Category Archives: Uncategorized

Hybrid CNN-LSTM Architecture For Automated Diabetic Retinopathy Detection With Clinical Explainability

Authors: Jayraj Patil, Yash Pavnekar, Siddhesh Nikam, Pratik More, Prof. Dr. Jyoti Chavan

Abstract: Every year, diabetic retinopathy (DR) threatens the vision of millions, but the screening process just can’t keep up. The current system moves slowly—specialists are overworked, results change from one doctor to the next, and too many patients learn they have DR only after their sight is already at risk. We wanted a fix. So, our team created an automated deep learning platform—a hybrid that stacks a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) layers. This model doesn’t just detect DR; it also grades its severity from plain retinal fundus photos. We didn’t stop after building one model. We tried three hybrid approaches—Custom CNN+LSTM, MobileNetV2+LSTM, and InceptionResNetV2+LSTM—and compared them to seven standard CNN-only baselines. To make sure even the subtle signs stand out, we used CLAHE (Contrast Limited Adaptive Histogram Equalization) on every image. Medical datasets are imbalanced by nature, so we rebalanced things through loss weighting, giving serious DR cases the extra attention they deserve. And for transparency, we turned to Grad-CAM, producing heatmaps so doctors can see exactly what our AI focused on. When it came down to results, the InceptionResNetV2+LSTM beat the rest: 91.4% accuracy, a Quadratic Cohen’s Kappa of 0.89, and a Macro F1-Score of 0.86 for multi-class DR grading. More than just numbers—two ophthalmologists validated our Grad-CAM maps and agreed with the AI’s focus 91% of the time. To make everything practical, we built a Streamlit web app layered with secure roles, live predictions, explainability, and instant PDF reports. This project pushes DR screening closer to where it needs to be. With smarter AI, clear explainability, and a clinic-ready platform, screening can be faster, fairer, and more dependable—catching DR cases that used to slip by, and backing up doctors with real confidence.

Artificial Intelligence Assisted Drug Discovery Of Noncommunicable Disease: Predictive Modelling And Optimization

Authors: Ayush Patel, Sangeeta Vhatkar, Namdeo Badhe

Abstract: AI and machine learning are shaping up drug discovery and it is about time. The old way- slow, expensive and full of dead-ends- are outdated. Tools like deep learning, graph neural networks, GANs and reinforcement learning are stepping up. These tools actually help scientists spot new targets, sift through virtual libraries for promising compounds, predict how molecules will behave, dream up brand new drug designs, find fresh uses for old drugs and even streamline clinical trials. Graph models, in particular, shine because they get the complicated shape and connections in molecules. These all let researchers simulate how tiny structures interact in the messy reality of biology. Generative AI pushes boundaries even further by designing all sorts of molecules- each tailored for certain properties- across an almost endless chemical universe. Technology is making and creating waves everywhere: cancer, heart conditions, brain disorders, infections-you name it. Across the board, the results are better predictions, smarter trade-offs, more molecular variety and a smoother path from lab to clinic. Of course, it’s not all smooth sailing. Challenges remain like messy data, black-box designing making, regulatory headaches and the tricky business of converting code into medicine. But even with those bumps, AI-powered drug discovery isn’t another upgrade. It is a real-shift: more data-driven, more scalable and a lot more personal. The evidence keeps piling up-AI is speeding up therapeutic breakthroughs and rewriting the future position of medicine, one algorithm at a time.

DOI: https://doi.org/10.5281/zenodo.19663484

Determination Of Varicose Veins Problems Using Concurrent Sensor Network With Heat Treatment Module

Authors: Karthikeyan D, Dhanush D, Harikrishnan S, Jagadesh J, Jagan K

Abstract: Varicose veins are a prevalent vascular disorder caused by weakened vein walls and malfunctioning valves, resulting in improper blood circulation and vein enlargement in the lower extremities. Early identification and timely intervention are essential to prevent complications such as venous ulcers and chronic discomfort. This paper presents a wearable healthcare system designed to detect and manage varicose vein conditions using a concurrent sensor network integrated with a heat treatment module. The proposed system employs multiple sensors, including photoplethysmography (PPG), temperature, infrared, and pressure sensors, to acquire physiological data related to blood circulation and skin temperature variations. The collected signals are processed using a microcontroller-based system that performs real-time analysis and identifies abnormal vascular patterns. Upon detecting irregularities, the system activates a controlled heat therapy module to improve blood flow and reduce discomfort. The integration of sensing and therapeutic functionality enables continuous monitoring and immediate intervention, enhancing patient convenience and reducing dependency on hospital visits. The proposed framework demonstrates the effectiveness of IoT-based wearable systems in improving vascular health monitoring and providing automated therapeutic response for varicose vein management.

Smart Lithium-ion Battery Monitoring, Protection And Automatic Switching System

Authors: H. M. Pawar, Kawar Arpita Chandrashekhar, Aher Vaishnavi Sanjay, Birari Prafull Pravin, Jadhav Rushikesh Hiraman

Abstract: The increasing demand for reliable and efficient energy storage systems has led to the widespread use of lithium-ion batteries in various applications such as electric vehicles, renewable energy systems, and portable electronics. However, these batteries are highly sensitive to conditions like overcharging, over-discharging, overheating, and short circuits, which can reduce their lifespan and pose safety risks. This paper presents a Smart Lithium-Ion Battery Monitoring, Protection, and Automatic Switching System designed to enhance battery performance and safety. The proposed system continuously monitors key parameters such as voltage, current, and temperature using embedded sensors and a microcontroller-based control unit. It incorporates protection mechanisms to prevent hazardous conditions and ensures optimal battery operation. Additionally, an automatic switching feature is implemented to seamlessly transition between power sources or backup batteries during faults or low charge conditions. The system improves reliability, efficiency, and longevity of lithium-ion batteries while minimizing human intervention. Experimental results demonstrate the effectiveness of the proposed system in real-time monitoring and protection, making it suitable for modern energy management applications.

DOI: https://doi.org/10.5281/zenodo.19666788

Iot Based Driving License Detection and Safety System

Authors: H. M. Pawar, Deore Shrawani Shashikant, Kapadnis Tejas Sudhakr, Pagar Shubham Manik

Abstract: With the rapid increase in road accidents and traffic violations, ensuring driver authenticity and safety has become a major concern. This paper presents an IoT-Based Driving License Detection and Safety System designed to verify the validity of a driver’s license and enhance road safety through real-time monitoring. The proposed system integrates RFID/QR code-based license identification with IoT-enabled devices to authenticate drivers before vehicle ignition. A microcontroller-based unit processes the data and checks it against a stored or cloud-based database. If the license is invalid, expired, or not detected, the system restricts vehicle operation and sends alerts to concerned authorities or vehicle owners. Additionally, safety features such as alcohol detection, seat belt monitoring, and accident detection are incorporated to minimize risks. The system uses wireless communication technologies to transmit real-time data and alerts. This approach not only prevents unauthorized vehicle usage but also promotes responsible driving behavior. Experimental results demonstrate that the system is efficient, reliable, and suitable for smart transportation and intelligent traffic management systems.

DOI: https://doi.org/10.5281/zenodo.19667350

Linguistic Structures And Power In Martin Luther King Jr. ’s Lincoln Memorial Speech: An FDG And CMT Analysis

Authors: Aye Pa Pa Myo, Liping Chen

Abstract: This study aims to explore the linguistic structures and the concept of power underlying King’s speech through structural – emotional perspectives, adopting the dual lens of Functional Discourse Grammar (FDG) and Conceptual Metaphor Theory (CMT). FDG provides a robust framework to examine the functional structures of King’s language at the syntactic, semantic, and pragmatic levels, while CMT allows for a nuanced understanding of how metaphors in the speech contribute to the construction of power, social change, and collective identity. The study employs a mixed quantitative -qualitative research method. Findings reveal that King prefers using linguistic structures at the phonological and morphosyntactic levels more than at the representational and interpersonal levels. He further emphasizes concepts of power using 15 instances of metaphors in his speech. His masterful employment of linguistic structures and metaphors brings ideology, stance, and power to his political discourse, grasping the attention of his audiences and making significant efforts in demanding rights for freedom, justice, equality, and job opportunities, as well as in promoting business in the Black community, which is being oppressed by the White Society. Future research could further explore King’s linguistic structures and metaphors by utilizing digitalization in the modern era.

DOI: https://doi.org/10.5281/zenodo.19668145

A Comprehensive Review Of Machine Learning And Deep Learning Approaches For Student Failure Rate Prediction: Towards An Enhanced Hybrid And Explainable Framework

Authors: Babandi Usman, Salim Ahmad, Zahraddeen Safyanu

Abstract: Student academic failure remains a persistent challenge in higher education, particularly in developing countries where late identification of at-risk students limits timely intervention. Recent advances in Educational Data Mining and Learning Analytics have enabled predictive modelling of student performance; however, many existing models suffer from poor interpretability, data imbalance, and limited integration of behavioral and socio-economic variables. This study presents a comprehensive review and synthesis aimed at guiding the development of an enhanced algorithm for student failure rate analysis. A systematic review methodology was employed, involving structured literature collection, screening, categorization of predictive techniques, and comparative analysis of statistical, machine learning, ensemble, and deep learning approaches. Algorithms were evaluated using established performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC, alongside qualitative criteria such as interpretability, scalability, and real-time applicability. The analysis reveals that while ensemble and deep learning models achieve superior predictive accuracy, they often lack transparency and struggle with imbalanced educational datasets. Based on these findings, the research proposes a hybrid and explainable predictive framework that integrates ensemble learning, neural networks, imbalance-handling techniques, and explainable AI methods. The review demonstrates that hybrid approaches provide the most promising balance between accuracy, interpretability, and early detection capability. The major contribution of this research lies in synthesizing fragmented literature into a unified framework for enhanced student failure prediction, identifying critical research gaps, and establishing a methodological foundation for developing a scalable, interpretable, and real-time predictive system to support data-driven academic interventions.

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Digital Surveillance In India: Constitutional Challenges And Implications For Civil Liberties

Authors: Jiya Bhatt, Manthan Khopkar

Abstract: Digital surveillance has emerged as an integral feature of governance in contemporary India. The desire for national security, prevention of crime, distribution of welfare services, and administrative efficiency has prompted the Indian state to increasingly adopt digital technologies in the areas of governance. The rapid expansion of digital technologies such as biometric technologies, facial recognition technologies, and extensive communication interception technologies has enabled the Indian state to increasingly use digital surveillance. The use of such technologies has not only provided efficiency in governance but has also posed significant challenges to civil liberties such as the right to privacy, freedom of expression, and the right to due process. The paper seeks to examine the evolution of digital surveillance in India, the constitutional basis of digital surveillance in India, and the implications of digital surveillance on civil liberties in India. The paper seeks to examine the implications of digital governance in India on the constitutional values of a democratic society. The paper seeks to examine the implications of digital governance in India on the constitutional values of a democratic society. The paper seeks to examine the implications of digital governance in India on the constitutional values of a democratic society.

DOI: https://doi.org/10.5281/zenodo.19672251

Intelligent Medicine Box For Patient Care

Authors: Mrs. S. Revathi, M. Aarthi Sree, N.Deepika, K.Rajalakshimi

Abstract: Medication management plays an important role in maintaining good health, especially for elderly individuals and patients undergoing long-term treatment. Forgetting medication times or improper organization of medicines can lead to serious health issues. A smart medicine box is developed to assist users in managing daily medication schedules in an efficient and reliable manner. The system operates using a microcontroller (Arduino) integrated with a real time clock to monitor predefined medication timings and generate timely reminders. Multiple medicine compartments are provided to store different medicines separately, reducing the chances of confusion and incorrect usage. Visual and audio alerts notify users at scheduled times, ensuring regular intake of medicines. The system also monitors medicine availability and provides alerts when medicine levels become low, helping users refill medicines on time. Simple controls and a user- friendly interface make the system suitable for home use without requiring technical knowledge. The smart medicine box enhances medication adherence, improves patient safety, and reduces dependence on caregivers. Such a system is especially useful in households with elderly people and patients requiring continuous medication, offering an effective solution for organized and timely medicine management.

DOI: https://doi.org/10.5281/zenodo.19672584

Automatic Hair Dryer With Temperature And Speed Control

Authors: R.Ranjith, S.Sudhakar, A.Adhithya, Dr. T. Sengolrajan

Abstract: This project develops a wirelessly connected hair drying system that automatically adjusts airflow speed based on real-time hair moisture detection through integration with existing hair dryer units. The system employs ESP32 microcontroller as the central processing unit with built-in Wi-Fi capability for cloud connectivity and mobile application interface. Capacitive moisture sensors continuously monitor hair wetness levels and transmit data to the ESP32 which processes the information through adaptive algorithms. DS18B20 temperature sensors monitor thermal output while solid-state relays control the heating element through PWM signals. Motor speed regulation utilizes to modulate AC motor performance across three operational levels high speed for very wet hair conditions, medium speed for moderately damp hair and low speed for nearly dry hair conditions. The touch control interface integrates mounted on the dryer surface for manual operation while the mobile application communicates through Firebase cloud platform enabling remote parameter adjustment. An OLED display module presents real-time operational data including moisture levels and temperature readings. The integration process involves mounting the moisture sensor near the dryer nozzle, installing temperature sensors adjacent to heating elements and housing the ESP32 module within the dryer handle.

DOI: https://doi.org/10.5281/zenodo.19675047

Service Provider & Job Seeker Application A Scalable Digital Platform For Service Marketplace

Authors: Amit Gupta, Mohammad Mokarram Siddiqui, Kaminee Pachlasiya, Santoshi Kharal

Abstract: To address these challenges, this study proposes a Service Provider & Job Seeker Ap-plication, a digital platform designed to connect customers directly with skilled workers through a centralized and interactive system. The application enables service providers to register, create professional profiles, and showcase their skills, while customers can search, compare, and hire workers based on ratings, location, and availability.The system architecture is built using modern technologies such as React Native for frontend development, Node.js with Express for backend services, and MongoDB/MySQL for data storage. It incorporates essential features such as realtime booking, secure digital payments, notifications, and a rating system to ensure trust and transparency.Unlike traditional service models, the proposed system not only simplifies the hiring process but also enhances employment opportunities for skilled workers by providing them with a digital platform. Additionally, features like role-based access control, real-time updates, and analytics improve system efficiency and scalability.Overall, the application the gap between service seekers and job seekers, promoting digital transformation and improving accessibility in the service industry.

DOI: https://doi.org/10.5281/zenodo.19675217

Lightweight Retrieval-Augmented Generation System For CPU-Only Document Question Answering

Authors: Pratik Halnor, Om Kale, Vishal Gore, Abhishek Kahar, Devyani Jadhav

Abstract: Retrieval-Augmented Generation (RAG) improves the factual accuracy of Large Language Models by grounding responses in external documents. However, most existing systems rely on dense em-beddings, vector databases, and GPU-based computation, making them unsuitable for low-resource environments. This paper presents a lightweight RAG system designed specifically for CPU-only environments. The system integrates PDF text extraction and Optical Character Recognition (OCR) using PyMuPDF and Tesseract, followed by a keyword-based retrieval mechanism. The retrieved context is then passed to a language model API for response generation. Experimental evaluation demonstrates that the system achieves an accuracy of 83.3% with an average response time of approximately 2.2 seconds. The results highlight that efficient document intelligence systems can be developed without heavy computational requirements

Renewable Energy Based Microgrid Power Management System

Authors: M. Saicharan, K.Hari Krishna, J. Krishna, Dr M.Sri Suresh

Abstract: A 2 kW Hybrid Microgrid with PV, Wind Turbine and BESS: Design, Modeling and Simulation. In this paper, design, modeling and simulation of a 2 kW hybrid renewable energy microgrid having solar PV, wind turbine generation and BESS are presented. Perturb and observe (P&O) MPPT algorithm is adopted for PV subsystem whereas dynamic wind speed model is used for wind turbine. In this model, a Fuzzy Logic Controller (FLC) controls the power flow between battery, load, grid, and renewable sources in real time. The DC link is connected to AC grid through a three-phase inverter and transformer. Simulation results in MATLAB/Simulink indicate that the proposed system can supply steady 2 kW load with proper battery charge/discharge control and it doesn’t need a lot of grid power. The proposed system can find applications in rural electrification, isolated communities, and smart grid installations.

DOI: https://doi.org/10.5281/zenodo.19675652

Pre-Ride Vision :Realtime Visual Crowd Guidance System For Railway Platforms

Authors: Ganapathy T, Thirumalai T, Anbarasan A J

Abstract: The Real-Time Visual Crowd Guidance System for Railway Stations is a smart safety and passenger management solution developed using Python and ESP32 with serial communication. A camera connected to the Python system continuously monitors the crowd near each train compartment and analyzes the density in real time using computer vision techniques such as OpenCV-based people detection. Based on the crowd level, the Python application sends serial commands to the ESP32, which controls the red, yellow, and green LEDs, buzzer, and LCD display to guide passengers. When the crowd is high, the red LED glows, buzzer alerts, and the LCD displays “Move to Next Compartment”; for moderate crowd, the yellow LED indicates caution; and for low crowd, the green LED with the message “You Can Enter” is shown. This system helps reduce congestion, improves passenger flow, and enhances safety in railway stations during peak hours through real-time visual and audio guidance.

DOI: https://doi.org/10.5281/zenodo.19676460

QuantumDrive: A Secure Architecture For Ephemeral QR-Based File Sharing

Authors: Aryan D. Vekariya

Abstract: When working across different devices, moving a file securely from a phone to a laptop is surprisingly annoying. People usually end up emailing files to themselves or logging into a cloud drive just for one small transfer. This not only wastes time but also leaves junk data sitting on third-party servers forever. To fix this, we built QuantumDrive, a simple web app that lets you transfer files directly without forcing you to make an account. We used React for the frontend and Node.js for the server. Instead of saving your files permanently, the app encrypts them right inside your browser. The receiver then downloads the file using a temporary QR code, and the file gets deleted from the server shortly after. This paper explains how we designed the app, how the encryption works, and what we learned while building it during our internship.

Decolonizing Masculinity: Rethinking Leadership And Gender In Chinua Achebe’s Fiction

Authors: E Umachandrika, Dr L Sangeetha

Abstract: The construction of masculinity in African literature, particularly in the works of Chinua Achebe, has often been interpreted through the lens of tradition, authority, and patriarchal dominance. However, a closer examination reveals that Achebe’s narratives not only depict but also interrogate and complicate these masculine ideals. This article explores how masculinity is constructed, performed, and ultimately destabilized in Achebe’s major novels, with particular attention to the relationship between gender and leadership. By analyzing key texts such as Things Fall Apart, No Longer at Ease, and A Man of the People, the study argues that Achebe simultaneously represents and critiques patriarchal structures embedded within Igbo society and postcolonial governance. Drawing on postcolonial theory and gender studies, the paper examines how leadership is coded as masculine and how this coding contributes to both personal and societal crises. Furthermore, it highlights the often-overlooked roles of female characters and alternative masculinities that challenge dominant norms. The article ultimately contends that Achebe’s fiction offers a nuanced critique of patriarchal authority, suggesting the need for a reimagined model of leadership that transcends rigid gender binaries.

Photonic Neural Networks And Optical AI Accelerators: A Comprehensive Review Of Architectures, Material Platforms, And System-Level Challenges

Authors: Abubakar Umar Hamza

Abstract: The rapid advancement of artificial intelligence (AI), particularly deep learning and large-scale neural networks, has created significant demand for high-performance and energy-efficient computing architectures. Conventional electronic processors such as GPUs and TPUs are increasingly constrained by power consumption, memory bandwidth limitations, and data movement bottlenecks. In response, photonic neural networks and optical AI accelerators have emerged as promising alternatives that exploit the properties of light to perform computation at high speed and low energy consumption. This paper presents a comprehensive systematic narrative review of photonic neural networks and optical AI accelerators, focusing on their architectures, material platforms, and key engineering challenges. The methodology employed involves structured literature collection from recent peer-reviewed studies, thematic classification of photonic architectures (including Mach–Zehnder interferometer meshes, microring resonator networks, and diffractive optical systems), and comparative analysis of material platforms and performance metrics such as energy efficiency, scalability, and computational latency. The results of the review indicate that photonic systems offer significant advantages over electronic computing, particularly in terms of energy per multiply–accumulate operation (femtojoule-level), ultra-high bandwidth (terahertz range), and low-latency computation. However, practical deployment remains limited by challenges in scalability, fabrication variability, noise sensitivity, and the lack of efficient optical training mechanisms. The analysis further shows that hybrid photonic–electronic architectures currently represent the most viable pathway toward near-term implementation, while heterogeneous material integration is essential for achieving fully functional photonic AI systems. The contribution of this research lies in providing a structured and critical synthesis of recent advancements in photonic AI hardware, identifying key technological bottlenecks, and outlining future research directions toward scalable and commercially viable optical computing systems. This work serves as a reference framework for researchers working at the intersection of photonics, electronics, and artificial intelligence.

DOI: https://doi.org/10.5281/zenodo.19686160

Happenings And Happiness: A Comprehensive Android-Based Wedding And Event Planning Platform With Real-Time Communication, Payment Integration, And AI-Ready Architecture

Authors: Darshankumar Patel

Abstract: The Indian wedding and event planning industry, valued at over INR 3 lakh crore annually, remains largely fragmented and dependent on manual processes. This paper presents Happenings and Happiness, a comprehensive dual-role Android application that digitizes the complete wedding planning lifecycle for both event organizers and service vendors. The system integrates Firebase Firestore for real-time data synchronization, Firebase Cloud Messaging (FCM) V1 API with JWT-based OAuth2 authentication for push notifications, RazorPay payment gateway for secure transaction processing, and Cloudinary for cloud-based media management. The application implements real-time bidirectional chat, vendor discovery and booking management, guest list management, budget tracking, and an earnings analytics dashboard. The dual-role architecture supports seamless interaction between users and vendors within a single application while maintaining strict role-based data access through Firestore security rules. Performance evaluation demonstrates sub-second message delivery in real-time chat, reliable push notification delivery across both user roles, and consistent UI performance across Android 10–14 devices. The system addresses critical gaps in existing wedding planning applications by providing an end-to-end digital solution tailored for the Indian market.

Machine Learning Based Portfolio Generator

Authors: Sunkara Sravallika, Banala Sai Revanth, Madasi Hema, M Efraiem, Bharat Tank

Abstract: The main focus of this project is to develop an intelligent, user-friendly portfolio generation platform and examine the effectiveness of template recommendations using machine learning techniques during the development and deployment phases. To carry out this project, two stages are considered — the frontend/template development phase and the backend/ML integration phase — with the completion of the frontend serving as the baseline milestone. The frontend/template phase covers the design and implementation of interactive UI components, template layouts, and live preview functionality, whereas the backend/ML phase covers API development, data handling, template recommendation algorithms, and export functionalities. The completed system uses data provided by users — including skills, education, experience, and achievements — as the sample input for generating customized portfolios. A recommendation engine, employing embedding-based similarity search and rule-based heuristics, is integrated to suggest templates best suited to the user’s industry and expertise. System evaluation considers performance metrics such as recommendation accuracy, export reliability, and user experience responsiveness as control variables. Findings from internal testing indicate that the frontend design and predefined templates significantly improve the user’s ability to customize portfolios efficiently during the pre-integration phase. In the post-integration phase, it is observed that the machine learning model enhances personalization, while backend optimizations ensure reliable export. In terms of system-specific factors, it is found that a clean template structure and low-latency API responses improve usability and adoption rates.

