced concrete by partially substituting steel fiber and marble dust powder is reviewed in this research. Concrete is one of the most important and widely utilized materials in the building industry. Marble dust powder MDP was a waste product from the marble industry that would harm the environment if it wasn't disposed of properly.The project's objective is to replace fine aggregate with marble dust powder. The addition of marble dust powder to concrete was done without compromising the material's mechanical qualities. Furthermore, steel fibers were included to improve the concrete's mechanical qualities.were examined in relation to different moisture contents and grades.

Improvement The Heat Transfer Rate Of Ac Evaporator By Optimizing Materials

Authors: Ranu Parste, Deepak Solanki

Abstract: Enhancing the heat transfer rate of air conditioning (AC) evaporators is a key objective in advancing energy-efficient thermal systems. This study investigates the optimization of evaporator material selection to improve thermal performance using a Genetic Algorithm (GA)-based approach. Traditional materials like copper and aluminum are evaluated alongside advanced composites and coatings based on criteria such as thermal conductivity, cost, weight, and corrosion resistance. The Genetic Algorithm is employed to identify the optimal material configuration that maximizes heat transfer while minimizing trade-offs. Simulation results demonstrate that GA effectively converges on optimal solutions, offering a 10–20% improvement in heat transfer performance over conventional materials. The integration of GA in material selection not only enhances evaporator efficiency but also provides a scalable method for intelligent design in HVAC systems. This research highlights the potential of evolutionary algorithms in solving complex multi-parameter engineering problems in thermal system optimization.

A Literature Review On Al₂O₃-Reinforced Epoxy Composites

Authors: Arun Patel, Dharmendra Kumar Tikle, Dr Rajeev Arya

Abstract: Aluminum oxide (Al₂O₃) has emerged as a prominent filler in polymer composites, enhancing mechanical, thermal, and electrical properties. This review critically examines recent research on Al₂O₃-reinforced epoxy, thermoplastic, and hybrid composites, with particular emphasis on particle modification, dispersion, and interfacial compatibility. Mechanical properties, including tensile, flexural, and impact strength, are analyzed alongside thermal conductivity, thermal stability, and glass transition temperature. Functionalization of Al₂O₃ particles, such as silane treatment or hybridization with graphene oxide, significantly improves filler-matrix adhesion, optimizing both stiffness and toughness. The review highlights the trade-offs between enhanced thermal performance and reduced ductility at higher filler loadings. Advances in fabrication methods, including melt compounding, hand lay-up, and bio-inspired approaches, are summarized. This work provides a comprehensive reference for researchers seeking to design high-performance Al₂O₃ polymer composites for structural, thermal management, and electronic applications.

BURNOUT OF SECONDARY SCHOOL TEACHERS IN RELATION TO THEIR JOB SATISFACTION

Authors: Dr. Sarmistha Choudhury, Sohail M Sangma

Abstract: Teaching at the secondary school level demands not only subject expertise but also sustained emotional engagement and adaptability in the face of diverse classroom challenges. These demands, coupled with institutional pressures, can contribute to a state of professional burnout is a phenomenon characterized by emotional weariness, depersonalization, and diminished sense of personal achievement. Such experiences may, in turn, influence how teachers perceive their work, shaping their overall sense of job satisfaction. This study investigates the rate of burnout among secondary school educators and explores patterns in their levels of job satisfaction. Further, it investigates how these two variables interact, providing insight into whether and to what extent burnout impacts teachers’ professional contentment. Using standardized measures — the Teachers’ Burnout Scale (TBS) by Prof. Madhu Gupta & Ms. Surekha Rani (2011) and the Teachers’ Job Satisfaction Questionnaire (TJSQ) by Dr. Amar Singh & Dr. T.R. Sharma (1999) — data were collected from a representative sample of educators. The analysis offers a profound understanding of the emotional and motivational dynamics within profession of teaching with implications for policy and practice aimed at enhancing teacher well-being and effectiveness

DOI: http://doi.org/10.5281/zenodo.17291769

Future-Proof: Thriving In The 21st Century Workforce Future-Proof: The Hottest Careers Of The 21st Century A Strategic Guide To Thriving In The Global Workforce

Authors: Dr Prince Blessing Lawal

Abstract: In an era defined by rapid technological evolution, shifting global priorities, and the rise of ethical governance, the 21st-century workforce demands a recalibration of career trajectories. This paper explores the concept of “future-proof” careers—professions that demonstrate resilience, adaptability, and sustained relevance amidst socio-economic disruptions and digital transformation. Drawing upon interdisciplinary research, global employment trends, and Sustainable Development Goal (SDG) frameworks, the study identifies key sectors poised for long-term growth, including ethical leadership, artificial intelligence, climate innovation, symbolic literacy, and inclusive education. It further examines the cultural and institutional imperatives that shape career viability, offering strategic insights for educators, policymakers, and aspiring professionals. By mapping the intersection of purpose, technology, and global impact, this paper serves as a ceremonial guide for navigating the hottest careers of the century with clarity, compassion, and foresight.

DOI: http://doi.org/10.5281/zenodo.17208157

Future-Proof: The Hottest Careers of the 21st Century

Authors: Dr Prince Blessing Lawal

DOI: http://doi.org/10.5281/zenodo.17208157

GrowthKAR: Outsourcing Services Platform

Authors: Asst. Prof. Khyati Zalawadia, Meet Jethwa, Shivang Meghnathi, Chetan Sharma, Besta Bharath

Abstract: Startups and small-to-medium enterprises (SMEs) play a critical role in global economic growth. However, they frequently face challenges related to scalability, operational inefficiency, accountability, and limited access to investors. Ex- isting freelancing platforms such as Upwork and Fiverr focus on flexibility but lack accountability, while consulting firms like Accenture provide reliability but at unaffordable costs. To address this gap, this paper proposes GrowthKAR, a unified outsourcing services platform designed specifically for star- tups and SMEs. The platform integrates AI-driven project monitoring, blockchain-enabled payment systems, and a pool of vetted professionals to ensure transparency, accountability, and scalability. GrowthKAR supports startups throughout their growth journey by offering tools for project execution, progress tracking, and mentorship access. A case study demonstrates cost savings of up to 30 percent, improved delivery timelines, and enhanced accountability compared to existing platforms. This work contributes a scalable model that merges affordability and accountability, empowering SMEs to compete more effectively in a dynamic global market.

DOI: https://doi.org/10.5281/zenodo.17212995

Marketing Metamorphosis: Bridging Traditional And Digital Sales Strategies In The Tech-Driven Age

Authors: Dr. Srinivasan Gopal Chari

Abstract: From the billboard to the byte, from the handshake to the hyperlink, marketing has changed drastically in the vast theater of 21st-century business. This research article explores the dynamic juxtaposition and convergence of conventional and digital marketing and sales techniques, therefore highlighting the tectonic changes in customer involvement, campaign orchestration, and technology mediation. Influencer marketing, CRM automation, and influencer marketing as business ecosystems migrate from the analog inertia of print advertisements and field sales into the turbulent digital storm of AI-driven analytics necessitate not just adaptation but also change from the toolset perspective. This study seeks to outline the philosophical undercurrents, historical background, and technical progress that have collectively rewritten the marketing playbook, therefore acting as a compass for contemporary professionals—an intellectual ready reckoner. In its golden age, traditional marketing depended on wide brushstrokes—mass communications across stationary media like print, radio, and television. Designed to mesmerize the collective consciousness, the campaign was monologic, one-directional. Newspapers column inches, the famous tagline, the television jingle—they were the currency of credibility. Their philosophy rested on emotional resonance, persuasion, and aspirational identity. These approaches were sometimes castles constructed on sand—grand in intention but precarious in responsibility—limited capacity for feedback, and measures based more on intuition than evidence.

Novel Approach To Implementation Of Channel Estimation In 6g Spectrum By Using Noma And Artificial Intelligence Hybrid Technique

Authors: Ajay Damor, Dr Nidhi Tiwari, Professor Madhavi S Bhanwar

Abstract: The emergence of sixth-generation (6G) wireless networks demands highly efficient spectrum utilization and robust communication strategies to support ultra-reliable, low-latency, and high-capacity services. One of the critical challenges in 6G is accurate channel estimation, especially in dense user environments where spectrum resources are limited. This paper proposes a novel hybrid approach for channel estimation that integrates Non-Orthogonal Multiple Access (NOMA) with Artificial Intelligence (AI)-driven algorithms. The NOMA framework enables simultaneous multi-user transmission within the same spectrum band, thereby enhancing spectral efficiency, while the AI-based module leverages deep learning and reinforcement learning models to perform adaptive and dynamic channel estimation under varying propagation conditions. The proposed methodology not only minimizes estimation errors but also reduces computational complexity compared to conventional estimation methods. Simulation results demonstrate significant improvements in spectral efficiency, bit error rate, and overall system throughput, validating the potential of the AI–NOMA hybrid approach for next-generation wireless networks. This work highlights the importance of intelligent channel estimation techniques in realizing the performance requirements of 6G communication systems.

Making Big Changes Stick: How A Supermarket Handled Smart Technology And Kept People Happy

Authors: MD Juman Hussan

Abstract: This paper looks at how a big Australian supermarket, Woolworths, put new smart technology (AI) into its business. We wanted to see if they followed the right steps to manage this big change. The move to use AI in things like checking stock and talking to customers (like with their chatbot, 'Olive') is a huge deal. This study uses common ideas like Kotter’s eight-step change model to see where the company did well and where they struggled. We also use the Organisational Culture Assessment Instrument (OCAI) idea and The Communication Diagnostic concept to check on the company's team spirit and how they talked about the changes. We found that while the smart systems made things faster and sales grew (like their online sales hitting 5.1 billion), the communication with warehouse teams caused problems, leading to disagreements. This shows that even the cleverest systems need simple, clear human leadership and a team culture that wants to learn new things (a growth mindset) to truly work well.

Water Quality Assessment of Chambal River by Using Multivariate Statistical Methods

Authors: Prateek Srivastava

Abstract: The present investigation assessed the spatiotemporal variation in the surface water quality at 27 monitoring stations on the ChambalRiver with the aid of multivariate statistics, and categorized the river stretch from least to heavily polluted utilizing the Water Quality Index (WQI). The WQI unveiled a distinct pollution spectrum in the river, while cluster analysis (CA) grouped the stations according and water chemical similarities due to various stressors.A clear gradient of organic pollution and nutrient enrichment has been identified as the key drivers of the aquatic disturbance. WQI, CA, and PCA collectively provided an efficient framework for differentiating pollution levels and sources, underscoring the necessity of targeted monitoring and management to safeguard aquatic environments.

DOI: http://doi.org/10.5281/zenodo.17231081

Testing Sustainable Material For Aerospace Application

Authors: Jagadeep Thota, Ellyssa Purdy

Abstract: Aerospace vehicles, such as a rocket, need to be light weight. They carry heavy payloads and critical flight instrumentation (avionics) which need to be protected. Typically, aerospace vehicles contain single use parts, some of which may be made of even nylon and polystyrene, that are not environmentally friendly. Such materials can harm soils and the ecosystems upon disposal. This paper looks at replacing some of these aerospace vehicle parts, mainly the parts protecting the rocket avionics, by a sustainable biodegradable material. This study looks at the performance of the rocket avionics when enclosed by such sustainable material. The work presented in this paper involves utilizing computer-aided design (CAD) modeling coupled with numerical flight simulation. The aerospace vehicle, with the sustainable material parts, is flight tested.

DOI: http://doi.org/10.5281/zenodo.17231851

Advanced AI Framework For Robust Fault Diagnosis In Industrial Systems

Authors: Dr. Ishaan Tamhankar

Abstract: The paper proposes a novel advanced AI framework for robust fault diagnosis in industrial systems that experience missing data in sensor measurements. The approach integrates Diffusion Model-based Imputation, Multi-Path Transformer-Graph Neural Network (MPT-GNN), and Uncertainty-Aware Federated Learning (UA-FL) to restore missing sensor readings, enhance fault detection accuracy, and preserve data privacy across distributed industrial environments. The framework combines short-term temporal convolutional networks, Transformers for long-term analysis, and GNNs for inter-sensor connectivity, resulting in improved precision and interpretability of fault diagnosis. Additionally, Bayesian Neural Networks are incorporated for reliable uncertainty estimation, while Elastic Weight Consolidation provides memory-efficient edge device deployment. Experimental results demonstrate fault detection accuracy of up to 98.7% on industrial machinery datasets, minimizing the impact of missing data and facilitating real-time, scalable, and robust deployment of industrial AI systems for predictive maintenance applications.

DOI: https://doi.org/10.5281/zenodo.17248679

Sustaining Himalayan Springs Amidst The Emerging Water Crisis

Authors: Prateek Srivastava, Sandeep Dubey, Shriyanshi Singh

Abstract: The significance of spring water is fundamentally integral to the livelihood of the Himalayan population. Springs are the chief providers of drinking water for households, agricultural, and industrial applications, especially in the Himalayan region, and contribute to the ecological richness and ecosystems in the Himalayas. Despite their crucial significance, springs continue to attract minimal attention. Over the last couple of decades, a noticeable drop of about 60% in low-discharge springs has been documented. With the escalation of population growth, relentless climate change, and rapid urbanization, springs face several significant threats to their survival. There is growing evidence that the springs of the Himalayas are experiencing desiccation, a reduction in discharge, and deterioration in water quality. In the Himalayan territories, springs hold significant importance in the context of cultural and religious beliefs. They are considered purest form of water and are frequently associated with different gods, rituals, and mythologies. These springs were regarded as sacred due to their intrinsic connections to regional deities and rituals of worshipping water. Heat, glacial melting and rainfall patterns are the anticipated alterations that are projected to influence the quality & quantity of water substantially. Springs rejuvenation could offer a climate-adaptive approach benefiting the Himalayan ecosystems and livelihoods, improve water accessibility, and help to accomplish any of the Sustainable Development Goals (SDGs). Spring-shed management based on aquifer systems combines scientific knowledge, community participation and collaborative partnerships in springs revival, thereby generating policy attention on spring water across the region.

DOI: http://doi.org/10.5281/zenodo.17241706

Legal and Ethical Aspect of Professional Development in Nursing

Authors: Umeh Elizabeth Egodu

Abstract: Permitting a new driver to get behind the wheel of a car requires the driver to know the laws governing driving; however, the laws do not tell the whole story. For example, what is a driver to do when entering an unprotected intersection? What governs the driver’s movement into the intersection? How does the driver account for the weather, vehicle, and road condition? What is the driver’s knowledge and experience level? Any new driver needs guidance or rules to manage the inherent risks. Inherent risk is also a part of nursing. Patients are ill; medications and treatments have benefits and adverse effects; clinical situations are undetermined, open ended, and highly variable Providing nursing care sometimes feels like the new driver navigating that unprotected intersection. As with the new driver, education and standards provided by laws and regulations designed to protect the public provide guidance in nursing practice. Nursing requires specialized knowledge, skill, and independent decision making. The practice of nursing ivolves behaviour, attitude and judgement, and physical and sensory capabilities in the application of knowledge, skills, and abilities for the benefit of the client. Nursing careers take widely divergent paths – practice focus varies by setting, by types of clients, by different disease, therapeutic approach or level of rehabilitation. Burses work at all points of service in the health care system (sheets, 1996).

DOI: https://doi.org/10.5281/zenodo.17248256

Digital Assessment And Evaluation In Modern Educational Landscape

Authors: Umeh Elizabeth Egodu

Abstract: Digital assessment refers to the use of technology to create, administer and evaluate assessments online, offering benefits like immediate feedback and data analysis. For nursing, this involves utilizing digital resources like learning management systems, online databases, and simulation software, etc It enhances learning by providing flexible, accessible, and interactive evaluation methods that cater to diverse student needs. This method allows educators to conduct assessments online, providing immediate feedback, thereby enhancing the learning experience.

DOI: https://doi.org/10.5281/zenodo.17248418

UrbanSync – Real Time Public Transport Tracking System

Authors: Mr. Atharv Antaram Gavali, Ms. Antara Sandip Hire, Ms. Arpita Shankar Gaikwad, Ms. Shruti Ramesh Mandale, Mrs. R. V. Shinde (Guide)

Abstract: Public transport is essential for mobility, especially in small and developing cities with limited private transport options. However, issues like unpredictable bus arrivals, long wait times, poor route information, and lack of real-time updates undermine its reliability and efficiency. This project proposes a Real-Time Public Transport Tracking System using GPS, mobile apps, and cloud-based data management. The system tracks the live location of public vehicles and provides updates via a passenger mobile app and display boards at bus stops. Commuters can view estimated arrival times (ETA), select optimal routes, and receive alerts on delays or route changes. Transport authorities benefit from backend monitoring, enabling real-time tracking, data-driven scheduling, and improved resource allocation. The system aims to reduce wait times, enhance commuter convenience, and increase public transport adoption. In the long term, it supports traffic decongestion and promotes sustainable urban mobility.

