Daily Archives: May 4, 2026

Authors: Vaibhav Aggarwal, Laxmi, Yashraj Sharma

Abstract: The rapid advancement of information technology has transformed the traditional education system into a more flexible and accessible digital learning environment. This research paper presents the design and implementation of an Online Course Management System (OCMS) developed using Django web framework with Python as the backend programming language and SQLite as the database management system. The proposed system provides a comprehensive platform for educational institutions to manage courses, track student progress, handle enrollments, and generate completion certificates automatically. The system implements a role-based access control mechanism supporting three distinct user roles: Administrator, Instructor, and Student. Each role has specific permissions and functionalities tailored to their requirements. The frontend is developed using HTML5, CSS3, and Bootstrap 5 framework, ensuring responsive design across various devices. The research demonstrates how modern web technologies can be leveraged to create an efficient, scalable, and user-friendly learning management system.

Published by: vikaspatanker

Authors: Dimple Khatri, Garima, Rajat Takkar

Abstract: Large Language Models (LLMs) have emerged as a major breakthrough in artificial intelligence, significantly improving how machines process and generate human language. These models, built on transformer architectures, are capable of performing a wide range of tasks such as text summarization, translation, question answering, and code generation. In this paper, we analyze the applications and limitations of LLMs in real-world systems through a qualitative study based on literature review and conceptual experimentation. Our findings suggest that while LLMs provide high accuracy and flexibility across domains like healthcare, education, and customer service, they still face critical challenges such as hallucination, bias, high computational cost, and lack of interpretability. The study highlights the importance of integrating validation mechanisms and ethical AI practices to ensure reliable deployment. We conclude that although LLMs are powerful tools, their practical adoption requires careful optimization and responsible usage strategies.

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Published by: vikaspatanker

Authors: Dr. Rajul Misra, Mr. Saurabh Saxena, Ritik Saini, Waseem, Shuaib Ali

Abstract: The Single Phase Induction Motor Protection and Control System using IoT is designed to improve the safety, reliability and performance of single phase induction motors by continuously monitoring important electrical parameters and controlling the motor through internet-based technology. Single phase induction motors are widely used in household appliances, agricultural pumps, fans, compressors and small industrial machines due to their simple construction and low cost. However, these motors are highly sensitive to abnormal operating conditions such as overload, overcurrent, overheating, voltage fluctuation, phase failure and short circuit. These faults can reduce motor efficiency, increase maintenance cost and sometimes permanently damage the motor. In traditional systems, protection devices such as fuse, MCB or overload relay provide limited safety and do not allow remote monitoring of motor condition. To overcome this problem, Internet of Things (IoT) technology is used in this project to create a smart monitoring and protection system. The proposed system uses sensors such as current sensor, voltage sensor and temperature sensor to continuously measure motor parameters. These sensors are connected to a microcontroller such as Arduino, which processes the data and compares it with predefined safe operating limits. When the motor operates under normal conditions, the system continuously sends real time data to an IoT cloud platform such as Blynk or ThingSpeak through Wi-Fi module. The user can monitor motor parameters such as current, voltage and temperature using a mobile phone, tablet or computer from any location. If any abnormal condition such as overload, overheating or high current occurs, the microcontroller automatically activates the relay module to disconnect power supply to the motor and prevent damage. The system also provides remote control facility, allowing the user to turn the motor ON or OFF using IoT mobile application. This feature is especially useful in agricultural applications such as water pumping systems where motor needs to be controlled from distant locations. The system helps in early fault detection, reduces maintenance cost and increases operational safety. Overall, the IoT based motor protection system is an efficient, low cost and reliable solution for improving motor life and reducing failure risk. The system can be further enhanced by adding features such as SMS alert, mobile notification, automatic fault diagnosis and data logging for performance analysis. This project demonstrates how IoT technology can be effectively used in electrical engineering applications to develop smart and intelligent protection systems.

DOI: https://doi.org/10.5281/zenodo.20024088

Published by: vikaspatanker

Authors: Mrs.W. Asha Princy, Pooja K.P., Pooja Shree S, Prathisha A, Shahira Banu S

Abstract: The rapid advancement of artificial intelligence (AI) in healthcare has created unprecedented opportunities for improving diagnosis, treatment planning, and clinical decision-making. This paper presents DonorSync — an AI-powered Digital Twin system designed to assist physicians in liver and kidney donor-recipient matching using machine learning and medical image analysis. The proposed system combines Logistic Regression-based clinical parameter analysis (age, bilirubin, albumin, creatinine, urea) with a ResNet-50-driven ultrasound image evaluation module to generate ranked donor compatibility scores and transplant success probabilities in real time. Built on a FastAPI backend with MongoDB data storage and an HTML/CSS/JavaScript frontend, the platform provides secure, scalable, and efficient access to donor matching services. Experimental evaluation confirms that the integrated dual-modality approach substantially reduces donor selection time and enhances prediction reliability compared to conventional manual processes. The system aligns with UN Sustainable Development Goal 3 (Good Health and Well-Being) and Goal 9 (Industry, Innovation and Infrastructure).