Wearable EDA Sensor Gloves Using Conducting Fabric And Embedded System

Authors: Mr.M.Aakash, K. Kaviya, S. Lavanya, K. Mounika

Abstract: Deep, coordinated reforms in the areas of energy, industry, cities, 6 and government are required by India’s Viksit Bharat 2047 aim. According to this analysis, if policy, funding, and infrastructure all work together, electric mobility can be a potent, all-encompassing tool that creates new industrial jobs, cleaner air, reduced greenhouse gas emissions, and increased energy security. Based on government plans (NEMMP; FAME I & II; PM-E-Bus Sewa; PLI for Advanced Chemistry Cells), major institutional reports (IEA; NITI Aayog; CEEW; WRI; TERI; World Bank), lifecycle and grid studies, and evidence at the state level, the paper summarizes findings on emissions savings, total cost of ownership, depot and charging needs, battery supply-chain risks, and institutional capacity gaps. Research indicates that electrifying high-use vehicles, such as buses and three-wheelers, results in the greatest reductions in emissions and improvements in air quality per rupee spent; those electrifying depots and coordinating with DISCOMs is necessary for dependable bus operations; and that increasing domestic battery capacity is essential to reducing reliance on imports and generating green manufacturing jobs. High upfront costs for fleets and STUs, metro-concentrated charger networks, geopolitical dangers surrounding vital minerals, and inadequate coordination between ministries and utilities are still major challenges. The analysis concluded that if electric mobility isViksit Bharat, it needs be integrated into a long-term, 2047-aligned roadmap that integrates battery circularity, innovative finance, renewable energy growth, and STU capacity building.

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Brain Tumor Detection Using Deep Learning Enhancing Diagnostic Accuracy, Early Detection, And Clinical Decision Support Through AI-Based Medical Imaging

Authors: Noyal Biju, Dharunkumar C, Aziz Pardiwala, Abhishek Pillai

Abstract: Accurate and timely detection of brain tumors is a critical challenge in medical imaging, directly influencing treatment planning and patient prognosis. Conventional diagnostic approaches based on manual interpretation of Magnetic Resonance Imaging (MRI) scans are often limited by subjectivity, inter-observer variability, and increasing workload on radiologists. This study presents a robust deep learning–driven framework for automated brain tumor detection and classification, leveraging advanced Convolutional Neural Network (CNN) architectures. The proposed model employs a transfer learning approach using a pre-trained VGG16 network, fine-tuned on a curated dataset of MRI images to capture domain-specific features. A comprehensive preprocessing pipeline—including image normalization, resizing, denoising, and intensity standardization—is integrated with data augmentation techniques to address class imbalance and enhance generalization. The architecture incorporates fully connected layers with dropout regularization to mitigate overfitting and improve model stability Model performance is rigorously evaluated using standard metrics such as accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Experimental results demonstrate high classification performance, indicating the model’s capability to effectively distinguish between tumor and non-tumor cases. Furthermore, comparative analysis with baseline models highlights the superiority of the proposed approach in terms of feature extraction efficiency and predictive accuracy. The system offers significant potential for real-world clinical integration by reducing diagnostic latency, minimizing human error, and providing decision support for radiologists. This research underscores the transformative role of deep learning in medical image analysis and establishes a scalable foundation for future advancements, including multi-class tumor classification and explainable AI-driven diagnostics.

Brain Stroke Detection Using Machine Learning And Deep Learning 

Authors: Kanuri jai sai Prakash, Challa uday kiran, Gugilla Harshith, V.vidya sagar

Abstract: With the aid of a specially designed Graphical User Interface (GUI), a combination of Machine Learning and Deep Learning techniques was used to detect brain strokes. Images of “Stroke” and “Normal” cases were categorized from a dataset. Following the loading of the dataset, preprocessing and feature extraction were carried out, and then the data was divided into training and testing sets. The Convolutional Neural Network (CNN) algorithm achieved a significantly higher accuracy of 98% than the Support Vector Machine (SVM) algorithm, which only managed 59%. CNN outperformed SVM in stroke image classification, according to comparative analysis. The trained CNN model was then applied to new test image prediction, effectively differentiating between normal and brain cases. These findings demonstrate how well deep learning techniques work for precise Brain stroke detection from medical images. A crucial medical application that makes use of contemporary technologies like machine learning (ML) and deep learning (DL) for early stroke diagnosis and prediction is brain stroke detection. The automatic detection of ischemic and hemorrhagic strokes from CT and MRI scan images is the main focus of this study. Support Vector Machines (SVM), Random Forest (RF), and Logistic Regression are important algorithms for classification tasks. Images are classified, features are extracted, and stroke-affected brain regions are segmented using deep learning models, specifically Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs). Key procedures for the project include image enhancement, data preprocessing, and model training with frameworks like PyTorch, TensorFlow, or Keras. Metrics like accuracy, precision, recall, and F1-score are used to assess these models’ performance. The accuracy of the model’s stroke prediction is improved by adding clinical data, such as blood pressure, diabetes, smoking patterns, and other risk factors. Building an effective clinical decision support system that can aid in the early detection of strokes is the ultimate goal, as it may lower the death and disability rates related to cerebrovascular accidents (CVA).

DOI: https://doi.org/10.5281/zenodo.19691995

Comparative Study Of Statistical Models For Customer Churn Classification

Authors: Jyoti Gupta, Ayush Patel, Siddharth Prabhudesai, Rahul Neve

Abstract: Customer churn prediction plays a vital role in helping businesses retain customers and minimize revenue loss in competitive markets. This study focuses on developing a predictive framework to identify customers who are likely to discontinue a service based on historical data. The dataset used in this project consists of customer demographic, behavioral, and financial attributes, which are preprocessed and transformed through feature engineering techniques to improve model performance. Multiple machine learning classification models are implemented and evaluated to determine their effectiveness in predicting churn. To address the issue of class imbalance, appropriate techniques are applied to ensure fair model training. The models are assessed using key performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, providing a comprehensive comparison of their predictive capabilities. The analysis highlights the importance of factors such as customer tenure, service usage patterns, and billing characteristics in influencing churn behavior. The results demonstrate that machine learning models can effectively capture underlying patterns in customer data and provide reliable predictions. This study offers valuable insights into churn prediction and presents a data-driven approach that can support businesses in designing targeted customer retention strategies.

DOI: https://doi.org/10.5281/zenodo.19692500

Supporting Slow Learners With Remedial Innovation

Authors: Aditiya Sawant, Devansh Sojitra, Rohan Maheta

Abstract: In modern educational environments, a significant number of students face learning difficulties due to slower cognitive processing, limited conceptual understanding, low confidence, and the inability of traditional teaching systems to address individual learning needs. These students, commonly referred to as slow learners, often require personalized academic support and innovative teaching strategies to achieve educational success. This paper presents Supporting Slow Learners with Remedial Innovation, an intelligent and inclusive educational support platform designed to enhance the academic performance and confidence of slow learners through customized remedial solutions. The proposed system integrates personalized learning plans, adaptive teaching methodologies, educator guidance, progress monitoring, and technology-assisted learning resources to create a supportive learning ecosystem. The platform allows educators to identify student weaknesses, assign customized study materials, track academic progress, and provide targeted interventions based on individual performance. Students can access simplified learning content, practice modules, motivational feedback, and continuous assessments according to their pace of learning. The system is developed using modern web technologies with a scalable frontend-backend architecture to ensure accessibility, usability, and performance. It supports multiple user roles including students, educators, and administrators for efficient management and monitoring. Experimental outcomes indicate improvements in student engagement, learning consistency, conceptual understanding, and confidence levels when compared with conventional classroom-only teaching approaches. This project demonstrates how remedial innovation combined with digital technologies can transform the educational journey of slow learners by promoting equal learning opportunities, reducing academic gaps, and creating an inclusive learning environment. The proposed framework can be extended in future with Artificial Intelligence, predictive analytics, and multilingual learning support for wider educational impact.

DOI: http://doi.org/

Maternal And Child Health Outcomes Among Rural Informal Women Workers In Bihar: Evaluation Of Janani Suraksha Yojana

Authors: Dr. Pravin Kumar, Abhinav Kumar

Abstract: This study evaluates the effectiveness of Janani Suraksha Yojana (JSY) in improving maternal and child health outcomes in Bihar. Using a mixed-method approach and regression analysis, the study finds that awareness and education significantly influence institutional delivery, while barriers such as cost and accessibility persist

DOI: https://doi.org/10.5281/zenodo.19693897

Deep Learning-Based Chest X-Ray Classification For Pneumonia Detection Using Transfer Learning

Authors: Srinithi G D, Hidhesh R M

Abstract: Pneumonia remains one of the leading causes of mortality worldwide, particularly among children under five and the elderly. Early and accurate diagnosis through chest X-ray interpretation is critical, yet manual analysis by radiologists is time-consuming, subjective, and prone to inter-observer variability. This paper presents a deep learning-based approach for automated pneumonia detection from chest X-ray images using transfer learning with pre-trained convolutional neural network (CNN) architectures. We evaluate the performance of three widely adopted models — ResNet50, VGG16, and DenseNet121 — on the publicly available Kaggle Chest X-Ray Images (Pneumonia) dataset containing 5,856 labeled images. The models are fine-tuned with data augmentation techniques to improve generalization. Our experimental results demonstrate that DenseNet121 achieves the highest classification accuracy of 93.27%, with a recall of 97.44% for pneumonia-positive cases, outperforming both ResNet50 (91.83%) and VGG16 (90.06%). The proposed framework offers a reliable, efficient, and scalable computer-aided diagnostic (CAD) tool that can assist radiologists in clinical decision-making, particularly in resource-constrained healthcare settings.

DOI: https://doi.org/10.5281/zenodo.19942378

Bridging Linguistic And Structural Gaps In Marathi Government Document Translation: A Survey Of Modern Approaches

Authors: Manasi Waghe, Danish Chandargi, Mohammad Aamir Rayyan, Raviraj Joshi, Dr. A.R. Deshpande

Abstract: The translation of government and legal documents from Marathi to English poses unique challenges due to linguis- tic complexity, domain-specific terminology, structural richness, and low-resource constraints. General-purpose machine translation systems often fail to maintain semantic fidelity, formatting, and terminological consistency required for administrative and legal texts. This survey explores recent advances in multilingual machine translation, domain adaptation techniques, OCR-driven document understanding, Marathi-specific NLP resources, and terminology- constrained translation methods. We examine the state-of-the-art in robust Marathi-to-English translation systems and highlight critical gaps, focusing on integrating layout-aware models and domain- specific constraints to improve translation quality and reliability for official government documentation.

Human Safety Device

Authors: Anam Siddiqui, Saima Shaikh, Sana Shaikh, Alfiya Shaikh, Prof. Nargis Shaikh

Abstract: Personal safety has become a critical concern in modern society due to the increasing rate of crimes and emergency situations. This paper presents a Human Safety Device that integrates Internet of Things (IoT) technology with dual communication systems, namely GSM-based SMS alerts and Telegram-based real-time notifications, to ensure reliable emergency response. The system is built using an ESP32 microcontroller interfaced with a GPS module for location tracking, a pulse sensor for heart rate monitoring, and an MPU6050 sensor for motion detection. In emergency conditions, triggered manually via a panic button or automatically through abnormal sensor readings, the system captures and transmits location and health data to predefined contacts. Experimental evaluation shows that the system achieves an average alert response time of 3–5 seconds for Telegram notifications and 5–10 seconds for GSM-based SMS delivery. The GPS module provides location accuracy within ±5–10 meters, while sensor readings maintain an accuracy of approximately 95% under normal conditions. Additionally, a web-based interface enables real-time monitoring and visualization of user data. The proposed system is compact, cost-effective, and highly reliable, making it suitable for real-world deployment in personal safety applications.

AI-Driven Talent Acquisition: Transforming Recruitment Efficiency Through Predictive Analytics In HRM

Authors: Viraja kanawally

Abstract: Artificial intelligence is becoming increasingly integrated into recruitment and is changing the paradigm in human resource management practices by helping organizations become more efficient in their hiring, decreasing time to hire, and improving quality of hire performance. The current paper explores how predictive analytics driven by AI technology can be applied within a recruitment process by automating resume screening, job-candidate match, and employee turnover predictions. Using survey data collected from 304 firms based in Europe who have adopted AI tools for recruiting purposes, it is found that AI can cut down time to hire by 48.8%, reduce cost per hire by 54.6%, and increase retention rates by 17.9%. Still, 15% of organizations adopt AI to predict internal mobility. The major reasons preventing them from doing so are fears about algorithmic bias, excessive costs associated with AI tool adoption, and resistance from applicants. A framework for predicting recruitment outcomes with the help of AI will be presented.

DOI: https://doi.org/10.5281/zenodo.19697173

Real-Time AI-Driven Traffic Management Using YOLOv8n For Adaptive Signal Control

Authors: Mr. Omesh Wadhwani, Bhagyashri Rahangdale, Dhanashree Dahake, Parika Pandharkar, Rishita Pokhare

Abstract: This research paper presents a detailed exploration of an AI-based traffic management system leveraging the YOLOv8n object detection model. The system aims to improve traffic flow, reduce congestion, and enhance overall road safety through real-time analysis of traffic conditions. The paper covers various aspects, including the system architecture, the implementation details of YOLOv8n for vehicle detection and tracking, the integration of detected data into a traffic management platform, and the experimental results demonstrating the system’s performance and effectiveness. The study also addresses challenges in deploying AI-based traffic management systems and suggests potential solutions for future research and development. The proposed system is trained using the COCO dataset along with custom traffic video data to ensure robustness under different environmental conditions. Performance evaluation is carried out using standard metrics such as precision, recall, and detection accuracy. Experimental results show that the model achieves a precision of 0.92, recall of 0.89, and overall detection accuracy of 91%, while effectively estimating traffic density in real-time scenarios. These results demonstrate the system’s capability to support adaptive signal timing and significantly improve traffic efficiency.

DOI: https://doi.org/10.5281/zenodo.19698223

Lung Cancer Detection Using Deep Learning Model

Authors: Naveen Kumar K,, Bhargav Simha N, Mahendra Chowdary V, Dr.S.Vijayaragavan

Abstract: Lung cancer is one of the leading causes of cancer-related mortality worldwide, and early detection significantly improves patient survival rates. Traditional diagnostic methods such as CT-scan interpretation are time-consuming and require high clinical expertise. In this project, we propose an automated lung cancer detection system using an Attention-Enhanced Inception NeXt–based deep learning model. The model integrates the representational efficiency of the Inception NeXt architecture with an attention mechanism that highlights discriminative lung regions, enabling more accurate identification of cancerous nodules A pre-processed dataset of CT scan images is used to train and evaluate the model. Image augmentation, normalization, and lung-region enhancement techniques are applied to improve data quality and reduce overfitting. The proposed hybrid architecture demonstrates superior feature extraction capabilities and improved sensitivity compared to conventional CNNs. Experimental results indicate that the model achieves high accuracy, precision, recall, and F1-score, making it a reliable tool for assisting radiologists in early lung cancer diagnosis. This system has the potential to support faster, more consistent, and more accurate clinical decision-making.

DOI: https://zenodo.org/records/19699961

Ranger’s Bad Luck: A Review On The Development Of A 3D Arcade Game

Authors: Prakhar Kulshrestha, Tanmoy, Anoushka Das, Shubhradip

Abstract: This paper reviews the design and development of Ranger’s Bad Luck, an arcade-style 3D video game built using agile methodologies. The game incorporates real-time graphics, a physics engine, and immersive sound effects to enhance the player experience. This review evaluates the advantages, limitations, and technological choices made during development, while situating the project within the broader context of game design practices. The findings indicate that the integration of agile development methods and modern tools such as Unreal Engine and Blender enabled efficient prototyping and implementation, though limitations regarding performance and scalability remain.

Smart Traffic Signal Control System

Authors: H. M. Pawar, Abhishek Kavthekar, Sanket Gaware, Kartik Chawan, Sunny Bhagawat

Abstract: Rapid urbanization and the continuous increase in vehicular traffic have made conventional traffic signal systems inefficient, leading to congestion, increased travel time, and higher fuel consumption. Traditional fixed-timing traffic lights fail to adapt to real-time traffic conditions, resulting in unnecessary delays even when certain lanes have minimal or no traffic.This project proposes a Sub-Smart Traffic Signal Control System, an intelligent yet cost-effective solution designed to optimize traffic flow using real-time data. The system utilizes sensors such as infrared (IR), ultrasonic, or camera-based modules to detect vehicle density on different lanes. Based on the collected data, the signal timing is dynamically adjusted, giving priority to lanes with higher traffic density while reducing idle time for less congested routes.Additionally, the system can be extended to include emergency vehicle detection, enabling automatic signal clearance for ambulances and fire services. The proposed solution aims to minimize traffic congestion, reduce fuel wastage, and improve overall road efficiency without the high infrastructure costs associated with fully smart traffic systems.The implementation demonstrates how a semi-automated (“sub-smart”) approach can significantly enhance traffic management in developing urban areas, making it a practical and scalable solution for modern cities.

IoT And 5G Integration: Enabling Next-Generation Smart Connectivity A Comprehensive Review

Authors: Roshni Dhruv, Omi Navadiya, Reena Desai

Abstract: In the last few years, talk about IoT and 5G has left the boardrooms and landed in real places—factories, hospitals, city streets, even farms. This paper digs into that shift. We look at what actually happens when you put 5G’s speed and low latency together with IoT’s huge reach, and why that pairing matters much more than each alone. You’ll see real-world deployments, the tough problems people are running into, and a peek at what’s next—security issues that still keep folks up at night, plus some genuinely promising ideas with AI-powered networks and ambient sensing. We get into the details of enabling tech like edge computing, digital twins, and network slicing, right alongside new standards, economic outlooks, and the rules and regulations steering all of this. The point here isn’t to hype things up—it’s to spell out what’s actually going on, what’s working, what’s tricky, and why you should care.

DOI: https://doi.org/10.5281/zenodo.19703853

Secure Learn LMS

Authors: Mr. Karthiban R, Dhanushya S, Akash S, Bharath Raj P, Mathan Kumar J

Abstract: This project presents SecureLearn Browser, a secure browser integrated with a Learning Management System (LMS) to ensure examination integrity while supporting flexible learning. It follows a dual-mode architecture with Practice Mode and Exam Mode, each designed for different educational needs. Users access the system through a centralized login interface. After authentication, Practice Mode allows unrestricted access to course materials, including Full Stack Development and HTML modules, enabling self-paced learning without time or security restrictions. In contrast, Exam Mode enforces strict controls such as time limits, password protection, and browser lockdown to prevent navigation, screen capture, and application switching. Based on Safe Exam Browser (SEB) principles, the system creates a secure environment for assessments while maintaining a user-friendly space for learning. The integrated HTML course supports hands-on practice within the same platform. Overall, the system balances security and flexibility by providing a unified platform for both learning and examinations, eliminating the need for separate tools.

DOI: https://doi.org/10.5281/zenodo.19705480

Blockchain-enabled Smart Contracts In Healthcare And Voting Systems: A Review Paper

Authors: Niral Parmar, Hetal Parmar, Krushi Savani, Fakhruddin Kamdar

Abstract: Everybody throws around the term “blockchain” these days, like it’s some secret sauce. But smart contracts are where things actually start to get interesting. Forget endless forms and relying on someone’s handshake; smart contracts handle things automatically. They’re just coded agreements that trigger themselves no middlemen, no second-guessing if someone’s being honest. You know what you’re getting. This review looks at how smart contracts are changing the game in two touchy areas: healthcare and voting, where trust and privacy can’t be taken lightly. Dealing with healthcare is usually a hassle. People lose records, insurance companies bounce you around, and privacy feels flimsy. With smart contracts, you’re in charge of your data, claims happen faster, and private info stays private. Doctors can share what they need to, without breaking the rules. Voting? It’s had trust issues forever people aren’t sure their votes count for anything. Smart contracts clean things up. They make voting more transparent, help stop fraud, and lock down the results. You can check your ballot and know nobody’s changing numbers behind the scenes. Of course, it’s not all smooth sailing blockchain slows down when things get big, laws haven’t caught up, some of the interfaces are confusing, and big organizations don’t like change. This paper covers what works, what needs help, and where things could go next.

DOI: https://doi.org/10.5281/zenodo.19707494

AI-Enabled Smart Glove For Real-Time Voice Translation Of Hand Gestures: Design, Implementation, And Evaluation

Authors: Nagul Nisok K S, Nidhish V, Nirmal B, Nivesh S, Sai Sarvesh P G

Abstract: Communication barriers faced by individuals with speech and hearing impairments represent a significant societal challenge. This paper presents an AI-enabled smart glove system designed to translate hand gestures into synthesized voice output in real time. The proposed system integrates an array of flex sensors, an inertial measurement unit (IMU), and surface electromyography (sEMG) electrodes embedded within a lightweight, wearable glove. Raw sensor data are transmitted wirelessly via Bluetooth Low Energy (BLE) to a companion edge-computing module, where a multi-stream convolutional neural network–long short-term memory (CNN-LSTM) architecture performs gesture classification. Classified gestures are subsequently converted to speech using a neural text-to-speech (TTS) engine. Evaluated on a 250-class American Sign Language (ASL) dataset comprising 48,000 gesture samples from 40 subjects, the system achieves a top-1 classification accuracy of 97.4 % and an average end-to-end latency of 68 ms. Power consumption is maintained at 84 mW during continuous operation, enabling up to 11 hours of use on a 1,000 mAh Li-Po cell. Comparative analysis demonstrates that the proposed design outperforms existing glove-based and vision-based translation systems in accuracy, latency, and portability. The findings highlight the potential of the system as an effective assistive device for the deaf and hard-of-hearing community.

DOI: https://doi.org/10.5281/zenodo.19707715

Smart Temperature Regulation Using Fuzzy Logic Controller (FLC)

Authors: Veena Vanamane, Vimala V, Pallavi C, M.Bharathi, Yashawini.C.K

Abstract: Achieving efficient and stable temperature regulation remains a challenge for both industrial and domestic applications, especially where conventional PID control methods require precise modelling and struggle with nonlinear or uncertain systems. This paper presents a fuzzy logic–based temperature control system that improves performance by mimicking human decision-making. By using temperature error and change in error as input variables and processing them with linguistic rules, the proposed controller effectively manages uncertainties to achieve smoother, more reliable control. Simulation and experimental data confirm that this fuzzy controller reduces overshoot, provides faster responses, and enhances stability compared to traditional methods. Its design shows clear potential for use in industrial heating, smart homes, and thermal management.