Ecosphere(E-commerce Site For Plants)

Authors: Himadri Vegad, Devid Vaghasiya, Krupal Gohil, Purvesh Ranpariya, Mehul Bhatiya, Dr. Harsh Khattar

Abstract: Ecosphere consists of an extraordinary online shop- ping platform that caters exclusively to botanical and environ- mentally conscious people. The platform acts as a marketplace, which is entirely dedicated to products related to plants, and users are enabled to shop and sell various commodities, including live plants, seeds, gardening tools, eco-friendly accessories, etc. Those who sell their products on the platform get to present them to a targeted group of the most enthusiastic buyers, whereas buyers can enjoy a selected shopping experience with the help of their own preferences and complete product reviews. Besides the marketplace, Ecosphere also offers a lively com- munity hub where users can post their gardening stories as if they were blogging. Members are free to take pictures of their plants, ruminate on their growing journeys, and share tips, ideas, etc. with others. This type of functionality brings more users to the platform and results in a higher degree of their presence, not only because of interaction but also because of contributions of common knowledge.

DOI: https://doi.org/10.5281/zenodo.17249926

Local Event Finder

Authors: Prof. Arunesh Pratap Singh, Divyang Rajput, Shubham Yadav, Himanshu Jena, Meet Hadiya

Abstract: Events are vital for strengthening cultural, educational, and professional connections within communities, yet smaller gatherings often suffer from poor visibility and outdated promotion methods. Traditional approaches such as posters and scattered social media posts rarely provide real-time updates or smooth booking options, making event discovery difficult for attendees and limiting reach for organizers. To overcome these challenges, the Local Event Finder platform was developed using the MERN stack. It enables users to search for nearby events, navigate to venues, receive instant updates, and book tickets securely, while giving organizers tools to manage and promote their events effectively. Core features include geolocation-based filtering, secure payments, and real-time notifications. Built with the Agile Scrum approach, the system evolved through iterative feedback and testing. A pilot study showed a 40% rise in attendance and greater engagement compared to traditional methods. With innovations like hyper-local targeting, role-based authentication, and WebSocket-based updates, the platform demonstrates its potential to improve event visibility, streamline management, and deliver a better user experience.

DOI: https://doi.org/10.5281/zenodo.17249871

Cybersecurity Solutions for Modern Threats

Authors: Harhit Suthar, Prathinav Kothia, Vishal Bharvadiya, Smit Bharatbhai Kanani, Ami Shah

Abstract: This project focuses on enhancing cybersecurity for websites and applications, protecting users from modern threats like phishing and data breaches. It aims to create a secure digital environment by implementing strong security measures. One of the key features of the project is a user-friendly complaint registration system, allowing individuals to report cyber fraud directly without having to visit a cybercrime office. Additionally, it provides users with real-time updates on the latest cybercrime incidents and ensures that their email and phone number are not exposed on external websites. To further assist users, an AI- powered chatbot is integrated into the system, offering real-time guidance on cybersecurity-related queries. The project tests the effectiveness of its security measures to ensure they can withstand real-world threats. Beyond protection, the project aims to educate users and businesses on best practices for staying safe online. The ultimate goal is to deliver a secure, easy-to-use platform that helps individuals and businesses stay protected from evolving cyber risks.

DOI: https://doi.org/10.5281/zenodo.17255051

Voice Command Door Lock System

Authors: Prem Narwekar, Shubham Nannware, Aditya Gupta, Sohan Londhe

Abstract: This project presents the design and implementation of a Voice-Activated Door Lock System integrated into a portable door unit constructed from glass fiber-reinforced polymer (GFRP), including a matching lintel beam. The primary objective is to enhance security, accessibility, and portability while maintaining structural durability and aesthetic appeal. The voice-controlled locking mechanism utilizes speech recognition technology to grant or deny access based on authorized voice commands. This hands- free approach to security offers a modern alternative to traditional key-based or keypad systems, ideal for users with mobility impairments or for smart home integration. The door and lintel beam are fabricated using glass fiber, chosen for its lightweight nature, high strength- to-weight ratio, corrosion resistance, and ease of transportation, making the system suitable for both temporary installations and permanent structures. The portability of the door unit allows flexible deployment in residential, commercial, or construction site environments. The system is powered by a microcontroller (e.g., Arduino) interfaced with a microphone module, voice recognition module, and electronic lock. Security is further enhanced through multi-level authentication protocols and real-time status feedback via a mobile app or local display. This project merges advanced materials with intelligent control systems, providing a robust, user-friendly, and portable access control solution for modern smart environments.

An Integrated Framework For Personalized Book Recommendations Combining Hybrid Filtering With A Full-Stack Architecture

Authors: Harsh N Sorathiya, Manthan Shah, Dhruv Shah, Ovesh Khatri, Professor Rahul Moud

Abstract: This paper delineates the design and implementation of an integrated platform for personalized book recommendations. The system is architected upon a decoupled three-tier model, featuring a dynamic user interface built with React.js and a robust backend service developed in Python-Flask. Central to the platform is a hybrid recommendation engine that synergizes item-item collaborative filtering—which employs Cosine Similarity on a pre-calculated similarity matrix—with a content-based fallback mechanism. This dual-strategy approach is specifically engineered to overcome the prevalent challenges of data sparsity and the cold-start problem. To ensure persistent personalization, user data and interaction histories are stored in a cloud-hosted PostgreSQL database and managed via the SQLAlchemy Object-Relational Mapper (ORM). Security is enforced through a stateless JSON Web Token (JWT) authentication protocol, which also underpins the system's role-based access control for administrative functions. This research provides a practical blueprint for the development of scalable, real-world recommender systems by synthesizing established algorithms with contemporary software engineering methodologies.

Reducing Phishing Attacks In Online/Mobile Wallet & Net Banking: A Comprehensive Framework For Enhanced Security

Authors: Arpan Garg, Nishchal KC, Pramish Bhandari, Mr. Nikhil Ranjan

Abstract: The increasing reliance on browser-based internet banking has amplified the threat of phishing attacks, which exploit human and system vulnerabilities to gain unauthorized access to sensitive financial information. This review exam- ines various phishing attack techniques targeting browser-based banking systems, categorizing them by their operational mech- anisms and identifying their strengths, weaknesses, and limi- tations. Existing approaches include deceptive website cloning, cross-site scripting, DNS hijacking, man-in-the-middle attacks, and malicious browser exten- sions. While some methods rely on social engineering and exploit user trust, others leverage technical flaws in browser or network infrastructure. Strengths of these at- tacks often lie in their low cost, scalability, and ability to bypass traditional security measures, while their weaknesses include dependence on user interaction, detectable behavioral patterns, and increasing resistance through multi-factor authentication and improved browser security. The analysis reveals persistent chal- lenges: phish- ing techniques continuously evolve, and defensive mechanisms often lag behind, requiring constant adaptation. This review synthesizes findings from peer-reviewed sources, including Applied Sciences (MDPI), Journal of Information Security and Applications (Elsevier), Computers Security (Elsevier), and International Journal of Network Security Applications (IJNSA), highlighting the need for integrated, proactive defense strategies combining technical safeguards, user awareness, and regulatory measures to effectively mitigate the evolving phishing threat landscape in online banking environments.

DOI: http://doi.org/10.5281/zenodo.17263409

Development Of A 3D Augmented Reality Application For Virtual Product Try-On

Authors: Divyanshu Bisht, Neeraj Narwat, Dakshesh Chaturvedi, Dr. Shelja Sharma

Abstract: The rapid evolution of e-commerce has created an urgent Immersion shopping experiences that bridge the gap between physical and digital retail environments are desperately needed, as e-commerce continues to grow at an accelerated rate. Conventional e-commerce sites have poor customer engagement, high return rates, and an inability to appropriately depict product attributes. Through real-time product visualization, spatial interaction, and customized retail environments, this study suggests a full 3D Augmented Reality (AR) platform that transforms online shopping experiences. Customers may see products in their real-world settings before making a purchase thanks to the platform's use of WebAR technology, the Three.js rendering engine, and machine learning algorithms for product suggestion. To provide smooth augmented reality experiences on many devices, the system combines cutting-edge computer vision algorithms, real-time 3D rendering, and cloud-based processing. The outcomes of the experiment show an 82% increase in customer satisfaction ratings, a 45% rise in conversion rates, and a 67% decrease in product returns. Size uncertainty, color accuracy, and spatial compatibility are some of the major issues in online retail that the platform tackles while offering scalable solutions to merchants of different product categories.

DOI: http://doi.org/10.5281/zenodo.17263630

Life Science In Genetics And Its Applications In Computer Science

Authors: Santosh Kumar Dash

Abstract: Genetics, a cornerstone of life sciences, explores the structure, function, and inheritance of genes. With the advent of advanced computational technologies, genetics has expanded beyond biological boundaries and entered the realm of computer science. Concepts such as genetic algorithms, DNA computing, and bioinformatics are directly inspired by genetic principles. This paper examines the interdisciplinary relationship between genetics and computer science, emphasizing how genetic models inspire computational techniques and how computational tools accelerate genetic research. Applications range from medical diagnostics and drug design to artificial intelligence, optimization problems, and cybersecurity. This convergence of life science and computer science illustrates the potential for transformative innovations across multiple disciplines

DOI: https://doi.org/10.5281/zenodo.17265109

Mathematical Modeling Of Atmospheric Pollutant Dispersion Under Periodic Emissions: Implications For Respiratory And Cardiovascular Health

Authors: Ashutosh Kumar Upadhyay, Meenakshi Vashisth, Amanpreet Kaur, Sapna Ratan Shah

Abstract: This study presents a mathematical model for the dispersion of atmospheric pollutants subjected to periodic emission sources and removal dynamics. Using an advection-diffusion-reaction framework, we derive and analyze the governing partial differential equation incorporating a sinusoidal source term and a constant atmospheric removal rate. The model captures real-world conditions such as diurnal emission cycles and steady pollutant decay. Analytical and numerical solutions are explored to understand the spatiotemporal behavior of pollutant concentrations. Numerical results highlight that pollutant concentration profiles evolve with time, showing spatial spreading, downstream advection, and amplitude attenuation due to decay. Furthermore, increasing the emission oscillation amplitude (α) leads to more pronounced temporal fluctuations in concentration at fixed locations. Importantly, given that the World Health Organization reports that air pollution contributes to nearly 7 million premature deaths annually, primarily due to respiratory and cardiovascular diseases, this work underscores the critical need for accurate pollution modeling to inform effective monitoring and mitigation strategies under oscillatory emission conditions.

DOI: https://doi.org/10.5281/zenodo.17276408

Artificial Intelligence (AI) And The Legal System: Opportunities, Challenges, And The Imperative For Effective Governance

Authors: Dr. Honey Sharma

Abstract: This research paper seeks to explore the profound and multifaceted impact of AI on the legal landscape by analyzing its potential benefits, inherent risks, and the broader implications for governance, accountability, and the rule of law. Through an in-depth examination of current global and national trends, evolving legal frameworks, and relevant case studies, this study provides a comprehensive understanding of the extent to which AI is reshaping the traditional contours of the legal profession, judicial decision-making, and regulatory compliance mechanisms. In conclusion, the growing influence of AI on the legal system represents both an unprecedented opportunity and a profound challenge. While AI promises to enhance efficiency, accuracy, and access to justice, it simultaneously raises critical ethical, legal, and governance concerns that cannot be ignored. Only through a balanced regulatory framework—one that combines technological innovation with robust oversight and human accountability—can societies ensure that AI contributes positively to the pursuit of justice and the protection of fundamental rights. The future of law in the age of AI will depend not only on the sophistication of our machines but also on the wisdom with which we choose to govern them. This research aims to examine the opportunities that AI brings to the legal field, the challenges associated with its deployment, and the urgent necessity for a comprehensive and ethical governance framework that balances innovation with accountability.

save life

Authors: Prem Narwekar, Shubham Nannware, Aditya Gupta, Sohan Londhe

Predictive Analysis For Crop Yield

Authors: Dixita Rajpopat, Heet Padhiyar, Rucha Chougule, Yash Kacha, Asst. Prof. Zulkifl khairoowala

Abstract: Agriculture plays a vital role in providing food and supporting the economy. However, farmers often face problems like unpredictable weather, poor soil conditions, pest attacks, and limited resources. These factors make it difficult to estimate crop yield accurately and result in losses. Traditionally, farmers rely on their personal experience or basic methods, which are not always reliable in today’s changing environment. This project, Predictive Analysis for Crop Yield, focuses on using machine learning techniques to forecast agricultural production by analyzing data such as weather patterns, soil type, and past yield records. Models like regression, decision trees, and neural networks are applied to reduce errors and provide dependable predictions. The goal is to design a reliable prediction system that helps farmers make better decisions and supports policymakers in ensuring food security.

DOI: https://doi.org/10.5281/zenodo.17293428

IMPACT OF ADULTERATION WITH PAWPAW EXTRACT ON THE PHSICOCHEMICAL CHARACTERISTICS OF PALM OIL.

Authors: Dr. Ugwuoke Malachy Okonkwo, Okozor Petrolina Nkeiruka, Ezea Boniface Chukwuebuka

Abstract: Palm oil is a widely consumed edible oil valued for its nutritional, economic, and industrial importance. However, adulteration with substances such as pawpaw (Carica papaya) extract has become a common malpractice aimed at enhancing color and yield, often at the expense of quality. This study examined the effects of adulteration with pawpaw extract on the physicochemical characteristics of palm oil over an eight-week storage period. Palm oil samples were adulterated at concentrations of 0.05–0.08 g/mL and evaluated for acid value, saponification value, iodine value, peroxide value, free fatty acid content, specific gravity, viscosity, melting point, moisture content, and color using standard AOAC (2019) and Codex (2019) procedures. Results showed that adulteration significantly altered both chemical and physical properties. The acid value increased from 2.41 to 6.38 mgKOH/g, while free fatty acids rose from 1.21% to 3.19%, indicating accelerated hydrolysis and reduced stability. Peroxide value increased sharply from 8.12 to 19.47 meq/kg, confirming enhanced oxidative rancidity. Saponification value increased from 195.1 to 209.3 mgKOH/g, suggesting incorporation of lower-molecular-weight fatty acids, whereas iodine value decreased from 53.6 to 41.2 g I₂/100 g, and indicating reduced unsaturation. Physical changes included increased specific gravity (0.903–0.923), viscosity (43.5–58.2 cP), and moisture content (0.18–0.41%), alongside a reduced melting point (36.4–32.8 °C) and a color shift from dark red to reddish-yellow. Finally, pawpaw extract markedly deteriorated the physicochemical quality of palm oil, promoting oxidation, rancidity, and moisture absorption. These changes compromise its edibility, shelf life, and industrial applicability. The findings underscore the urgent need for stricter monitoring and enforcement of food quality regulations to prevent adulteration and ensure consumer safety.

DOI: http://doi.org/10.5281/zenodo.17301002

AI Resume Analyzer: A Review And Case Study Of An NLP-Driven Recruitment System

Authors: Sunny Ramchandani, Preksha Jain, Ameya Shivhare, Maddu Akhil, Shashank Pandey

Abstract: The growing demand for efficiency in recruitment has accelerated the adoption of AI-powered resume screening systems. This paper reviews the evolution of resume analysis approaches, from keyword-based filters to large language models (LLMs) and Retrieval-Augmented Generation (RAG) pipelines. A critical analysis compares accuracy, scalability, fairness, and regulatory compliance across methods. Ethical concerns, includ- ing bias and privacy, are discussed alongside recent regulations such as the EU AI Act and GDPR. To complement the review, we present a case study of an AI Resume Analyzer system, followed by experimental validation. The paper concludes with limitations and future research directions for trustworthy, scalable, and fairness-aware recruitment systems.

DOI: http://doi.org/10.5281/zenodo.17305016

Brain Tumor Detection From MRI Images Using CNN-Based Deep Learning Models

Authors: Rishi Kumar Mishra, MD Yasin Alam, Shivam Pisudde, Mohammed Abubakar

Abstract: Early and accurate detection of brain tumors from magnetic resonance imaging (MRI) is critical for patient care. This paper presents a CNN-based pipeline for binary brain tumor detection using grayscale MRI images, built on a transfer-learning backbone (EfficientNetB0) with targeted preprocessing, augmentation, and explainability via Grad-CAM. We describe dataset handling, model architecture, training strategy, and evaluation metrics including accuracy, AUC, precision, recall and confusion analysis. Empirical results on commonly used MRI image collections demonstrate that the proposed workflow achieves competitive performance while remaining computationally efficient. We conclude with a discussion of limitations, reproducibility practices, and recommended future extensions.

