Category Archives: Uncategorized

Authors: Varun Garg, Shruti Rajak, Arpita Maravi, Anupama Awadhiya, Sarah Khan

Abstract: The increasing presence of generative artificial intelligence (AI) in educational settings is transforming the way students engage with learning. Tools powered by AI are making information more accessible, enabling quicker completion of academic tasks, and offering personalized support tailored to individual needs. While these benefits are undeniable, there is a growing concern that continuous dependence on such technologies may gradually reduce students’ active cognitive involvement in the learning process. This study explores how the use of generative AI tools influences student learning behaviour, particularly focusing on critical thinking and problem-solving skills. To gain a comprehensive understanding, a mixed-method approach was adopted, combining survey responses with a comparative evaluation of tasks completed with and without AI assistance. The results suggest that although AI enhances efficiency and convenience, overreliance on these tools can limit deeper cognitive engagement and independent reasoning. The findings emphasize the importance of mindful and balanced use of generative AI in education, ensuring that technological support complements rather than replaces essential learning processes.

DOI: https://doi.org/10.5281/zenodo.20050504

Published by: vikaspatanker

Authors: Dr. Prahlad Singh

Abstract: Mathematics was often portrayed as the story of great men, but history proves otherwise. In ancient times, the Early Modern Era, during the development of the research university in the nineteenth century, and through the modern scientific state, women played roles in the development of mathematical thought and practice. They commented on mathematics, taught mathematics, produced textbooks, and made groundbreaking contributions to number theory, algebra, logic, geometry, elasticity theory, and computing. However, their success did not always translate into recognition. Women lacked access to education, entry into academies and universities, resorted to anonymous publications, received precarious or unwaged appointments, and were known for their relationships with eminent men. In this essay, it will be argued that the major historical trend lies in women's active involvement in mathematics coupled with their systematic exclusion from the processes of certifying achievements, awarding rewards, archiving accomplishments, and recalling mathematicians in institutional memory. Among the most notable women who worked in the field of mathematics will be mentioned Hypatia, Maria Gaetana Agnesi, Sophie Germain, Sofia Kovalevskaya, Emmy Noether, Grace Hopper, Julia Robinson, Euphemia Lofiton Haynes, Maryam Mirzakhani, Karen Uhlenbeck, and Maryna Viazovska.

DOI: https://doi.org/10.5281/zenodo.20049877

Published by: vikaspatanker

Authors: Dr. Prahlad Singh

Abstract: Ancient Indian mathematics and astronomy evolved as part of a tightly linked scholarly tradition wherein numbers, geometry, trigonometry, calendars, and planetary astronomy were practiced and theorized simultaneously rather than as distinct and separate activities. In such an intellectual environment, Aryabhata, whose Aryabhatiya was written around 499 CE, emerged as the pivotal classical scholar. He provided not only mathematical formulas but also geometrical facts, sine values, solutions to indeterminate equations, models for planetary motion, theories of eclipses, and the remarkable proposition that the observed daily rotation of the stars is due to the rotation of the Earth. But what made Aryabhata influential was more than just his own contributions, since other Indian mathematicians and astronomers such as Bhaskara I, Brahmagupta, Lalla, and Bhaskara II preserved, disputed, modified, and elaborated on his teachings, resulting in one of the most impressive pre-modern mathematical astronomical traditions found anywhere in the world. This paper contends that the accomplishments of Aryabhata and the Indian tradition based on his work can be considered an important scholarly tradition in which mathematics worked for astronomy, astronomy inspired mathematics, and the practice of commentary was a powerful force of innovation.

DOI: https://doi.org/10.5281/zenodo.20049527

Published by: vikaspatanker

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

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.

Published by: vikaspatanker

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

Published by: vikaspatanker