Daily Archives: May 6, 2026

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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.

Published by: vikaspatanker