Harnessing Diatoms To Mitigate Microplastic Pollution: A Review_199

Authors: Prateek Srivastava, Abhishek Kumar Sharma, Prishita Singh, Saleha Naz

Abstract: Microplastic (MP) pollution has become a critical environmental issue, with particles originating from consumer products and plastic degradation now pervasive in aquatic, terrestrial, and atmospheric systems. MPs pose ecological risks by disrupting feeding, growth, and reproduction in aquatic organisms and potentially entering human food chains. Traditional mitigation strategies remain insufficient, prompting exploration of biological alternatives. Diatoms, photosynthetic microalgae with silica frustules, show strong potential for MP remediation. Through biofilm formation, extracellular polymeric substance (EPS) secretion, and adhesion, diatoms facilitate MP aggregation, sedimentation, and partial degradation. Their interactions with bacteria further enhance plastic breakdown, while large-scale cultivation enables integration into wastewater treatment and hybrid remediation systems. Despite limitations such as incomplete degradation and environmental dependence, diatoms represent an eco-friendly, scalable, and sustainable strategy. Advances in engineered consortia, genetic modification, and field validation may establish diatoms as a viable biotechnological tool for mitigating microplastic pollution

DOI: http://doi.org/10.5281/zenodo.17312397

The Foundation of Structural Vibration Frequency Analysis and Its Applications in Structural Design

Authors: Phương Ngo Nam

Abstract: Thanh Tran Xuan, 2 Phương Ngo NamVibration is a common phenomenon in nature and in engineering. All structures subjected to external forces will vibrate and may experience the phenomenon of resonance during operation. Vibration and resonance are often the cause of, or at least a contributing factor to, many operational problems in structures and machinery, leading to shaking, noise, and even component failure, even when the applied force has not exceeded the material's strength limit. When designing structures, machinery, and civil works, engineers routinely account for the effects of vibration. This includes calculating the structure's natural frequencies, predicting the operational frequency range of the structure, and designing the structure to mitigate adverse effects while utilizing beneficial vibratory characteristics. To fully understand the vibration and resonance issues of a structure, the factors causing vibration and resonance must be identified and quantified. A common approach to achieve this is to study the dynamic properties of the mechanical structure under dynamic excitation: its natural frequencies, corresponding mode shapes, and damping ratios.

DOI: https://doi.org/10.5281/zenodo.17312488

Multipurpose Agriculture Machine

Authors: Harsh Janware, Nishant Bawangade, Preetesh Moroliya, Tilak Kothurwar, Prof. Rajendra Dhandre

Abstract: Automated agricultural robotics integrates robotics, sensors, IoT communication, and AI to modernize cropping systems. This review expands on the user's uploaded summary of a modular field robot (microcontroller-based control, LoRa communication, seed metering, irrigation, and fertigation modules). We synthesize recent literature on agricultural robots and precision-farming technologies, evaluate sensing and communication choices, discuss autonomy levels and AI integration, and outline environmental and socio-economic implications. Key challenges and future research directions are energy autonomy, reliable low-cost sensing, robust perception for unstructured fields, and equitable deployment are highlighted

Smart Plant Health Monitoring System

Authors: Mukesh Sahni, Raj Mayaskar, Rajan Vankar, Yash Parikh, Vikram Kaushik

Abstract: This paper introduces a novel Smart Plant Health Monitoring System to identify and forecast plant health problems in real-time, facilitating data-driven decision-making for enhanced crop yield and sustainability. In contrast to conventional manual approaches, the system combines IoT sensors, cloud computing, and artificial intelligence to monitor environmental parameters like soil moisture, pH, temperature, and humidity constantly. Convolutional Neural Networks (CNN) are employed for plant disease identification in images, while sensor data is processed to provide an early warning for water stress or nutrient deficiencies. An easy-to-use web and mobile app, developed using Flask and Python, offers farmers actionable information. Automated irrigation monitoring and alert features are also integrated within the system to minimize wastage of resources and enhance crop management efficiency. With the integration of IoT-based sensing, machine learning, and real-time analytics, this product constitutes a major leap in precision agriculture, fostering sustainable agriculture and improved productivity.

DOI: https://doi.org/10.5281/zenodo.17320399

EcoXchange: AI-Powered Reuse & Thrifting Marketplace

Authors: Aparna Mote, Vaishnavi Patil, Sayali Pawar, Shruti Pawar, Saundarya Surana

Abstract: This paper introduces EcoXchange, an AI-based platform with the goal of supporting sustainable practices through thrifting, gamification, and waste categorization. The platform stimulates reuse by allowing users to trade pre-owned products in a thrifting marketplace. Gamification tactics, including reward-based models, are incorporated to maximize user participation in sustainable activities. Moreover, artificial intelligence technologies such as image recognition are used for waste classification, enhancing the efficiency of recycling. The study examines user activity in the thrifting market, participation through gamified functions, and the operation of AI-based waste sorting. The results demonstrate the promise of integrating thrifting, gamification, and AI to enable environmental sustainability.

Artificial Intelligence And Human Resource Analytics: An Integrated Approach

Authors: Chinnathambi. A, Dr S.Maruthavijayan

Abstract: This paper examines the integration of Artificial Intelligence (AI) into Human Resource (HR) Analytics, using primary survey data collected via a Google Form. The survey captured respondent demographics, awareness and perceptions of AI in HR, adoption levels, perceived benefits, and ethical concerns. Findings indicate strong awareness of AI in HR among respondents, with most considering it important for the future of HR Analytics. Recruitment, training, and employee engagement emerged as the top HR functions benefiting from AI, while data privacy and lack of expertise were identified as key challenges. The study concludes that while AI offers significant potential for improving HR decision-making and efficiency, its successful adoption requires robust data governance, ethical oversight, and capacity building for HR professionals.

Comparative Study On Green And Sustainable Practices In Indian Agri-Businesses: A Case Analysis Of EcoFarms India, Sresta (24 Mantra Organic), And WayCool Foods

Authors: Dr. Shweta B. Karadipatil, Shreyas Dewangan, Sandesh Rajput, Manish Sheramkar

Abstract: The study critically analyses three prominent Indian agri-business firms—EcoFarms India Ltd., Sresta Natural Bioproducts (24 Mantra Organic), and WayCool Foods—to evaluate their adoption of green and sustainable practices across production, processing, and supply chains. Using a comparative case study methodology, the paper examines environmental, social, and economic indicators to assess sustainability performance. Results reveal that while EcoFarms emphasizes farmer-centric organic exports, Sresta integrates sustainability through its farm-to-retail organic brand model, and WayCool drives technological and circular innovations in its logistics and value chain. The paper concludes that these three models collectively represent the emerging architecture of sustainable agribusiness in India, combining environmental stewardship with commercial scalability

Blueprint Analysis Of A Hybrid Solar-Inverter System For Uninterrupted Power Supply In Government Vocational School Workshops In Port Harcourt

Authors: Hachimenum Nyebuchi Amadi, Mutiu Oluseyi Lawal, Richeal Chinaeche Ijeoma

Abstract: This study presents a blueprint analysis of a hybrid solar-inverter system designed to provide uninterrupted power to government vocational school workshops in Port Harcourt. Frequent grid outages and unreliable supply compromise hands-on training, damage equipment, and reduce instructional hours, challenges that this research addresses by combining solar photovoltaic generation with intelligent inverter-based energy management and battery storage. The paper develops site-specific system architecture, sized through load surveys of typical workshop equipment (welding machines, drills, compressors, lighting and power tools), local solar resource assessment, and operational duty cycles. Key components include PV arrays; a bidirectional inverter/charger with surge-and-islanding capability, a battery energy storage system sized for critical loads during peak outage periods, and a supervisory energy management system that prioritizes loads, schedules charging, and supports seamless transition between grid, PV and battery modes. Using techno-economic modelling and scenario analysis, performance metrics system availability, autonomy duration, levelized cost of energy (LCOE), and payback period are evaluated under Port Harcourt’s irradiance and tariff conditions. Results indicate that a properly sized hybrid solution can achieve >99% uptime for critical workshop operations, reduce energy expenditures, and extend equipment life by smoothing supply disturbances. Sensitivity analysis shows that battery cost and duty-cycle demand are the most influential variables on financial viability. The blueprint also outlines installation best practices, safety and grounding considerations for educational environments, routine maintenance schedules, and guidelines for integrating the system into vocational curricula as a live teaching resource. The proposed blueprint offers a scalable, replicable model for other government training institutions aiming to improve practical training continuity, build local technical capacity, and progress toward resilient educational infrastructure.

DOI: http://doi.org/10.5281/zenodo.17325762

Role of Artificial Neural Network in Process Control and Monitoring in Bioprocessing

Authors: Bilal Abdullahi Shuiabu, Binghua Yan

Abstract: Bioprocessing plays an essential role in the large-scale production of biological products, where accurate monitoring and control are key for both yield and quality. This work aims to develop and assess a predictive framework based on Artificial Neural Networks (ANN) for estimating product yield in bioprocess operations. A multi-phase approach was implemented, beginning with data collection from online sensors and laboratory analyses, followed by preprocessing steps that included normalization, outlier removal, noise filtering, and feature engineering, utilizing dimensionality reduction through Principal Component Analysis. A hybrid ANN model was created, integrating Feed-Forward Neural Networks (FNN) for steady-state predictions, Long Short-Term Memory (LSTM) networks for learning temporal sequences, and Convolutional Neural Networks (CNN) for interpreting spectroscopic data.The model, trained using supervised learning and cross-validation, achieved strong predictive performance with a Mean Squared Error (MSE) of 1.0139 and a coefficient of determination (R²) of 0.9756, capturing 97.6% of yield variance. Predicted versus actual values showed high consistency, confirming robustness for real-time monitoring. Minor overfitting was observed at extreme values, highlighting the need for dataset expansion and regularization. Overall, the results demonstrate that ANN-based modeling effectively captures nonlinear dynamics in bioprocessing, supporting proactive optimization, disturbance detection, and integration into industrial-scale monitoring systems.

DOI: https://doi.org/10.5281/zenodo.17324969

Design Of A 1kVA Smart Inverter For Office Energy-Backup With IoT-Based Monitoring In MTN Offices In Port Harcourt

Authors: Hachimenum Nyebuchi Amadi, Solomon Philip Itsabuma, Richeal Chinaeche Ijeoma

Abstract: The reliability of power supply remains a critical challenge in Nigeria, particularly for corporate offices such as MTN in Port Harcourt, where continuous operation of ICT facilities and customer service platforms depends on stable electricity. Conventional inverters have provided backup solutions, but their limitations in efficiency, monitoring, and maintenance create gaps in long-term reliability. This study focuses on the design and simulation of a 1kVA smart inverter with Internet of Things (IoT)-based monitoring to address these challenges. The proposed system comprises a 24V DC battery bank, sinusoidal pulse width modulation (SPWM) control, an H-bridge inverter stage, LC filter, and a step-up transformer. MATLAB/Simulink was used for modelling and performance evaluation of the inverter, including harmonic analysis, efficiency testing, and assessment of output waveform quality. Simulation results indicated that the system delivered a stable 220V AC sinusoidal output with total harmonic distortion (THD) reduced to acceptable IEEE standards. The inverter achieved peak efficiency of 88.7% at 700W loading conditions and maintained above 80% efficiency across varying loads. The integration of IoT-enabled sensors enabled real-time monitoring of voltage, current, and battery state of charge through a cloud-based dashboard, facilitating predictive maintenance and informed decision-making. The findings demonstrate that a smart inverter with IoT integration provides a sustainable and scalable solution for office energy-backup applications, supporting uninterrupted operations in environments with frequent grid outages.

DOI: http://doi.org/10.5281/zenodo.17325564

Zinc Oxide Nanophotocatalysts In Textile Dye Degradation: A Mini Review

Authors: Jawaria Ehsan, Abdul Ghafoor, Rakia Ali, Manahal Abbas, Sadia Shabbir, Shahzaib, Nouman Ahmad, Muhammad Azeem Akbar, Amin Abid

Abstract: Water is the essence of the universe. In recent years, Industrialization have put adverse impacts on our environment especially aquatic ecosystem. Industrial dyes are one of the main waste pollutants [1]. Dyes contaminate water ecosystem and this effects aquatic habitat. These dyes also have severe impacts on human health. So, this is alarming to treat wastewater containing hazardous coloring agents(dyes) specially belonging to the textile industry. The textile industry is of the biggest contributors for water pollution, due to large amounts of synthetic dyes released into water bodies and enhance pollution. These dyes can cause serious health and environmental issues because they resist natural degradation. The best solution to this problem is photocatalytic degradation in which nanomaterials help to the breakdown of these dyes which are basically pollutants. Zinc oxide nanoparticles have gained a lot of attention among various photo catalysts due to their strong photocatalytic activity, cost-effectiveness and environment friendliness. This review explores how the zinc oxide nanoparticles break down the dyes, the factors that influence their efficiency, and recent developments in improving their performance. In this review we also highlight future directions, which include green synthesis methods and integration of zinc oxide with renewable energy sources for applications that are more environmentally friendly.

DOI: https://doi.org/10.5281/zenodo.17334811

Machine Learning Algorithms For Financial Risk Assessment In Indian Institutions: A Comprehensive Analysis Of Performance, Implementation, And Regulatory Compliance

Authors: Nijrup S. Visani

Abstract: The integration of machine learning (ML) algorithms in financial risk assessment has emerged as a transformative force within Indian banking and financial institutions. This study presents a comprehensive analysis of ML algorithm performance, implementation strategies, and regulatory compliance frameworks specifically tailored to the Indian financial ecosystem. Through systematic evaluation of seven primary ML algorithms—Neural Networks, Random Forest, Support Vector Machines (SVM), Logistic Regression, Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—this research demonstrates significant performance improvements over traditional risk assessment methods. Neural Networks achieved the highest accuracy of 91.5% with precision of 89.8% and recall of 87.7%, while Random Forest demonstrated robust performance at 90.7% accuracy. The study reveals that ML-based approaches improve risk assessment accuracy by 16-22 percentage points across credit risk (91% vs 75%), market risk (88% vs 70%), operational risk (85% vs 65%), liquidity risk (87% vs 68%), and fraud risk (94% vs 72%) compared to traditional methods. Analysis of regulatory compliance shows a dramatic improvement from 25% in 2021 to 95% in 2025, coinciding with the deployment of over 820 ML models across Indian financial institutions. The research incorporates case studies from major Indian banks including HDFC Bank, ICICI Bank, and State Bank of India, demonstrating practical implementation success with operational efficiency improvements of 40-65%. The study addresses the Reserve Bank of India's Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) released in August 2025, highlighting the regulatory landscape's evolution toward ML adoption. This research contributes to the growing body of knowledge on ML applications in financial services while providing actionable insights for practitioners, regulators, and researchers in the Indian financial sector.[1][2][3][4][5][6][7][8][9][10]

A Survey On Digital Health Care Data Analysis Techniques For Developing Machine Learning Models

Authors: Khaleel Khan Mohammed

Abstract: Data is expanding rapidly, driving advances in technology and algorithms. In healthcare and biomedicine, this growth enables early disease prediction, better patient care, and improved community services through Machine Learning and AI. Since disease patterns vary across regions, AI adoption has the potential to radically transform the entire healthcare industry. This paper has brief various models proposed by the researcher for disease detection. Techniques of machine learning for disease prediction was elaborate in the paper. Challenges of prediction models for accuracy was summarize in the work under different condition. Finally paper has brief some of major evaluation parameters of for comparing healthcare models

DOI: http://doi.org/10.5281/zenodo.17339813

Examining The Inhibitive Effect Of Alanine On Corrosion Of Aluminium In Acidic Medium

Authors: Erienu Obruche Kennedy,, Ikechukwu S.Chikwe, Eziukwu. Chidi Charles, Paul Nnamdi Ogbu, Ibe Michael Onyebuchi, Amaechi Justice Nzegwu

Abstract: The study examined how alanine inhibits corrosion of aluminium coupons in an acidic environment using a 0.5M HCl solution. The weight loss method was used for synthesis. Aluminium sheets with a purity of 98.98% were cut into rectangular coupons measuring 2cm x 4cm and were 1.0 mm thick. The total surface area of each coupon was 8 cm2, which was reduced by washing with absolute ethanol and drying in acetone. These rectangular coupons were fully immersed in the acid solution with different concentrations of the inhibitor for time intervals ranging from 30 minutes to 3 hours. The inhibitor at the optimal concentration reduced aluminium corrosion by about 14.6%, and the inhibition efficiency varied significantly with different alanine concentrations. The presence of heteroatoms in the inhibitor helped absorb onto the metal surface, displacing water molecules and creating a protective barrier. The findings indicated that alanine in 0.5M HCl has some inhibiting properties for lowering the corrosion rate of aluminium. However, the results also showed that alanine is not an effective inhibitor for aluminium corrosion due to its low inhibition efficiency.

DOI: https://doi.org/10.5281/zenodo.17356213

Life Cycle Assessment And Cost Analysis Of Green Concrete Mixtures For Sustainable Construction Using SimaPro Software.

