Category Archives: Uncategorized

Authors: Pradeep Kumar, Dr. Sunil Maggu

Abstract: Brain tumors represent life-threatening neurological conditions requiring precise classification for effective treatment planning. This paper presents a Multi-Class Brain Tumor Classifier capable of distinguishing between Glioma, Meningioma, Pituitary, and No Tumor classes from MRI scans. Unlike standard binary classifiers, the system employs an Ensemble of five supervised Machine Learning algorithms — Random Forest, XGBoost, SVM, KNN, and Naive Bayes — combined through Soft Voting for robust decision-making. Texture Analysis using GLCM (Gray-Level Co-occurrence Matrix) and LBP (Local Binary Pattern) feature extraction provides explainable, biologically interpretable features rather than opaque deep-learning representations. The system is deployed as a Flask web application that automatically generates standardized PDF Medical Reports for clinical documentation. Experimental evaluation on the Kaggle Brain Tumor MRI Dataset (7,023 images) confirms that the ensemble approach achieves superior accuracy, with Random Forest and XGBoost leading individual classifier performance at 90.68% and 90.53% respectively.

Published by: vikaspatanker

Authors: Prof.F.J.Sayyad, Kale Tejas Popat, Ganeshkar Shraddha Santosh, Kucheker Priti Dattaray

Abstract: Electric vehicles (EVs) represent a promising and sustainable mode of transportation that reduces greenhouse gas emissions and dependence on fossil fuels. battery and wiring harness playing key roles. This abstract provides an overview of the selection of batteries and wiring harnesses for electric vehicles. Battery selection involves evaluating various parameters, including energy density, power density, cycle life, and cost. Lithium-ion batteries are the most commonly used technology due to their high energy density, long cycle life, and low self-discharge rates. The wiring harness in an electric vehicle is a complex network of wires and connectors that connects various electrical components, including the battery, motor, inverters, and other vehicle systems appropriate wiring harness is critical to ensure the efficient flow of power and data throughout the vehicle.

Published by: vikaspatanker

Authors: Paul Binu, Paulu Wilson, Ronal Shoey George

Abstract: This paper presents an emotion-aware voice-based conversational therapy assistant that integrates speech recognition, con-versational AI, and emotional text-to-speech synthesis into a unified pipeline. The system captures user speech through a microphone, transcribes it to text, generates context-aware empathetic responses using a large language model (Gemini AI), and synthesizes emotion-ally expressive speech output using IndexTTS2 with zero-shot voice cloning. The architecture follows a modular design comprising four major modules: Voice Input, Processing and AI, Emotion Analysis, and Speech Synthesis. The emotion mapping subsystem identifies user affect and selects an appropriate response emotion to guide TTS output. Evaluation against two baselines (generic neutral TTS and rule-based keyword approach) demonstrates that the proposed model achieves the highest overall score of 74.51, significantly outper-forming both baselines in holistic end-to-end quality. The system balances emotion recognition accuracy, response relevance, and audio naturalness, making it suitable for mental health support, virtual assistants, and human-centered AI applications. The results confirm that combining emotional conditioning with contextual response generation yields substantially better conversational quality than neutral or rule-driven approaches.

DOI: https://doi.org/10.5281/zenodo.20045982

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Authors: Pranjal Sharma, Kunal Lariya, Ghanendra Kumar Joshi, Dipesh Patel, Prof. Sanjay Kumar Dewangan

Abstract: The growing demand for eco-friendly and high-efficiency solar energy technologies has driven the exploration of lead-free perovskite materials as viable alternatives to traditional lead-based compounds. This project investigates the photovoltaic performance of a novel lead-free chalcogenide perovskite, BaHfS₃, under pressure-tuned conditions (0 GPa and 25 GPa) using SCAPS-1D simulation software. At 25 GPa, BaHfS₃ exhibits a direct bandgap of 1.30 eV — nearly ideal for single-junction photovoltaic applications under the Shockley–Queisser limit. A total of 64 device configurations were tested by varying Electron Transport Layer (ETL) and Hole Transport Layer (HTL) materials. The optimal structure identified was FTO/CdS/BaHfS₃/NiO/Au, achieving a power conversion efficiency (PCE) of 28.71% with a Voc of 0.9591 V, Jsc of 34.42 mA/cm², and FF of 86.98%. Key parameters including absorber layer thickness, doping concentration, defect density, and series/shunt resistance were systematically optimized. The study confirms BaHfS₃ as a sustainable and efficient absorber layer with significant potential for next-generation non-toxic and stable photovoltaic technologies.

DOI: https://doi.org/10.5281/zenodo.20045558

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Authors: Sunik Kumar Sharma, Aman Chandrakant Nagle

Abstract: The way networks work is changing fast, and that means we are more open to Cybersecurity threats. Old security systems do not work well together. They do not give us a clear picture of what is happening right now. This paper is about the design and implementation of a Real- Time Threat Detection Dashboard. This dashboard uses open-source tools to keep an eye on network threats all the time, analyze them, and show them in a way that is easy to understand. The system uses Suricata to detect intrusions, Nmap to find assets, and a web-based dashboard built using Flask and React. This framework lets us process security events in time and gives us useful information through visual analytics. We tested the system in a controlled environment. It worked well, detecting and showing threats with very little delay.

