Authors: Ms. Nandhini R, Mr. Mohan Kumar

Abstract: Financial literacy plays a significant role in enabling individuals to make informed and effective financial decisions in their daily lives. In today’s rapidly changing financial environment, college students are increasingly required to manage personal finances, including budgeting, saving, investing, and controlling expenses. However, many students lack adequate financial knowledge and awareness, which may lead to poor financial behaviour and long-term financial instability. This study aims to assess the level of financial literacy among college students and examine its influence on their financial decision-making behaviour. The research also focuses on identifying the major sources of financial information used by students and analysing the relationship between financial literacy and saving habits. The study highlights the importance of financial education in developing responsible financial behaviour among young adults. By identifying gaps in financial awareness, the research provides useful insights and recommendations for improving financial literacy programs and promoting better financial management practices among students for a financially secure future.

DOI: https://doi.org/10.5281/zenodo.20354178

Published by: vikaspatanker

Authors: Dr.K.Srinivasan, Dr. M. K. Vediappan

Abstract: We propose and analyze the Proximal Adaptive Momentum with Variance Reduction (PAMVR) algorithm, a novel first-order method for solving nonconvex composite optimization problems of the form min F(x) = f(x) + g(x), where f is a smooth nonconvex function and g is a proper convex, lower-semicontinuous regularizer. PAMVR integrates three complementary mechanisms: (i) a momentum-corrected gradient estimator with adaptive step sizes, (ii) a periodic variance-reduction snapshot strategy inspired by SVRG, and (iii) a proximal operator for handling the nonsmooth component. Under standard Lipschitz-gradient and bounded-variance assumptions, we establish global convergence to an epsilon-approximate stationary point with a sample complexity of O(n + n^{2/3}/epsilon^2) stochastic gradient evaluations, matching the best-known bounds for this problem class while requiring weaker algorithmic assumptions than existing momentum-based methods. We further prove almost-sure convergence of the iterate sequence under a Kurdyka-Lojasiewicz (KL) regularity condition, obtaining explicit convergence rates depending on the KL exponent. The theoretical findings are validated on benchmark nonconvex problems including sparse logistic regression, matrix completion, and neural network training, demonstrating consistent improvements of 15–32% in convergence speed over PROX-SVRG, ProxGD-M, and Spider-Boost baselines. These results establish PAMVR as both a theoretically sound and practically competitive method for large-scale nonconvex optimization.

DOI: https://doi.org/10.5281/zenodo.20354108

Published by: vikaspatanker

Authors: Lalita Sonawane

Abstract: Smart classrooms and digital learning are changing education with the help of technology. Tools like smart boards, projectors, online classes, artificial intelligence, and virtual classrooms help students learn in an easy and interesting way. During the COVID-19 pandemic, online learning became very important because schools and colleges were closed. This research paper explains the meaning, benefits, challenges, and future of smart classrooms and digital learning. The information for this paper was collected from books, journals, websites, and research articles. The study shows that smart classrooms improve communication between teachers and students, increase student participation, and provide flexible learning opportunities. Students can study anytime and anywhere through digital platforms. However, there are also some problems like poor internet connection, high technology cost, lack of digital skills, and cyber security risks. The paper concludes that smart classrooms and digital learning are important for the future of education and need proper support, training, and infrastructure.

Published by: vikaspatanker

Authors: Prof. R. Raveendhra

Abstract: Artificial intelligence (AI) is transforming pharmaceutical research by enabling rapid molecular prediction, virtual screening, and biological data integration. However, many current AI systems lack energetic realism and mechanistic interpretability. This manuscript presents a conceptual framework termed Adaptive Quantum-Generative Optimization (AQGO), integrating generative AI, molecular transformers, quantum-mechanical screening, molecular docking, and expert pharmacological validation. The framework is designed to improve lead optimization by combining data-driven molecular generation with physics-based energetic evaluation. The article reviews current advances in AI-driven drug discovery, the role of quantum chemistry in molecular simulation, translational challenges, and future directions for hybrid AI–quantum systems. Emphasis is placed on explainability, reproducibility, ethical deployment, and scientific transparency. The proposed architecture highlights the potential of combining generative intelligence with quantum-mechanical validation to support more efficient and reliable pharmaceutical discovery pipelines.