Authors: Sakshi Pazai, Dr. Ketan A. Salunkhe, Sachin Pagar

Abstract: This study assesses green concrete made by replacing Ordinary Portland Cement (OPC) with Ground Granulated Blast Furnace Slag (GGBS) and Fly Ash. Experimental tests and life cycle analysis using SimaPro v9 and Ecoinvent show the mixes meet M30 compressive strength while cutting CO₂ emissions, acidification, and resource depletion. Reduced clinker use lowers embodied energy and costs, enhancing economic feasibility. Findings align with global research, confirming technical reliability and sustainability

DOI: http://doi.org/10.5281/zenodo.17356530

Sleep As An HR Metric: Should Companies Track Rest To Boost Productivity? (Investigating HR Programs That Encourage Healthy Sleep Habits For Performance). A Non-Doctrinal Study

Authors: Gowtham AG, Dr S.Maruthavijayan

Abstract: In today’s competitive and high-pressure work environments, employee well-being has emerged as a key factor influencing organizational success. Among the various determinants of well-being, sleep plays a vital yet often underestimated role in shaping productivity, creativity, and overall job satisfaction. This research explores the concept of sleep as a measurable Human Resource (HR) metric and investigates whether companies should track or promote healthy sleep habits to enhance performance. The study examines the link between sleep quality and work efficiency, analyzing how insufficient rest contributes to errors, stress, absenteeism, and burnout. It also reviews existing HR wellness programs, such as flexible work hours, nap spaces, and wearable sleep tracking, implemented by leading global organizations. Furthermore, the research evaluates the ethical implications of monitoring employees’ rest patterns, focusing on issues of privacy, autonomy, and data security. Through literature analysis and employee survey data, this study aims to identify the potential benefits, challenges, and limitations of integrating sleep-focused initiatives into HR management. The findings suggest that while sleep tracking and wellness incentives can foster a more engaged and productive workforce, they must be implemented with ethical safeguards and voluntary participation.

A Study On Extracellular Tyrosinase Producing Actinomycetes

Authors: Harisha. H, Shilpa M.P, Jyothi Hiremath, S. Shivaveerakumar

Abstract: Actinomycetes isolation, characterisation, and biotechnological uses, with a focus on Streptomyces species for the synthesis of extracellular tyrosinase. Using morphological, biochemical, and molecular criteria, actinomycetes were identified from samples taken from a variety of habitats, including freshwater, soil, and marine systems. Their capacity to produce enzymes, beneficial chemicals, and most notably melanin through copper-dependent tyrosinase activity is highlighted in the paper. Enzyme yield and melanin synthesis were maximized by optimizing fermentation and physicochemical conditions. Important discoveries include the discovery of strong Streptomyces isolates with high tyrosinase activity, which have uses in environmental remediation, bioprocessing, and biosensors. Techniques including qRT-PCR, SDS-PAGE, and UV-V is spectrophotometry confirmed the genetic expression, activity, and purification of tyrosinase. This study highlights the strain-specific adaptability of actinomycetes, indicating that more screening and genetic advancement will enable them to reach their full potential in industry and medicine. The complete examination of the attached files served as the foundation for all findings and conclusions, guaranteeing the incorporation of all pertinent information

DOI: http://doi.org/10.5281/zenodo.17357348

Architecture As Cultural Storyteller: Weaving Narrative Spatial Imagination Into Libraries And Museums

Authors: Ar. Ghazal Gujral

Abstract: What if museums and libraries could do more than store books and artifacts – what if they could move us, draw us into the heart of stories, and make memory feel present and alive? This essay explores how “narrative spatial imagination”, a concept inspired by both literature and architectural theory, can transform public cultural spaces into immersive storytellers. By analyzing diverse global architectural examples, the paper demonstrates how spatial storytelling can transform public cultural institutions into meaningful environments that foster identity, discovery, and connection. The essay concludes by highlighting the transformative potential of architecture as a living storyteller.

DOI: http://doi.org/10.5281/zenodo.17358403

Real Time Auction Monitoring System

Authors: Satyam Kumar, Rohan Asawale, Himanshu Tiwari, Rajeshwari Girase, Priti Patel

Abstract: The traditional digital auction houses started conduct- ing online auction systems driven by online bidding tech- nology which lined auction systems with a higher rate of accessibility compared to previous online auction systems. Despite faster serving of online auction systems, it is im- portant to know that most auctions operate at a net loss due to a lack of divorce settlements over unclaimed as- sets. Instead of focusing on divorce settlements, many operators of online auction systems prefer to obscure un- claimed assets by focusing on technological services and ‘the art of transaction’. With a special emphasis on se- curing, speedy, and reliable transactions, highly auction- able resources are put up for auction to garner competitive pricing. This serves as a loss-leader for the online auction houses and unclaimed assets, which otherwise would make a loss, serve as the resource. Instead, focused technology which enhances the competitive auction proves to attract more bidders and garner higher net profits. Available tech- nologies such as Real-Time Bidding (RTB) become a more focused, streamlined approach to garner higher net profits. The climax of most online auction houses is regret as they ‘sell’ information at a loss to attract bidders and gar- ner subsequent revenue through premium services. Auc- tion houses which operate at a loss defy the simple eco- nomic principle of supply and demand. This indicates a lack of competition amongst auction houses, tellingly termed as the auction cartel. The net outcome of such a cartel manifests as a loss of economic resources. Focusing on unclaimed assets, the auction cartel proves the con- cept of economically valuable information. Over-relying on RTC auction systems renders auction houses unable to accurately auction minimal resource packages, leading to economically valuable information. Despite serving as a platform, the auction cartel offers valuable resources to researchers and professionals focused on e-auction systems.

Zero Trust Security Model for Microservices: Principles, Benefits, and Challenges

Authors: Md. Abdul Momin, Md. Ezharul Islam

Abstract: Microservices are widely used to build modern applications, but their distributed design brings serious security risks that traditional perimeter-based models cannot handle. Once attackers bypass the perimeter, they can move across services unchecked. Zero Trust Architecture (ZTA) addresses this problem with its “never trust, always verify” principle. It secures microservices through continuous authentication, least-privilege access, micro-segmentation, and encrypted communication. This paper examines the core principles of ZTA, its primary benefits, such as enhanced security, regulatory compliance, resilience, and scalable security, and the challenges of adoption, including complex policy management, performance overhead, integration with legacy systems, skill shortages, and a lack of standardization. To overcome these barriers, best practices like Zero Trust Architecture, enabling tools, automated policy management, and unified governance are discussed. The paper also highlights the role of AI and ML in making ZTA smarter through adaptive authentication and real-time threat detection. Overall, ZTA offers a flexible and powerful approach for protecting microservices in cloud-native environments.

DOI: https://doi.org/10.5281/zenodo.17365282

Feasibility Studies On Welding Of Titanium With Stainless Steel With Different Filler Material Combination Using Electron Beam Welding (EBW)

Authors: Dr Suresh Arjula, Vankudoth Ravinder

Abstract: This review paper unequivocally highlights the essential role of machine learning models in enhancing weld quality and optimizing welding processes, with a strong focus on the revolutionary applications of ML in electron beam welding. By adopting these innovations, the industry can confidently unlock new levels of efficiency and excellence in welding technology. The formation of brittle Ti–Fe intermetallic compounds (IMCs) and the mismatch in thermal properties make welding titanium and stainless steel difficult. These dissimilar metals can be joined using Electron Beam Welding (EBW), which is done under high vacuum and provides precise control with little contamination. Main objective statement: The objective of this project is to investigate the feasibility of welding titanium alloy (Ti-6Al-4v) with stainless steel (304L/316L) using electron beam welding and different filler materials(ni,cu,v). The study aims to analyze the effect of filler materials o the weld quality ,micro structure, and mechanical properties of the joints in order to identify the most suitable filler for high- strength applications. Main task the main task of this project is to weld titanium alloy (Ti-6Al-4v) with stainless steel (304L/316L)using electron beam welding and different filler materials (nickel,copper, vanadium). The welds will be analyzed for microstructure and mechanical properties to determine the most suitable filler material for producing strong and defect-free joints. filler materials (nickel, copper, and vanadium) to fuse titanium alloy (Ti-6Al-4v) with stainless steel (304L/316L). In order to identify the best filler material for creating robust and flawless joints, the microstructure and mechanical characteristics of the welds will be examined.

Mixed Nanoferrites: Fabrication and Uses in Biomedical And Sensor Domains

Authors: Dr. S. Thenmozhi, Dr. S. R. Chitra2

Abstract: For several novel applications, the synthesis and characterization of nanoferrites are crucial. Their synthesis techniques have a significant impact on their electrical and magnetic characteristics, which are important in many applications. The resultant ferrites can have different characteristics depending on the process used, including sol-gel (SG), SG auto-combustion, self-combustion, co-precipitation, reverse micelle, micro-emulsion, glass crystallization, precursor, and hydrothermal procedures. The synthesis, characterization, and applications of mixed nanoferrites with the formula MFe2O4 are reviewed in this study. M can represent a variety of elements, including Cu, Fe, Mg, Mn, Ni, and others. Excellent magnetic characteristics, such as strong coercivity, high anisotropy, high Curie temperature, and mild saturation magnetization, are displayed by nano-sized ferrites. They also possess noteworthy mechanical qualities including considerable hardness and desired electrical qualities like high electrical resistance and minimal eddy current losses. According to our investigation, mixed nanoferrites show better qualities than single-component ferrites, which make them attractive options for a range of cutting-edge applications. This paper tries to give a comprehensive overview of the characteristics, synthesis methods, and possible uses of mixed nanoferrites, highlighting the latter are potential for major practical effect. We concentrate on the effects of these materials' form, size, and cation dispersion on their electrical and magnetic characteristics. Furthermore, we investigate the possible uses of mixed nanoferrites in a number of domains, such as: Superior magnetic and dielectric materials for electronics and sensors High-performance magnetic resonance imaging (MRI) contrast agents Biomedical uses, such as medication administration and the management of hyperthermia.

The Transformative Role Of Artificial Intelligence In Higher Education And Research & Development: Opportunities, Challenges, And Future Directions

Authors: Sateesh Kumar Beepala

Abstract: Artificial intelligence (AI) is rapidly transforming higher education (HE) and research and development (R&D), enabling individualized learning, automating administrative procedures, and speeding up research workflows from literature discovery to data analytics. This review summarizes recent empirical and review literature (2019-2025), identifies key opportunities (adaptive learning, intelligent tutoring, research assistance, administrative automation), and highlights major challenges (academic integrity, bias and fairness, data privacy, governance, workforce readiness). We suggest a framework for responsible AI adoption that strikes a balance between educational objectives, technical capabilities, and ethical precautions, as well as research priorities and policy recommendations for institutions and donors. Finally, the article provides realistic implementation instructions and assessment criteria to assist universities and research institutions in securely and effectively integrating AI.

A Procedural Framework For The First Filling Of The Water Conductor System At The Tehri Pumped Storage Plant (4×250 MW)

Authors: Rajeev Prasad, Vinod Jhinkwan

Abstract: The first filling of a Water Conductor System (WCS) is the most critical non-operational test for any large-scale underground hydropower or pumped storage project. This process validates the structural integrity, water tightness, and geo-mechanical interaction of the system with the surrounding rock mass under hydrostatic pressure for the first time. This paper presents the philosophy and step-by-step methodology adopted during the first filling of the water conductor system at the 1000 MW Tehri Pumped Storage Plant. It also provides valuable insights and reference information for conducting first filling operations in similar large-scale hydroelectric projects. As a critical pre-commissioning activity, the first filling is designed to test the integrity and performance of all hydraulic components under controlled conditions for the first time. The methodology adheres to Indian Standard codes (IS 12633:1989) and project-specific requirements, implementing a phased, step-wise approach to pressurize the system gradually. The process is segmented into four distinct stages: Stage 1 involves filling the Tail Race Tunnel (TRT) up to the downstream surge shaft; Stage 2 covers the Head Race Tunnel (HRT) from the intake to the Butterfly Valve Chamber (BVC); Stage 3 entails filling the pressure shafts from the BVC to the Main Inlet Valve (MIV); and Stage 4 completes the balance filling of the upstream surge shaft. This paper provides detailed volumetric computations, filling discharge rates, prescribed waiting periods for strata stabilization, and a complete timeline for each stage. The total cumulative filling time, inclusive of all mandatory stabilization gaps, is calculated for two potential upper reservoir level scenarios (EL. 775 m and EL. 780 m), ensuring a controlled and safe commissioning process for the Tehri PSP.

Challenges And Bottlenecks In Integrating AI To Teach 21st-Century Skills In Primary, Middle & Secondary Education In Rural Schools Of District Bandipora, Kashmir, India.

Authors: Javid Ahmad Bhat

Abstract: The integration of Artificial Intelligence (AI) into teaching 21st-century skills—critical thinking, creativity, communication, collaboration, and digital literacy—presents transformative opportunities for learning. Yet rural regions such as District Bandipora in Jammu & Kashmir face acute barriers that limit adoption and impact. This study examines infrastructural, pedagogical, socio-cultural, administrative, and equity-related challenges to integrating AI-driven pedagogy in primary, middle & secondary schools across Bandipora. Drawing on district-level facts (approximately 850 schools; only 60 ICT labs and about 120 schools with any computer facility), direct observations, stakeholder interviews, and a review of relevant literature, the paper maps the main bottlenecks: lack of computers and technological facilities in schools and homes; non-availability of reliable internet; scarcity of well-trained teachers in AI and digital pedagogy; limited manpower and administrative bottlenecks; disproportionate and irregular parent–teacher meetings (PTMs); and low parental interest in technology-enabled learning. The majority of students lack mobile devices or internet access at home, which exacerbates inequities and weakens the continuity of learning beyond school. We propose a phased, equity-focused strategy combining low-bandwidth and offline AI tools, intensive teacher professional development, infrastructure and maintenance planning, community engagement to boost parental interest, and governance reforms to address procurement, data protection, and human resource constraints. The paper concludes with a prioritized action plan for district-level implementation and an agenda for evaluation and future research..

Mechanochemical Synthesis of Ultrafine Cuprous Oxide Using Glucose as Reducing Agent

Authors: YongChol Kim, ChungIl Kim, JongGuk Kim

Abstract: Cuprous oxide is used as an antimicrobial and fungicide of fruit trees and is widely used as a catalyst for photodegradation of organic contaminants in the visible light region. Cuprous cuprous oxide was successfully prepared by simple mechanochemical method at any place by varying the amount of reactants added in the system consisting of copper sulphate, sodium hydroxide and glucose without any heat source or complex device, the mode of addition of reactants, the grinding time and the aging conditions of the product. First, CuSO4∙5H2O and NaOH were ground to a size of about 100 μm, respectively. Then, the ground copper sulfate was homogeneously mixed with glucose powder, a reducing agent and dodecyl sodium sulfate (SDS), a dispersing stabilizer. It was then mixed with a suitable amount of the ground NaOH and underwent a simple mechanochemical method of ball milling with an agate mortar to prepare ultrafine cuprous oxide in solid phase consisting of copper sulfate, sodium hydroxide and glucose. The results show that ultrafine cuprous oxide was obtained after 30 min of ball-milling without combustion of glucose as reducing agent when adding NaOH twice. The as-prepared ultrafine cuprous oxide was characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) and it correspond to a cubic structure with size of 30-40 nm.

DOI: https://doi.org/10.5281/zenodo.17424480

Passenger Experience And Facilities In Indian Railway Stations

Authors: Ashitosh Honmale

Abstract: Railway stations are important public spaces in India. They shape the comfort and satisfaction of millions of travelers every day. Passenger experience depends not only on train services but also on the availability and quality of station facilities. This paper looks at passenger experience regarding amenities at Indian railway stations. It compares regional stations like Parbhani Junction and Akola Junction with urban hubs such as Chhatrapati Shivaji Maharaj Terminus (CSMT, Mumbai) and New Delhi Railway Station. Findings from studies and observations show that while urban stations provide modern facilities like escalators, digital display boards, and mechanical cleaning, regional stations often lack basic amenities, especially sanitation, accessibility, and seating. Closing this gap is crucial for improving overall passenger satisfaction across Indian Railways.

Architecture as a Cultural Storyteller: Weaving Narrative Spatial Imagination into Libraries and Museums

Authors: Ar. Ghazal Gujral

DOI: http://doi.org/10.5281/zenodo.17358403

Aquatic Macrophytes As Natural Filters For Microplastic Pollution In Freshwater Ecosystems

Authors: Prateek Srivastava, Prishita Singh, Abhishek Kumar Sharma

Abstract: Microplastic pollution poses a growing threat to freshwater habitats, impacting aquatic life and ecological balance. Conventional removal methods are often tended to be expensive and ineffective, prompting interest in environment friendly and sustainable alternatives. This review highlights the potential of aquatic macrophytes as natural biofilters for microplastic remediation. It covered the sources and characteristics of microplastics influencing their interaction with plants, and the primary removal mechanisms, including physical entrapment, surface adsorption, and root-mediated retention. It emphasizes the role that rhizosphere and biofilm microbial communities play in aggregation and degradation processes. Aspects that impact removal efficiency are being examined, including plant morphology, microplastic properties, and water factors. The ecological implications and potential risks of microplastic–macrophyte interactions are also considered. Finally, significant research gaps are identified, highlighting the need for long-term, field-based, and integrative studies. Overall, macrophytes offer a promising, sustainable approach for mitigating microplastic contamination in freshwater environments.