Exploring The Strength Of Machine Learning Techniques For Detection Of Cancer: A Review

Authors: Mrinalinee Singh

Abstract: Cancer remains one of the leading causes of mortality worldwide, necessitating early and accurate detection mechanisms to improve patient survival rates. Traditional diagnostic methods, while effective, often face challenges regarding time efficiency, inter- observer variability, and sensitivity. In recent years, Machine Learning (ML) and Deep Learning (DL) have emerged as pivotal tools in oncology, offering automated, high-precision diagnostic capabilities. This paper reviews the strengths of various ML paradigms—including Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN)—in the detection of malignancies. We critically analyze the performance of these algorithms across different cancer modalities, such as breast, lung, and skin cancer. Furthermore, the review highlights the transition from feature-based classical ML to automated feature extraction via Deep Learning, discusses current challenges such as data heterogeneity and model interpretability, and proposes future directions for integrating AI into clinical workflows.

DOI: https://doi.org/10.5281/zenodo.19708441

Green Solvents In Organic Synthesis: A Comprehensive Review Of Sustainable Alternatives, Performance Evaluation And Industrial Applications

Authors: Fatima Ibrahim Baiwa, Amina Ibrahim Baiwa

Abstract: The environmental and health hazards associated with conventional organic solvents have intensified the global shift toward sustainable chemical processes. This review critically examines the role of green solvents in modern organic synthesis, with emphasis on their physicochemical properties, reaction performance, environmental impact, and industrial applicability. A systematic review methodology was adopted, involving the analysis of peer-reviewed literature, industrial reports, and green chemistry databases. Studies were selected using defined inclusion criteria based on reaction efficiency, toxicity, recyclability, energy consumption, and economic feasibility. Comparative evaluation was performed across six major solvent classes: water, supercritical carbon dioxide, ionic liquids, deep eutectic solvents, bio-based solvents, and solvent-free systems. The analysis reveals that green solvents consistently demonstrate improved reaction yields (typically 85–99%), enhanced selectivity, reduced volatile organic compound emissions, and significantly lower energy requirements compared to traditional solvents. Water-mediated and solvent-free reactions showed the highest sustainability performance, while deep eutectic solvents and bio-based solvents emerged as the most promising scalable alternatives due to their low cost, biodegradability, and high recyclability. Industrial case studies further indicate substantial reductions in hazardous waste generation and regulatory burden following adoption of green solvent technologies. This review contributes a comprehensive comparative framework for evaluating green solvent performance and identifies key research gaps, including the need for standardized sustainability metrics and long-term toxicity assessment of emerging solvent systems. The findings reinforce the critical role of green solvents in advancing sustainable organic synthesis and highlight future opportunities in AI-assisted solvent design, switchable solvent systems, and circular solvent economies.

DOI: https://doi.org/10.5281/zenodo.19711912

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Green Solvents In Organic Synthesis: A Comprehensive Review Of Sustainable Alternatives, Performance Evaluation And Industrial Applications

Authors: Fatima Ibrahim Baiwa, Amina Ibrahim Baiwa

Abstract: The environmental and health hazards associated with conventional organic solvents have intensified the global shift toward sustainable chemical processes. This review critically examines the role of green solvents in modern organic synthesis, with emphasis on their physicochemical properties, reaction performance, environmental impact, and industrial applicability. A systematic review methodology was adopted, involving the analysis of peer-reviewed literature, industrial reports, and green chemistry databases. Studies were selected using defined inclusion criteria based on reaction efficiency, toxicity, recyclability, energy consumption, and economic feasibility. Comparative evaluation was performed across six major solvent classes: water, supercritical carbon dioxide, ionic liquids, deep eutectic solvents, bio-based solvents, and solvent-free systems. The analysis reveals that green solvents consistently demonstrate improved reaction yields (typically 85–99%), enhanced selectivity, reduced volatile organic compound emissions, and significantly lower energy requirements compared to traditional solvents. Water-mediated and solvent-free reactions showed the highest sustainability performance, while deep eutectic solvents and bio-based solvents emerged as the most promising scalable alternatives due to their low cost, biodegradability, and high recyclability. Industrial case studies further indicate substantial reductions in hazardous waste generation and regulatory burden following adoption of green solvent technologies. This review contributes a comprehensive comparative framework for evaluating green solvent performance and identifies key research gaps, including the need for standardized sustainability metrics and long-term toxicity assessment of emerging solvent systems. The findings reinforce the critical role of green solvents in advancing sustainable organic synthesis and highlight future opportunities in AI-assisted solvent design, switchable solvent systems, and circular solvent economies.

DOI: https://doi.org/10.5281/zenodo.19711912

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Credit Wallet System

Authors: Faisal Chaudhary, Ms. Ayushi Sanjiv Desai

Abstract: The Wallet App is an innovative digital financial system designed to provide users with a credit-based mining system. Users earn credit at a predefined mining speed, which increases with continuous usage and referrals. The app implements a unique referral tree structure, encouraging engagement and organic user growth. The mined credit can be utilized within the app ecosystem to purchase essential goods and services, including medical expenses, through affiliated service providers. The wallet does not support external transactions, ensuring that all financial activities remain within the ecosystem. Users progress through different stages, unlocking benefits and higher mining speeds. Additionally, the system rewards active users by transferring 1/10th of their annual credited amount as a bonus. Key features include app-to-app transfers, daily credit mining, referral-based growth, transaction verification by admins, and stage-based progression. The app is designed to function as a closed-loop financial service, reducing dependency on traditional banking while promoting financial inclusion. With an intuitive UI and robust backend, the wallet app provides a secure, engaging, and rewarding financial experience for users.

DOI:

AI-Based Github Security Scanner

Authors: Ms.S.Hari Priya, Akalya M, Anupriya S, Bala.G, Dhanusurya.S

Abstract: With the rapid growth of software development, platforms like GitHub have become essential for code sharing and collaboration. However, many developers, especially students and beginners, often upload code without proper security checks, leading to vulnerabilities such as hardcoded credentials, exposed API keys, and insecure coding practices. This project presents an AI-Based GitHub Security Scanner designed to automatically analyze repositories and identify potential security risks. The system integrates with GitHub to scan source code using a combination of static code analysis and AI-driven techniques. It detects common vulnerabilities, misconfigurations, and sensitive data exposure in real time. The AI component enhances detection accuracy by learning patterns from known security issues and suggesting improvements to developers. Additionally, the tool provides detailed reports and recommendations, helping users understand and fix vulnerabilities effectively. By automating security analysis, this project aims to improve coding practices, reduce risks, and promote secure software development. Overall, the proposed system offers a scalable and intelligent solution for early detection of security flaws in GitHub repositories, making it especially useful for students, developers, and organizations.

DOI: https://doi.org/10.5281/zenodo.19755384

The Convergence Of Silicon And Carbon: The AI-Driven Transformation Of Biotechnology

Authors: Kriti.R. Shukla

Abstract: As of 2026, the biotechnology sector has undergone a fundamental paradigm shift from a traditional “wet-lab first” experimental model to an “in silico first” computational framework. This evolution is driven by the maturation of generative artificial intelligence (AI), geometric deep learning, and multi-modal foundational models. This article explores the current state of AI in biotechnology, focusing on protein engineering, generative chemistry, genomic interpretation, and bioprocess optimization. We examine how the integration of Large Language Models (LLMs) and diffusion-based generative models has accelerated the drug discovery pipeline, reduced R&D costs, and enabled the design of de novo biological systems with unprecedented precision.

Cybersecurity And Fraud Prevention in Financial Institutions (Matlab)

Authors: Dr. Dhanalakshmi S, B. Sasi Prabha

Abstract: In an era where financial transactions are increasingly digital, the threat of cyber fraud has become a growing concern for both institutions and individuals. With every swipe, click, or transfer, there’s a risk that sensitive data could be exploited by attackers using sophisticated techniques. As fraudsters become smarter, our defenses must evolve too. This chapter presents a practical approach to fraud detection using MATLAB, focusing on a simple, transparent, and explainable rule-based system. Rather than relying on complex machine learning models that can act as “black boxes,” this method uses intuitive rules based on transaction amount, time, and location to flag potentially fraudulent activity. The system is built with ease of implementation in mind, making it ideal for financial institutions looking for an interpretable starting point or a lightweight solution for early warning detection. The model is demonstrated on simulated transaction data, and its results are visualized clearly to show the difference between normal and suspicious behavior. By the end of this chapter, readers will not only understand how to build a basic fraud detection system in MATLAB, but also appreciate the importance of balancing technical rigor with real-world usability in cybersecurity efforts.

DOI: https://doi.org/10.5281/zenodo.19718898

Lorawan Iot-Enabled Trash Bin Level Monitoring System

Authors: Yaminideavi A, Elakkiya N S

Abstract: The rapid expansion of urban populations has significantly intensified waste generation, straining the efficiency of traditional collection methods that rely on static, fixed schedules. Such conventional systems often result in overflowing bins, inefficient collection routes, and escalated operational costs. This paper proposes a comprehensive Long Range Wide Area Network (LoRaWAN) infrastructure designed to modernize Smart City waste management. Unlike existing single-task architectures, the proposed framework integrates a multi-tiered hierarchy of LoRaWAN device classes to manage services of varying complexity. At the foundational level, smart bins utilize ultrasonic sensors and low-power microcontrollers to monitor fill levels and environmental conditions. Higher-level smart drop-off containers facilitate user interaction and support asynchronous downlink queries for real-time data exchange. Data is transmitted via LoRa gateways to a centralized cloud-based dashboard, enabling municipal authorities to monitor bin status and dynamically optimize collection routes. Experimental results suggest that this scalable, energy-efficient IoT paradigm not only prevents bin overflow through automated threshold alerts but also reduces fuel consumption and environmental impact. The integration of diverse LoRaWAN node classes provides a robust, cost-effective solution for real-time urban process control within the Smart City ecosystem.

Exploring The Stigma Gap: A Comparative Study of Schizophrenia Literacy and Social Distance Across Generations

Authors: Riya Srivastava, Dr. Shilpi Aggarwal

Abstract: This study delves into the diverse perceptions of mental health, with a particular focus onSchizophrenia, across different generational cohorts. By examining how perceptions have evolved over decades, from the “Gen Z” cohort to older generations, this research aims to broaden our understanding of the disorder and its impact on individuals and society. The study encompasses an extensive analysis of Schizophrenia, covering its historical evolution, contemporary awareness, and societal attitudes. Through a comparative lens, it investigates how perceptions of Schizophrenia and the resulting social distance vary among individuals of diverse ages. Employing a mixed-methods approach, primarily utilizing an online survey, this research captures a comprehensive picture of mental health literacy and stigma across these generations. This work contributes valuable, actionable data to the field of mental health advocacy and education. Ultimately, it advocates for a more inclusive and supportive society, where mental health is understood, accepted, and supported across all generations.

DOI: https://doi.org/10.5281/zenodo.19728938

Avoidance Of Train Collision System

Authors: N.Santosh Kumar, V.Kavya, M.Harshitha, P.SudheerKumar, V.Karthik

Abstract: Train collisions are one of the critical safety concerns in railways, which could result from human error, signal failure, communication delay, or reduced line of sight owing to complex terrain. This paper proposes a low-cost intelligent TCPS designed using ESP32 microcontrollers, ultrasonic sensors, ESP-NOW wireless communication, and an automatic servo-based braking mechanism. The trackside unit constantly monitors the movement of the train through two ultrasonic sensors and calculates real-time distances for potential head-on collision detection. When the system identifies a threat, it issues an instantaneous emergency braking signal to the onboard units, which trigger the servo-driven brake assembly. Further, the proposed system is integrated with regenerative braking to recover the kinetic energy for recharging the lithium-ion battery supply present in the system. Experimental testing on a prototype railway track has shown high detection accuracy, quick wireless communication, and reliable automatic braking. The proposed system gives a scalable, modular, and energy-efficient alternative to conventional railway safety mechanisms that can be integrated with state-of-the-art signalling and AI-based prediction in future applications.

EV Battery Management System With Charge Monitoring

Authors: N. Santosh Kumar, U. Reshma, V. Sandhya, S. Santhi vardhan

Abstract: This project presents an innovative Battery Management System (BMS) for electric vehicles, leveraging Arduino UNO to ensure optimal battery performance, safety, and longevity. The system is designed to monitor critical parameters such as voltage, current, temperature, and state of charge (SOC), while also assessing the state of health (SOH) of the battery. The BMS supports both fast and slow charging modes, intelligently managing charging processes to prevent overcharging and thermal runaway. User-friendly interfaces, including real-time data displays, offer intuitive insights into battery status, empowering users with actionable information. This project combines cost-effective hardware with comprehensive safety and performance monitoring, making it a significant step toward safer and more efficient electric vehicle batteries. Real-time SOC/SOH evaluation, and versatile charging management, which collectively advance the capabilities of conventional battery management solutions.

Artificial Intelligence In Achromatopsia: A Comprehensive Review Of Diagnosis, Genetic Insights And Emerging Therapeutic Strategies

Authors: Dr. A Senthilkumar, Dr. Tintu George, Dr. Ginne M James

Abstract: Achromatopsia is a rare inherited retinal disorder characterized by the absence of cone photoreceptor function, resulting in color blindness, photophobia, nystagmus, and reduced visual acuity. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have transformed ophthalmic diagnostics and opened new avenues for early detection and treatment planning. This review paper presents a comprehensive analysis of achromatopsia, focusing on its clinical features, genetic basis, diagnostic approaches and therapeutic developments, with a strong emphasis on AI-driven methodologies. The paper also explores the integration of AI in retinal imaging, genotype–phenotype correlation, and gene therapy optimization. Finally, challenges, limitations and future research directions are discussed.

DOI:

Design And Analysis Of PID Controller For Water Level Regulation In A Tank System

Authors: Veena Vanamane, S Zuha Afsheen, Thrisha M, Preksha Malipatil, Chirasvi S N

Abstract: In many industrial applications, maintaining a steady water level in tank systems is crucial. The design and analysis of proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) controllers for a single-tank water level control system are presented in this study. MATLAB/Simulink is used to create and construct a mathematical simulation model. Rise time, settling time, overshoot, and steady-state error are used to assess the controllers. The P controller has steady-state error, the PI controller decreases error but increases overshoot, and the PID controller offers ideal performance with better stability and quicker reaction, making it appropriate for efficient water level control, according to the results.

Performance and analysis of Single-channel and Multiple channel based Approximate Distributed Arithmetic Filter Design

Authors: Santhosh Babu K C, Chirakshitha S, Eashanya K R, Eashanya K R, Ganavi A S, Gowthami G

Abstract: Efficient digital filtering is critical for modern signal processing applications. This work presents an Adaptive Distributed Arithmetic (ADA)-based FIR filter designed for single-channel and scalable multi-channel configurations on FPGA. The proposed design incorporates error- controlled approximation and optimized computation to reduce LUT usage, power consumption, and processing delay. Implementation using the Xilinx Vivado environment demonstrates improved area efficiency and speed while maintaining acceptable signal quality. The results indicate that the ADA approach is well- suited for low-power, high-throughput FPGA-based DSP applications.

DOI:

From Waste to Value: Recovery, Optimization, and Reuse of Magnesium Hydroxide from Zero Liquid Discharge Wastewater for Sustainable Industrial Applications

Authors: Pooja KR

Abstract: Zero Liquid Discharge (ZLD) systems are increasingly adopted to minimise industrial wastewater discharge; however, they generate concentrated brine streams rich in dissolved minerals. This study focuses on the recovery of magnesium hydroxide from ZLD wastewater using a controlled precipitation method and optimisation of key process parameters. The influence of pH (9–12), temperature (25–60°C), and reaction time (30–120 min) on recovery efficiency was systematically evaluated. Maximum recovery efficiency of approximately 91% was achieved at pH 11, 45°C, and 90 minutes reaction time. The recovered magnesium hydroxide exhibited satisfactory purity and demonstrated effective performance in neutralisation and contaminant removal tests. These findings are consistent with earlier studies on magnesium recovery from brine systems (Almousa et al., 2024; Yong et al., 2024).

DOI: https://doi.org/10.5281/zenodo.19757760

Nutri-Fit Kitchen: A Technology-Driven Personalized Meal Planning And Delivery System For Fitness And Wellness

Authors: Pruthvik k c, Khushboo Khanum, Vinay Kumar, Aaditha, Prajwal, Dr Saumya patel

Abstract: The growing trend of the fitness culture and preventive nutrition has led to the demand of organized and evidence-based systems of meal-preparations. Nutrition based kitchens respond to this demand offering calorie controlled, macro based and goal oriented food that promotes muscle building, fat loss as well as athletic performance. The studies have always indicated that, when people are fed on professionally prepared, nutritionally optimized food, their level of dietary adherence, metabolic response, and training outcomes are better than when people prepare the food on their own. Also the ready-made healthy food assists in coping with the most widespread limitations, including time, decision fatigue, and irregular nutrient consumption. Research on personalized and sports nutrition points out the fact that diets that are consistent with individual fitness objectives enhance energy balance, body make-up and post- workout management. Protein rich and nutrient-dense functional diets also increase satiety, enhance muscle protein synthesis, and overall diet quality especially in individuals with a high physical activity. Nutrition-oriented kitchens use such principles as dietitian regulation, exact portion- control, and calculated macro- distribution in relation to clinical and sports nutrition practices. In contemporary nutrition services, technology is an important ingredient. The digital platform and mobile applications that focus on the user allow customization of the meal, tracking progress, and motivation of behaviour. Properly designed UI/UX systems have a great positive influence on engagement, compliance, and user satisfaction. In addition, meal-preparation services paired with app-observation proved to reinforce the long-term adherence and health outcomes. Generally, studies have indicated that nutrition-prep kitchens could be used as a remedy to modern dietary issues because they combine the element of convenience and scientifically tested nutritional habits. They fill the disconnect between individualized dieting planning and the actual practice, and assist athletes, gym-goers and common consumers to attain long-term health and fitness objectives. The paper assesses the operational model, digital integration, user experience, and nutritional efficiency of Nutri-Fit Kitchen by making it a revolutionary system in the contemporary health- conscious food-oriented business.

Performance Evaluation Of A Diesel Engine Utilising Varying Ratios Of Ethanol-Butanol Additives Department Of Mechanical Engineering

Authors: Bejini Chidananda Krishna, Naveen KR, D.Ravi

Abstract: This study examines how a diesel engine behaves when ethanol and butanol are mixed with regular diesel fuel in different proportions. The aim is to determine whether these cleaner, renewable fuels can partially replace diesel without compromising engine performance while reducing harmful emissions. To do this, several fuel blends were prepared and tested in a diesel engine under different load conditions and ratios (D85E7.5B7.5, D80E10B10, D75E12.5B12.5). During the experiments, important performance factors such as fuel efficiency, fuel consumption, and exhaust temperature were recorded. At the same time, emissions such as carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and smoke levels were measured to assess the environmental friendliness of each blend. The results show that adding ethanol and butanol improves the combustion process because these fuels contain oxygen, which helps the fuel burn more completely. This leads to lower emissions of pollutants like CO, HC, and smoke, making the engine cleaner compared to using pure diesel. However, when the proportion of ethanol and butanol is increased too much, the engine may consume more fuel and show a slight drop in efficiency, mainly because these fuels have lower energy content than diesel. In some cases, higher blends can also affect how smoothly the engine runs. Overall, the study suggests that using an optimal mix of ethanol and butanol with diesel can provide a good balance between performance and emission reduction, making it a practical and eco-friendly alternative for diesel engines.

DOI: https://doi.org/10.5281/zenodo.19761417

A Secure Blockchain-Based Framework For Academic Certificate Authentication And Validation

Authors: Sarwesh kumar, Keshav Kumar

Abstract: With the increase in the adoption of digital education and online certifications, there is a remarkable need for certificate verification systems that are secure and reliable. Certificate issuance and validation are traditionally conducted through a manual process relying on centralized databases that is easily forged, manipulated, and fails in the occurrence of an accidental data loss. A big cause of concern for educational institutions, employers and government agencies is fake certificates. To alleviate such problems, a tamper-proof and distributed certificate verification mechanism is proposed on the basis of block chain technology. The blockchain technology offers a decentralized and unchangeable ledger, like cannot change the data once stored in that ledger. The new system stores the verified certificates into a blockchain network securely after being issued by institutions. It assigns a unique cryptographic hash value for each certificate to ensure its authenticity and integrity.

DOI: https://doi.org/10.5281/zenodo.19761949

Reducing Phishing Attacks In Online Banking Using A Multi-Layered Machine Learning Framework

Authors: Nikhil Kumar, Shekhar Kumar Purbe, Dr. Jyoti Gautam

Abstract: Phishing attacks are now considered to be the greatest cyber security threats in online banking, mobile wallet, and other online financial systems. Attackers launch such attacks not only exploit the vulnerabilities in systems but also exploit human factors to steal sensitive financial information and cause huge monetary losses and destroy users’ credibility. Current defense strategies are blacklist based URL filtering and static rules based detection, which are unable to cope with modern phishing attacks. Modern phishing attacks are carried out by employing advanced techniques such as domain spoofing, adversary-in-the-middle (AiTM) attacks, and dynamic web contents . This paper proposes a multi-layered intelligent phishing detection architecture to defend online banking platforms. The proposed system uses URL analysis, content inspection, and transaction behavior analysis to protect online banking systems from different angles. The system uses machine learning algorithms such as Random Forest, Support Vector Machine, and Logistic Regression to classify phishing attacks based on the features extracted from URL, web pages, and user transactions. Unlike previous approaches which only use single layer detection, this paper proposes a hybrid system architecture with real-time detection and behavioral analysis to detect phishing attacks. The system is trained with datasets collected from multiple repositories which are publicly available phishing repositories. The experimental results show that the model trained by the proposed method achieves an accuracy of 96.5% with high precision and recall and low latency to be applied in real-time systems. The system also provides an alert and response mechanism to notify users and stop fraudulent transactions as soon as possible.

DOI: https://doi.org/10.5281/zenodo.19762735

LLM-Powered Cloud Log Analyzer With Root Cause Explaination

Authors: Subasree, Harshavardhini N, Dhaarani S, Avinash R K, Karthik Prakash M

Abstract: The rapid expansion of cloud computing has led to the continuous generation of massive system log data, making manual analysis difficult, time-consuming, and prone to errors [1][2][6][10]. This work proposes an LLM-based cloud log analyzer that automates the interpretation of logs and assists in identifying root causes using Artificial Intelligence. The system gathers logs from cloud platforms such as AWS CloudWatch and CloudTrail, processes them to extract meaningful attributes, and applies Large Language Models (LLMs) for efficient log analysis [1][2][3][4]. The proposed approach detects anomalies, recognizes patterns, and identifies root causes including permission-related issues, resource limitations, network configuration errors, and application-level failures [5][8][12][13]. In addition, it produces clear human-readable explanations and suggests automated corrective actions, thereby reducing reliance on domain experts and lowering system downtime [12][14]. A web-based dashboard is also implemented to present error summaries, root cause insights, and recommended solutions in an understandable format. By combining cloud computing with Generative AI, the system improves operational efficiency, strengthens cloud reliability, and supports the evolution of AIOps in modern IT environments [3][5][8][12].

