Category Archives: Uncategorized

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.

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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

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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.

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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.

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Authors: Yuvraj Singh, Mohd Wali Abbas, Shardool Vikram Singh, Raziya Siddiqui

Abstract: Explainable Artificial Intelligence (XAI) has emerged as a critical field in modern healthcare, addressing the limitations of traditional “black-box” AI systems that lack transparency and interpretability. Your project focuses on developing an interpretable AI framework to assist clinicians in diagnosis, treatment decision-making, and patient management. This review summarizes the motivation, existing literature, research gaps, methodological framework, and potential clinical impact, while also interpreting the conceptual diagrams provided. The work highlights how XAI can improve clinician trust, ensure accountability, reduce bias, and enhance patient outcomes by making AI decisions understandable and actionable.

DOI: https://doi.org/10.5281/zenodo.20020249

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Authors: Dr.K.N.Kazi, Phule Pragati Bhagwan, Kashid Mukta Rajendra, Rajmane Prajkta Pandurang

Abstract: An Integrated Solid Waste Management System for Power Generation and Agricultural Usage is a sustainable approach to manage waste effectively while producing useful outputs. The system focuses on collecting, segregating, and processing different types of solid waste such as organic, recyclable, and non-recyclable materials. This system helps in reducing environmental pollution, minimizing landfill usage, and promoting energy generation. It also supports farmers by providing low-cost organic manure, improving soil fertility and crop yield. By integrating waste management with energy production and agriculture, the project contributes to sustainable development and efficient resource utilization. Overall, the system offers an eco-friendly, cost-effective, and practical solution to address waste disposal problems while generating power and supporting agricultural activities.

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Authors: Khushpreet Kaur, Anrika, Kashish, Ekta, Dr. Rajat Takkar

Abstract: The inability of existing online learning platforms to adapt to individual learner needs remains a fundamental and unresolved challenge in educational technology, contributing to persistently high dropout rates and poor knowledge retention across self-paced digital learning environments. This research proposes and evaluates a conversational AI assessment framework combined with dynamic personalised learning plan generation as a viable solution to this challenge. The study investigates whether natural, dialogue-based learner profiling yields more meaningful personalisation than conventional form-based or performance-data-driven approaches, and whether adaptive, quiz-based feedback integrated with multimodal resource matching improves learner engagement compared to uniform content delivery. A prototype platform named Flint was developed to implement and evaluate these research propositions, employing a dual-AI-engine architecture that addresses reliability and hallucination concerns identified in prior literature. Results demonstrate that conversational profiling successfully captures richer individual learner profiles, that dynamically generated plans align more closely with individual needs than static curricula, and that integrated gamification sustains motivation across extended learning engagements. The findings provide practical evidence to the growing body of research on AI-driven personalised education and demonstrate the feasibility of deploying large language model-powered individualised learning experiences at scale.

DOI: https://doi.org/10.5281/zenodo.20012543

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Authors: Kodavati Ram Sanjay, Ruban Kumar S, Saran Raj S, Dr. Arun Kumar

Abstract: Automated detection of crop leaf diseases and classification of remote sensing land cover categories remain challenging owing to complex backgrounds, illumination vari-ability, spectral distortions, and high intra-class visual similarity. Existing frameworks provide strong baselines but commonly suf-fer from scale-sensitive segmentation, redundant feature fusion, limited contextual representation, and slow convergence in high-dimensional feature spaces. This paper proposes the Adaptive Recurrent FusionNet (ARFusionNet) framework — a Flask-based web application that integrates four coordinated inno-vations: Multi-Scale Adaptive Contrast Normalisation (MACN) for illumination-robust preprocessing; Graph-Based Superpixel Attention Segmentation (GSAS) for adaptive Region-of-Interest extraction; Bidirectional Gated Recurrent Units (BiGRU) embed-ded within Residual Efficient Convolution Blocks with Adaptive Weighted Feature Aggregation (AWFA); and Hybrid Binary Differential Evolution controlled Particle Swarm Optimisation (BDE-PSO) for efficient feature selection. DenseNet121 serves as the backbone feature extractor. We validate the system on the Plant Pathology 2020 dataset (1,821 high-resolution apple leaf im-ages; four disease classes). ARFusionNet achieves 98.2% classifi-cation accuracy, surpassing the state-of-the-art baseline (97.6%), while reducing training time by approximately 78 seconds and remaining fully executable on a standard CPU laptop without GPU dependency. The accompanying web application exposes eight interactive diagnostic modules including leaf visualisation, Canny edge display, convolved feature maps, neural network architecture visualisation, and real-time per-image prediction.

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Authors: Dr.T.V.Deokar, Namrata Manoj Bandgar, Ankita Dilip Thombare, Bhagyashri Ashok Bhong

Abstract: A Realtimeloadflowmonitorindistributionsystemthisprojectdevelopsareal-timeloadflowmonitoring system for a distribution network using a bulb and a motor. The system continuously monitors electrical parameters to detect overload and fault conditions. A GSM module is used to send instant alerts to the user, while a buzzer provides local warning. This ensures quick response, improved safety, and reliable operation. The project also demonstrates the effect of different load types onsystem performance and serves as a simple, cost-effective model for smart power distribution

Published by: vikaspatanker