DOI: http://doi.org/10.5281/zenodo.17432321

Barium Titanate As A Sustainable Energy Harvester: A Review On Materials, Mechanisms And Devices

Authors: Manjit Borah

Abstract: The increasing demand for sustainable, miniaturized and eco-friendly power sources has spurred significant interest in nanogenerators for self-powered electronics. Among the materials explored, barium titanate (BT), a lead-free ferroelectric perovskite has emerged as a promising candidate owing to its high dielectric constant, strong piezoelectric response, and environmental compatibility. This review highlights the evolution of BT from its early discovery as a ferroelectric ceramic to its modern applications in energy harvesting systems. The fundamental aspects of BT, including its perovskite crystal structure, ferroelectric behavior, and piezoelectric mechanism, are discussed to establish its role as an effective energy transducer. Strategies for enhancing its modest intrinsic piezoelectric properties such as domain alignment, chemical doping, phase boundary engineering, grain texturing and composite or nanostructure design are thoroughly examined. Advances in device engineering have demonstrated the utility of BT nanostructures, including nanowires, nanotubes, and thin films, in piezoelectric nanogenerators (PENGs), triboelectric nanogenerators (TENGs) and hybrid nanogenerators (HNGs). Comparative insights into these systems reveal BT’s dual role as both a primary energy harvester and a dielectric performance enhancer. Finally, the review underscores BT’s technological relevance in wearable electronics, biomedical implants and Internet-of-Things (IoT) devices, positioning it as a sustainable alternative to lead-based ferroelectrics for next-generation self-powered systems. PACS Nos.: 77.84.-s, 77.65.-j, 84.60.-h, 84.60.Rb, 81.07.-b

DOI: https://doi.org/10.5281/zenodo.17439503

Computer-Aided Design And Manufacturability Analysis Of An Automotive Door Trim Panel Using CATIA V5

Authors: R. Sridhar, S. Jacob, T. GopalaKrishnan, K. Karunakaran, G. Sathish Kumar, Aravind Y, Arun Vikram M P

Abstract: The growing emphasis on lightweighting, cost efficiency, and ergonomic refinement in modern vehicles has intensified the need for computer-aided product design methodologies in interior component development. This study presents the computer-aided design (CAD) and manufacturability analysis of an automotive door trim panel using CATIA V5. The research outlines a structured workflow beginning with the import of a Class-A aesthetic surface, followed by the generation of Class-B and Class-C surfaces, definition of tooling axes, and incorporation of essential engineering features such as doghouses, push pins, heat stakes, and gussets to ensure assembly integrity and durability. A bottom-up assembly approach was employed to integrate the armrest, lower substrate, and map pocket into a unified structure. The manufacturability of the resulting geometry was verified through draft analysis, confirming adequate ejection feasibility and surface continuity for injection moulding. The findings highlight that applying a parametric and feature-driven CAD approach significantly enhances precision and design efficiency, ensuring compliance with design-for-manufacturing (DFM) standards. Furthermore, the proposed workflow demonstrates potential for reducing tooling errors, optimizing material usage, and supporting sustainable production by minimizing rework iterations. This research provides a replicable framework for transitioning conceptual surface designs into manufacturable components, with broader implications for ergonomics, cost reduction, and eco-friendly automotive interior design.

Multiple Disease Prediction using Machine Learning Algorithm

Authors: Binay P, Anil H, Ankith G, Tanya B, Prof. Arathi H L

Abstract: Machine learning techniques like Logistic Regression, The Support Vector Machine i.e. (SVM) classifiers, The Random Forest classifiers i.e. (RFC), The Decision Tree classifiers i.e. (DTC), and K-Nearest Neighbor (KNN), as well as basic metrics like heart rate, blood pressure, cholesterol, and pulse rate, the goal of this project is to forecast the occurrence of various diseases like diabetes, heart disease, and Parkinson's disease. The most accurate calculation is used to train the dataset, while Python pickling and streamlit are used to record the model behavior. By entering pertinent disease- related information, the initiative seeks to determine the risk factors for the diseases and provide users a prognosis of whether they have the condition or not. This program can assist people in keeping an eye on their health and taking the necessary actions to prolong.

DOI:

Nano-Engineered Atomic Clocks For Ultra-Precise Military Positioning

Authors: Kabir Kohli

Abstract: Nano-engineered atomic clocks represent a major leap in precision timing, especially for military applications where size, weight, and power (SWaP) constraints are critical. By leveraging nanotechnology such as quantum dots, nanophotonics, and MEMS, these clocks offer ultra-precise timing in compact formats suitable for GPS-independent navigation, encrypted communications, and weapon synchronization. Traditional atomic clocks, while highly accurate, are often too bulky and energy-intensive for deployment in mobile or embedded military systems. In contrast, nano-engineered versions benefit from advanced material science, offering enhanced robustness, energy efficiency, and miniaturization. This paper delves into the core design principles of atomic clocks, elucidates the role of nanotechnology in transforming these systems, and explores their applications in military contexts. The discussion covers key nanotechnological components, such as MEMS for integration, quantum dots for enhancing signal fidelity, and nanophotonics for precise light manipulation. Case studies from DARPA, NIST, and ESA demonstrate real-world implementations and validate the technology's viability. Despite challenges such as fabrication complexity, radiation sensitivity, and thermal management, the future trajectory of nano-engineered atomic clocks appears promising. With developments in AI-driven stabilization and integration into quantum computing and communication systems, these clocks are poised to become indispensable assets in next-generation defence infrastructure. Their ability to function independently of GPS in contested or denied environments grants them a strategic edge, fundamentally redefining how military forces navigate, synchronize, and communicate in modern warfare.

DOI: http://doi.org/10.5281/zenodo.17474418

Optimization Of Production Routes Of Various Radioisotopes Used For Industrial Applications_405

Authors: Heera Singh

Abstract: Radioisotopes are radioactive isotopes of an element that emits radiations to achieve stability through processes like alpha, beta, and gamma decay. They are used in industry to trace, test and also in several industrial processes and space operations. Radioisotopes are use in RTGs (Radioisotopes thermoelectric generators). RTGs provide electric power using heat from the decay of radioactive isotopes like Plutonium-238 (in the form of plutonium oxide) etc. Works on the principal of Seebeck effect. The selection of fuels for RTGs, there are some criteria that isotopes must have like ability to produce high radiation energy, long half life for regular production of energy, high heat power to mass ratio and cross section. This study is focused mainly on study of cross section area of radioisotopes. The cross section is very important factor, it represents the probability of a nuclear reaction occurring when a particle such as neutron interacts with the nucleus of an atom. This study calculates the cross-sectional area of some radioisotopes with the help of statistical model code EMPIRE 3.2 and compare it with the experimental data available.

Adversarial Attacks On AI-Based Security Systems

Authors: Anakha R Varma, Angelina Sennichan, Ujjwal Agrawal, Shrey Keyal, Shashank Modi, Dr. Umamaheswari M

Abstract: Generative Adversarial Networks (GANs) have rapidly become a cornerstone of modern artificial intelligence research since their introduction in 2014 [1][2][3]. By pitting two neural networks—the generator and the discriminator—against each other in a zero-sum game, GANs have enabled breakthroughs in realistic data generation, computer vision, natural language processing, and creative AI [2][10]. This adversarial learning paradigm not only allows machines to learn distributions of real-world data but also to generate synthetic data that is almost indistinguishable from reality [1][3]. In this paper, we provide a structured overview of GANs, covering their core architectures, evolution into advanced variants, real-world applications, algorithmic workflow, and challenges [10][14]. We also analyze open research issues and future directions to guide practitioners and researchers [5][15]. To make this study comprehensive, references are drawn from 15 peer-reviewed sources across computer science, engineering, and applied domains, and illustrative figures of GAN workflow, algorithm, and application scenarios are included to aid conceptual understanding [8][11].

DOI: https://doi.org/10.5281/zenodo.17452368

Nutrisense-Food And Nutrition Hub

Authors: Asst.Prof.Nidhi Pandey, Sahil Harkude, Dhruv Tiwari, Chaitanya Darekar, Vaibhav Khatore

Abstract: During our research, we discovered that while tracking nutrition is essential for maintaining good health, existing apps frustrate users with tedious manual data entry requirements. This led us to de- velop NutriSense, an innovative solution that eliminates the hassle by automatically analyzing meal photographs. Our platform integrates Google’s Gemini AI to recognize food items from images and in- stantly compute comprehensive nutritional breakdowns including calories, proteins, carbohydrates, and fats. Additionally, we implemented an intelligent chatbot that provides users with personalized dietary guidance based on their eating patterns. The application runs seamlessly across mobile devices and desktop computers through responsive web design, built using React for the frontend and Node.js for backend operations. Our evaluation phase revealed promising results, with the system accurately iden- tifying various food types and delivering reliable nutritional calculations. The platform enables users to visualize their daily nutritional intake, analyze long-term dietary trends, and receive customized rec- ommendations from our AI nutritionist. Through this work, we demonstrate the practical potential of combining artificial intelligence with modern web development to create accessible tools that genuinely support healthier lifestyle choices.

DOI: https://doi.org/10.5281/zenodo.17452604

Smart Blind Stick With Automated Navigation, Ditch And Object Detection System

Authors: Dr. Pankaj Malik, Mudita Sharma, Kabir Jaiswal, Ayush Modi, Priyansh Jain

Abstract: This research presents the design and implementation of a smart blind stick aimed at improving the mobility and safety of visually impaired individuals through automated navigation, obstacle detection, and ditch recognition. The system integrates ultrasonic sensors to detect frontal and lateral obstacles, infrared sensors to identify sudden drops such as ditches or stair edges, and a GPS module for real-time navigation. A microcontroller processes the sensor data and triggers feedback mechanisms including a vibration motor and buzzer to alert the user. The prototype was tested in both indoor and outdoor environments with varied terrain and object placements. Experimental results show that the system achieved an obstacle detection accuracy of 98% for static objects and 91% for moving obstacles. Ditch detection was successful in 95% of test cases, while GPS-based navigation delivered 93% accuracy in open environments. The average system response time ranged from 0.3 to 0.5 seconds, offering real-time feedback and effective user alerts. The results confirm the system’s effectiveness in enhancing mobility, reducing accident risk, and promoting independent navigation for visually impaired users.

A Comprehensive Analysis Of Algorithmic Strategies For Long-Term Investment Optimization

Authors: Smit Khandelwal, Bhavyam Sanghavi, Kiran Kankariya, Ayush Agrawal, Prof. Parag Jambhulkar

Abstract: Long-term investment strategies have been an essential component of portfolio management for both institutional and retail investors. With advancements in computational finance and artificial intelligence, algorithms play a crucial role in optimizing portfolio allocation, risk management, and return maximization. This survey paper provides a comprehensive overview of long-term investment algorithms, including traditional models, rule-based strategies, and machine learning approaches. Furthermore, it compares their advantages and disadvantages, highlighting areas where algorithmic methods can outperform human decision-making and where limitations still exist.

Self-Adaptive Quantum Buffering For Cross-Domain Networks

Authors: Manoj R Chakravarthi, Sunil Kulkarni

Abstract: As networks continue to grow in complexity, managing network congestion and reducing packet loss has become a critical challenge. Traditional methods for buffer management often fail to adapt quickly to dynamic and unpredictable traffic patterns, leading to poor network performance, especially in cross-domain networks (e.g., between ISPs, data centers, and wireless networks). This paper proposes a novel approach, Self-Adaptive Quantum Buffering for Cross-Domain Networks (SAQBS), which combines quantum computing for congestion prediction with machine learning algorithms for real-time buffer management. By predicting network congestion more accurately, the proposed system reduces packet loss, increases throughput, and lowers latency compared to traditional methods. Through detailed simulations, we show that this approach provides significant improvements in network performance.

DOI: http://doi.org/10.5281/zenodo.17455043

Integrated Enhancement Systems For Next Generation Legged Robotics

Authors: Dhrisya S. Anil, Densil Emmanuel, Gokul Krishna G, Vishnu S, Dr Abhilash S. Vasu

Abstract: Next-generation legged robots are evolving into highly intelligent, adaptable, and resilient systems through the integration of advanced add-on components spanning multiple technological domains. Advanced Sensor Systems such as 3D LiDAR, stereo vision, IMUs, and environmental sensors enable precise perception, mapping, and situational awareness across diverse terrains. Power and Energy Modules, including high-density batteries, supercapacitors, and energy-harvesting systems, ensure extended operational endurance and efficient energy management. AI and Control Add-Ons, like onboard edge processors and reinforcement learning modules, enhance autonomy, adaptive gait control, and real-time decision-making capabilities. Meanwhile, Mechanical and Structural Enhancements—such as compliant joints, soft actuators, and modular leg designs—improve mobility, flexibility, and durability during locomotion. Complementing these are Communication and Networking Modules, including 5G/6G and mesh network radios, which facilitate high-speed data exchange, swarm coordination, and remote teleoperation. Safety and Redundancy Systems, featuring fail-safe controllers, predictive maintenance tools, and collision avoidance sensors, ensure reliability and secure human-robot coexistence. Additionally, Specialized Functional Add-Ons like manipulators, payload mounts, and environmental sampling kits expand the robots mission versatility, while Human–Robot Interface (HRI) Add-Ons, including AR/VR interfaces, voice and gesture recognition, and haptic feedback systems, enable intuitive and immersive interaction. Together, these integrated technologies form a comprehensive framework for developing the next generation of legged robots—capable of autonomous, adaptive, and safe operation in complex real-world environments.

DOI: https://doi.org/10.5281/zenodo.17461921

Network Intrusion Detection Using Machine Learning: A Comparative Study Of Logistic Regression, KNN, And Random Forest

Authors: Aparna Mote, Vaishnavi Patil, Sayali Pawar, Shruti Pawar, Saundarya Surana

Abstract: This paper introduces EcoXchange, an AI-based platform with the goal of supporting sustainable practices through thrifting, gamification, and waste categorization.The platform stimulates reuse by allowing users to trade pre-owned products in a thrifting marketplace. Gamification tactics, including reward-based models, are incorporated to maximize user participation in sustainable activities. Moreover, artificial intelligence technologies such as image recognition are used for waste classification, enhancing the efficiency of recycling. The study examines user activity in the thrifting market, participation through gamified functions, and the operation of AI-based waste sorting. The resultsdemonstrate the promise of integrating thrifting, gamification, and AI to enable environmental sustainability.

Integration Of Real-Time NetFlow Streaming Analytics For Adaptive Network Attack Detection

Authors: Pavithra V, Dr. D. Rajinigirinath, T. Saranya

Abstract: In modern digital infrastructures, network intrusion detection systems (NIDS) require real-time capabilities to effectively identify and prevent ongoing cyberattacks. This paper presents the integration of real-time NetFlow streaming analytics into an adaptive AI-based attack detection framework. Traditional machine learning approaches are often limited to offline datasets, making them less effective for live monitoring. The proposed system employs streaming NetFlow data collection, continuous preprocessing, and online inference using Random Forest and Support Vector Machine models. A real-time analytics dashboard built using Streamlit provides live alerts and visualization of suspicious flows. Experimental results indicate a detection latency reduction of 35% and a 10% improvement in adaptive accuracy when using continuous learning updates. This work demonstrates that integrating real-time NetFlow analytics significantly enhances the responsiveness and scalability of intrusion detection systems.