DOI: https://doi.org/10.5281/zenodo.19763530

Design And Simulation Of A Quasi Z-Source Inverter For Photovoltaic Energy Conversion

Authors: Kura Sairam, Kurva Saisharath, Dr. P. Kowstubha, A. Sai Aditya

Abstract: Renewable energy sources such as solar power are highly dependent on environmental conditions, which often leads to fluctuations in output voltage and current. These variations create challenges for conventional inverter systems like Voltage Source Inverters (VSI), Current Source Inverters (CSI), and even traditional Z-Source Inverters (ZSI), affecting their efficiency and reliability. To address these issues, this paper focuses on the design and simulation of a Quasi Z-Source Inverter (QZSI) for photovoltaic (PV) energy conversion. The QZSI is an improved version of the ZSI, achieved by modifying the impedance network. This topology offers several advantages, including the ability to perform both buck and boost operations in a single stage, reduced component stress, and a continuous input current, which is particularly beneficial for PV systems. Additionally, the QZSI allows the use of shoot- through states without damaging the inverter, enabling effective voltage boosting under varying input conditions. In this work, the operating principle, voltage boost capability, and control strategy of the QZSI are studied. A simulation model is developed using MATLAB/Simulink to evaluate system performance under different operating scenarios. The results demonstrate that the QZSI provides improved voltage stability and overall efficiency, making it a suitable choice for renewable energy applications.

DOI: https://doi.org/10.5281/zenodo.19766037

Roboclean: Automated Garbage Collection With Conveyor Mechanism

Authors: P Sudhakar Reddy, Pothireddy Nithisha, Sakamuru Hari priya, Saggam Ranjith Kumar, Panditi Prem Kumar, Yagnam setty chaithanya kumar

Abstract: Increasing water pollution due to floating solid waste in rivers, lakes, and drainage canals has become a major environmental concern, and manual waste collection in water bodies is inefficient, unsafe, and time-consuming. This project presents Roboclean, an ESP32-based automated garbage collection system designed specifically for collecting floating waste from water surfaces using a conveyor belt mechanism. The system employs dual conveyor belts driven by DC motors through motor driver modules to lift and transfer waste from water to a collection bin. An ESP32 microcontroller acts as the central control unit, coordinating motor operations and system monitoring. IoT connectivity using the Blynk platform enables real-time remote control and monitoring of the system through a mobile application. A 16×2 LCD display provides on-site status information, while a regulated power supply ensures reliable operation. By automating floating waste collection and enabling remote supervision, the proposed system reduces manual labor, improves safety, and enhances cleanliness of water bodies. Roboclean offers a cost-effective and scalable solution suitable for rivers, lakes, sewage canals, and smart city environmental management applications.

DOI:

Exploring Trends In Job Postings And Salaries Across Different Industries

Authors: Ms.C. Harivarshini, Ms.M. Shubhashree, Dr.R. Karthik

Abstract: The global workforce is undergoing rapid evolution. Current data-driven research into worldwide employment trends thus has become a pressing need. The objective of the present study was to conduct a comprehensive analysis of job advertisements and salary trends by reviewing 999 job records collected from 213 countries; these included a total of 13 data points. As part of its analytic process, the present study utilized a data preprocessing pipeline that involved the passing of data through multiple stages – data cleansing, data type conversion, aggregation, data partitioning, normalization, etc., prior to submission to various data visualisation techniques; these included bar charts, histograms, box plots, scatter plots, correlation heat maps, skills frequency heat maps, pie charts, violin plots, and choropleth maps. Among the most significant findings of the present study were the following: job advertisements show evidence of consistent salary levels based on both level of education and type of job; however, geographic region and industry sector appear to play an important role in determining salary levels. Additionally, the study concluded that the most common skills necessary for attaining such salaries are as follows: management skills; analytical skills; design skills; communication skills; and technical/data oriented skills. Consequently, the authors propose a framework that can be used to better understand the trends of employment and provide actionable insights into the employment market.

DOI: https://doi.org/10.5281/zenodo.19816389

Design Of A DC-DC Buck Converter With ClosedLoop Control For Low-Power Applications

Authors: Sareddy Prasanna Reddy, Dr. P. Kowstubha, Parameshwari Rathod, Barla Ananda Sagar

Abstract: This paper presents the design and implementation of DC-DC buck converter using a digital PI control technique. The system converts a 24V DC input into a regulated 12V DC output. An microcontroller is used to implement closed-loop control and generate PWM signals. The controller continuously monitors the output voltage and adjusts the duty cycle to maintain stable output under different load conditions. The converter achieves an efficiency of around 90% with good voltage regulation. The results show that the proposed system is suitable for low-power embedded applications.

Ai Based Dynamic Pricing Engine

Authors: Arayan Gandre, Swaraj Sakpal, Unmesh Nhavelkar, Vedant Gaikwad, Prof. Smita Pawar

Abstract: In today’s highly competitive and data- driven marketplace, pricing strategy has become a decisive factor in determining a company’s profitability, customer satisfaction, and long-term sustainability. Traditional static pricing models, which rely on fixed markups or manually updated price lists, are often inadequate in responding to the dynamic nature of modern markets. These methods struggle to adapt to frequent fluctuations in consumer demand, competitor actions, supply chain disruptions, and seasonal influences. This research presents the design and development of an Artificial Intelligence (AI)-based Dynamic Pricing Engine that autonomously predicts and optimizes product prices in real time. The proposed framework integrates a variety of heterogeneous data sources — including historical sales transactions, customer purchasing behavior, inventory levels, market demand elasticity, and competitor pricing trends — to generate context-aware pricing recommendations. The system employs a hybrid machine learning approach: regression-based models are used for short- term price prediction, while reinforcement learning techniques enable continuous self-improvement through feedback-driven optimization. A prototype implementation was tested using real-world re- tail and e-commerce datasets to evaluate its effectiveness. The experimental results demonstrate that the AI-driven dynamic pricing model significantly enhances revenue optimization, profit margins, and inventory turnover compared to traditional rule- based or static pricing systems. Moreover, the model exhibits rapid adaptability to demand shifts and improved decision- making accuracy under volatile market conditions. The findings highlight the transformative potential of AI in automating strategic business decisions and emphasize the scalability and robustness of intelligent pricing systems. This study contributes to the broader field of intelligent commerce by providing a data-centric, adaptive, and scalable solution for modern enterprises seeking to maintain competitiveness in the evolving digital economy.

ExplorAR Glasses: An Intelligent Augmented Reality Travel Assistance System Using Geolocation, Contextual Intelligence, And Multimodal Services

Authors: Yashvi Rajiv Vyas, Mohammad Armaan, Rida Sadiqa, Sarayu Anand Gongada, Mohammed Sufyan, Dr. Chandrasekhar V (Project Giude)

Abstract: ExplorAR Glasses is an intelligent augmented reality (AR)-based travel assistance system designed to enhance real-world exploration through contextual digital augmentation. The system integrates geolocation, artificial intelligence, computer vision, and real-time API services to deliver immersive, hands-free assistance to users. By combining GPS-based location tracking, AI-generated contextual insights, OCR-based translation, weather forecasting, and voice interaction, ExplorAR enables users to interact with their surroundings in a seamless and intuitive manner. The system is built using a modular architecture consisting of a lightweight mobile client and a cloud-based backend. The backend leverages large language models (LLMs) for contextual information generation, while external APIs provide navigation, translation, and environmental data. The frontend prototype, developed using a cross-platform framework, serves as an intermediary between device sensors and backend services. The solution is designed to be scalable and adaptable for integration with wearable AR devices such as smart glasses. This project demonstrates how emerging technologies can be combined to create a real-time, context-aware digital assistant that improves accessibility, travel experience, and user interaction with physical environments.

DOI: https://doi.org/10.5281/zenodo.19828864

Design Of Reinforcement Learning Grid World Navigation System Using Rewards And Penalties: Q-Learning, SARSA And Double Q-Learning

Authors: Prachi Durge, Mahek Shribas, Mohanish Lanjewar, Parth Gadwal, Pranay Wadibhasme, Pranjali Nakhate

Abstract: This paper presents a systematic comparative study of three tabular reinforcement learning (RL) algorithms—Q-Learning,State-Action-Reward-State- Action (SARSA), and Double Q-Learning—deployed within a configurable stochastic GridWorld environment. The environment incorporates slip-based stochastic transitions, trap cells, potential-based reward shaping grounded in the theoretical guarantees of Ng et al. [1], and partial observability modes. The central research hypothesis investigates whether Double Q-Learning’s decoupled selection-evaluation mechanism demonstrably reduces maximization bias compared to vanilla Q-Learning, particularly under elevated stochastic transition probabilities. An interactive web-based research platform is developed using Flask and Chart.js, enabling real-time policy visualization, value-function heatmaps, Q-table analysis, and multi-seed benchmark comparisons with confidence intervals. Experimental results across three canonical grid configurations demonstrate that Double Q- Learning achieves superior convergence stability and reduced overestimation in high-slip environments, while SARSA exhibits inherently conservative on-policy behavior that trades off peak performance for robustness near traps.

DOI: https://doi.org/10.5281/zenodo.19830361

Comparative Analysis Of Machine Learning Regression Techniques For Used Car Price Prediction: Linear Regression Versus Random Forest

Authors: Dr. Jasjit Singh Samagh, Urvita and Chandan

Abstract: Accurate valuation of used automobiles remains a critical challenge in the automotive resale market, where traditional manual estimation methods suffer from inconsistency, subjectivity, and limited scalability. This paper presents a comprehensive comparative analysis of two fundamental machine learning regression techniques—Linear Regression and Random Forest—for automated car price prediction. We developed and evaluated two complete prediction systems: a web-based application using Linear Regression integrated with Streamlit, and a desktop GUI application employing Random Forest with Tkinter interface. Both systems were trained and tested on comprehensive used car datasets comprising over 6,700 vehicle records with features including brand, manufacturing year, kilometers driven, fuel type, transmission type, ownership history, engine specifications, and market pricing. The Linear Regression model achieved an R² score of 0.87, Mean Absolute Error (MAE) of 0.34 lakhs, and Mean Squared Error (MSE) of 0.18, while the Random Forest approach demonstrated superior performance with R² score of 0.94, MAE of 0.28 lakhs, and MSE of 0.60. Our comparative analysis reveals that Random Forest’s ensemble learning approach captures non-linear relationships more effectively, achieving 7% higher variance explanation than Linear Regression, though at increased computational complexity. Statistical significance testing confirms that Random Forest’s performance improvement is statistically significant (p < 0.01). Both systems provide real-time predictions through user-friendly interfaces—web-based for broader accessibility and desktop-based for offline usage. This research contributes practical insights into algorithm selection for automotive price prediction, demonstrating trade-offs between model simplicity, interpretability, and accuracy while providing deployment-ready solutions for diverse stakeholder requirements.

DOI: https://doi.org/10.5281/zenodo.19830626

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Intelligent Monitoring Of Water Quality: Leveraging Data Science And Machine Learning For Environmental Sustainability

Authors: Uzair Aman Syed, Prof. Sangeeta Vhatkar

Abstract: Water pollution poses significant threats to human health and the environment. The existing approaches to water quality measurement through hand sampling and the use of chemicals have two significant weaknesses: they are slow in delivery and do not cover all fields. According to the researchers, the AI based system that combines sensor networks with machine learning algorithms and real-time predictive models was designed to accomplish the following objectives: The system ensures continuous monitoring of the indicators of water quality. The system applies the correct techniques to estimate the concentra- tions of water pollutants. The system creates helpful measures that are used to deal with cases of water contamination. The experimental results have shown that the method proposed is very accurate in detection and response time is better than those of the conventional methods therefore making optimal decisions regarding environmental agencies and policymakers.

DOI: https://doi.org/10.5281/zenodo.19836127

Anesthesia Prediction For Optimizing Patient Sedation Using Support Vector Regression,XG Boost And Transformer Model

Authors: Ms.M.Devika, Mandyam Rohith Reddy, Kale Umamaheshwara Rao, Tamilarasan

Abstract: To maximize patient safety and comfort during medical procedures, effective anesthesia management requires closely monitoring and administering anesthesia for every procedure performed. If medications are not given to the appropriate degree of sedation, there could be potential complications or issues with correctly and efficiently completing the procedure. This paper will cover the development of an AI-based system using machine learning algorithms, including support vector regression (SVR), extreme gradient boosting (XGBoost), and transformer-based (Txb) models, to predict dosage(s) of anesthesia based on clinical information from the patient (demographics/vital signs/medical history) as well as characteristics associated with the procedure. Previous experiments have shown that the advanced machine learning methods discussed above yield greater accuracy and reliability than established methodology currently employed in anesthesia practice to estimate ideal anesthesia dosages. The proposed system will allow anesthesiologists to determine the appropriate dosage(s) of anesthesia to reduce exposure to risk and improve healthcare delivery efficiency through quality data to support better informed decisions.

CLARA.AI: An On-Premise LLM-Powered Academic Administration and Analytics Platform

Authors: Dhyanesh M, Dharshini S, Deepak P, Aisha Amna A

Abstract: Indian engineering institutions face significant administrative bottlenecks, ranging from repetitive circular drafting to manual, error-prone data entry for university mark sheets. CLARA.AI (Comprehensive LLM-powered Academic Resource Administrator) is a full-stack, AI-driven platform designed to automate and augment these critical workflows. Operating entirely on-premise to ensure data privacy, the system integrates a local Large Language Model (Llama 3.1 and 3.2 via Ollama) with a Django-based Model-View-Template architecture. Key innovations include an AI Circular Generator that overlays dynamically drafted text onto institutional letterheads, and an Intelligent Academic Analytics engine that utilizes coordinate-based table extraction and LLM metadata enrichment to parse complex PDF mark sheets. Furthermore, CLARA.AI features a hybrid Natural Language Query (NLPQ) pipeline and a robust four-tier Role-Based Access Control (RBAC) system. By seamlessly unifying data management and generative AI without relying on external cloud APIs, CLARA.AI represents a paradigm shift in secure, intelligent educational administration.

Deep Learning-Based Detection Of Plant Diseases Using Leaf Image Analysis

Authors: Shweta Patnaik, Stanli Jena

Abstract: Now a days Plant diseases significantly affect agricultural productivity and food security by reducing crop yield and quality. Traditional methods of disease detection rely on manual inspection, which is time-consuming, labour intensive, and often prone to human error. To overcome these limitations, automated approaches based on computer vision and deep learning have been developed for accurate plant disease detection. This study presents a method for identifying and classifying plant diseases using leaf image analysis. The proposed system utilizes computational models to analyse visual features of leaf images and detect disease patterns with improved accuracy. Image preprocessing techniques, including noise removal, resizing, and normalization, are applied to enhance image quality and ensure consistency in model input. The performance of the system is evaluated using standard metrics such as accuracy and precision. The results demonstrate that the proposed approach provides more reliable and efficient disease detection compared to conventional methods. Furthermore, the system offers a cost-effective solution that can assist farmers in early diagnosis and management of plant diseases. This approach highlights the potential of image- based automated systems in supporting precision agriculture and improving crop health monitoring.

DOI: https://doi.org/10.5281/zenodo.19844732

Machine Learning based Wind Energy Forecasting for Energy Management in Microgrid Applications

Authors: P. Hemeshwar Chary, Akula Nikhila, Balusuguri Navya, Kotaraviteja

Abstract: This paper is about building hardware for a machine learning system that forecasts wind energy and ties it into an energy management setup for microgrids. It seems like the main idea is to use this optimized thing called Variational Mode Decomposition along with CNN-LSTM for the predictions, and then a Deep Reinforcement Learning approach for handling the energy side. What stands out is how they actually built a real prototype to test it, not just simulations like a lot of other studies do. The setup includes emulating wind data, some microcontroller to control things, a battery for storage, loads that can be adjusted, and a way to connect to the grid. I think that makes it more practical, you know. They ran experiments and got better accuracy in forecasting, plus the energy dispatch worked efficiently in real time. It feels like this could help make microgrids more reliable, cut down on costs, and keep everything running stable. Some parts of the implementation might still need tweaking, but overall it shows promise. The forecasting part with VMD and the neural nets seems key to why it performs well. Index Terms—Wind energy is something thats getting a lot more attention these days, especially with all the push for renewable stuff. Forecasting how much power the wind will give is tricky because wind changes so much, right. I think using models like CNN and LSTM can help predict it better. CNN is good for spotting patterns in data, like images but here its time series from wind speeds. Then LSTM handles the sequences over time, remembering past stuff to guess future outputs. It seems like combining them makes the forecasts more accurate, at least from what Ive read. VMD comes in too, which I believe stands for Variational Mode Decomposition. Its a way to break down the noisy wind data into smoother parts, so the model doesnt get confused by all the ups and downs. Without that, predictions might be off. I might be oversimplifying this, but it feels like preprocessing the signal with VMD first improves everything. For energy management systems, once you have a good forecast, you can plan better. Like deciding when to store extra power or switch sources. In a microgrid, thats super important because its small scale, maybe for a community or island. Hardware implementation is the next step, turning the software models into real devices. Ive seen papers on using FPGAs or something for that, to make it fast and efficient on actual turbines. Microgrid applications tie it all together. Wind forecasting with these tools helps balance the grid, reduces waste. Some people say its not perfect yet, others think its ready for more use. That part stands out to me, how it could really change things but still has challenges like cost. Overall, this approach seems promising, though Im not totally sure about the hardware side yet.

The Role Of Telemedicine In Post-Pandemic Healthcare

Authors: Mohammed Afsal

Abstract: The COVID-19 crisis reshaped healthcare systems across the world in ways never seen before. As hospitals struggled to manage rising infection rates, traditional face-to-face consultations quickly became risky. In response, healthcare providers rapidly turned to telemedicine as a safer and more practical alternative. What initially began as an emergency response soon demonstrated long-term value. Virtual healthcare services have since proven effective in expanding access, improving chronic disease management, reducing operational costs, and maintaining continuity of care. This paper examines how telemedicine evolved during the pandemic, the technologies that support it, the benefits and limitations it presents, and its growing importance in shaping the future of global healthcare delivery.

DOI: https://doi.org/10.5281/zenodo.19845945

Advancement In Parkinsons Treatment

Authors: Aviraj Sanjay zure, Tushar ravsaheb shingade , Vinay Vilas patil , Abhishek Mallinath Sutar , Sonali Mahadev Patil , vaishnavi Deepak Pawar , Anita Rangrao Pujari

Abstract: Current advancements in the treatment of Parkinson’s disease (PD) are shifting from purely symptomatic management to a dual approach: refining the delivery of existing dopaminergic therapies and developing experimental disease-modifying and neurorestorative interventions. While levodopa remains the gold standard, its long-term use is complicated by motor fluctuations and dyskinesia, prompting the development of novel delivery systems like continuous subcutaneous infusions (e.g., Vyalev and Onapgo) and inhaled levodopa to provide steadier symptom control. Simultaneously, emerging therapies including stem cell transplantation (e.g., bemdaneprocel), gene therapy (e.g., AB-1005), and immunotherapy targeting -synuclein are advancing through late-stage clinical trials with the goal of replacing lost neurons or halting disease progression.

Applications Of Intelligent Sensors In Smart Homes: A Review

Authors: Nana Boa Benfor, Zhang Aiqiang, Isyaku Muhammad, Kelvin Gyamfi Boadu

Abstract: Smart homes are swiftly evolving into intelligent, autonomous ecosystems that improve home comfort, security, and energy efficiency. The integration of intelligent sensors, aided by the Internet of Things (IoT), artificial intelligence (AI), and sophisticated wireless communication protocols, is key to this change. This paper provides a comprehensive overview of the types, functions, and applications of intelligent sensors in smart homes, including motion detection, energy management, indoor air quality monitoring, moisture and leak detection, and flame and dangerous gas detection. Edge/fog computing, cloud platforms, and federated learning technologies that enable intelligent sensing are rigorously studied, along with system-level architectures that support seamless automation. Despite the potential, issues such as data privacy, interoperability, system stability, and the limitations of low-cost sensors remain. This study emphasizes on future research approaches focused on robust security frameworks, decentralized intelligence via federated learning, and improved sensor accuracy, all of which aim to achieve scalable, resilient, and truly intelligent smart homes.

DOI: https://doi.org/10.5281/zenodo.19861721

A Study on AI–Driven Social Media Monitoring with Special Reference to Coimbatore City

Authors: Mrs. Swathi M, Ms. Anushka N

Abstract: This study examines the role of Artificial Intelligence (AI) in enhancing social media monitoring, with a focus on businesses in Coimbatore district. AI technologies such as sentiment analysis, machine learning algorithms, and automated data analytics help organizations track online mentions, understand consumer behavior, and manage brand reputation effectively. The research investigates how businesses are adopting AI-driven social media monitoring tools, the benefits they achieve in marketing and customer engagement, and the challenges faced during implementation. The findings highlight the importance of AI in modern social media management and provide strategies for effective adoption. Results indicate that AI-driven monitoring significantly improves decision-making, enhances brand perception, and strengthens competitive advantage.

DOI: https://zenodo.org/records/19863709

Customer Relationship and Service Management in Digital Insurance Platforms

Authors: Assistant Professor Ms.J.Abinaya, Mr. M. Dhilipkarna

Abstract: Customer Relationship and Service Management in digital insurance platforms has become a critical area of study due to the rapid digital transformation of the insurance industry. This study focuses on how digital platforms enhance customer engagement, streamline service delivery, and improve overall customer satisfaction through the use of advanced technologies such as artificial intelligence, chatbots, and data analytics. It examines the effectiveness of digital tools in managing customer relationships, addressing grievances, and ensuring seamless communication. The study also highlights the challenges faced by insurers in maintaining personalized interactions while operating in a digital environment. Overall, it emphasizes the growing importance of technology-driven service management in building long-term customer loyalty and competitive advantage in the insurance sector.

DOI: https://zenodo.org/records/19864230

A Study on Shift in Consumer Preference from Rice and Wheat to Millets in Coimbatore

Authors: Assistant Professor Ms.G. Revathi, Mr. Gowtham MR

Abstract: The transformation of food consumption patterns has become a significant subject of study in the field of marketing and consumer behavior. This research investigates the shift in consumer preference from traditional staple grains such as rice and wheat to nutrient-rich millets in Coimbatore city. The study aims to analyze awareness levels, key influencing factors, and consumer purchasing behavior towards millet-based products. A descriptive research design has been adopted, with primary data collected from 100 respondents through structured questionnaires. The findings indicate that increasing health consciousness, rising incidence of lifestyle diseases, medical recommendations, and media influence are the primary drivers behind this shift. Consumers perceive millets as a healthier alternative due to their high fiber, mineral content, and low glycemic index. Despite growing acceptance, challenges such as pricing, limited accessibility, and lack of awareness in certain segments still persist. The study concludes that millets possess strong market potential and represent an emerging segment in the food industry. Strategic marketing, awareness campaigns, and supply chain improvements can further accelerate this transition

DOI: https://zenodo.org/records/19864623

AI Chatbots In Education: Ethical, Cognitive, And Security Implications For Children

Authors: Ashita Chhabra, Ansh Popli, Jaskirat Kaur, Ansh Rehan

Abstract: The rising popularity of AI chatbots in the sphere of education is transforming the learning experience of children and the manner in which educators provide academic assistance. They offer personalized feedback and flexible assistance, interactive and interactive learning experiences, which can increase motivation and increase accessibility of learning. However, their growing incorporation into child,oriented education brings about myriad of moral, mental and safety issues. Other ethical concerns of the use of AI chatbot also feature in the paper as comprising academic dishonesty, unfairness and algorithm favoritism. It however, also researches a mental outlook on the excessive use of automatic reactions, and the loss of critical analytic faculties, creativity, and independent learning capability, in society. Finally, the paper concludes with security issues, which are the vulnerabilities of data security, misinformation and transparency in AI. The findings suggest that in the realm of digital learning, there is need to employ conscientious methods of AI technology to ensure the well,being of children is not jeopardized and this could be made possible through appropriate data protection, ethical practices and child-centered policies. Although the positive side of the application of AI chatbots in education is obvious, it is also important to define the way the latter should be applied in a responsible manner.