Published by: vikaspatanker

Authors: Jag Pratap Singh Yadav

Abstract: Systems in reality are characterized by uncertainties. There are uncertainties associated with nature, economics, communications, healthcare delivery, production systems, and social systems, among others, which cannot be modeled using deterministic equations. Stochastic processes facilitate the formulation of mathematical models of systems whose behavior is affected by some random elements. They help in assessing risks, optimizing resource allocations, forecasting future behaviors, and enhancing system robustness. The paper centers on stochastic processes as techniques for dealing with uncertainty in systems. First, the concept of stochastic processes will be defined. Major types of stochastic processes, including Markov chains, Poisson processes, Brownian motion, random walks, and queueing models, will be discussed. Then, applications of stochastic models in various areas, such as financial modeling, engineering, health care, climate studies, operations management, telecommunications, and machine learning, will be explored. Additionally, this paper will examine advantages and disadvantages associated with stochastic modeling. In particular, problems associated with assumptions used in building stochastic models and computational complexities of such models will be analyzed. It will be concluded that stochastic processes represent powerful tools for studying and managing uncertain systems because they enable turning randomness from an issue into a quantifiable phenomenon.

DOI: https://doi.org/10.5281/zenodo.20021901

Published by: vikaspatanker

Authors: Prof. Pushpa T, Dheeraj P Aradhya, K Prajwal, Pramod Hegde, Kiran MR

Abstract: The global surge in non-communicable diseases (NCDs) necessitates a transition from episodic clinical care to continuous, data-driven personal health management. This paper details the development of the Smart Health and Fitness Tracker (SHFT), a scalable ecosystem built on the MERN stack. Unlike localized tracking applications, SHFT employs a centralized NoSQL architecture to provide longitudinal health data analysis. The system integrates real-time telemetry tracking—including caloric balance, hydration, and sleep hygiene—with automated BMI and BMR computation. By utilizing a React-based interactive dashboard and Node.js middleware, the platform achieves high data integrity and low-latency feedback. Experimental results demonstrate that the system enhances user engagement and supports informed decision-making for long-term wellness.

Published by: vikaspatanker

Authors: Ayush Pravin Kudale

Abstract: The exponential growth of social media has positioned user-generated content as a rich yet underexploited resource for understanding collective human behaviour, opinion dynamics, and information propagation. Existing analytical solutions are largely confined to individual platforms and often rely on opaque machine-learning pipelines, limiting transparency, reproducibility, and regulatory compliance. This work presents a novel API-driven social media intelligence framework that integrates heterogeneous data from Twitter, Reddit, and YouTube into a unified analytical pipeline. The proposed architecture synthesises three analytical dimensions: semantic text understanding through Natural Language Processing (NLP), structural interaction modelling via graph-theoretic methods, and decision transparency through Explainable Artificial Intelligence (XAI). A layered, modular design addresses the dual challenges of data heterogeneity and ethical governance. Empirical evaluation confirms that cross-platform data fusion yields measurably superior analytical stability and reduced platform-induced bias relative to single-source baselines. Beyond its research contributions, the framework is deliberately architected to serve as a deployable foundation for a final-year academic project.

DOI: https://doi.org/10.5281/zenodo.20021338

Published by: vikaspatanker

Authors: Mansi Ankush Thakare, Dr Vikas Kumar

Abstract: Agricultural mechanization plays a critical role in enhancing productivity, operational efficiency, and sustainability in modern farming. However, the substantial financial burden associated with purchasing farming equipment makes ownership impractical for many small and medium-scale farmers. Renting farming equipment emerges as a viable alternative, offering affordability, flexibility, and optimal resource utilization. This paper examines the benefits, challenges, and economic implications of renting farming equipment, backed by global case studies and emerging trends. Despite logistical and financial constraints, advancements in digital platforms and AI-driven rental services have significantly improved accessibility and efficiency. Additionally, this study explores policy measures and economic strategies that can enhance the adoption of rental services in the agricultural sector, thereby contributing to sustainable and inclusive farming practices.

Published by: vikaspatanker

Authors: Dr. Rajul Misra, Mr.Bhaskar Chauhan, Mr. Saurabh Saxena, Vivek Kumar, Kritika Singh

Abstract: This paper presents the design and development of an IoT-based automated irrigation system that maintains uniform soil moisture across agricultural fields. The system integrates distributed soil moisture sensors, a microcontroller-based control unit, and IoT connectivity to regulate water delivery through solenoid valves and motor-driven pumps without requiring on-site human supervision. An active drainage subsystem prevents waterlogging when moisture exceeds safe thresholds. As soil conditions are constantly monitored, the system infuses water only when it must be and eliminates excess water when necessary. Using this method not only helps save water but also keeps the soil conducive for growing crops. The model is cost-efficient, scalable, and suitable for precision agriculture.

DOI: https://doi.org/10.5281/zenodo.20021005

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Authors: Prof. K.S. Tamboli, Javalekar Shubham Shivaji, Katkar Sanyukta Jivan, Solavane Shubham Bharat

Abstract: This paper presents the design and implementation of a hybrid solar-grid water pumping system with automatic power switching to ensure reliable irrigation. The system utilizes a photovoltaic (PV) array as the primary energy source and integrates grid supply as a backup during low solar conditions. An intelligent control algorithm is developed using MATLAB/Simulink to monitor system parameters and automatically switch between power sources. The system improves energy efficiency, reduces dependency on conventional electricity, and ensures uninterrupted water supply. Simulation and experimental results validate the effectiveness, reliability, and cost-efficiency of the proposed system.

Published by: vikaspatanker