DOI: http://doi.org/10.5281/zenodo.17471011

Environmental Impact of Waste Disposal Sites in Zaria Metropolis, Kaduna State, Nigeria

Authors: Umudi Ese Queen, Oluchukwu B. Chikwe, Ikechukwu S. Chikwe, Chidi Henry, Njor Oru Ogar, Onwugbuta Godpower Chukwuemeka, Okorocha Cyrilgentle Ugochukwu, Success Uchechi Chidebere, Uzogbo Helen Njideka, Chukwunenye Ozioma Rosita

Abstract: The research evaluated how dumpsites affect their surrounding environments. Soil samples from the waste were gathered during both dry and wet seasons, while gaseous pollutants and other field data were measured on-site using portable gas sensors. The concentration levels of CO, H2S, FL, SO2, NO2, and NH3 varied across the seasons, ranging from 1.50 (CTR) to 11.40 (SA), 0.001 (CTR) to 0.0039 (RA), 0.001 (CTR) to 0.0085 (SA), 0.001 (CTR) to 0.039 (SH), BDL (CTR) to 0.0039 (JK), and 0.001 (CTR) to 8.65 (SH) ppm. The levels of particulates, relative humidity, and air temperature near the dumpsites during the seasons were 0.105 (KU) to 19.305 (RA) ppm, 6.35 (AJ) to 77.35 (CTR) %, and 27.25 (CTR) to 38.1000C (RA), respectively. Generally, these levels exceeded standard limits, with a few exceptions. The results for the physical and chemical properties of the refuse waste soils and control samples for both dry and wet seasons showed: pH levels ranging from 7.40 (CTR) to 10.25 (JK) and 6.40 to 9.8 (DA), EC ranging from 0.35 dscm-1 (CTR) to 11.05 dscm-1 (RA) and 0.08 (CTR) to 10.10 dscm-1, cation exchange capacity (CEC) from 33.60 (CTR) to 62.35 Cmol/Kg (KU) and 15.81 (CTR) to 56.01 CmolKg-1 (DA). The NO2-N levels ranged from 0.056 (RA) to 0.530 mg/kg (SH) and 0.035 (CTR) to 0.369 (DA). NO3-N levels ranged from 0.026 to 0.164 mg/kg (SH) and 0.011 (CTR) to 0.113 (DA), SO42- from 1.011 (CTR) to 84.60 (JK) and 2.115 (CTR) to 90.57 mg/Kg (JK), PO33—P from 15.765 to 120.76 (BG) and 28.10 (DA) to 148.76 mg/kg (AJ), Chloride from 1.00 (CTR) to 42.80 (RA) and 0.60 (CTR) to 45.65 mg/kg (RA), organic matter (OM) from 0.425 (CTR) to 11.600 (KU) and 0.110 (CTR) to 14.14% (DA), and CO32- ion from BDL (AJ, CTR, JK, and NTC) to 2.50 (SA) and BDL (AJ, CTR, DA, SH, KU, and PR) to 4.95 % (JK). The results for heavy metals and controls for dry and wet seasons were as follows: Zn levels ranged from 194.15 (CTR) to 1,135.30 (SA) and 115.10 (CTR) to 553.44 (SH) mg/kg. For Pb, the values were between 14.41 (BG) and 77.17 (RA), and from 1.20 (BG) to 5.13 mg/kg (CTR). Cu levels varied from 1.123 (BG) to 899.50 (RA) and 5.90 (BG) to 60.70 mg/kg (JK). Cd ranged from 1.02 (BG) to 3.48 (RA) and 0.72 (CTR) to 2.96 mg/kg (AJ). Hg levels were between 169.60 (JK) and 731.00, and from 33.39 (CTR) to 233.90 mg/kg (BG). The analysis results indicated that the refuse waste soil and air are significantly polluted; therefore, the Kaduna State Environmental Agency (KEPA) should work to reduce hazardous waste generation and provide proper refuse waste disposal facilities.

DOI: https://doi.org/10.5281/zenodo.17471212

Comparative Analysis Of Machine Learning Algorithms For Network Attack Detection Using NetFlow Data

Authors: Pavithra V, Dr. D. Rajinigirinath, T. Saranya

Abstract: Machine learning has become an efficient approach for automating network intrusion detection. This paper compares three supervised algorithms—Random Forest (RF), Support Vector Machine (SVM), and Gaussian Naïve Bayes (GNB)—for classifying NetFlow traffic into normal and attack categories. The preprocessed dataset developed in the previous phase was used for model training and validation. Each algorithm was evaluated using accuracy, precision, recall, and F1-score metrics. Results show that Random Forest achieved 92% accuracy, outperforming SVM (85%) and GNB (78%). This demonstrates that ensemble-based learning provides better generalization for adaptive network attack detection systems.

DOI: http://doi.org/10.5281/zenodo.17471269

Design And Implementation Of An AI-Powered Early Warning System In Higher Education

Authors: Dipti D. Mehare

Abstract: Early Warning Systems (EWS) in higher education use data to identify students at risk of poor academic outcomes so institutions can deliver timely interventions. This paper presents the design, implementation, and evaluation of an AI-powered Early Warning System that integrates institutional records, learning management system (LMS) activity, and assessment data to predict at-risk students and produce actionable, explainable alerts for instructors and student support teams. The system architecture combines feature engineering, an ensemble predictive model (XGBoost), and model-agnostic explainability (SHAP) to provide both accurate predictions and interpretable risk drivers. An experimental evaluation on a multi-year dataset of undergraduate records demonstrates the feasibility of the approach and highlights operational considerations, privacy/ethical issues, and best practices for deployment. The work contributes a reproducible design blueprint and practical lessons for institutions seeking to implement AI-driven EWS.

Comparative Study Of Transformer Architectures For Natural Language Processing

Authors: Dr. G Ramasubba Reddy, M Prathap, J.Sunil

Abstract: Natural Language Processing (NLP) has undergone a paradigm shift with the advent of Transformer-based architectures. Traditional RNN and CNN models struggled to capture long-range dependencies, but Transformers revolutionized this domain through self-attention mechanisms. This paper presents a comparative study of major Transformer architectures — BERT, RoBERTa, DistilBERT, and GPT-2 — focusing on text classification performance. Using a common dataset (IMDb reviews), models were fine-tuned and evaluated on accuracy, training time, and parameter efficiency. The results highlight trade-offs between accuracy and computational efficiency, providing insights into model selection for various NLP applications.

Phase Analysis Of The ScandiumContaining Minerals And Prediction Of The Presence Of Scandium: Ferrocolumbite, Samarskite, Fergusonite

Authors: Thae-Min Ro, Un-Hui Jang, JinSim Kim

Abstract: Sc is a common dispersed element in the crust, but the presence of Sc and scandiumcontaining minerals is also newly revealed, as the studies of pegmatites with high REE content have been intensified. In particular, the research on ferrocolumbite, samarskite and fergusonite has also been extensively studied. However, there are few studies comparing the crystalline structure of the Sc and sample preparation to perform phase analysis in these three minerals. Ores containing rare and rare earth elements, such as monazite, zircon, cassiterite, ferrocolumbite, samarskite, fergusonite and garnet, with quartz, feldspar and mica as the major minerals, were fractionated according to the mineral species by microscopy, and the phases were determined after calcination of ferrocolumbite, samarskite and fergusonite by Xray fluorescence analysis, and the presence and presence of these minerals were compared with those of these minerals. Ferrocolumbite, a scandiumcontaining mineral, appears as a crystalline phase, whereas samarskite and fergusonite are amorphous phases, thus confirming the precalcination and postcalcination phase at 950℃. The Sc content was determined by XRF and atomic absorption spectrometry, and XRD structure analysis was carried out to predict the presence of Sc.

Smart Surveillance: Harnessing Deep Learning For Real-Time Violence Recognition

Authors: Sakshi Keshri, Nitin Namdev

Abstract: In video content analysis, differentiating between violence and non-violence remains a formidable challenge, primarily due to the dynamic, high-dimensional, and temporally complex nature of video streams. Traditional architectures such as ResNet50 and MobileNetV2, though powerful for static image classification, struggle to capture the evolving temporal dynamics inherent in sequential video data. To address this limitation, we introduce a hybrid deep learning framework that unites the spatial abstraction power of InceptionV3 with the sequential learning capacity of Long Short-Term Memory (LSTM) networks. The pipeline begins with robust preprocessing—noise suppression, effective feature extraction, and data refinement—ensuring the model is trained on high-quality representations. By fusing InceptionV3’s rich spatial embeddings with LSTM’s temporal modeling, the system achieves a remarkable accuracy of 99.86% and a validation accuracy of 92.48%, significantly surpassing baseline models. Beyond establishing state-of-the-art results, this hybrid approach underscores the transformative potential of deep learning in advancing intelligent video surveillance and enabling fine-grained, real-time behavioral analysis in safety-critical environments

Machine Learning Approaches To Anomaly Detection In Credit Card Fraud: A Comprehensive Review

Authors: Priyesh Mahajan, Nitin Namdev

Abstract: Credit card fraud is a growing problem in today’s digital world. It causes heavy financial losses for companies and puts consumer security at risk. Traditional rule-based systems help to some extent, but they often fail against advanced fraud tricks.Machine learning (ML), especially anomaly detection, offers a better solution. It can spot unusual patterns in large datasets, making fraud detection faster and smarter. Researchers are exploring many methods, such as autoencoders, hybrid models, and new feature selection techniques.Still, there are challenges. Fraud data is highly imbalanced, and real-time detection is difficult to achieve. Even so, ML-based anomaly detection shows great promise. With continuous improvements, it could make financial transactions much safer in the near future

Machine Learning Approaches For Skin Cancer Diagnosis: A Systematic Review

Authors: Om Dwivedi, Neelam Singh Parihar

Abstract: Skin cancer remains one of the most prevalent and potentially fatal forms of cancer worldwide, emphasizing the need for early and accurate detection. This systematic review explores recent advancements in machine learning (ML) techniques applied to skin cancer diagnosis. It examines supervised and deep learning models such as CNN, SVM, and ensemble classifiers across dermoscopic and clinical image datasets. The study highlights their effectiveness in lesion segmentation, classification, and feature extraction, achieving accuracy levels surpassing traditional diagnostic methods. Additionally, it discusses the challenges of data imbalance, model interpretability, and clinical deployment. The review concludes that integrating ML with explainable AI and multimodal imaging holds significant potential for improving diagnostic precision and supporting dermatologists in real-world decision-making.

A Survey On Web Based Application On Geo-Fencing System

Authors: Kuldeep Tiwari, Sachin Rahangdale, Saundarya Waje, Isha Adhaoo, Ayush Upadhyay, Mrudula Nimbarte, Rashmi Jain

Abstract: Attendance management has evolved from manual methods to intelligent, automated verification systems. Many existing solutions rely solely on facial recognition or GPS-based verification, but few integrate both in real time. This paper presents a Live Photo and Location-Verified Attendance Verification System, which combines real-time photo capture, GPS coordinates, and timestamp validation through a unified RESTful API. The system prevents proxy attendance by ensuring all data—image, location, and time—are captured and transmitted in a single authenticated transaction. The proposed architecture uses React.js for the frontend, Node.js/Express for the backend, and MongoDB for storage, secured with JSON Web Tokens (JWT). Comparative analysis shows improved reliability, integrity, and scalability compared to existing models [1]– [12].

DOI: https://doi.org/10.5281/zenodo.17480718

A Survey On Medical Diagnosis Of Retinopathy And Detection Techniques

Authors: Rahul Patidar, Dr. Anubhav Sharma

Abstract: Diabetic Retinopathy is a frequent complication of diabetes mellitus that leads to retinal lesions affecting vision. Without timely detection, it can result in permanent blindness. Sadly, Diabetic Retinopathy is irreversible, and medical interventions only help preserve existing vision. Early identification and treatment of DR play a crucial role in minimizing the risk of vision impairment. However, manual diagnosis of retinal fundus images by ophthalmologists is time-consuming, labor-intensive, costly, and susceptible to errors, unlike automated computer-aided diagnostic systems. This paper has summarized the type of retinopathy and various stages before getting blindness. Many of researcher proposed models that find the disease in early stages by analyzing medical images. Various techniques of image optimization, analysis and classification were discussed. Paper has summarized the image features that were used in different research article for identify the retinopathy image class.

DOI: http://doi.org/10.5281/zenodo.17480907

Agnipath Scheme And Artificial Intelligence In Indian Military

Authors: Omkar Gore

Abstract: The Union Cabinet on 14 June had approved a recruitment scheme for Indian youth to serve in the Armed Forces. The scheme is called AGNEEPATH and the youth selected under this scheme will be known as Agniveers. Agniveers will be given an attractive customised monthly package along with Risk and Hardship allowances as applicable in the three services. According to the government, AGNEEPATH scheme has been designed to enable a youthful profile of the Armed Forces. It will provide an opportunity to the youth who may be keen to don the uniform by attracting young talent from the society who are more in tune with contemporary technological trends and plough back skilled, disciplined and motivated manpower into the society.

E-check Generation And Management System: A Secure Microservice-Based E-Check Generation System With Fraud Analytics And Bank Aggregation Integration

Authors: Sangeeta Manna

Abstract: The author presents a cloud-native, secure and scalable electronic check (E-Checks) generation and management system built using Spring Boot microservices, AWS Cloud infrastructure and banking-grade integrations with DataSeers for fraud analytics and compliance and Plaid for bank account aggregation and verification. The proposed system automates digital check creation, secure verification, fraud detection and user notifications, targeting businesses needing faster and safer money movement without relying on traditional paper checks.

DOI: https://doi.org/10.5281/zenodo.17483299

Eco-Friendly Cutting Fluids Sustainability: Comparing Non-Edible Vegetable Oils With Fuzzy AHP In Machining Processes

Authors: Dr. Manoj Kumar Singh, Roshan Kumar

Abstract: Growing environmental concerns and stricter industry regulations have amplified the need for sustainable machining methods and eco-friendly cutting fluids. Traditional mineral oil-based lubricants pose significant environmental and health risks due to their non-biodegradable nature and toxic ingredients. This research presents a structured decision-making framework for selecting sustainable cutting fluids using the Fuzzy Analytical Hierarchy Process (FAHP). Four options—Neem seed oil, Jatropha oil, Pongamia oil, and mineral oil — were evaluated based on four main criteria: biodegradability, health effects, tool life, and surface finish. The FAHP model was validated through sensitivity analysis and experimental testing during the turning of AISI 1045 steel. Results show Neem seed oil achieved the highest overall sustainability score, supported by its excellent tool life (0.18 mm wear) and surface roughness (Ra = 0.46 µm). The model's consistency ratio was below 0.1, confirming dependable decisions. This combined FAHP experimental approach offers a robust, reproducible framework for selecting sustainable fluids, aiding Indian machining industries in replacing mineral oils with biodegradable, non-toxic, and cost-effective alternatives.

DOI: http://doi.org/10.5281/zenodo.17484238

Removal Of Heavy Metal Ions From Wastewater By Chemically Modified Plant Wastes As Adsorbents.

Authors: Pradeep Kumar Jaiswal, Rakesh Kumar Yadav, Manish Kumar Tiwari

Abstract: The application of low-cost adsorbents obtained from plant wastes as a replacement for costly conventional methods of removing heavy metal ions from wastewater has been reviewed. It is well known that cellulosic waste materials can be obtained and employed as cheap adsorbents and their performance to remove heavy metal ions can be affected upon chemical treatment. In general, chemically modified plant wastes exhibit higher adsorption capacities than unmodified forms. Numerous chemicals have been used for modifications which include mineral and organic acids, bases, oxidizing agent, organic compounds, etc. In this review, an extensive list of plant wastes as adsorbents including rice husks, spent grain, sawdust, sugarcane bagasse, fruit wastes, weeds and others has been compiled. Some of the treated adsorbents show good adsorption capacities for Cd, Cu, Pb, Zn and Ni.

A Systematic Review Of Standalone And Hybrid Convolutional Neural Network Models For Rice Leaf Disease Detection

Authors: Lele Mohammed, Aminu Aliyu Abdullahi

Abstract: Rice leaf diseases pose a serious threat to global food security, reducing yield quality and quantity across major rice-producing regions. Conventional detection methods relying on manual inspection are often time-consuming, subjective, and inefficient. In recent years, deep learning particularly Convolutional Neural Networks (CNNs) has emerged as a transformative tool for automated plant disease diagnosis. This study presents a systematic review of recent advancements in standalone and hybrid CNN–machine learning (CNN–ML) models for rice leaf disease detection. Following the PRISMA 2020 framework, four major databases (IEEE Xplore, Scopus, Web of Science, and Google Scholar) were searched for studies published between 2020 and 2025. A total of 37 peer-reviewed articles were included after applying rigorous inclusion and exclusion criteria. Data were extracted on model architectures, datasets, performance metrics, and computational efficiency, and were analyzed both quantitatively and qualitatively using descriptive statistics and comparative synthesis. Results reveal that standalone CNN models such as ResNet, DenseNet, and EfficientNet achieved a median accuracy of 96.4%, while hybrid CNN–ML models (e.g., CNN–SVM and CNN–Random Forest) recorded a median accuracy of 95.2% with notably reduced inference times and model sizes. Lightweight models like MobileNet and EfficientNet-Lite demonstrated optimal trade-offs between accuracy and resource efficiency, supporting their suitability for edge deployment in low-resource environments. Despite these advancements, only 30% of studies utilized field-based datasets, highlighting a persistent generalization gap between controlled and real-world conditions. Furthermore, explainable AI tools were employed in merely one-third of studies, limiting interpretability and trust in AI-driven diagnostics. This review emphasized the growing maturity of deep learning for smart agriculture while identifying critical gaps in dataset diversity, interpretability, and deployment feasibility. It concludes that future research should prioritize standardized datasets, explainable and lightweight architectures, and energy-efficient edge intelligence to ensure sustainable, inclusive, and transparent AI adoption in rice disease management.