The Role of Artificial Intelligence in Customer Segmentation and Target Marketing

Authors: Dr. G. Arutgeevitha, Mr. M. Kavin Kumar

Abstract: The role of Artificial Intelligence in customer segmentation and target marketing has gained significant importance in the modern business environment. This study examines how AI technologies such as machine learning, predictive analytics, and data mining enhance the accuracy and efficiency of identifying customer groups and delivering targeted marketing strategies. AI enables organizations to analyze large volumes of customer data, identify patterns, and predict consumer behavior, leading to more personalized marketing efforts. The study also explores the benefits and challenges associated with AI adoption in marketing practices, highlighting its impact on improving customer engagement, conversion rates, and overall marketing performance.

DOI: https://zenodo.org/records/19865248

Detoxify: An Automated System to Recalibrate YouTube Recommendation Algorithms Using Intent-Based Content Surfing

Authors: Prof. Vikas More, Sagar Mahajan, Yash Kadam, Yuvraj Singh, Yashraj Khandar

Abstract: In the modern banking environment, customer experience has become a critical factor influencing customer satisfaction, loyalty, and overall business performance. With the rapid advancement of digital technologies and increasing competition in the financial sector, banks are required to move beyond traditional service models and adopt customer-centric approaches. This study focuses on enhancing customer experience in the Indian banking sector through the application of business analytics and the development of a personalization framework. The primary objective of this research is to analyze how business analytics can be used to understand customer behavior and improve service delivery. The study also aims to identify customer expectations regarding personalized banking services and to examine the existing gaps in service quality. Primary data for the study was collected through a structured questionnaire administered to 50 respondents. The collected data was analyzed using simple statistical tools such as percentages and pie charts to derive meaningful insights. The findings of the study reveal that while customers are generally satisfied with banking services, there is a significant demand for personalized services. Most respondents expressed that banks do not fully understand their needs and expect more customized offerings. The study also highlights that digital banking, particularly mobile banking, is widely preferred due to its convenience and accessibility.

A Study on Arketing Strategies and Consumer Behaviour Analysis with Special Reference to Coimbatore District

Authors: Assistant Professor Mr. Eknath Prasath M, Mr. Vasanth Kumar S.

Abstract: Marketing strategies play a significant role in influencing consumer behaviour and shaping purchasing decisions in modern markets. Businesses operating in competitive environments must adopt effective marketing techniques to attract customers and maintain long-term relationships. Understanding consumer behaviour is essential for organizations to develop products, pricing strategies, promotional activities, and distribution systems that satisfy customer needs. This study aims to analyze marketing strategies and examine consumer behaviour with special reference to Coimbatore district. The research focuses on the factors that influence consumer buying decisions, the role of digital marketing, and the effectiveness of promotional strategies adopted by businesses in the region. The study mainly relies on secondary data collected from journals, research articles, and online sources. The findings suggest that marketing strategies such as branding, advertising, pricing policies, and digital promotions significantly influence consumer purchase behaviour in Coimbatore. Factors such as brand reputation, product quality, promotional offers, and social media marketing play a crucial role in shaping consumer preferences. The study also highlights the growing importance of digital platforms in influencing consumer decision-making.

DOI: https://zenodo.org/records/19865755

Rebook

Authors: Sanskriti Solse, Swarali Karkar, Saneeya Shaikh, Shubham Patil, Ms. Manila Gupta, Prof. Mohammad Juned, Dr. Varsha Shah

Abstract: The escalating cost of academic textbooks in India presents a significant financial burden to engineering students, many of whom purchase books for a single semester only to leave them unused thereafter. This paper presents ReBook, a full-stack, location-aware web platform that facilitates the buying, selling, and donation of second-hand academic books among students. The system employs Java Spring Boot for the backend REST API, MySQL 8.0 for persistent storage, and a JavaScript single-page application for the frontend. Key innovations include GPS-based distance sorting using the Haversine formula, WhatsApp seller integration, a pincode-level hyper-local search filter, and an AI-powered camera-based book condition detection module built on the Claude API (Anthropic). All ten planned feature modules were implemented and verified functional across Chrome, Firefox, and Android mobile browsers. ReBook directly addresses the affordability and sustainability challenges of academic publishing for students across India.

A Review Of Federated Learning: Privacy-Preserving Machine Learning

Authors: Rathod Neha, Mojidra kirtika, khandhediya Isha, Harkishan Gohil

Abstract: Federated Learning (FL), was created by McMahan et al (14), has become of interest because it offers a decentralized machine learning framework for developing large scale ML models. This allows many users (or clients) to collaborate on training a shared model while retaining control of their own data. FL is ultimately designed to provide a solution to the conflict between the data demands of machine learning systems and the desire of individuals/companies to keep their personal and commercial data private. This paper is a review of the privacy and confidentiality aspects of Federated Learning. A critical review of the fundamental algorithms used in FL, possible attacks against FL systems, and the four primary techniques for enhancing privacy in FL; Differential Privacy (DP), Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), and hardware based Trusted Execution Environments (TEE), is provided. We will review aggregation protocols, determine the strength of FL systems against poisoning and inference attacks, and compare various FL systems implemented in three industries; healthcare, mobile communication and finance. A detailed review of FL reveals research issues related to; statistical heterogeneity, communication overhead, system heterogeneity and fairness. Finally, this review presents a prioritized set of research objectives for the next ten years, with an emphasis on situating FL within the larger context of privacy-preserving ML and potential regulatory developments.

DOI: https://doi.org/10.5281/zenodo.19878825

Prediction Of Strength Parameters Of Poly Propylene Fiber Reinforced Concrete Using Multiple Regression Analysis (Mra)

Authors: K. Sagar, K.Ashok, G. Vijay Kumar

Abstract: This investigation explains the effect of addition of polypropylene fibers and Nano Silica into concrete. This investigation is divided into two phases. First Phase deals with calculations of Compressive and Split Tensile strength. Here we have done compressive strength tests for calculating the optimum percentage of Nano Silica with variation from0% to 3% of cement which is replaced with cement in concrete. Now polypropylene fiber is added to concrete from 0% to 1.4% of cement and those specimens were tested for compressive and Split Tensile strength and obtained the maximum percentage of fiber at which strengths maximum. In second phase, a modal equation is developed using Multiple Regression Analysis (MRA) for compressive and Split Tensile strength based on experimental results which are found in phase one. By using obtained modal equations we will calculate Predicted strength and their residuals and their graphical representation is shown.

DOI: https://doi.org/10.5281/zenodo.19879606

Real-Time Retail Forecasting And Anomaly Detection Using Hybrid ARIMA And Neural Network Models

Authors: Khadija Elkattany, Md Mutasim Billa

Abstract: This paper presents a hybrid machine learning framework that addresses scalability and accuracy challenges in retail inventory management by integrating real-time demand forecasting with anomaly detection, evaluated using Walmart’s historical sales data. Traditional approaches face a trade-off: maintaining individual models for each product category is computationally prohibitive, while generalized models often underperform for dissimilar items, resulting in stock outs or overstocking. To address this, we propose a department-level aggregation strategy that balances specificity and generalization, combined with a hybrid methodology: ARIMA for linear trend and seasonality modeling, cubic spline interpolation to capture nonlinear residual patterns, and neural networks for complex interactions. The framework dynamically adjusts predictions using real-time sales streams and applies residual-based anomaly detection with threshold triggers to identify sudden demand spikes or supply disruptions. Experiments on a filtered Walmart dataset (12 months, 15 departments) indicate an 18% reduction in mean absolute error (MAE) compared to exponential smoothing baselines, while spline-enhanced neural networks achieve a 24% improvement over standalone ARIMA. The anomaly detection module identifies 92% of simulated irregularities with a 7% false-positive rate. The proposed framework provides three principal advantages: (1) scalable department-level modeling without per-product customization, (2) real-time adaptability to fluctuating demand, and (3) cost-efficient inventory optimization through integrated anomaly alerts. This work offers a practical blueprint for retailers to enhance forecasting precision, mitigate supply chain risks, and reduce operational costs in volatile markets.

DOI: https://doi.org/10.5281/zenodo.19880805

Simulation-Driven Lightweight Design Of An Automotive Reducer Housing Using FEA-Coupled Topology Optimization

Authors: Acharjee Partho Protim, Wei Zhang

Abstract: Lightweight design is essential in modern automotive systems to improve energy efficiency, reduce emissions, and enhance performance. This study presents a simulation-driven framework for the lightweight design of an automotive reducer housing using finite element analysis (FEA) and topology optimization (TO). A baseline reducer housing is analyzed under multiple load conditions, including maximum torque, emergency braking, and cornering. Stress distribution and deformation behavior are evaluated to identify structurally redundant regions. A Solid Isotropic Material with Penalization (SIMP)-based topology optimization method is applied with a volume reduction constraint to minimize compliance while maintaining stiffness. The optimized topology is reconstructed into a manufacturable design considering casting constraints. Comparative FEA validation shows significant mass reduction while preserving structural integrity, safety factor, and stiffness. The proposed methodology provides an effective and practical framework for lightweight automotive component design.

DOI: https://doi.org/10.5281/zenodo.19880845

Bias Propagation Analysis In AI Chatbots Using Prompt-Based Fairness Evaluation

Authors: Rajat Takkar, Gunjan Lathwal, Devanshi Dadwal, Bhumika Aggarwal, Gaurang Batra

Abstract: AI chatbots and large language models show up almost everywhere these days – customer support, healthcare, schools, even hiring. While they’re good at handling language, there’s still a big question about bias. These systems often pick up biases from their training data and then reflect them back in their answers. That includes biases related to gender, jobs, places, or wealth. This study explores whether chatbots respond to demographic-based questions with built-in bias. We used a set of structured prompts and gathered answers from several AI chatbots, recording all responses for analysis. Every answer was examined using sentiment analysis and a neutrality scoring method. This measured how fair or unbiased each system was. We performed all our analysis using Python tools like Pandas, TextBlob, and Matplotlib. Our expectation was that chatbot responses would usually be objective, but some subtle biases could sneak in depending on how you ask the question or what the topic is. Some questions just lead to more bias than others. By scoring fairness, we can actually quantify differences and see which systems are more neutral. This approach helps assess how these AI tools deal with real-world issues and fairness.

Data Protection And Cybersecurity Issues In Autonomous Vehicles Under Indian Law

Authors: Nv Subhasri, Madhunisha. A, Shruthi. T

Abstract: This dissertation examines the growing intersection of law, technology, and regulation in the context of autonomous vehicles (AVs) in India, with particular emphasis on issues of privacy, data protection, and cybersecurity. It analyzes existing Indian legal frameworks, such as the Information Technology Act and the proposed Personal Data Protection Bill, to assess whether they are equipped to handle the unique challenges posed by AV technology. The study also compares India’s approach with international standards, including the GDPR and regulatory models followed in countries like the United States and China. Through this comparative perspective, the research highlights existing gaps and suggests areas where India can strengthen its legal and regulatory response.

A Multi-Model Fusion Framework For Cardiovascular Risk Prediction

Authors: Dr. Meghna Utmal, Sakshi Singh, Kunti Uikey, Vaishali Gupta, Sajal Pandey

Abstract: — Heart disease remains a major health concern worldwide, affecting a large proportion of the global population. According to reports by the World Health Organization (WHO), approximately 17.9 million deaths occur annually due to cardiovascular diseases. In the context of the COVID-19 pandemic and its post-infection complications, cardiac failure has emerged as a commonly observed condition, highlighting the critical need for early diagnosis and prediction of heart disease to enable effective prevention. Timely detection can significantly reduce mortality rates. Recent advancements in machine learning techniques have greatly contributed to the healthcare sector, particularly in the prediction of heart diseases, thereby saving numerous lives. This paper presents an efficient ensemble-based machine learning approach for predicting heart-related disorders, achieving an accuracy of 88.52%.

DOI: https://doi.org/10.5281/zenodo.19882012

Mitigating Credit Card Fraud Using SMOTE Sampling And Artificial Neural Networks

Authors: Vansh Sharma

Abstract: Banking and financial institutions are increasingly encountering the challenges of credit card fraud. Statistics suggest that each year financial institutions incur losses close to billions of dollars globally due to such frauds .Hence it is evident for financial institutions to continue to invest in advanced fraud detection systems to minimize the impact of credit card fraud on their bottom line and protect their customers from financial losses.Before deep diving into the solutions which can be proposed to solve the problem of credit card fraud , it is important to know the ways in which these frauds are taking place and what loopholes are being misused to catalyze these frauds .Hence in our research paper we first look at ways in which these frauds are taking place. Moreover, one of the other challenges to proposing a solution to this problem is the presence of highly imbalanced datasets to train the model , which motivates us to apply various techniques such as Synthetic Minority Oversampling Technique (SMOTE) to make the datasets balanced which will allow us to train the model better .We implement Artificial neural network + Recurrent neural network with auto-encoder architecture to make a model for one-class classification . The model uses these relationships to make predictions about the likelihood of fraud in new transactions. ANNs can be used to process large amounts of data and are particularly effective in detecting non-linear relationships between variables.

DOI: https://doi.org/10.5281/zenodo.19884020

Blockchain And AI-Based Fraud Detection System For Digital Payments

Authors: Avinash, Anshika, Shivam

Abstract: We know digital payment systems are growing faster all over the world, and as they grow, they have some consequences. One big problem among them is digital payment fraud, which rapidly increases as payment systems grow. Fraudsters can surpass rule-based detection systems as they adapt; they have new patterns for doing fraud. They find loopholes in the main architecture from where they manipulate data and do fraud. We study both problems and reach a very solid solution to track down all fraud patterns. We added artificial intelligence and the Hyperledger Fabric blockchain, which is used to detect the pattern of fraud, and a blockchain, which is used to make the payment system tamper-proof. All data related to the payment system are stored in a single system, which is very secure and not able to be encrypted. The detection system runs on four methods. For unstable workflow it uses LSTM networks. For rule-based classification, we used a random forest classifier. For fraud detection, we used a GraphSAGE network, and last, for any suspicious activity, we used an autoencoder. All the things are watched by a meta-learner, which analyzes and combines their output and provides data to trigger a smart contract response, which works automatically. Different financial institutions are used to train their systems without using shared row transaction data to make privacy learn their module detection. We concluded our study, but two public benchmarks are set by PaySim (6.35M transactions) and IEEE-CIS (590K transactions). In PaySim we succeed with up to 98.3% accuracy and an AUCROC of 0.991. Adversarial robustness testing shows the team requires 3.2 times larger to prevent any mistake for success for a single model. These results show much need of AI and blockchain. Using AI and blockchain is very efficient; they are better than anything else to detect fraud.

AI-Driven Approach To Student Performance Analysis System

Authors: Rajat Srivastava, Mr. Ankit Singh, Sneha Mehrotra, Shaifali Singh, Shreyansh Srivastav

Abstract: There’s a lot more to student performance than just marks. Some kids barely pass written exams but shine in group projects or sports. The problem is, most colleges still judge students almost entirely by their test scores. That’s like judging a fish by its ability to climb a tree. By the time a teacher realizes someone’s struggling, that student might already be failing or even thinking of dropping out. So what if we could spot trouble earlier — way before the report card says it all? That’s what this paper is about. We used machine learning to sift through student data — attendance, past grades, even family background — and predict who might fall behind. Not just for the sake of prediction, but to actually give teachers a heads-up so they can step in and help. The results were pretty solid. Our model caught most at-risk students with over 90% accuracy. Not perfect, but a lot better than waiting till the end of the semester.

Decision Intelligence For AI And Emerging Technologies: The AEGIS-DM Framework For Trustworthy, Cost-Aware, And Low-Latency Decision Making

Authors: Prudvi Saisaran Ponduru

Abstract: Recent advances in foundation models, multimodal learning, reasoning-oriented large language models, agentic workflows, and edge AI have expanded the capabilities of artificial intelligence systems. However, practical decision-making remains brittle because many systems optimize prediction quality while under-modeling intervention effects, uncertainty, safety constraints, latency budgets, and human accountability. This paper introduces AEGIS-DM, an adaptive, edge-aware, governed, interventional, and safe decision-making framework designed for AI systems deployed across emerging technology settings including agentic assistants, cyber-physical systems, healthcare decision support, and enterprise automation. The framework combines five layers: multimodal state representation, predictive scoring, causal effect estimation, simulator- or planner-based long-horizon optimization, and a governance layer for calibration, fairness, policy checks, logging, and human override. We further propose a cross-domain evaluation protocol using public resources such as Adult, D4RL, WebShop, ALFWorld, MIMIC-IV Demo, NASA CMAPSS, and M5, together with open-source tooling including OpenAI Evals, Responsible AI Toolbox, OpenSpiel, RecSim NG, Stable-Baselines3, and RLlib. Because this manuscript is a methods-and-benchmark contribution, the quantitative section reports deterministic scenario-based simulation results under the stated protocol rather than production deployment measurements. Under the reference protocol, the proposed hybrid approach is expected to outperform rule-based, supervised-only, offline-RL-only, and prompt-only agent baselines in composite decision quality and robustness while maintaining substantially better latency and cost than cloud-only frontier-model pipelines.

DOI: https://doi.org/10.5281/zenodo.19968793

Intelligent Surveillance For Suspicious Activity Detection

Authors: Wasim Riyajoddin Kazi, Om Vitthal Devakate, Vishal Popatrao Jagadale, Kavita Shinde

Abstract: In recent years, the issues related to public safety and security have increased significantly, which resulted in a surge of demand for automated surveillance systems. However, traditional monitoring systems based on CCTV require constant human surveillance, which is not only wasteful but also error- prone. This paper proposes a deep learning-based surveillance system that can automatically detect suspicious activities in videos. The proposed model utilizes CNNs to classify video frames into normal and suspicious categories. Upon detection of suspicious activity, the system captures the frame and sends an automated email notification to the registered system administrator using the SMTP protocol. The proposed system utilizes OpenCV for video processing, TensorFlow/Keras for training and predicting the models, and SQLite to securely store administrator information within a database.

Enhancing Security And Privacy In Multi-Tenant Cloud Computing: A Framework-Based Study

Authors: Kasarla Vanitha, B.Archana

Abstract: Cloud computing has transformed the IT landscape by providing scalable, flexible, and cost-effective services. However, its multi-tenant infrastructure introduces significant security and privacy challenges due to shared resources and virtualized environments. This paper examines established security and privacy frameworks, threat models, and protective architectures designed to address these concerns. Through a comparative analysis of existing literature and technical frameworks, the study identifies key vulnerabilities and effective mitigation strategies in multi-tenant cloud environments. Additionally, graphical models and charts are included to demonstrate how shared resource access and virtualization can be secured using encryption, tenant isolation, and dynamic authentication mechanisms.

DOI: https://doi.org/10.5281/zenodo.19909008

A Multi-Modal AI-Based Health Intelligence Framework For Integrated Disease Risk Assessment And Lifestyle Analysis

Authors: Rishi Raghav Singh, Rohan Singh, Rajat Takkar

Abstract: More than 30% of worldwide deaths involve diseases caused by cardiovascular and lifestyle factors (WHO, 2023) As awareness of early risk identification advances, accessible practical screening tools for use in primary care continue to be either very expensive, reliant on specialists or both. In this paper, we propose a Multi-Modal AI-Based Health Intelligence Framework with an explicit focus on two interrelated concepts encapsulated in the form of two specialized individual modules: Disease Risk Assessment (DRA) module and Lifestyle Analysis (LSA) module. After systematic preprocessing and class-balancing, the DRA module trains LR, SVM, and RF on the Cleveland Heart Disease dataset (303 patients). The LSA module takes user-reported behavioral behaviors — BMI, physical activity, sleep, dietary quality, and stress — to calculate a composite Lifestyle Risk Index (LRI). Both modules are provided through a Streamlit web application that provides real time predictions with SHAP-based explanation. Amongst all the classifiers we evaluated, Random Forest performed best with a 91.8% accuracy, AUC-ROC = 0.956 It powers a sub 60 ms response time for the system and is deployable in the cloud.

An AI-Assisted Skill-Based Candidate Evaluation System For Automated Recruitment Pipelines

Authors: Arghadeep Nath, Rajat Takkar

Abstract: Early-stage hiring processes continue to depend on resume-based and keyword-based filtering, which does not reliably capture a candidate’s actual abilities. This paper presents an AI-assisted skill evaluation system that prioritizes demonstrated performance over resume content. The system models candidate screening as a multi-stage pipeline: skill profiling, dynamic assessment delivery, automated rule-based and NLP evaluation, and weighted score aggregation. A competency model maps candidate skills to standardized assessment criteria, enabling objective cross-candidate comparison. Evaluation on simulated data (n=100) yields a Spearman rank correlation of 0.91, a false-positive shortlist rate of 12%, and a top-quintile precision of 78% — all substantially better than a conventional ATS baseline. The proposed framework is scalable, modular, and designed to reduce bias inherent in resume-centric screening.

Challenges In Adopting Microservices Architecture: A Systematic Review Of Data Consistency And Fault Tolerance

Authors: Devang Sethi, Dr. Rajat Takkar

Abstract: Microservices architecture has gained significant attention as a dominant paradigm for building scalable and cloud- native applications by decomposing monolithic systems into independently deployable services with decentralized data ownership. However, this architectural approach introduces challenges related to distributed data management and system reliability. This paper presents a systematic literature review examining data consistency and fault tolerance mechanisms in microservices environments. The study analyzes research published between 2016 and 2026 collected from major academic databases including IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, Google Scholar, and arXiv. The findings indicate that strict consistency models often limit system scalability and availability, leading many architectures to adopt eventual consistency and BASE principles. Saga-based transaction management patterns are increasingly preferred over traditional Two-Phase Commit protocols due to improved resilience, although they introduce additional implementation complexity. The review also highlights the lack of standardized evaluation frameworks for benchmarking distributed resilience strategies. Overall, the study emphasizes the importance of balancing consistency, scalability, and fault tolerance when designing reliable microservices-based systems.