Published by: vikaspatanker

Authors: Govardhan Jadhav, Anand Ahire, Hitesh Kalal, Rishi Mishra, Prof.S.R.Agrwal

Abstract: Social media usage has increased significantly in recent years, leading to concerns about addictive behavior and its impact on users’ productivity and mental well-being. This paper presents a Social Media Addiction Tracker system designed to monitor, analyze, and manage user engagement across various platforms. The proposed system collects data such as screen time, frequency of usage, and interaction patterns, and applies data analytics and machine learning techniques to identify signs of excessive usage and potential addiction. Based on the analysis, the system provides real-time feedback, usage reports, and personalized alerts to help users regulate their social media habits. Experimental evaluation demonstrates that the system effectively raises user awareness and supports behavior modification. The proposed solution aims to promote healthier digital habits and improve overall well-being.

Published by: vikaspatanker

Authors: Govardhan Jadhav, Anand Ahire, Hitesh Kalal, Rishi Mishra, Prof.S.R.Agrwal

Abstract: Social media usage has increased significantly in recent years, leading to concerns about addictive behavior and its impact on users’ productivity and mental well-being. This paper presents a Social Media Addiction Tracker system designed to monitor, analyze, and manage user engagement across various platforms. The proposed system collects data such as screen time, frequency of usage, and interaction patterns, and applies data analytics and machine learning techniques to identify signs of excessive usage and potential addiction. Based on the analysis, the system provides real-time feedback, usage reports, and personalized alerts to help users regulate their social media habits. Experimental evaluation demonstrates that the system effectively raises user awareness and supports behavior modification. The proposed solution aims to promote healthier digital habits and improve overall well-being.

Published by: vikaspatanker

Authors: Mohammed Munyim Hussain V, Poorvaj K P, Prashanth S R, Preetham M, Mr P Prasanna

Abstract: Agriculture remains a cornerstone of economic activity across developing nations, yet smallholder farmers routinely face yield gaps caused by uninformed decisions on crop selection, soil nutrition, and disease management. This paper presents AGRI HUB, a web-based Crop and Soil Management System that unifies several machine-learning and deep-learning services behind a single Flask-driven interface. Four core modules are delivered: (i) smart crop recommendation using a Random Forest classifier trained on seven agro-climatic parameters, achieving 99.55% accuracy across 22 crop classes; (ii) soil nutrient analysis and fertilizer recommendation through NPK deficit computation against crop-specific thresholds; (iii) plant disease detection using a ResNet-9 convolutional neural network capable of classifying 38 disease categories from leaf photographs; and (iv) real-time, weather-driven activity planning by consuming OpenWeatherMap API data to generate seven-day farming calendars. An AI chatbot powered by the Google Gemini large language model supplements the analytical modules with conversational agronomic guidance. A crop profitability comparison dashboard rounds out the system, enabling evidence-based economic decisions. Experimental evaluation confirms that the integrated platform consistently outperforms single-module alternatives in both accuracy and decision breadth, offering a scalable, cost-effective tool for precision agriculture.

Published by: vikaspatanker

Authors: Parag Hossain

Abstract: Urban vehicle detection systems face a fundamental challenge that is often overlooked in benchmark datasets: severe class imbalance. In real-world traffic scenes, common vehicles such as cars appear thousands of times more frequently than critical but rare categories including ambulances, e-bikes, and motorcycles. This imbalance causes standard detectors to become biased toward majority classes, leading to unacceptable failure rates for minority class detection in safety-critical applications. In this paper, we propose a novel Class-Balanced Knowledge Distillation (CBKD) framework specifically designed to address this challenge on the challenging CAVI-14 dataset, which contains fourteen urban vehicle categories with up to fifteen-fold class imbalance. Our method integrates three key components: class-balanced sampling to ensure equal exposure to all classes during training, focal loss with class-specific weights to down-weight easy majority examples, and knowledge distillation from a teacher model pretrained on a synthetically balanced dataset. Extensive experiments demonstrate that CBKD achieves perfect mean average precision at 0.50 intersection-over-union threshold (mAP50) of 1.000 and near-perfect mAP50-95 of 1.000 after one thousand training epochs. Per-class F1 scores consistently exceed 0.97 across all fourteen categories, including the rarest classes. Qualitative results on validation images show accurate detection even under heavy occlusion and challenging lighting conditions. Our approach establishes a new state-of-the-art on the CAVI-14 dataset and provides a practical, reproducible solution for imbalanced object detection in intelligent transportation systems.