DOI: http://doi.org/10.5281/zenodo.17499057

Van Management System

Authors: Kedar Pinniboyina, Divya Kuwar, Adarsh Vidyarthi, Aditya Raj, Soundharya Iyer

Abstract: Managing school transportation is important because it helps keep students safe, avoid delays, and use resources better. Old ways of doing this, like using paper records or calling to assign buses, are not very reliable and don't offer much visibility. To fix this, we created a Van Management System using HTML, Tailwind CSS, and Type- Script for the look, Node.js (JavaScript Runtime) to control how it works, and MySQL to store data. The system helps with important tasks like planning routes, assigning buses and drivers, booking seats, and helping people like admins, drivers, and parents communicate. The automation in the system stops mistakes like double bookings, makes things more fair, and improves communication. Unlike GPS-based systems that need expensive hardware and work only with the internet, this is cheaper and better suited for schools with fixed bus routes. Testing showed the system helps with better scheduling, fewer delays, and more transparency. Plans include adding AI for better route planning, predicting when things might go wrong, and optional GPS or IoT features to make it even more reliable.

DOI: https://doi.org/10.5281/zenodo.17491818

Enhancing Diagnostic Imaging Through Ai-Driven Radiomics for Advancing Precision Medicine

Authors: Aminu Jafar, Abubakar Muhammad Miyim, Zahraddeen Sufyan, Nuraddeen Jaafar Ibrahim

Abstract: Radiomics has emerged as a promising paradigm for transforming standard medical images into high-dimensional quantitative features that can support early cancer detection and treatment planning. However, clinical adoption of radiomics-based artificial intelligence (AI) has been hindered by computational inefficiency, long inference times, and limited generalizability across diverse imaging settings. This research addresses these challenges by developing a hybrid CNN–Vision Transformer (ViT) pipeline combined with Principal Component Analysis (PCA) and Light Gradient Boosting Machine (LightGBM) for the diagnostic imaging of breast, lung, and colorectal cancers. A multi-institutional dataset of 6,360 cases was curated from The Cancer Imaging Archive (TCIA), MICCAI repositories, and two Nigerian hospitals (ABUTH and UCH). Radiomic features (n = 1,834) were extracted in compliance with the Image Biomarker Standardisation Initiative (IBSI). CNN and ViT models were used for complementary feature extraction (local and global contexts), followed by feature fusion and dimensionality reduction with PCA. The reduced features (150 principal components, retaining 99.2% variance) were classified using LightGBM. The system was evaluated using Accuracy, Sensitivity, Specificity, F1-score, and AUC-ROC, with statistical significance assessed via DeLong, McNemar, and bootstrap confidence intervals. The proposed model achieved 94.2% overall accuracy, with per-cancer accuracies of 94.7% (breast), 92.3% (lung), and 93.8% (colorectal), and AUC values ≥ 0.923. Sensitivity and specificity averaged 0.93 and 0.92 respectively. Crucially, the pipeline achieved an average inference latency of 1.7 ± 0.3 seconds per image, representing a >99% reduction in computational time compared to baseline CNN (4.1 minutes) and ViT (3.2 minutes) models. Ablation studies confirmed the incremental value of each component: CNN-only accuracy (87.2%), ViT-only (89.4%), hybrid without PCA/LightGBM (91.3%), versus full hybrid (94.2%). Explainability analysis using SHAP identified texture heterogeneity, shape irregularity, and intensity statistics as the most influential feature families, improving model transparency and clinical trust. The findings demonstrate that the proposed hybrid radiomics-AI model simultaneously delivers state-of-the-art diagnostic accuracy and near real-time inference speed, directly addressing key barriers to clinical adoption. Its validated performance across international and Nigerian cohorts highlights strong generalizability, and its interpretability makes it suitable for deployment in real-world oncology imaging workflows. This work establishes a new benchmark for high-speed, explainable AI-driven radiomics in cancer diagnostics.

DOI: http://doi.org/10.5281/zenodo.17498894

Phytochemical Characterization and Antioxidant Evaluation of Selected Ethnomedicinal Plants of Achanakmar Biosphere Reserve, Chhattisgarh

Authors: Nikher Sharada Sahu, Rashmi Verma, Manish Tiwari

Abstract: Achanakmar Biosphere Reserve in Chhattisgarh, India, is bio-diversely rich in ethnomedicinal plants and has been used traditionally for the treatment of various diseases. This research focused on the phytochemical content and antioxidant activity of leaf extracts of Bauhinia purpurea (Kanchan), Buchanania lanzan (Chironji), and Boerhavia diffusa (Punarnava). Sequential solvent extraction of varying polarities (petroleum ether, chloroform, ethyl acetate, methanol, ethanol, and water) was carried out, followed by qualitative and quantitative phytochemical screening. Screening showed the universal occurrence of alkaloids, flavonoids, phenolics, tannins, saponins, steroids, and terpenoids, with B. diffusa also having anthraquinones and coumarins. Quantitative analyses showed methanolic and ethanolic extracts to have increased total phenolic (TPC), flavonoid (TFC), and alkaloid (TAC) content over non-polar solvents, validating their effectiveness in pulling out bioactive molecules. Antioxidant activity was determined using DPPH, ABTS, and FRAP assays. Of the extracts, Boerhavia diffusa methanol fraction had the maximum radical scavenging activity (IC₅₀ = 73.1 µg/mL; ABTS inhibition = 95.6%; FRAP = 348 µM Fe²⁺/g), followed by Buchanania lanzan and Bauhinia purpurea. Comparative examination showed solvent-dependent differences, wherein polar extracts invariably showed greater antioxidant activity. The findings confirm the ethnomedicinal significance of these species and determine their utility as natural antioxidants for therapeutic and nutraceutical use. This research further underscores the significance of indigenous flora in forming sustainable approaches to drug discovery and natural products research.

Agnipath Scheme in Indian military.

Authors: Omkar Gore

DOI: https://doi.org/10.5281/zenodo.17492636

Graph Databases For Modeling Complex Relationships

Authors: Miss. M. Saranya

Abstract: Traditional relational databases frequently find it difficult to effectively describe, store, and query rich relationship-centric data in an era characterized by increasingly complex and interrelated data. A persuasive answer to this problem has been provided by graph databases, which provide a data model in which entities are represented as nodes and relationships as edges. This allows for high-performance traversal of links and intuitive representation. The use of graph databases to simulate intricate interactions in a variety of fields, including fraud detection, biological networks, recommendation systems, and social networks, is examined in this research. By treating relationships as first-class citizens, graph databases provide smooth relationship queries, even at scale, in contrast to relational databases that depend on expensive joins. The paper illustrates how graph databases improve query performance, simplify schema evolution, and enhance data insight through comparative research, real-world case studies, and schema design examples. We also look at use scenarios where graph-based models perform better than conventional relational systems and talk about important tools like Neo4j, Amazon Neptune, and Apache TinkerPop. The purpose of this presentation is to demonstrate the useful benefits of graph databases and their revolutionary role in releasing the potential of linked data.

On Road SOS

Authors: Kunjan A. Minama, Vishnu S. Zaru, Bharat Odedara, Prince Odedara, Zulkifl Khairoowala

Abstract: Vehicle breakdowns along the road are a common but quite serious issue that car owners face globally, which mostly result in long waits, safety risks, and money loss. The current systems of help are broken up and not effective, particularly for EVs, where the charging infrastructure is scarce. This article introduces ON ROAD SOS, a mobile and web application created to provide on-the-spot help to the roadside by linking the users to the closest garages, tow trucks, and charging stations that are available. The app uses GPS-based location tracking, the Google Maps API, and Firebase cloud infrastructure for rapid and reliable service matching. Some of the features are emergency SOS messaging, multi-vehicle (EV and ICE) support, and centralized service directories. Our execution shows that the waiting times are cut down, the safety is enhanced, and the communication is made more efficient. The next steps are AI-based predictive maintenance, encrypted digital payments, blockchain-based service verification, and collaboration with smart city infrastructure.

From Teacher To Innovator: Redefining The English MFL Teacher’s Role In Kazakhstan’s AI-Integrated Classroom

Authors: Mahmud Choudhury, Aidana Tulegenovna

Abstract: The rapid integration of Artificial Intelligence (AI) into education is transforming the pedagogical landscape of English and Modern Foreign Language (MFL) instruction worldwide. In Kazakhstan, this shift is particularly significant as the nation advances its digitalization agenda and redefines educational priorities for the 21st century. This paper explores the evolving role of English MFL teachers as they transition from traditional knowledge transmitters to innovative facilitators, designers of learning experiences, and co-creators of AI-enhanced pedagogy. Drawing on theoretical frameworks of digital literacy, teacher agency, and constructivist learning, the study examines how AI technologies—such as adaptive learning platforms, intelligent tutoring systems, and automated assessment tools—reshape instructional practices, curriculum design, and learner engagement. Through an analysis of emerging practices in Kazakhstani schools and universities, the paper highlights the need for continuous professional development, ethical awareness, and interdisciplinary collaboration to ensure that teachers remain central to the educational process. Ultimately, this research argues that embracing innovation and AI fluency empowers teachers to become catalysts for educational reform, fostering linguistic proficiency, creativity, and critical thinking among learners in an increasingly AI-driven classroom environment.

Virtual Laboratories: A Modern Approach to Learning and Experimentation

Authors: Zeel Bhatt, Raushan Kumar, Manav Patel, Pavan, Asst.Prof.Ankita Saxena

Abstract: Laboratories are an essential component of learning, yet traditional facilities demand significant physical resources and cannot always be accessed by every learner. Advances in cloud computing provide an opportunity to create virtual laboratories (VLabs) that are available anytime and from anywhere. This paper introduces a cloud-based virtual laboratory developed using the MERN (MongoDB, Express, React, Node.js) stack. The system integrates secure user authentication, experiment execu- tion, and remote access to simulations through a web interface. Our findings show that cloud-enabled VLabs offer improved scalability, lower infrastructure costs, and wider accessibility compared to conventional laboratories.

The Transformative Power Of Non-Native English Educators In ESL Teaching: Reconceptualizing Excellence In Language Education

Authors: Dr. Deng Diing Diing

Abstract: This comprehensive examination challenges prevailing assumptions about native speaker supremacy in English language teaching by presenting compelling evidence for the distinctive pedagogical advantages of non- native English speaking (NNES) educators. Drawing from extensive empirical research, sociolinguistic theory, and contemporary pedagogical frameworks, this article demonstrates that NNES educators possess unique competencies that not only equal but often surpass those of native English speaking (NES) teachers in facilitating meaningful language acquisition. Through critical analysis of metalinguistic awareness, learner empathy, cultural mediation, and explicit grammar instruction capabilities, this work repositions NNES educators as exemplary professionals whose lived experience of language learning constitutes an invaluable pedagogical asset. The article synthesizes research-based findings from applied linguistics, second language acquisition theory, and educational psychology to construct a comprehensive argument for reconceptualizing excellence in ESL instruction beyond the limiting paradigm of native-speakerism.

From Teacher To Innovator: Redefining The English MFL Teacher’s Role In Kazakhstan’s AI-Integrated Classroom

Authors: Mahmud Choudhury, Aidana Tulegenovna

Blockchain-Based Secure Biometric Identification In Cloud

Authors: S.Hemanth, B.Shymala Devi

Abstract: In the ever-growing world of digitalization, securing and protecting the privacy of biometric information in cloud-based data sets have become central concerns. This work provides a secure representation of the blockchain technology and incorporation of sophisticated iris recognition technology to allow a method of a secure and non-repudiable biometric identification in the cloud condition. With the help of the MMU Iris Dataset, comprising 460 datasets of iris images of 46 individuals, the proposed system will deploy the utilisation of the pre-processing methods that may involve segmentation, normalisation, and denoising to improve the quality of the images. Discriminative and compact feature vectors are produced by applying deep learning techniques such as InceptionV3 and classical Log-Gabor filters on feature extraction. To maintain confidentiality and integrity, these vectors are encrypted with the AES-256 and hashed with the SHA-256. The hash-coded templates are subsequently enrolled and published on a permissioned block chain network with smart contracts to receive identity verification, admittance restrictions and withdrawal. With homomorphic encryption, biometric templates may be securely matched, without decryption and without breaching an end-to-end privacy. The proposed model reached the recognition accuracy of iris definition of 97.8%, which was much better than the recognized models of recognition such as ResNet50 (95.7%) and VGG16 (94.1%). The system also has a low encryption latency and has a high blockchain throughput (1200 TPS) which is scalable to the real world. This framework exhibits a combination of the benefits of biometric authentication and decentralization of blockchain, thus meeting both of the criteria of cloud-based identity management data security and trustworthiness. The proposed model can, therefore, offer a trusted, transparent and privacy-wise solution to next-gen biometric systems.

Digital Image Processing Technique For Detection Of Different Plant Diseases Using Machine Learning

Authors: Sahana S A, Lisha N Singh, Priyanka R N, Rekha D

Abstract: Agriculture plays a vital role in India’s economy, and early detection of plant diseases is essential to prevent crop loss and ensure food security. Traditional manual inspection methods are often inefficient and prone to error. This paper presents a lightweight and interpretable plant disease detection system based on classical machine learning and image processing techniques. The proposed approach uses K-means clustering for image segmentation to isolate infected regions from plant leaves. Texture and colour features are extracted using the Grey- Level Co-occurrence Matrix (GLCM) and HSV colour histograms. These extracted features are classified using a Support Vector Machine (SVM) to identify various plant diseases. The system achieves high accuracy while remaining computationally efficient, making it suitable for low-resource environments. By avoiding complex deep learning architectures, the proposed model ensures faster processing and better interpretability, supporting early disease diagnosis and promoting sustainable agricultural practices.

DOI: https://doi.org/10.5281/zenodo.17512518

Solar Wireless Electric Vehicle Charging Station

Authors: Dr.H.N.Suresh, Intiyaz T, Manoj T

Abstract: The growing popularity of electric vehicles (EVs) has contributed to cost reduction and environmental protection. This project presents a solar-powered wireless charging system that removes the need for wired plug-in connections. It utilizes renewable solar energy to drive an inductive charging mechanism capable of charging EVs wirelessly. Key elements include solar panels, converters, transformers, regulator circuits, and LCD displays. The approach aims to create an efficient, sustainable, and eco-friendly charging infrastructure suitable for future mobility

Authenticated Access Control For Vehicle Ignition Using Driver’s License And Fingerprint

Authors: Dr. G Shivakumar, Harshitha BK, Harshitha M, Likhith BK, Mahesh LR

Abstract: Vehicle theft and unauthorized access are persistent global challenges. Traditional key-based ignition systems and remote keyless entry mechanisms have proven insufficient due to vulnerabilities like duplication, hot-wiring, and relay attacks. This paper presents a dual- authentication ignition system that integrates smart driver’s licence verification with fingerprint recognition, ensuring that only authorized and licensed drivers can start the vehicle. The system employs an RFID-enabled driver’s licence and biometric sensor, managed by an Arduino Uno microcontroller. Only successful dual verification enables ignition. The prototype demonstrates improved security and accountability compared to traditional systems.

Off-Grid Rf-based Communication Through Lo-Ra

Authors: Amiras Patel, Vraj Bhingradiya, Harsh Upadhyay, Shashank Patel

Abstract: In environments where conventional communication infrastructure is unavailable or has failed, a critical need arises for independent and resilient connectivity solutions. This project details the design, implementation, and performance of a proof- of-concept off-grid communication system that facilitates peer- to-peer (P2P) text messaging using LoRa (Long Range) tech- nology.1 The system is architected to be entirely infrastructure- independent, requiring no cellular networks, gateways, or cen- tralized servers.1 The hardware implementation consists of portable, low-power nodes built with an ESP32 microcontroller, an SX1278 LoRa transceiver, and an OLED display.1 Real- world testing in a semi-urban environment validated the system’s effectiveness, achieving reliable message delivery with a Packet Delivery Ratio (PDR) exceeding 90.

MolGraphormer: An Interpretable GNN-Transformer For Uncertainty-Aware Molecular Toxicity Prediction

Authors: Akshay Balaji

Abstract: Accurate and Interpretable toxicity prediction re- mains fundamental in computational chemistry and drug discov- ery. We propose MolGraphormer, a Transformer-GNN hybrid architecture integrating Graph Neural Network message passing with self-attention mechanisms for molecular property prediction. Our model incorporates substructure-aware embeddings via multi-head attention, edge-conditioned message passing, and hierarchical graph aggregation, enabling both local and global molecular reasoning. Evaluated on the Tox21 benchmark dataset, MolGraphormer achieves competitive performance with F1-Score of 0.6697 and AUC-ROC of 0.7806, while maintaining strong recall (0.7787) for identifying toxic compounds. We employ Monte Carlo Dropout and Temperature Scaling for uncertainty quantification, Combined with uncertainty quantification and attention-based interpretability, MolGraphormer offers a practical framework for drug safety assessment and regulatory toxicology.