DOI: https://doi.org/10.5281/zenodo.19909639

AI Bylaws: A Framework For Ethical Governance

Authors: Keshav Mittal, Jobanpreet Singh, Kartik Kumar, Jasnoor Kaur

Abstract: The field of Artificial Intelligence (AI) has been launched at a rapid pace in many areas including health, finance, administration, and law. Despite the efficacy and automation of AI technologies that remain unexamined, such technologies are accompanied by grave ethical and legal concerns such as algorithmic prejudice, misinformation, abuse of deepfakes, and cybersecurity concerns. These concerns have brought about the realization that there exists a great need in structured governance instruments and mechanisms that regulate AI practices and require prudent application. The other recent concept of the field is AI bylaws that can be described as operational guidelines and regulations of governance to regulate the development of AI systems, their implementation, and their interactions with users. The discussed research paper examines the concept of AI bylaws and addresses the problem of ethical compliance of AI systems with reference to the experimental data consisting of ethically sensitive prompts, related to discrimination, cybercrime, deepfake abuse, and harmful behavior.. The experiment measures the responses of AI and compares them against pre-established measures of ethical compliance. The findings show that AI systems tend to reject dangerous instructions and follow security protocols, but the discrepancies in the detail of the explanation and context-specific logic can be observed. Judging by these results, the present paper suggests a system of AI bylaws that is based on transparency, accountability, fairness, and prevention of misuse. The study indicates that the evaluation through experimentation would be useful in determining what is weak in the current AI governance methods and direct the creation of stronger ethical principles of AI systems.

DOI: https://doi.org/10.5281/zenodo.19909649

Rise Of UPI Fraud In India: Vulnerability Analysis And Prevention Framework

Authors: Aniket Garg

Abstract: The rapid growth of the Indian digital payments ecosystem which is controlled mainly by the Unified Payments Interface (UPI) has improved financial inclusion whilst alleviating transaction friction. Meanwhile, the magnitude, speed, and functionality of UPI have increased vulnerability to phishing, impersonation, scams, and synthetic identities, mule accounts, and AI-enforced social engineering. The paper under consideration investigates the UPI fraud proliferation in India through the qualitative analysis of official circulars, payment data, cybersecurity reports, and the latest regulatory interventions. It has been shown in the analysis that user confusion, ineffective verification conduct, quick payment rails that cannot be reversed, and more advanced threat agents are the proximal factors influencing the rise in fraud. A multi-level framework of prevention that incorporates beneficiary authentication, concatenation of devices, behavioural danger rating, mule-account recognition, consumer knowledge, and amplified inter-institutional reports is proposed. The paper concludes that future achievements in minimizing fraud through the integration of scale-induced innovation with security-by-design and timely redress framework will be dependent on it. [1], [3], [4], [5].

DOI: https://doi.org/10.5281/zenodo.19910031

Digital Supply Chain Transformation and Business Performance of Manufacturing Firms in the Democratic Republic of Congo During COVID-19

Authors: Ummi Yusuf Adam, Habibu Yusuf Adamu

Abstract: The COVID-19 pandemic led to disruptions in global supply chains, exposing vulnerabilities in organizations that were not adequately prepared for digital operations. This study investigates how digital transformation in supply chain management has influenced the business performance of manufacturing companies in the Democratic Republic of Congo amid the pandemic. Utilizing organizational information processing theory and the dynamic capabilities perspective, a conceptual framework was created to connect the digital environment, digital capabilities, digital supply chain transformation, and business performance. Data were collected through a structured survey of 233 senior logistics managers and the model was tested using partial least squares structural equation modeling (PLS-SEM). Measurement validation confirmed reliability and discriminant validity of the constructs. The results reveal that both digital environment (β = 0.271, p = 0.005) and digital capabilities (β = 0.304, p = 0.003) significantly drive digital supply chain transformation, which in turn exerts a strong positive effect on business performance (β = 0.597, p < 0.001). Mediation analysis further shows that digital supply chain transformation significantly mediates the effects of digital environment on business performance. These findings emphasize the importance of developing robust internal digital capabilities alongside an enabling external digital environment to enhance supply-chain agility in turbulent contexts.

DOI: https://doi.org/10.5281/zenodo.19910152

Ai-Driven Adaptive Traffic Signal Control System

Authors: Pranavvikraman. A, Dr. M. Sakthivanitha

Abstract: Traffic congestion is a critical challenge in rapidly urbanising cities, and conventional fixed-time traffic signals fail to adapt to dynamic real-time variations, leading to longer waiting times, fuel wastage, emissions, and delays in emergency response. To address this, the project designs and implements an AI-Driven Adaptive Traffic Signal Control System at a six-road intersection near Adyar Bridge, Chennai, Tamil Nadu, India. The system integrates a Python backend powered by OpenAI’s GPT-5.4-nano model with a real-time HTML/CSS/JavaScript frontend, connected through Flask and Socket.IO. The AI receives time slot inputs, determines traffic density ranges from a lookup table based on real-world observations, and predicts realistic vehicle counts for nine lane paths: R1-R4, R1-R5, R1-R2, R6-R2, R6-R4, R6-R5, R3-R5, R3-R2, and R3-R4. Using these counts, it calculates signal timings for five units — S1, S2, S3, and pedestrian signals P1 and P2 — across five traffic cases (C-1 to C-5). Signals operate independently through Green → Yellow → Red phases, with transitions occurring only when all signals reach red. A midnight mode between 12:01 AM and 4:59 AM switches all signals to blinking red. The dashboard features a dark theme with LCD-style countdown timers and a manual override for emergencies. Economically viable at USD 0.20 per million tokens, the GPT-5.4-nano model demonstrates practical use of AI in structured decision-making for critical infrastructure. Results show reduced delays, improved throughput, and safer pedestrian crossings.

Nutrition & Balanced Diet

Authors: Heli Dholariya

Abstract: Nutrition plays a vital role in maintaining overall health and well-being. A balanced diet provides essential nutrients required for the proper functioning of the body, including growth, repair, and energy production. In recent years, unhealthy eating habits and lifestyle changes have led to an increase in nutritional deficiencies and chronic diseases such as obesity, diabetes, and cardiovascular disorders. This research paper presents a comprehensive study on the importance of nutrition and a balanced diet, including its components, benefits, and impact on human health. It also highlights the consequences of poor nutrition and suggests strategies to maintain a healthy diet. The findings emphasize that proper nutrition is essential for improving quality of life and preventing diseases.

Design Of A Fire-Fighting Robot For Various Applications

Authors: Krishna Pratap Singh Gaur, Om Prakash Sondhiya

Abstract: Innovative solutions that reduce human exposure to life-threatening risks are required because to the increasing frequency and severity of fire accidents in industrial settings, including petrochemical refineries, warehouses, power generation facilities, and chemical manufacturing plants. The methodical design, development, and experimental validation of an autonomous firefighting robot created especially for industrial deployment are presented in this work. The suggested platform combines an embedded multi-sensor array consisting of a FLIR Lepton 3.5 infrared thermal imager, Hamamatsu UV flame sensors, a Velodyne VLP-16 three-dimensional LiDAR, and electrochemical gas detectors with a thermally insulated omnidirectional Mecanum-wheel chassis. On an NVIDIA Jetson Orin NX computation module, FireDetNet-v2, a lightweight convolutional neural network trained on 45,000 annotated industrial fire pictures, achieves a mean average precision (mAP@0.5) of 97.6% at 30 frames per second. A 150-liter onboard water-AFFF suppression module uses a two-degree-of-freedom pan-tilt nozzle gimbal to administer agent at up to 12 bar, with a maximum throw range of 15 meters. GPS-denied autonomous navigation is made possible via simultaneous localization and mapping (SLAM) using the Cartographer framework using VLP-16 LiDAR data.

DOI: https://doi.org/10.5281/zenodo.20053261

Identification of Missing Persons and Unidentified Bodies’ Recognition Using GAN-Based Reconstruction

Authors: R. Oviyashree, M. Loganathan, S.P. Manoj, A.S, Vasunthra, Dr.R.Punithavathi

Abstract: Every year, forensic investigators and humanitarian groups are overwhelmed by thousands of missing people and unidentified bodies. However, traditional methods of identifica-tion are still very slow and prone to mistakes. AMPUIS, the AI-based missing person and Unidentified Body Identification System, solve this problem with a smart, real-time forensic framework. The system uses a ResNet-10-based Single Shot Multi-Box Detector to find faces, Open-face to extract deep embeddings that don’t change with pose, and a Support Vector Machine classifier to give probability scored identity verification. AMPUIS is built on a secure Flask web architecture that lets law enforcement and NGOs access it based on their roles. It has automated case management, severity-based alerts, and a live forensic dashboard. Experimental results show that this method is more accurate and faster than traditional biometric methods.It is also scalable and can be used all over the world for modern forensic identification.

DOI: http://doi.org/

Hyperlocal Real Estate Price Forecasting: A Case Study of the Noida Market

Authors: Kavya Sharma

Abstract: The residential property market in Noida is complex due to its structured sector-based planning and the coexistence of Authority-developed plots and private high-rise housing societies. These two categories follow different pricing patterns, even within nearby areas. This study aims to develop a transparent price prediction model using Multiple Linear Regression to analyze the impact of hyperlocal features, particularly Metro connectivity, on property prices. A historical dataset of Noida properties was utilized and processed using Python and Pandas. The finalized regression model achieved approximately 85% accuracy on the testing dataset, revealing that Sector Location and Metro Connectivity are the most influential factors, often outweighing flat size. This demonstrates that a transparent regression approach can effectively support fair pricing in high-variance markets.

DOI: http://doi.org/

MRI-Based Brain Tumor Detection Using Deep Learning

Authors: Professor Rajendra Pawar, Omkar Walunj, Pranav Hole, Sarthak Thigale, Sohan Sandbhor

Abstract: Early detection of brain tumors is crucial for effective treatment and improved patient outcomes. This study presents an automated system for brain tumor classification using deep learning techniques. A convolutional neural network based on the VGG16 architecture is utilized to analyze MRI images and classify them into different categories such as glioma, meningioma, pituitary tumor, and normal cases. The system includes image preprocessing, model prediction, and a web-based interface developed using Flask for easy user interaction. Users can upload MRI images and receive instant predictions along with confidence scores. Additionally, a PDF report is generated to present the results in a structured format. The proposed approach demonstrates reliable performance and can assist medical professionals in making faster and more accurate preliminary diagnoses.

Comparative And Explainable Machine Learning Framework For Fake News Detection: A Trust Gap And Cross-Dataset Robustness Analysis

Authors: Akash Suri, Aryan Pathania, Divyayush Verma, Rajat Takkar

Abstract: The proliferation of fake news on social media plat- forms poses significant threats to public discourse and democratic processes. While numerous machine learning approaches have been proposed for fake news detection, limited attention has been given to understanding why different models classify news as fake and whether these explanations are consistent across algorithms. This paper presents a comparative and explainable machine learning framework that addresses two critical research questions: (1) Do different ML models agree on which textual fea- tures indicate fake news? (Trust Gap Analysis), and (2) Do fake news patterns learned from one domain generalize to another? (Cross-Dataset Robustness). We evaluate four classical machine learning algorithms—Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest—using TF-IDF features on two distinct datasets: ISOT (political news, 44,898 articles) and WELFake (general news, 72,134 articles). Using SHAP (SHapley Additive exPlanations) for model interpretability, we compute Jaccard similarity and Spearman rank correlation to quantify agreement between model explanations. Our results reveal that different models exhibit varying levels of agreement on fake news indicators, with implications for model selection in real- world deployment. Furthermore, cross-dataset analysis identifies “universal” fake news features that generalize across domains versus “topic-specific” features that are domain-dependent. This work contributes a novel analytical framework for evaluating the trustworthiness and generalizability of fake news detection systems.

DOI: https://doi.org/10.5281/zenodo.19916551

WiFiShield: Real-Time Detection of Public Wi-Fi Network Vulnerabilities

Authors: Daksh Jasrotia, Associate Professor Dr.Simarjit Kaur

Abstract: Public Wi-Fi networks in places like airports or cafés is convenient, but they are not very safe. Most people do not realize that these networks don’t usually have strong security and because of that, attackers can use methods like Man-in-the-Middle (MITM) attacks. One common method of MITM attacks is called ARP spoofing. In this method, a hacker tricks your device into sending data through their system instead of the real network. This usually happens in the background and the users don’t notice anything suspicous. As a result, important and personal information can be exposed and stolen without the user knowing. In this paper, I am proposind WiFiShield, a lightweight system or rather an application designed to detect such vulnerabilities in real time. Instead of being a heavy security suite, the application or system runs quietly in the background, sniffing network packets and double-checking the ARP table for any sudden, suspicious changes or spikes in the ARP requests. If the system sees a gateway address “flapping” or changing unexpectedly, it assigns a risk score and alerts the user immediately. I am aiming to make WiFiShield highly effective at spotting these hijacks without slowing down the computer. The application should be a simple, low-power solution for anyone who needs to stay connected on the go without leaving their personal data wide open to hackers.

DOI: http://doi.org/

Socio Mind AI: Multi-Channel Digital Behavioral Footprint Analyzer

Authors: Udit Tripathi

Abstract: SocioMind AI is an AI-powered analytical framework that quantifies psychological states and personality traits through the automated processing of heterogeneous social media data. Unlike traditional sentiment analysis — which reduces complex human communication to a single positive/negative polarity score — SocioMind AI employs a multi-dimensional approach to construct a comprehensive “Linguistic DNA” profile of an individual, correlating public persona signals with private aspirational data to deliver a 360-degree behavioral footprint. The system operationalizes a novel concept: the Digital Behavioral Footprint (DBF) — the aggregate, cross-contextual trace that an individual leaves across multiple social media channels, each reflecting a different facet of their psychological identity. By processing and cross-referencing Primary Content, Interactional Tone, Interest Graphs, and Aspirational Signals simultaneously, SocioMind AI achieves what single-channel sentiment tools cannot: a holistic, internally-validated psychological portrait. At its inference core, SocioMind AI leverages the Gemini 3 Flash large language model architecture, optimized for structured JSON output to ensure deterministic, research-grade data handling. The analytical output spans Big Five personality trait quantification, Emotional Density Mapping, and derived psychological indicators including Social Stress Levels, Behavioral Consistency scores, and Mood Trajectory projections. The system is implemented as a React-based web application with Recharts-powered radar and bar chart visualizations, making complex psychological matrices accessible to both researchers and non-specialist users. Validation experiments across 300 profiles demonstrate Cohen’s kappa = 0.74 for Big Five dimensions and Pearson r = 0.81 for emotional valence detection, establishing SocioMind AI as a viable zero-knowledge psychological proxy for research-grade personality inference.

DOI: http://doi.org/

Comparative Study Between Polyethylene Glycol-400 (PEG-400) and Polyvinyl Alcohol (PVA) for Self-Curing Concrete

Authors: Manish S. Bansode, Tejas S. Mokal, Saad S. Pathan, Karan K. Rathod, Professor Yash S. Shet, Professor Hemanth K.Thakur, D. N. Jaiswal

Abstract: The rapid increase in construction activities has significantly increased the demand for water used in concrete curing. Conventional curing methods require continuous external water supply, which is often impractical in regions with water scarcity. This research focuses on self-curing concrete using Polyethylene Glycol (PEG-400) and Polyvinyl Alcohol (PVA) as internal curing agents. The study evaluates the mechanical properties of concrete, particularly compressive strength, by varying the percentage of these agents. The results demonstrate that self-curing concrete improves hydration, reduces shrinkage, enhances durability, and minimizes water consumption. The study concludes that PVA shows better performance compared to PEG in terms of strength and water retention.

DOI: http://doi.org/

FaceTrace: An AI-Based Missing Person Detection System Using Deep Learning Facial Recognition

Authors: Sourabh Vijay Patil, Vaishnav Maruti Kadam, Ajay Angad Ahir, Altaf Yasin Mahat

Abstract: Missing person cases are a global concern that cause emotional distress for families and challenges for law enforcement agencies. Traditional search methods such as posters, manual surveillance, and public announcements are slow and inefficient. This paper proposes FaceTrace, an artificial intelligence based missing person detection system that uses deep learning facial recognition to identify individuals from images and surveillance streams. The system leverages ArcFace embeddings, computer vision techniques, and a centralized MySQL database to match uploaded images with stored records. The proposed system enables faster identification and improves accuracy compared to manual methods.

DOI: http://doi.org/

Improving Security and Privacy in Attribute-Based Data Sharing in Cloud Computing

Authors: Dr. Shrikant V. Sonekar, Professor Rohan B Kokate, Miss. Samiksha S Raut

Abstract: Cloud computing has revolutionized the way data is stored, processed, and shared by providing scalable, flexible, and on-demand access to computational resources over the internet. It has enabled individuals, enterprises, and government organizations to efficiently manage large volumes of data without investing heavily in physical infrastructure. Despite these advantages, the rapid adoption of cloud platforms has introduced significant challenges related to data security, privacy preservation, and fine-grained access control. Since data is stored on third-party servers, users lose direct control over their sensitive information, increasing the risk of unauthorized access, insider threats, and data breaches. Traditional encryption techniques such as symmetric and asymmetric cryptography ensure data confidentiality but fail to provide flexible and scalable access control mechanisms in dynamic, multi-user cloud environments. These methods rely heavily on complex key management systems and are not suitable for scenarios where access permissions need to be defined based on user roles, attributes, or contextual conditions. To address these limitations, Attribute-Based Encryption (ABE) has emerged as a powerful cryptographic approach that enables secure and flexible data sharing by enforcing access policies based on user attributes rather than identities. In particular, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) allows data owners to define access structures directly within the encrypted data, ensuring that only users whose attributes satisfy the defined policies can decrypt and access the information. This paper presents the design and implementation of a secure and privacy-preserving data-sharing framework based on CP-ABE in cloud computing environments. The proposed system incorporates advanced security features such as fine-grained access control, secure key generation and distribution, user authentication, and protection against common attacks including collusion attacks and unauthorized data access. Additionally, privacy-preserving mechanisms are integrated to ensure that sensitive user attributes and data remain protected even from cloud service providers. The system architecture includes key components such as data owners, attribute authorities, cloud servers, and data users, working together to provide a secure and efficient data-sharing environment. Experimental evaluation demonstrates that the proposed framework significantly improves data security, reduces the risk of data breaches, and enhances access control efficiency compared to traditional encryption-based systems.

DOI: https://zenodo.org/records/19924638

Application of Machine Learning in Enhancing the Efficiency Performance of Solar Power Plant

Authors: Dr. Shrikant V. Sonekar, Professor Rohan B Kokate, Miss. Vaishnavi R Tandulkar

Abstract: The rapid growth in global energy demand, coupled with increasing environmental concerns, has accelerated the transition toward renewable energy sources, with solar power emerging as one of the most promising and sustainable alternatives. Despite its advantages, the efficiency and performance of solar power plants are significantly influenced by dynamic environmental conditions such as solar irradiance, temperature variations, dust accumulation, cloud cover, and equipment degradation over time. Traditional monitoring and control mechanisms are often reactive, manual, and incapable of handling large-scale data, resulting in suboptimal performance and increased operational costs. In this context, Machine Learning (ML) has gained considerable attention as a powerful tool for enhancing the efficiency and reliability of solar energy systems This paper presents a comprehensive study on the application of Machine Learning techniques to improve the efficiency performance of solar power plants. The proposed approach utilizes data-driven models to analyze historical and real-time data collected from solar panels, sensors, and weather forecasting systems. Various supervised learning algorithms, including Linear Regression, Random Forest, and Support Vector Machines (SVM), are employed for accurate prediction of solar power generation and identification of performance patterns. Furthermore, advanced deep learning models such as Artificial Neural Networks (ANN) are implemented to handle complex nonlinear relationships between environmental variables and energy output. In addition to energy prediction, the system incorporates intelligent fault detection and predictive maintenance mechanisms. Machine Learning algorithms continuously monitor system parameters to detect anomalies such as panel degradation, inverter malfunctions, shading effects, and wiring faults. Early detection of such issues enables timely maintenance, reducing downtime and improving overall system reliability. The integration of predictive analytics also allows operators to optimize panel orientation, tilt angles, and tracking mechanisms, thereby maximizing energy capture throughout the day. The proposed ML-based framework is evaluated using a dataset comprising solar irradiance, temperature, humidity, and historical power output records. Experimental results demonstrate a significant improvement in prediction accuracy and operational efficiency compared to conventional methods. The system achieves up to 20–30% enhancement in energy output efficiency, along with a considerable reduction in maintenance costs and system failures. Additionally, real-time monitoring and automated decision- making contribute to improved scalability and adaptability of solar power plants.

DOI: https://zenodo.org/records/19925745

Bridging Accuracy And Latency: An Edge- Centric Study Of Lightweight Deep Neural Architectures

Authors: Rajat Takkar, Disha Sharma, Hridyesh Sharma

Abstract: The rapid growth of edge computing has changed how artificial intelligence is deployed on devices with limited resources such as smartphones, embedded systems, and IoT devices. In such environments, constraints related to memory, power, and storage make it difficult to use traditional deep learning models directly. Although modern neural networks perform well in tasks like computer vision, they often need high computational resources, which limits their practical use on edge devices. In this work, we focus on lightweight deep learning architectures that are designed to operate efficiently under these constraints. Specifically, we examine three widely used models—MobileNetV2, SqueezeNet, and EfficientNet-B0—for real-time inference on edge devices. The CIFAR-10 dataset is used as a benchmark to evaluate model performance. To improve training efficiency, we also apply transfer learning by utilizing features from pre-trained models. In addition, optimization techniques such as structured pruning and dynamic quantization are used to reduce unnecessary parameters and improve computational efficiency without significantly affecting performance. These methods help in lowering model size and speeding up inference, making deployment more feasible in resource-limited environments. The experimental results show noticeable differences in performance across the selected models. EfficientNet-B0 achieves the highest classification accuracy of 92.06%, while SqueezeNet provides faster inference due to its compact architecture and fewer parameters. MobileNetV2 offers a balanced trade-off between accuracy and latency, making it suitable for practical applications. Overall, the findings highlight the importance of selecting appropriate lightweight architectures along with effective optimization strategies when deploying deep learning models on edge devices. This work provides useful insights into balancing accuracy, model size, and inference speed, which are key factors in real-world edge computing scenarios.

DOI: https://doi.org/10.5281/zenodo.19926432

Areenabook : A Django Driven Sports Facility Booking And Scheduling Platform

Authors: G Adithya Kumar Dubey, S Gokul, A Daarshan, Dr. R. Bharathi

Abstract: The demand for managing sports facilities efficiently is. We need better digital tools to handle bookings. Traditional methods often lead to scheduling conflicts. Are not very efficient. This paper talks about Arenabook a web-based platform for booking and scheduling sports facilities. It was built using the Django framework. With Arenabook users can see what’s available in time make reservations and manage their bookings. Administrators can control scheduling, pricing and resource allocation. Here’s how Arenabook works to prevent bookings: it uses a check-lock-confirm-update mechanism. This ensures that everything runs smoothly and consistently. The platform has secure user authentication and role-based access control. It works well on devices. The backend of Arenabook uses a database. This helps with handling data and processing queries. Arenabook makes managing sports facilities easier reduces the need for work and improves the user experience. Tests show that Arenabook is scalable, reliable and suitable for sports facility management. Arenabook can handle a lot of users and data making it a great solution, for sports facilities. Arenabook is a platform that can improve the way sports facilities are managed.