DOI: https://doi.org/10.5281/zenodo.20351972

Published by: vikaspatanker

Authors: Natalie Brooks, Grace Mitchell, Charlotte Evans, Amelia Foster, Naveen Kumar

Abstract: The rapid adoption of cloud-native architectures has increased the demand for high-performance Java applications capable of delivering scalability, reliability, and operational efficiency across distributed enterprise environments. This research paper explores intelligent JVM tuning and cloud scaling strategies designed to optimize the performance of Java-based cloud applications operating in modern hybrid and multi-cloud infrastructures. The study examines critical performance optimization techniques including garbage collection tuning, heap memory optimization, thread management, JVM parameter configuration, container-aware resource allocation, and real-time application monitoring. Additionally, the paper investigates the role of cloud orchestration platforms, Kubernetes-based auto-scaling, AI-driven observability systems, and predictive resource management frameworks in enhancing application responsiveness and infrastructure utilization. Intelligent automation mechanisms integrated with JVM performance analytics enable dynamic workload balancing, anomaly detection, and proactive remediation of performance bottlenecks. The research further analyzes the impact of microservices architectures, distributed caching systems, and continuous deployment pipelines on improving scalability and operational agility. Security, governance, and cost optimization considerations associated with enterprise-scale Java cloud deployments are also discussed. The findings demonstrate that intelligent JVM tuning combined with adaptive cloud scaling significantly improves application throughput, reduces latency, enhances fault tolerance, and minimizes operational overhead in high-volume enterprise computing environments. This research provides a comprehensive framework for organizations seeking to modernize Java application infrastructures while maintaining performance stability, business continuity, and long-term cloud operational efficiency.

DOI: http://doi.org/10.5281/zenodo.20351776

Published by: vikaspatanker

Authors: Michael Anderson, Matthew Collins, Daniel Foster, Christopher Hall, Naveen Kumar

Abstract: Modernizing legacy financial systems has become a strategic priority for enterprises seeking to improve operational agility, scalability, security, and digital service delivery in rapidly evolving financial ecosystems. Traditional financial platforms built on monolithic architectures and outdated technologies often suffer from high maintenance costs, limited interoperability, performance inefficiencies, and reduced adaptability to modern cloud-native environments. This research paper explores enterprise-scale re-engineering approaches for transforming legacy financial systems through Java-centric software paradigms integrated with intelligent cloud automation frameworks. The study examines the role of Java-based microservices architectures, containerization, API-driven integration, Infrastructure as Code (IaC), DevOps practices, and AI-powered cloud orchestration in enabling scalable and resilient modernization strategies. Furthermore, the paper analyzes how intelligent automation technologies, including machine learning, predictive analytics, automated deployment pipelines, and autonomous monitoring systems, enhance system reliability, operational efficiency, and infrastructure optimization across hybrid and multi-cloud financial environments. The proposed framework emphasizes secure migration methodologies, continuous compliance validation, self-healing operational capabilities, and cloud-native application modernization for mission-critical financial services. Additionally, the research discusses implementation challenges such as legacy system complexity, regulatory compliance, cybersecurity risks, data migration constraints, and organizational transformation requirements. The study concludes that the integration of Java-centric re-engineering methodologies with intelligent cloud automation frameworks provides a robust foundation for achieving sustainable enterprise modernization, accelerated digital transformation, improved customer experience, and long-term technological adaptability within modern financial institutions.

DOI: http://doi.org/10.5281/zenodo.20351718

Published by: vikaspatanker