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Authors: Mrs. K. Senbagam, Dhanush S, Gopinathan S, Dilli Babu K

Abstract: Flooding is one of the most severe natural hazards, leading to significant losses in human life, infrastructure, and economic resources, particularly in flood-prone regions such as India. Rapid and reliable identification of inundated areas is essential for effective disaster response, mitigation planning, and resource allocation. Conventional flood mapping techniques are often labor-intensive, time-consuming, and limited by environmental constraints. In particular, optical satellite imagery is highly affected by cloud cover and poor visibility during extreme weather conditions. To address these limitations, this study proposes an automated flood assessment framework utilizing satellite-based remote sensing data. The approach primarily leverages Synthetic Aperture Radar (SAR) imagery, which enables consistent data acquisition irrespective of weather conditions or illumination. The proposed framework integrates image preprocessing, change detection, and region extraction techniques to identify flood-affected areas by analyzing temporal variations between pre-event and post-event images. The system is designed to efficiently highlight newly formed water bodies and quantify flood impact through statistical and visual outputs. A web-based interface is incorporated to enhance accessibility and interpretation of results. Experimental observations demonstrate that the proposed method provides reliable flood detection across diverse terrains, including urban and vegetation-covered regions. This work contributes toward developing a scalable and efficient solution for large-scale flood monitoring, supporting timely decision-making and improving disaster management strategies.

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Authors: Nousheen, Balasubbareddy Mallala

Abstract: A novel protection coordination approach utilizing artificial neural networks (ANNs) is introduced in this work for meshed high-voltage transmission systems. Existing overcurrent and distance relay coordination methods in meshed topologies are prone to relay blinding, zone overreach, and incorrect operation during power swing events. The developed ANN model is trained using an extensive fault scenario dataset generated through simulation of a 9-bus, 230 kV benchmark network in MATLAB/Simulink. The proposed architecture—with 18 inputs, three hidden layers containing 36, 24, and 12 neurons respectively, and a 9-output trip signal layer—delivers improved coordination speed, selectivity, and sensitivity over traditional relay configurations. Testing results demonstrate a fault classification accuracy of 98.54% on previously unseen data. On average, fault clearance times are shortened by 56.8% in comparison to conventional coordination approaches, and dependable detection of high-impedance faults is also achieved. The approach provides a flexible and adaptive protection solution well-suited to contemporary interconnected power grids.

DOI: https://doi.org/10.5281/zenodo.20045309

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Authors: Swati Pawar, Shreyash Karpe, Thanshu Agarkar, Mohit

Abstract: In today’s world, where we’re flooded with information, having a smart and efficient search system is more important than ever. Traditional search engines like Google rely on keywords and fixed ranking systems such as PageRank. While these methods work well, they often fail to truly understand what a user means, handle complex multi-step questions, or deliver deeply personalized results beyond just rewording queries. Recent advancements in AI, especially large language models (LLMs), have given rise to tools like Perplexity.ai and You.com, which combine search results into easy-to-read summaries. However, these tools still have limitations they lack deep personalization, emotional understanding, field-specific tuning, and adaptability to a user’s evolving search journey. This study presents a next-generation AI-powered search engine that bridges these gaps. It combines Google’s Custom Search API for scalability with advanced natural language processing for contextual understanding and intelligent recommendation systems. What sets this system apart is its ability to build a growing map of a user’s knowledge over time. It dynamically adapts to multi-step queries and continuously refines results to match the user’s needs and learning path. Our approach aims to connect the precision of keyword-based searches with the flexibility of conversational, chat-style searches. The result is more relevant answers, reduced search fatigue, and a smoother, more personalized experience especially valuable for academic research, technical exploration, and other knowledge-intensive tasks.

DOI: https://doi.org/10.5281/zenodo.20044339

Published by: vikaspatanker

Authors: Khushbu Rajput, Bhavesh Jain

Abstract: Agriculture is an important sector in terms of food security and economic development, especially in developing nations. Precise crop yield estimation is required for efficient agricultural planning and management in the context of the increasing effects of climate change. Crop yield is affected by various factors, including climate variability, soil type, and availability of nutrients. Conventional crop yield estimation techniques, which rely on average values and traditional knowledge, are not reliable due to the complexities involved in crop yield estimation. Proposed in this paper is a framework for crop yield prediction using machine learning, incorporating climatic and soil variables. The climatic variables of rainfall, temperature, and humidity, and soil variables of soil pH and necessary nutrients (nitrogen, phosphorus, and potassium) are used as input variables. Three supervised machine learning algorithms—Linear Regression, Random Forest, and Gradient Boosting—are applied and compared to assess their predictive capability. Linear Regression is applied as a baseline algorithm, while ensemble methods are applied to deal with non-linearities in agricultural data. The performance of the models is measured using typical regression evaluation criteria, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). The experimental outcomes show that the models based on ensemble methods perform better than the baseline model in terms of prediction accuracy and generalization ability. The results confirm that the combination of climatic and soil properties helps to improve crop yield prediction.

DOI: https://doi.org/10.5281/zenodo.20044195

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Authors: Bantu Tejasri, K. Shree Vatsal, Dharavath Mahesh, Assistant Professor Dr. Sukanth T.

Abstract: This paper presents the design and practical implementation of a solar-driven, self-navigating vacuum robot intended for use in indoor settings. The system harnesses photovoltaic energy to eliminate grid dependency, uses a multi-sensor arrangement for real-time obstacle detection, and incorporates an Arduino Mega 2560 microcontroller for centralized decision-making. The prototype was subjected to rigorous testing across multiple indoor scenarios, where it recorded a 97% obstacle detection accuracy, approximately 94% cleaning coverage, and a continuous runtime of 60–70 minutes following a 3–4 hour solar charge. The outcomes confirm that merging renewable energy with embedded robotics yields a cost-effective and sustainable alternative to conventional cleaning appliances.

DOI: http://doi.org/

Published by: vikaspatanker