Pediatric Pneumonia Detection With A Lightweight, Cross-Operator Vali-dated Deep Learning Model

Authors: Ayushi Rathour

Abstract: Pneumonia remains a leading cause of mortality in pediatric populations globally, with an estimated 740,000 deaths annually in children under 5 years. Early accurate diagnosis is critical for timely intervention, yet diagnosis remains challenging in re-source-limited settings where radiologist expertise is scarce. While chest radiography is the primary diagnostic tool, interpretive variability and limited radiologist availability constrain diagnostic accessibility in low- and middle-income countries. This study developed and validated a lightweight deep learning model for automated pediatric pneumonia detection from chest X-rays, incor-porating rigorous cross-operator validation to assess real-world generalizability. Using MobileNetV2 transfer learning, the model was trained on 1,750 balanced chest radiographs and evaluated on internal validation (n=259) and cross-operator validation (n=485) datasets from the Guangzhou Women and Children's Medical Center. The model achieved 94.8% accuracy with 89.6% sensitivity on internal validation. Critically, on cross-operator validation with different radiologists and imaging equipment, the model maintained 96.4% sensitivity (242/251 pneumonia cases detected correctly) with 86.0% overall accuracy, representing an acceptable 8.8% degradation and demonstrating robust real-world performance. The lightweight 14MB architecture enables sub-second inference on mobile devices, and the maintained high sensitivity demonstrates the model learned generalizable disease patterns rather than dataset artifacts. The combination of high sensitivity (96.4%), strong ROC-AUC (0.964), and deployment fea-sibility through a prototype clinical framework demonstrates this approach can augment pneumonia screening in resource-limited pediatric clinics. These results bridge academic validation with practical clinical deployment, suggesting that rigorously validated AI-assisted diagnosis can improve childhood pneumonia detection in global health contexts where radiologist availability remains constrained.

DOI: https://doi.org/10.5281/zenodo.17531598

Effect Of Strategic Management On Financial Performance

Authors: Henry Kehinde FASUA, Francis Kehinde EMENI, Oluwabunmi Akindele OLAWAYE

Abstract: This study examines the effect of strategic management practices on the financial performance of listed manufacturing firms in Nigeria. The research was motivated by inconsistent empirical findings and limited studies integrating strategic position, cost-leadership, differentiation, and strategic control within the Nigerian context. An ex post facto design was adopted, utilizing secondary data extracted from annual reports of 55 manufacturing firms listed on the Nigerian Exchange between 2014 and 2023. Return on Assets (ROA) was used as the proxy for financial performance, while strategic position, cost leadership, differentiation, and strategic control served as the independent variables, with gross profit as a control variable. The panel data were analyzed using Panel Least Squares, Fixed Effects, and Random Effects models, with the Hausman test guiding model selection. Findings reveal that strategic position, cost-leadership strategy, and strategic control significantly and positively influence financial performance, whereas differentiation strategy shows an insignificant effect. The results highlight the importance of competitive positioning, cost efficiency, and robust control mechanisms in driving profitability among manufacturing firms in Nigeria. The study concludes that firms should adopt an integrated strategic management approach to improve financial outcomes and sustain long-term competitiveness. It recommends that management prioritize cost-focused strategies, continuous environmental assessment, and effective monitoring frameworks to strengthen performance. Future studies may incorporate additional governance and macroeconomic variables to enhance explanatory power and provide broader insights.

Stabilization Of Clayey Soil Using Industrial Waste Products

Authors: Neha Dongre, Dr.Sunil Sugandhi

Abstract: Soil stabilization can be explained as the alteration of the soil properties by chemical or physical means in order to enhance the engineering quality of the soil. The main objectives of the soil stabilization is to increase the bearing capacity of the clay soil, it’s resistance to weathering process and soil permeability. The long-term performance of any construction project depends on the soundness of the underlying soils. Unstable clay soils can create significant problems for pavements or structures. Therefore soil stabilization techniques are necessary to ensure the good stability of clay soil so that it can successfully sustain the load of the superstructure especially in case of clay soil which are highly active, also it saves a lot of time and millions of money when compared to the method of cutting out and replacing the unstable soil. This paper deals with the complete analysis of the improvement of clay soil properties and its stabilization using industrial waste sand and lime. The experimentation is carried out keeping 20% of lime as constant and industrial waste sand 10%, 20%and 30%. Disposal of these waste materials is essential as these are causing hazardous effects on the environment. With the same intention literature review is undertaken on utilization of solid waste materials for the stabilization of soils and their performance is discussed.

Design & Calculation Of Aquatic Clean-up Bot (ARASAG)

Authors: Ankon Chakma, Dr. Yan Guoping

Abstract: In recent years, the problem of river garbage pollution has become increasingly serious. The traditional manual dredging method has low cleaning efficiency and cleanliness, high labor costs, and safety hazards for personnel. There is an urgent need for innovative systems to protect freshwater ecology and river systems. This paper proposes a design scheme for an automated and efficient river cleaning machine, aiming to increase the total amount of floating pollutants collected autonomously, enhance the efficiency of river environment regular management, reduce costs by replacing manual labor with machines, and achieve the management goals of "clean water, clear river, clear taste, and beautiful scenery." Based on the requirements of the topic, this paper proposes and designs a high-automation river cleaning machine through theoretical, design, and modeling analysis. It mainly consists of an overall platform, interception system, conveyor system, sensor-equipped mechanical arm, garbage bin system, power system, and control system. Through scheme evaluation and design calculation, the buoyancy of the overall platform is optimized. The interception system is realized using a double-sided blocking support, and the conveyor system is used to collect floating pollutants in small areas of the river. The mechanical arm with sensors is used to intercept and collect large areas of floating pollutants in the river. After fully considering efficient waste compression and convenient unloading, the garbage bin system of the machine is designed. This paper studies and calculates each key component of the devices mentioned above, completes the corresponding main design and verification, and provides some guidance for the design and application of a highly automated river cleaning machine. This plays an important role in realizing the full automation of river cleaning machines to replace traditional manual work and reduce costs and has a positive significance for promoting the modernization and intelligent development of the river environmental protection industry.

DOI: http://doi.org/10.5281/zenodo.17542341

An Integrated Inventory Model With Price Negotiation And Two-Level Credit Policy Under Price-Sensitive And Stock-Dependent Demand

Authors: Awanish Kumar, Dr. Pinky Pandey

Abstract: This paper formulates a joint inventory model, which considers the realities of today’s supply chain system through considering three main features: price-sensitive and stock-dependent demand, a vendor–buyer price bargaining policy, and bi-level credit. The nature of the proposed model is unlike those used in previous inventory models with fixed price and simple credit which do not consider situations reflecting real life where consumer behaviour along financial constraints plays a crucial role on the demand. The model incorporates a weighted bargaining factor (α) to account for dealing power in transfer pricing and combines a distributed credit policy (T₁ and T₂) to coordinate order-taking and payment leverage. Inventory dynamics are further extended to deterioration by which the model becomes valid for industries processing perishable, and quick obsoleted products. The total cost per cycle is minimized over an integral cost structure, which includes holding/stockout costs, ordering costs, purchasing costs and deterioration and interest costing. Gui simulations programmed in MATLAB show how the negotiation results, credit terms and deterioration rates affect demand, cost and inventory level together. The results highlight the need for a balance between operational efficiency and financial strategies and offer practical implications for vendor-managed inventory systems, wholesale distribution networks, and long-term B2B relationships.

DOI: https://doi.org/10.5281/zenodo.17550506

The Rise Of Predictive Analytics In Pharma Marketing: Transforming Insights Into Strategy

Authors: Harsh Shah, Dr Hitesh Ruparel

Abstract: The pharmaceutical world is changing fast, driven by data, technology, and the need to make smarter marketing choices. Predictive analytics is now helping companies move from guesswork to insight-based decisions. By using machine learning and advanced data models, marketers can study past trends and predict how doctors, patients, and markets might behave in the future. This new approach helps brands understand which doctors are more likely to prescribe, what communication channels work better, and where market opportunities truly lie. The power of predictive analytics lies in connecting many data points, such as CRM data, prescription trends, and digital engagement, to form one meaningful story. It helps marketing teams plan campaigns with higher accuracy and improve their return on investment. However, in India, its adoption is still slow due to fragmented data, lack of analytical skills, and the comfort of traditional methods. This paper explores how predictive analytics is changing the way pharma marketing is done, what models are used, the main barriers in Indian settings, and what steps can help in better adoption. The goal is to show how predictive thinking can turn raw insights into powerful brand strategies that drive growth and long-term competitiveness.

DOI: https://doi.org/10.5281/zenodo.17557302

AI-Driven Career Guidance System Using Psychometric Profiling And Machine Learning

Authors: Raj Guru, Mahak Devi, Rudrakshi Narayan Srivastava, Divyanshu Salwan, Alankrita Agarwal

Abstract: Career selection is a crucial yet often confusing stage for students, especially in technology-related disciplines where numerous options exist. Traditional counseling methods mainly rely on academic performance or general aptitude, overlooking the influence of personality and learning preferences. This paper presents an AI-driven career guidance system that integrates psychometric profiling with machine learning models to gener- ate personalized career recommendations. The system analyzes individual traits using validated psychological frameworks—Big Five Personality Traits, Myers–Briggs Type Indicator (MBTI), and VAK learning styles—together with self-assessed technical skill ratings. A custom dataset was developed through an online survey of undergraduate computer science students, combining psychometric attributes, technical competencies, and preferred career roles. Multiple machine learning algorithms, including Logistic Regression, Support Vector Machine, Random Forest, and Multi-Layer Perceptron, were trained and evaluated using accuracy, precision, recall, and F1-score. The best-performing model achieved an accuracy of [insert your actual result]%, demonstrating that combining psychometric and technical fea- tures significantly improves prediction reliability. The system further incorporates a hybrid recommendation module to suggest relevant courses and estimate salary ranges. Deployed as a web- based application, it provides accessible, explainable, and data- driven career advice for students and educators. The proposed framework establishes a foundation for future enhancements such as adaptive learning and integration of real-time labor market analytics.

DOI: https://doi.org/10.5281/zenodo.17568203

AgileCopilot: An AI-Powered Assistant for Enhanced Agile Software Development Using RAG and Role-Based Automation

Authors: S Jeyalakshmi, A Akhash Kumar, SP Goutham, N Sethumadhavan, D Sheik Mohamed Rashid

Abstract: Agile software development methodologies face persistent challenges in maintaining consistency across user story creation, effort estimation, and test case generation. These inefficiencies often lead to project delays, quality issues, and resource misallocation. This paper presents AgileCopilot, an AI-powered assistant that leverages Large Lan- guage Models (LLMs), Retrieval-Augmented Generation (RAG), and role-based automation to streamline Agile workflows. The system inte- grates historical sprint data stored in MongoDB to provide context-aware assistance for Business Analysts, Developers, Testers, and Product Man- agers. AgileCopilot employs a novel role-specific prompting mechanism combined with similarity-based retrieval to generate accurate user sto- ries, estimate story points, create comprehensive test cases, and provide sprint insights. Our approach demonstrates alignment with UN Sustain- able Development Goals, particularly SDG 4 (Quality Education), SDG 9 (Industry Innovation), SDG 12 (Responsible Consumption), and SDG 17 (Partnerships), by promoting efficient resource utilization and knowl- edge sharing in software development teams. Early projections and trial outcomes indicate improvements in story consistency, estimation accu- racy, and test coverage compared to traditional manual approaches.

Topic : The Role Of Blockchain In Transforming Healthcare Data Management

Authors: Samrat Shailendra Thakur, Ishwari Dadasaheb Dhakane

Abstract: A ground-breaking method for tackling important issues in healthcare data management is blockchain technology. Conventional healthcare systems frequently have security flaws that jeopardize patient privacy and trust, fragmented data storage, and poor interoperability. This study investigates how blockchain technology can improve data integrity, transparency, and efficiency in healthcare ecosystems. Healthcare organizations can safely store, exchange, and validate patient data amongst hospitals, labs, and insurance companies by utilizing blockchain's decentralized and unchangeable ledger. The study examines important research demonstrating blockchain's uses in healthcare information exchange, mobile health, and clinical data sharing. It also examines how blockchain might enhance clinical trials, health insurance claims processing, and supply chain management. Research shows that blockchain can give patients more control over their medical records while drastically lowering fraud, data breaches, and administrative inefficiencies. But there are still issues with scalability, legal compliance, and legacy system integration. All things considered, the study highlights blockchain as a potentially useful tool for safe, open, and patient-centered healthcare data management.

DOI: https://doi.org/10.5281/zenodo.17570316

Dynamic Credit Scoring With Real-Time Transactional & Behavioral Data Using Deep Reinforcement Learning

Authors: Dr. Pankaj Malik, Vaidika Kaul, Gautam Jagthap, Harshit Jamley, Hitansh Chopra

Abstract: Traditional credit scoring models depend on static, historical data and fail to adapt to the rapidly changing financial behavior of borrowers. This paper introduces a Dynamic Credit Scoring Framework that leverages Deep Reinforcement Learning (DRL) to update borrower creditworthiness in real time using transactional and behavioral data streams. The credit assessment process is formulated as a Markov Decision Process (MDP), where a DRL agent continuously learns optimal credit decisions—such as loan approval, limit adjustment, or monitoring actions—based on evolving borrower states. The model employs a temporal feature encoder for real-time transaction analysis, coupled with an actor–critic architecture for decision optimization and a reward function that balances profitability, default risk, and fairness constraints. Experimental evaluation was conducted using three years of anonymized banking transaction data from 12,000 customers. Results show that the proposed DRL-based system improves default prediction accuracy by 18.7%, enhances long-term portfolio profitability by 23.4%, and reduces false approval rates by 21.6% compared to traditional gradient-boosted and logistic regression models. Furthermore, the model demonstrates strong adaptability under concept drift, maintaining performance stability with only minor retraining. These findings indicate that integrating DRL with real-time behavioral analytics can significantly enhance credit risk assessment, enabling financial institutions to make faster, fairer, and more dynamic lending decisions.

Influence Of Carbon Fibre Reinforced Concrete In Architecture And Everyday Life

Authors: Swathi.Sa, Er. K. Sri Pranapb, Ar. N. Debakc

Abstract: In our everyday lives, we are surrounded by many elements and materials. Some bring comfort but aren’t affordable; some are affordable but aren’t as high in quality comparatively. Our goal here is to identify materials that withstand extreme conditions and have a long life. Such materials have been researched and discovered only in the past few years. This paper will show how Concrete fibre reinforced concrete is being seen and used in our everyday lives.

Linguistic, Cognitive, And Environmental Factors Influencing Listening Comprehension In EFL Contexts: Evidence From Abu-Essa College, Zawia University

Authors: Aisha Omran Salem Alghahwash

Abstract: This research investigates the issues and difficulties faced by students at Abu Essa College, Zawia University, in developing English listening skills, as well as the strategies they employ to overcome these difficulties. This study addressed a significant gap in English as a Foreign Language (EFL) education by examining listening comprehension challenges, as this skill often receives less attention than reading, writing, grammar, and vocabulary instruction. Using a questionnaire administered to 25 second-semester EFL students, the study examines linguistic, cognitive, and environmental factors affecting listening comprehension. The findings indicate that students experience considerable difficulties with listening comprehension, with particular challenges related to speech rate, accent variation, vocabulary limitations, and psychological factors. Based on the results, this study recommends enhancing listening strategies, equipping educational institutions with necessary technological tools for effective listening instruction, and encouraging students to improve their overall language proficiency through varied exposure to authentic listening materials.

Farming Tech Solutions: A Next Generation Approach to Precision Agriculture

Authors: Amit Kumar, Deep Samanta, Arnab Bhunia,, Atish Kumar Sah, Kabeeta Chaudhary

Abstract: Agriculture faces challenges of resource inefficiency, climate variability, and the need for sustainable food production. This paper introduces Farming Tech Solutions, a precision agriculture framework integrating IoT, artificial intelligence, and data analytics to support smarter farming practices. The system employs real-time sensors for soil and crop monitoring, predictive models for weather and yield estimation, and machine learning techniques for crop recommendation and disease detection. A prototype is developed and assessed on scalability, accuracy, and responsiveness. Results demonstrate improved irrigation efficiency, early detection of crop stress, and reduced manual intervention. The study highlights the potential of digital farming ecosystems to empower farmers with actionable insights and promote sustainable agricultural growth.

The Transformative Role Of Artificial Intelligence In Higher Education And Research & Development: Opportunities, Challenges, And Future Directions

Authors: Sateesh Kumar Beepala

DOI: http://doi.org/10.5281/zenodo.17669403