DOI: https://doi.org/10.5281/zenodo.19926939

Data As An Insolvency Asset In The Digital Age: Balancing Data Valuation, Asset Maximisation Under The Ibc And Dpdp Act.

Authors: Dr. Satish Chandra, Kritika Tyagi , Ritesh Kumar

Abstract: The digital age has placed data as an extraordinary intangible asset in the insolvency domain, yet its monetization sharply collides with the protective measures that provide privacy. The Insolvency and Bankruptcy Code (IBC) of 2016 has made it mandatory for corporate debtors to maximize the value of the asset, usually through asset-wise sale under CIRP Regulation 29 or liquidation. This would include digital assets such as customer databases and proprietary user data. High-profile cases like Jet Airways have shocked the international community with the lifeblood of the company in question, viz. JetPrivilege: Passenger Data; eventually, such information is furnished for sale, raising questions on how it could be misused. Valuing such data is a Herculean task, given the varied methodologies followed–be it market, income, or cost approach–emulating the peculiar difficulties in IP asset valuation. Concurrently, the Digital Personal Data Protection Act (DPDP) of India 2023 provides wide-ranging rights to data principals and sets out obligations for data fiduciaries regarding consent, purpose limitation, and cross-border transfers. Enforcement will fall upon the newly set-up Data Protection Board. Some insolvency-related data processing (for example, through NeSL) might be spared from the full reach of the Act’s legitimate-uses carve-out. The conflict between the creditor-oriented goal of maximizing asset value and the demands of data privacy creates a regulatory dilemma. This study proposes a synchronized legal framework, consisting of valuations standardized uniformly, specifications setting out IBC interfaces with DPDP, and procedural safeguards enabling speedy insolvency resolutions while safeguarding individual privacy rights.

DOI: https://doi.org/10.5281/zenodo.19927399

Strategic Home Completion & Financial Planning For New Residential Construction: An Engineering Economic Perspective

Authors: Er. Sanju Surendran Girija

Abstract: Residential construction projects demand the coordinated integration of engineering execution, financial planning, architecture, and long-term usability. In many emerging economies, homeowners frequently prioritize full completion of structural, architectural, and interior works prior to occupancy. Although this approach offers immediate convenience and aesthetic satisfaction, it often imposes substantial financial pressure, accelerates decision-making under time constraints, and limits adaptability to future technological or lifestyle changes. This paper critically examines two dominant residential completion strategies: full pre-occupancy completion and phased post-occupancy development. Through engineering-economic analysis and practical construction management perspectives, the study evaluates their impacts on capital expenditure, lifecycle cost, material efficiency, flexibility, and occupant satisfaction. Findings indicate that phased completion—where essential functional systems are completed first and non-critical enhancements are deferred—can significantly improve cash flow management, reduce debt exposure, and enable future integration of advanced materials and smart technologies. The paper concludes that a hybrid strategy, combining immediate structural readiness with planned incremental enhancements, provides the most sustainable and economically rational solution for modern homeowners.

DOI: https://doi.org/10.5281/zenodo.19934779

IoT Based Greenhouse Monitoring And Control System

Authors: Ashwajit Kamble, Utkarsha Lodha, Rushabh Dhakane, Prof. Kiran Khedkar

Abstract: To develop and operate an IoT-based Smart Greenhouse Monitoring and Control System, first install environmental sensors such as DHT22 for temperature and humidity, soil moisture probes, and LDRs for light intensity inside the greenhouse to continuously collect data on growing conditions. Connect these sensors to a microcontroller like Arduino Uno and integrate a WiFi module such as ESP8266 or NodeMCU to enable real-time wireless data transmission to an IoT cloud platform for remote monitoring and storage. Once data is available online, analyze it through dashboards or mobile apps to observe trends and make informed decisions. When environmental parameters deviate from optimal levels, the system should automatically trigger actuators—such as fans, sprinklers, or grow lights—to maintain ideal conditions. Throughout the cultivation cycle, data logging and analysis help identify patterns for predictive control and resource optimization, reducing manual intervention and improving crop yield and quality. The system should be operated continuously to maintain stability and can be enhanced over time by adding AI algorithms for predictive adjustments, renewable power sources for sustainability, and scalability to hydroponic or commercial setups, ensuring consistent productivity and energy-efficient farming year-round.

DOI: https://doi.org/10.5281/zenodo.19936180

ANALYSIS OF IRREGULER STRUCTURE USING P DELTA EFFECT

Authors: Karan Arvindkumar Patel, Hiral Apurva Dave

Abstract: In metropolitan areas, high-rise structures are built with various irregularities in their design and loading conditions. These irregular structures can experience sudden and significant effects when subjected to different types of loads, which is why additional considerations are necessary to prevent undesirable outcomes. Past earthquakes have demonstrated the adverse consequences that can occur in such structures. To mitigate these adverse effects, nonlinear analysis techniques like the P-Δ effect have been investigated in this current study. The P-Δ effect refers to the additional actions exerted on a structure due to its deformation resulting from applied stresses. In the study, the axially loaded columns of G+18 story structures were analysed using ETABS software under nonlinear dynamic time history conditions, taking into account the influence of the P-Δ effect. The displacement and drift response analysis revealed that these values tend to be higher as the height of the structure height increases. This finding underscores the importance of considering the P-Δ effect in structural analysis. By comparing the results with and without the consideration of the P-Δ effect, it was observed that there was an approximately eight percent variation in the outcomes. This indicates that neglecting the P-Δ effect could lead to significant discrepancies in the analysis results, further highlighting its significance in accurately predicting structural behaviour.

Skill Bridge: A Community-Centric AI Platform For Click To Edit Master Title Style

Authors: Hemanth KR, Abinav R, Aneesh Kumar R, Jayamoorthy S

Abstract: India’s rural population continues to face substantial challenges in accessing quality digital education due to persistent structural and technological constraints. Language limitations, inconsistent internet connectivity, and the absence of reliable skill certification mechanisms significantly hinder effective learning and restrict employability. Most existing digital education platforms are designed with an urban-centric approach, assuming English proficiency, continuous online access, and high digital literacy — assumptions that exclude large segments of rural youth and lead to underutilization of rural talent despite growing demand for skilled professionals. To address these challenges, Skill Bridge is proposed as an AI-powered, multilingual digital learning platform aimed at enabling inclusive and outcome-driven skill development. The platform leverages artificial intelligence to deliver personalized learning pathways and adaptive skill assessments, ensuring learners progress systematically according to individual competency levels. Blockchain technology is further integrated to provide secure, tamper-proof, and verifiable skill certifications, thereby enhancing trust and credibility among employers. By aligning learning outcomes with job readiness and employability requirements, Skill Bridge seeks to bridge the gap between digital education and workforce participation, creating sustainable skill development opportunities for rural youth and women across India.

DOI: https://doi.org/10.5281/zenodo.19947769

“Gsm Based Health Monitoring System”

Authors: Mr. Abhishek Gadade, Ms. Priyanka Dharmul, Ms. Pratiksha Kamble, Prof. Krashna Rathi

Abstract: This project presents the development of a GSM-based health monitoring system using Arduino, designed to enhance patient care through real-time tracking and remote diagnostics. It integrates heart rate, temperature, and oxygen saturation sensors to continuously monitor vital signs, making it suitable for hospitals, elderly care, and home-based applications. The system displays readings on an LCD for local observation and transmits data via a GSM module to a mobile number or cloud server, ensuring remote accessibility. A buzzer alerts caregivers when any parameter exceeds safe thresholds, enabling prompt medical response. The GSM module serves as the communication backbone, facilitating SMS alerts and bridging the gap between patients and healthcare providers. The system’s modular design, centered around Arduino, allows for scalability and future upgrades such as cloud integration or mobile app support, highlighting the role of GSM technology in modern, accessible healthcare solutions.

DOI: https://doi.org/10.5281/zenodo.19942002

Pragmatics In Human-AI Interaction: A Linguistic Study Of Conversational Agents

Authors: Dr. S. Thivyanathan, Dr. R. Anusha

Abstract: The unprecedented growth of conversational artificial intelligence agents has had a revolutionary impact on human-machine communication, but pragmatic competence—the capacity to understand and produce contextual meaning—is still an open problem for present-day technologies. This research provides a thorough linguistic study of pragmatics in human-AI interaction, which focuses on processing and producing meaning within the contexts of conversational agents’ implicatures, presuppositions, speech acts, and common ground. Based on an empirical analysis of 50 transcripts of human-AI conversations, along with experimental work with 36 participants in the comparison of five conversational agents (ChatGPT-4, Google Bard, Microsoft Copilot, Claude 2, and LLaMA 2), the research concludes that although rule-based conversational agents stick to strict literal understanding, transformer models show emergent pragmatic competence through successful interpretation of indirect speech acts in 76% of the cases. However, Gricean implicatures remain difficult (recognized in only 34% of instances) and cross-turn common ground challenging (consistent in only 41% of examples).

DOI: https://doi.org/10.5281/zenodo.19946123

Grid Connected Solar Maximum Power Tracking (Mppt)

Authors: R.Thilakar, Dr.A.Venkatesh, Dr.M.Malarvizhi

Abstract: Maximum Power Point Tracking (MPPT) is one of the most important enablers in the field of grid-connected photovoltaic (PV) systems. This paper provides an extensive literature review on various MPPT methods used for grid-connected PV systems. The review includes both conventional approaches and advanced optimization algorithms like intelligent control schemes and metaheuristics. The article highlights some of the recent developments in the field of MPPT methods for grid-connected photovoltaic systems such as the utilization of HOA to tune fractional-order PI controllers, which can achieve a rise time of 0.0073 seconds and power generation capacity of 100.72 kW , using PSO to achieve power extraction up to 7.5% higher than P&O with only 1.54% THD, and Second Order Sliding Mode Control that achieved convergence in 0.009 seconds with 76.29% THD reduction . The comparative analysis demonstrates that although conventional methods have the advantage of ease of implementation, advanced optimization algorithms outperform in terms of faster dynamic response and global maximum point tracking.

DOI: https://doi.org/10.5281/zenodo.19946327

Fractional Calculus-Based Modeling For Intelligent Healthcare Prediction Systems

Authors: Dr. Sharada H N, Dr. Sandhya S V

Abstract: Early-stage hiring processes continue to depend on resume-based and keyword-based filtering, which does not reliably capture a candidate’s actual abilities. This paper presents an AI-assisted skill evaluation system that prioritizes demonstrated performance over resume content. The system models candidate screening as a multi-stage pipeline: skill profiling, dynamic assessment delivery, automated rule-based and NLP evaluation, and weighted score aggregation. A competency model maps candidate skills to standardized assessment criteria, enabling objective cross-candidate comparison. Evaluation on simulated data (n=100) yields a Spearman rank correlation of 0.91, a false-positive shortlist rate of 12%, and a top-quintile precision of 78% — all substantially better than a conventional ATS baseline. The proposed framework is scalable, modular, and designed to reduce bias inherent in resume-centric screening.

DOI: https://doi.org/10.5281/zenodo.19946619

EnviroSense-ML: IoT And Machine Learning Framework For Real-Time Environmental Monitoring And Prediction

Authors: Dr. Dolley Srivastava

Abstract: The increasing problem of environmental pollution requires a new level of innovation going beyond the scope of existing monitoring systems. In this paper, we propose EnviroSense-ML – an end-to-end architecture leveraging IoT sensors together with machine learning algorithms for environmental monitoring and predictions. Our solution consists of a combination of inexpensive electrochemical sensors, LoRaWAN-based communication channels, and novel approaches in the field of hybrid machine learning techniques, which include the spatiotemporal GCN-LSTM model and CNN-BiGRU model using 8-bit quantization. The performance evaluations performed using the real-world dataset showed that our GCN-LSTM model demonstrated the highest interpolation accuracy (R² = 0.96), due to the inclusion of additional information about altitude and land cover into graph connections of the sensors. At the same time, 8-bit quantization resulted in 66% compression of the model’s size with less than 1% degradation of its accuracy. Moreover, experiments showed that ML algorithms can improve sensor measurements’ accuracy up to 46%. Also, our two-stage approach based on XGBoost reached near-perfect Air Quality Index prediction results (R² = 1.00, MAE = 0.35).

DOI: https://doi.org/10.5281/zenodo.19946681

Comparative Study Of Lexicon, Machine Learning, And Transformer-Based Models For Airline Sentiment Analysis

Authors: Ansh Jena, Sujit Kakade, Arya Kedar

Abstract: Sentiment analysis can help track passengers’ per- ceptions and improve the service offered by an airline due to the increasing importance of social media, such as Twitter. It is about conducting a comparative analysis of three models of natural language processing, namely lexicon-based, machine learning, and transformer-based classification techniques for determining sentiments of airline tweets. Twitter US Airline Sentiment was chosen to be analyzed as it comprised labeled tweets from the major U.S. airlines. Data quality was improved by applying methods of text preprocessing, such as removing noise, tokeniz- ing, and eliminating stopwords. Lexicon-based sentiment analysis relied on VADER polarity baselines, machine-learning approach entailed extraction of TF-IDF features and further application of Random Forest classification technique while transformer model applied RoBERTa to identify the context of sentiment. As a result of the analysis, it was found out that while the lexicon model was faster and provided more easily understandable results, machine- learning model allowed identifying sentiments more accurately. Transformer-based RoBERTa performed the best in terms of handling more complex linguistic structures, such as negations and sarcasm.

DOI: https://doi.org/10.5281/zenodo.19948612

Adaptive Control Based Multi-Level Inverter For Solar PV Applications

Authors: Mr. Harish B N, Prarthana S, Phalguni M H, Anushree T D, Sinchana K L, S N Meghana

Abstract: Plant diseases are also known to place huge burden on food security structure and agriculture to the global world; it is approximated that all plant diseases development costs a giant (an estimated 220 billion/year). To address this, the computer vision -specific and deep learning based automated disease detection systems are expandingly viewed as rather interesting as an option instead of the traditional forms of diagnosing that involve a significant amount of new employees . However, the literature screening is saturated with models that have been alleged to be super high in accuracy with regard to classifications when they are under some form of controlled conditions in the laboratory that must in no way imply any trustworthy depiction that they can be relayed over the situation in the real field. It can be said that such discrepancy in performance can stress the idea that there is a dire necessity to carry out more related and stiffer analysis of existing measures of data mining and optimization. This article has such an experimental alloy of which the plant disease variable models can be detected multi faceted in, which is discussed in detail on three axes parametric axis, combinatorial axis, computational axis. The rate of model performances to the hyperparameter options enshrined in the parametric assessment that may also be the optimizers are called counting. The combinatorial work involves the study of connections pertaining to the utility of various Convolutional Neural Network Convolutional designs, as well as the use of spectacular measures of data augmentation and fold up learning methods. The computational verification provided is a practical test of the feasibility of the model, comparison of statistics on the training time, model complexity, and speed of inference. According to the opinion that our experimental findings indicate, our individual models (as well as our EfficientNet) that come with the highest classification performance of about above 98 percent accuracy would always be the best trade off between accuracy and efficiency whereas ensemble models would adopt a combination of soft voting as the best trade off prerogative. The paper further estimates the radical performance augmentation with the generative data augmentation models against the conventional geometric transformations to apply the models in the truly competitive use. The primary accomplishment of this project is the system, which surpasses those pathetic signs of precision and rests upon the familiarization of scientists and performers with how to create, alter, and put to practical practice the scaleable, resilient, and effective plant disease detection methods used in the enhancement of the designated work in the agricultural forerunners.

AI-Based Wildlife Monitoring and Behavior Analysis System

Authors: Sharvil M. Palvekar, Shreyas P. Jadhav, Ninad V. Sarpole, Soham B. Gharat, Dr. Sandeep B. Raskar

Abstract: Wildlife conservation increasingly relies on auto-mated monitoring systems to overcome the limitations of tradi-tional field-based observation methods, which are labor-intensive, subjective, and constrained in spatial and temporal coverage. This paper presents an AI-based animal monitoring and behavior analysis framework that integrates deep learning-based object detection, multi-object tracking, and spatio-temporal analytics for real-time wildlife surveillance. A YOLO26l detection model is employed to identify animal species from camera trap im-agery and video streams, followed by location-aware tracking to analyze movement patterns and population density. Heatmap-based visualization and statistical analysis are used to infer behavioral trends across different time intervals. Experimental results demonstrate robust detection accuracy and reliable species classification, supported by confusion matrix-based evaluation. The proposed system offers a scalable and interpretable solution for intelligent wildlife monitoring and conservation planning.

DOI: http://doi.org/

Communicating Workforce Restructuring: “Ethical Corporate Crisis Communication Strategies For Organizational Trust And Employee Retention”

Authors: Nirnayak Talukdar, Vulli Sai Rishika, Dr. Sadiya Nair. S

Abstract: Workforce restructuring has become a persistent feature of corporate life in the modern global economy. Driven by technological disruption, shifting market conditions, mergers, and competitive pressures, organizations regularly resort to workforce reductions and operational downsizing. The financial and strategic rationale behind such decisions has attracted considerable scholarly attention, but the way organizations communicate those decisions to employees has remained comparatively underexamined. This white paper examines internal corporate communication during workforce restructuring crises. The central argument is that the problem facing organizations in such circumstances is not the restructuring decision itself, but the quality, timing, tone, and ethical character of how that decision is communicated to employees. Evidence drawn from organizational communication theory, crisis communication scholarship, psychological contract research, and documented corporate case studies shows that poor internal communication during restructuring consistently produces measurable, lasting damage: trust erodes, rumours spread, morale falls, and voluntary turnover among retained employees rises substantially. The paper is organized around three concerns. First, it reviews and synthesizes the academic literature on internal communication, crisis communication, organizational trust, and psychological contracts. Second, it identifies research gaps that persist in the field, particularly the absence of structured, employee-centred communication frameworks and the limited empirical attention given to message tone, narrative framing, and listening mechanisms. Third, it proposes an original conceptual model, the Ethical Workforce Crisis Communication (EWCC) Model, offering a four-stage framework for guiding organizational communication through the full arc of a restructuring event. The paper concludes with ten targeted recommendations for corporate leaders, HR professionals, and communication practitioners. The core recommendation is that ethical, transparent, and empathetic communication is not merely a courtesy extended to departing employees; it is a strategic necessity for organizational continuity, survivor morale, and long-term institutional legitimacy.

DOI: https://doi.org/10.5281/zenodo.19956659

Online Subsidy Management System Using Machine Learning (algorithm- Logistic Regression, Random Forest, Decision Tree)

Authors: Soundrya Mallappa Biradar, Nikhil Gurudev Lonari, Aniket Ramesh Bhandare, Vishwaraj Pradip Pawar, Mrs. Pallavee Bavane-Patil

Abstract: Government subsidy programs play a crucial role in socio-economic development by supporting vulnerable populations in sectors such as agriculture, education, healthcare, energy, and food security. However, traditional subsidy management systems are often plagued by inefficiencies, fraud, leakage, lack of transparency, and poor targeting. The advent of digital governance and data-driven technologies has opened new avenues for reforming subsidy allocation and monitoring mechanisms. Machine learning (ML), in particular, offers powerful tools for automating eligibility assessment, predicting beneficiary behavior, detecting anomalies, and optimizing policy outcomes. This review paper presents a comprehensive analysis of online subsidy management systems integrated with machine learning techniques, with a specific focus on Logistic Regression, Decision Tree, and Random Forest algorithms. The paper discusses system architecture, data sources, preprocessing methods, algorithmic frameworks, evaluation metrics, real-world use cases, challenges, ethical considerations, and future research directions. The review aims to serve as a ready reference for researchers, policymakers, and system designers working toward intelligent, transparent, and efficient subsidy management platforms.

DOI: https://doi.org/10.5281/zenodo.19959139

Artificial Intelligence Based Framework For Academic Performance Visualisation

Authors: Khushi, Rajat Takkar, Mugdha, Himanshi, Muskan

Abstract: Schools are embracing the use of data-driven information to track student achievement and performance. Conventional ways of tracking performances are not effective in the intricate nature of the relationships among diverse academic contributions. This research works on the necessity of an efficient, but simple, artificial intelligence based framework to examine and visualize the factors mentioned and to take proactive measures that will help find out students who might not need more than a top-quality academic assistance. The main purpose of the research is to come up with a machine learning model that is easy to interpret, has the predictive strength of the end-of-year academic scores and classifies students into groups of “pass” and “fail.” Also, the research will visualise the relationship between particular inputs (ex: hours of study, attendance) and performance in general and characterize a feature importance analysis to determine which factors have the most profound impact on student achievement. The research works with the artificial data including major academic variables: study hours, attendance, assignment grades, internal grades, and past GPA. The methodology will use two different machine learning models: Linear Regression to predict continuous performance scores and Decision Tree Classification to perform binary categorisation (Pass/Fail). Visualisation tools were combined to plot interactions among variables, and parameter analysis was performed in terms of standard accuracy measurements of regression as well as classification problems. The results indicate that the most important predictors of academic success are study hours, internal marks and attendance. Linear Regression model largely was able to predict final scores with high correlation to input data whereas the Decision Tree classifier offered a simple, interpretable logic with which students can be categorised. Analysis of feature importance provided a reason on why the consistent engagement and incremental assessment has a greater influence on the outcome rather than just the previous GPA. The offered AI-based framework is a scalable and understandable research proposal method of analysing educational data. The system facilitates informed, data-driven decisions made by educators by highlighting its critical performance drivers, and it helps to deliver timely interventions to at-risk students. Further work would entail the application of the framework on bigger datasets, and real-life contexts to improve predictive accuracy in diverse education settings.

DOI: https://doi.org/10.5281/zenodo.19960357

AquaVision BI: An Intelligent AI-Based Irrigation Monitoring System Using IoT Sensors

Authors: Vrushabh Jitendra Patil, Pratik Prakash Patil, Ganesh Rajendra Mote

Abstract: Traditional irrigation systems depend heavily on manual inspection to detect leaks, pipe blockages, and abnormal water flow, resulting in significant water wastage, reduced irrigation efficiency, and increased maintenance expenditure. This paper presents AquaVision BI, an intelligent IoT-enabled irrigation monitoring system that integrates three Hall-effect flow-rate sensors, an ESP32 Wi-Fi microcontroller, and an AI-driven differential-threshold anomaly-detection algorithm to achieve real-time surveillance of irrigation pipelines. The system continuously samples sensor pulse counts at one-second intervals, computes volumetric flow rates, and applies pairwise differential analysis to localise leakage to specific pipeline segments (upstream, mid-stream, or downstream). Upon anomaly detection, automated alerts are dispatched via the Blynk IoT cloud dashboard and a local buzzer actuator. Experimental evaluation on a controlled testbed confirms accurate leak localisation across all three sensor nodes, with end-to-end alert latency consistently below two seconds. The proposed system significantly reduces water wastage, lowers operational costs, and promotes sustainable agricultural water management practices.

Innovations in Dairy Management Systems: Towards Smart, Sustainable Practices

Authors: Vishal Tandale, Shabbir Ahmed, Hitesh Shewale

Abstract: The dairy sector remains a cornerstone of Indian agriculture, facing persistent challenges such as manual inefficiency, data fragmentation, and increasing demands for quality and traceability. This paper analyzes contemporary dairy management systems, focusing on the adoption of digital technologies—Internet of Things (IoT), Artificial Intelligence (AI), and integrated Enterprise Resource Planning (ERP)—to streamline operations, optimize productivity, and improve animal welfare. Evidence from recent deployments and technology pilots demonstrates that technologically- augmented management not only boosts efficiency but also aligns the sector with Food Safety and Standards Authority of India (FSSAI) compliance and export requirements. Implementation challenges and recommendations for scalable, farmer-friendly solutions are discussed.

Heart Disease Prediction System Using Machine Learning

Authors: Asst. Prof. Rutuja Gautam, Prof. Rohan B. Kokate, St. Ankit R. Dhole

Abstract: Heart disease is one of the leading causes of death worldwide, making early prediction and diagnosis extremely important. This review paper focuses on the use of machine learning techniques for predicting heart disease based on medical data. Various algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine are analyzed for their effectiveness in prediction. The system uses patient health parameters like age, blood pressure, cholesterol level, and heart rate to determine the risk of heart disease. A web-based application is also discussed, developed using Python for backend processing and HTML/CSS for user interaction. The results show that machine learning models can significantly improve prediction accuracy and assist doctors in decision-making. This paper highlights the importance of data preprocessing, model selection, and performance evaluation in building an efficient heart disease prediction system.

DOI: https://doi.org/10.5281/zenodo.19975051

ECHO-DR: An Event-Centric Hierarchical Orchestration Architecture for Scalable AI Workflows in Real-Time Disaster Response

Authors: Prudvi Saisaran Ponduru

Abstract: Scalable artificial intelligence (AI) workflows increasingly fail not because individual models are weak, but because the surrounding architecture cannot process heterogeneous, bursty, high-stakes evidence at operational speed. This paper proposes ECHO-DR, an Event-Centric Hierarchical Orchestration architecture for real-time disaster response. The real-world problem addressed is the difficulty of turning social media, remote sensing, UAV imagery, weather alerts, seismic feeds, and incident reports into timely, auditable, and trustworthy operational intelligence during floods, earthquakes, wildfires, and storms. ECHO-DR introduces four core contributions: an event-centric memory plane that unifies vector retrieval, geospatial indexing, lakehouse lineage, and structured event graphs; a hierarchical routing policy that escalates only high-value or uncertain items to expensive multimodal reasoning; a stage-disaggregated serving design that independently scales encoders, prefill workers, decoders, and tool calls; and a governance plane that embeds auditability, human review, and zero-trust access control into the workflow. A formal utility-constrained routing model, event-linking algorithm, fusion rule, and capacity model are developed to show how the architecture scales under large workflows. The paper also provides an implementation blueprint, clean system diagrams, benchmarking methodology, ablations, and simulated evaluation results. Simulated trace-driven experiments indicate that the proposed gated architecture can reduce p95 provisional alert latency relative to a monolithic multimodal pipeline while maintaining evidence traceability and limiting deep-model cost. The work demonstrates that scalable AI for future big workflows should be designed as a compound, event-centered, policy-aware system rather than as a single model endpoint.

Machine Learning-Based House Price Prediction in Chennai and Bengaluru

Authors: Associate Professor Dr. S. Thaiyalnayaki, Janga Kishore, Kareti Manoj, Jogu Ganesh, Kasaragadda Gopi Chand

Abstract: The rapid growth of urbanization in metropolitan cities has significantly influenced real estate markets and housing prices. Accurately estimating property values has become increasingly important for buyers, sellers, and real estate investors. This study presents a machine learning-based house price prediction system designed to analyze housing data and estimate property prices based on multiple influential factors. The dataset used in this research includes property attributes such as location, square footage, number of bedrooms, and number of bathrooms collected from metropolitan regions including Chennai and Bengaluru. The proposed system applies data preprocessing techniques to improve the quality of the dataset before model training. These preprocessing steps include handling missing values, encoding categorical variables, and performing feature scaling to ensure consistent data representation. After preprocessing, a predictive model based on Linear Regression is implemented to analyze the relationship

DOI: https://zenodo.org/records/19981757

AI-Driven Payroll Anomaly Detection In Oracle Cloud Payroll System

Authors: Mahesh Ganji

Abstract: This research study examines incorporation of an AI based anomaly detection method of Oracle cloud Payroll to make the payroll more accurate, reduce risks, and enhance compliance. The performance of different machine learning models such as Isolation Forest, One-Class SVM, Neural Networks, and Logistic Regression is tested in terms of their performance in detecting payroll data anomalies. Findings indicate that models such as Logistic Regression performed moderately well but other models did not cope with false positives and poor anomaly detection. The future is to perfect the models, involve deep learning, and realize the real-time anomaly to make the payroll management in large organizations more efficient and accurate.

DOI: https://doi.org/10.5281/zenodo.19988211

HealthGuard AI: A Multi-Stage Machine Learning Framework for Personalized Disease Risk Stratification and Adaptive Health Recommendation

Authors: Ashwani Kumar, Dr. Sunil Maggu

Abstract: The intersection of machine learning and preventive healthcare offers transformative potential for earlydisease detection and personalized health guidance. However, most existing systems either producebinary classification outcomes without contextual risk stratification or provide static, non-adaptivehealthrecommendations disconnected from individual prediction confidence scores. This paper introducesHealthGuard AI, a novel multi-stage predictive framework that integrates supervised machinelearningclassification with probability-based risk stratification and a dynamically adaptivehealthrecommendation engine. The system simultaneously addresses three major chronic diseasedomains—Type 2 Diabetes Mellitus, Coronary Heart Disease, and Parkinson’s Disease — using clinicallyvalidatedfeature sets drawn from UCI Machine Learning Repository datasets. Beyond binary prediction, HealthGuard AI applies predict_proba() outputs to stratify individual disease risk into Low(≤0.40), Medium (0.41–0.70), and High (> 0.70) categories, each triggering a distinct, evidence-alignedhealthrecommendation profile. An additional Body Mass Index and Basal Metabolic Rate estimationmoduleemploying the Mifflin-St Jeor equation further extends the system’s scope into nutritional healthanalytics. Deployed as an interactive web application via Streamlit Community Cloud, HealthGuardAIachieves classification accuracies of 78.5%, 81.3%, and 87.2% for Diabetes, Heart Disease, andParkinson’s Disease respectively. The system demonstrates that probability-aware risk stratification, when combined with adaptive, risk-tiered recommendations, produces a meaningfully richer andmoreclinically actionable output than conventional binary prediction pipelines. Experimental results, systemarchitecture, and the clinical relevance of risk-tiered adaptive recommendations are discussedindetail.

DOI: https://doi.org/10.5281/zenodo.19993977

Real time load flow Monitorin Distribution System

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

A Hybrid Enhancing And Optimizing Crops Disease And Land Cover Classification Using Adaptive Recurrent FusionNet Framework

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.

AI-based Personalised Learning System

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

Integrated Solid Waste Management System For Power Generation And Agricultural Usage

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.

Explainable AI For Transparent Decision Making In Healthcare

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

Development Of Hybrid Solar-Grid Water Pumping System With Automatic Power Switching In MATLAB

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.

IoT Based Intelligent Automated Irrigation System With Uniform Moisture Control And Active Drainage

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

Farming Equipment Rentals System

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.

API-Driven Cross-Platform Social Media Intelligence: An Integrated Framework Leveraging NLP, Graph Analytics, And Explainable AI

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

A Secure Full-Stack Ecosystem For Integrated Health And Fitness Telemetry

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.

Ai-Powered Digital Twin Approach For Personalized Organ Transplantation

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).

Smart Induction Motor Protection And Control System Using Iot

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

Applications And Challenges Of Large Language Models In Real-World Systems

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.

DOI:

Online course management system

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.

Design And Simulation Of A Bidirectional Battery Charger Integrating V2g, G2v, And Active Power Filter Capabilities, Controlled Via A Bluetooth Module

Authors: Mr. D. Harsha, N. Soumya, G. Nandavardhan Reddy, K. Sai Sreesh

Abstract: The rapid growth of electric vehicles (EVs) has increased the demand for efficient and intelligent charging systems capable of supporting modern power grids. This paper presents the design and simulation of a bidirectional battery charger that enables Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G), and active filter operations within a single integrated system. The proposed configuration consists of a bidirectional AC–DC converter connected to the grid and a bidirectional DC–DC converter interfaced with the battery through a regulated DC link. An LCL filter is employed to reduce harmonic distortion and ensure high-quality grid current. A control strategy based on pulse width modulation (PWM) and reference current polarity is implemented to achieve smooth transition between operating modes. In G2V mode, the system provides controlled battery charging with near unity power factor, while in V2G mode, stored energy is effectively supplied back to the grid. Additionally, the system operates as an active filter to compensate for harmonics caused by non-linear loads. Simulation results demonstrate stable DC link voltage, reliable bidirectional power flow, and improved power quality. A hardware prototype with microcontroller-based control and Bluetooth communication further validates the practical feasibility of the proposed system.

DOI: https://doi.org/10.5281/zenodo.20033587

Arduino-Based Real-Time Gas Leakage Detection System: Design, Implementation, And Performance Evaluation

Authors: V. Irfan Ahamed, J. Lokeshwar, Dr. K. Rohini

Abstract: Gas leakage accidents involving liquefied petroleum gas (LPG), methane, and related hydrocarbons represent a significant and persistent safety hazard in both residential and small-scale industrial settings. Conventional reliance on human olfactory detection is inherently unreliable, particularly under conditions of poor ventilation, occupant absence, or odorant threshold variability. This paper presents the design, hardware implementation, and systematic performance evaluation of a low-cost, embedded gas leakage detection system built around an Arduino Uno microcontroller (ATmega328P) and a Figaro MQ-2 semiconductor gas sensor. The sensing element operates on the principle of surface resistance modulation upon exposure to combustible gases, with the resulting analogue voltage mapped to a 10-bit ADC value for threshold-based decision logic. Alert output is delivered through a dual mechanism comprising an 85 dB piezoelectric buzzer and a visual LED indicator, ensuring notification under varied ambient conditions. Over 40 controlled trials spanning four gas concentration levels, the system achieved an overall detection accuracy of 92.5%, with a sub-1.2 second response time at high exposure levels and an alert latency of 180–210 ms. The false-positive and false-negative rates were 5.0% and 2.5%, respectively. Environmental characterisation identified ambient temperature and relative humidity as the primary factors influencing baseline drift and sensitivity attenuation. The results confirm that the proposed system provides a technically sound, cost-effective safety solution, with a clear upgrade pathway toward IoT-enabled remote monitoring.

DOI: https://doi.org/10.5281/zenodo.20035126

YOLOv8-Driven Adaptive Traffic Signal Management Using Real-Time CCTV Video Feeds: Architecture, Implementation, And Performance Evaluation

Authors: S. Ashwin, S. Brittlin, K. Rohini

Abstract: Conventional fixed-time traffic signal systems are structurally incapable of responding to the stochastic variability of urban traffic flow, resulting in prolonged vehicle waiting times, suboptimal intersection throughput, and unnecessary fuel consumption. This paper presents a complete, edge-deployed adaptive traffic signal management system that uses real-time video input from existing CCTV infrastructure and the YOLOv8 deep learning object detection model to continuously estimate lane-wise vehicle density and dynamically compute optimised signal phase durations. The architecture is modular, comprising video acquisition, frame preprocessing, YOLOv8-based vehicle detection and classification, density estimation, decision logic, and signal control modules. The system avoids cloud dependency through localised edge processing, ensuring end-to-end signal update latency below 250 ms. Experimental evaluation across four simulated intersection lanes demonstrates an overall vehicle detection mAP@0.5 of 92.9% at 46 frames per second, a 38.3% reduction in average vehicle waiting time, and a 37.5% improvement in intersection throughput relative to a fixed-time baseline. Comparative benchmarking against Faster R-CNN, SSD, and YOLOv3-based approaches confirms the superiority of the proposed implementation on both detection accuracy and real-time responsiveness. The system is deployable without additional roadside hardware investment, making it a cost-effective and scalable solution for smart urban traffic management.

DOI: https://doi.org/10.5281/zenodo.20035179

Enhancing Oral Lesion Classification Using Diffusion Models: A Deep Learning Approach

Authors: Sony V Hovale, Manu K C, Naresh Patel, Pavithra B, Shradha G Vernekar

Abstract: Early detection and classification of oral lesions are essential for the prevention of oral cancers, and yet, manual diagnosis is still a challenge due to variations in the appearance of lesions, quality of images, and limited clinical datasets. This research explores the use of diffusion models, a recent class of generative models renowned for their stable training and high-fidelity reconstruction, to improve the automatic classification of oral lesion images. The proposed system includes dataset collection from open-source platform kaggle, preprocessing of dataset, a diffusion- based denoising and feature extraction pipeline, and finally, a classification stage to categorize the normal, precancerous, and cancerous lesions. By leveraging the forward and reverse process of diffusion, the model improves the clarity of the images and effectively extracts discriminative features, mitigating problems of noise, imbalance, and low-quality clinical images. In a deep learning approach combining CNN-based classification with the concept of enhancement provided by diffusion mechanisms, the generalization performance is boosted. The system will be evaluated based on accuracy, precision, recall, and F1-score, and the results provide promising improvements compared to the state-of-the-art traditional deep learning methods. This paper has found that diffusion models provide a robust, scalable, and clinically valuable pipeline for early oral lesion detection, with strong potential to be deployed in real-world diagnostic pipelines and future research on medical imaging and we obtained a very good accuracy i.e., 96% while training the model. This paper establishes the diffusion model as a promising approach for medical image analysis, particularly in the early detection and classification of oral lesions, paving the way for future research and clinical applications in healthcare.

DOI: http://doi.org/

QR Based Online Payment System For Enhanced Convivence Using ML

Authors: Prof. Rahul D. Ingle, Prof. Rohan B. Kokate, Jyoti Ramesh Lanjewar

Abstract: The rapid advancement of digital technology has significantly transformed financial transactions, leading to the widespread adoption of cashless payment systems. Among these, QR code-based payment systems have emerged as one of the most convenient and efficient methods for conducting fast and contactless transactions. However, despite their growing popularity, these systems still face critical challenges such as transaction fraud, unauthorized access, phishing attacks, and security vulnerabilities. To overcome these limitations, there is a need to integrate intelligent technologies that can enhance both security and user experience. This project presents the design and development of a QR Based Online Payment System for Enhanced Convenience Using Machine Learning (ML). The primary objective of the system is to provide a secure, fast, and user-friendly digital payment platform that allows users to make payments simply by scanning QR codes. The system eliminates the need for physical cash, card swiping, or manual bank details entry, thereby reducing transaction complexity and improving efficiency. A key feature of the proposed system is the integration of Machine Learning-based fraud detection mechanisms. The ML model continuously analyzes transaction patterns, user behavior, device information, and payment history to identify unusual or suspicious activities. By using classification and anomaly detection techniques, the system can detect potential fraud in real time and prevent unauthorized transactions before they are completed. This enhances the overall trust and reliability of the payment platform. The system also includes essential modules such as secure user authentication, dynamic QR code generation, transaction processing, payment history tracking, and notification services. Each transaction is securely encrypted and stored in a centralized database to ensure data integrity and confidentiality. The platform is designed using modern web technologies to ensure scalability, responsiveness, and compatibility across multiple devices. From a functional perspective, the system supports both users and merchants, enabling seamless peer-to-merchant and peer to peer payments. Merchants can generate unique QR codes linked to their accounts, while users can scan and complete payments instantly. The inclusion of real-time alerts and dashboards helps users track their financial activities efficiently.

DOI: https://doi.org/10.5281/zenodo.20036097

Early Disease Detection Using Artificial Intelligence

Authors: Rohit Dhamale, Prathamesh Sonawane.

Abstract: Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern healthcare. It has the potential to significantly improve the accuracy and efficiency of disease diagnosis and treatment. One of the most important applications of AI in healthcare is early disease detection. Early detection allows medical professionals to identify diseases in their initial stages, enabling timely treatment and improving patient survival rates. AI technologies such as machine learning, deep learning, natural language processing, and predictive analytics can analyze large volumes of medical data quickly and accurately. These systems can process electronic health records, laboratory reports, medical images, and patient history to identify patterns that indicate the early onset of diseases. AI-based systems assist doctors by providing data-driven insights and predictions that help in clinical decision-making. This research paper explores the role of artificial intelligence in early disease detection and its impact on modern healthcare systems. The study examines different AI technologies used in disease diagnosis, the methodology used to implement these systems, and the challenges associated with AI integration in healthcare environments. Furthermore, the paper discusses the benefits of AI-driven diagnostic systems in improving healthcare efficiency, reducing medical errors, and enhancing patient outcomes.

DOI: https://doi.org/10.5281/zenodo.20036666

Ardunio Based Controlled System Robatic Arm by Pick and Place

Authors: Dr. K.N.Kazi, Miss. Kolekar Akshata Bharat, Miss. Adling Snehal Bhagwat, Mr. Chorage Mahesh Santosh

Abstract: Automation plays a vital role in modern industries by improving efficiency, accuracy, and productivity. This project presents the design and development of a Packing Controlled Robotic Arm using Arduino. The system is designed to perform automated pick-and-place operations for packing applications in small-scale industries. The robotic arm is controlled using Arduino Nano, with servo motors providing movement to each joint and a gripper mechanism handling objects. Joy Sticks are used to detect the presence of items for packing, while Arduino coordinates motion control through pre-programmed instructions. The developed system aims to reduce manual labor, minimize errors, and provide a cost-effective automation solution. The prototype demonstrates the potential of using simple, low-cost components for effective packaging automation in educational and industrial setups.

DOI: http://doi.org/

Smart Campus Engagement System: An Integrated Web Platform With AI-Assisted Learning, Geolocation Attendance, And Real-Time Campus Services

Authors: B. Anief, M. Sakthivanitha

Abstract: Contemporary higher education institutions operate a fragmented portfolio of digital tools — separate learning management systems, manual attendance registers, paper-based hostel outpass forms, and WhatsApp-group announcements — that impose coordination overhead on students and staff while producing no integrated data trail for institutional analytics. This paper presents the design, implementation, and evaluation of the Smart Campus Engagement System (SCES), a cloud-deployed, role-aware web platform that unifies nine functional modules — user management, AI-assisted learning, attendance and academics, hostel and outpass management, events and activities, communication and alerts, complaints and feedback, campus services, and analytics — within a single authenticated interface. The system is implemented using a Next.js 14 frontend, a FastAPI Python backend, a PostgreSQL cloud database, and a Groq API–powered LLaMA-3.3-70B language model for an AI assistant. Containerised deployment via Docker Compose supports horizontal scaling. System testing across eight functional scenarios at up to 200 concurrent users demonstrates API response times below 900 ms and a peak-load error rate of 2.8%. Security testing confirms resistance to SQL injection, JWT tampering, cross-site scripting, and unauthorised role escalation. Comparative analysis against four published smart campus systems confirms that the proposed implementation is the only system combining LLM-based AI assistance, geolocation-verified attendance, digital outpass workflow, and real-time push notifications in a single unified deployment. The system establishes a replicable, open-architecture blueprint for next-generation campus digitalisation.

DOI: https://doi.org/10.5281/zenodo.20037223

Emotion Detection from Text using CNN, LSTM and Hybrid CNN-LSTM Deep Learning Model

Authors: Sufiyan Ansari, Arhaan Shaikh, Usaid Khairdi, Moaiz Kazi

Abstract: Text classification has numerous applications in real world scenarios. Emotion detection from text is one of the vital tasks within natural language processing, which has gained significant attention of researchers over the years. In this study, emotions are detected and classified for better human–computer interaction, sentiment analysis, health management, and smart chattingbots. A number of deep learning models are developed and outperform the traditional models for the classification. Con- volutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid of CNN and LSTM are used for emotion classification. Furthermore, two traditional machine learning ap- proaches including Logistic Regression and Naive Bayes are also implemented for comparison purpose. Preprocessing is a very important step for a good model. Text normalization, stop words removal, tokenization, and padding are used for data preparation. Word embeddings, specifically pre-trained word2vec, are used to capture the semantic relationship of text features learned from deep learning models. The performance evaluation of these models is done using accuracy, precision, recall, F1-score, and confusion matrix. The experimental results show that the deep learning models have outperformed the traditional models. The hybrid CNN-LSTM model achieved the best results to classify emotions in multi-class problem.

Smart Attendance Tracking System Using and QR Code Technology

Authors: Dr. Anand Singh Rajawat, Mayur Devare, Vaibhav Ingale, Mohit Deshmukh, Prathmesh Ingle

Abstract: Traditional attendance marking is a labor-intensive process prone to human error and proxy attendance. This paper presents an automated Smart Attendance Tracking System (SATS) that utilizes Quick Response (QR) Code technology for instant identification. The system works by generating unique encrypted QR codes for each student, which are then scanned using a standard high-definition webcam. Built on a Java-based technical stack with a MySQL backend, the system processes image data to decode information in real-time. Experimental results demonstrate that the system can process an individual scan in less than 0.8 seconds with 100% accuracy in standard lighting. By eliminating physical registers and dedicated scanners, this system offers a cost-effective and highly scalable solution for educational institutions.

DOI: https://doi.org/10.5281/zenodo.20038072

AI‑Assisted Load Testing And Failure Prediction

Authors: Aaryan Kansal, Aditya Garg, Rajat Takkar

Abstract: Modern software systems must handle unpredictable user loads while maintaining performance and reliability. This paper proposes an AI-based load testing framework using asynchronous load generation, machine learning models, anomaly detection, and digital twin simulation. The system predicts latency, detects anomalies, and estimates failure thresholds, enabling proactive performance optimization for high-traffic applications.

DOI:

AI-Driven Autonomous Software Engineering System (Jellyfish AI)

Authors: Jay Pradip Deshmukh, Siddhi Kadam, Prajakta Badhe, Professor Snehal Awatade, Professor Bhagyashri Ramchandra Gunjal

Abstract: The use of Generative AI has started changing how software is developed today. Many tools are now available that help developers write code faster or fix errors, but most of them only support specific tasks. They do not handle the complete software development process from In this paper, we propose a system called JellyfishAI ,which aims to automate the full software development lifecycle. The idea is simple — a user can describe their requirements in normal language, and the system will generate a complete, ready-to-use application. It combines different technologies like natural language processing, code generation models, testing, and deployment into one system. Technology is advancing very fast, and modern applications need to be scalable, reliable, and developed quickly. Because of this, traditional development methods are under a lot of pressure. Usually, software development includes many steps like requirement analysis, design, coding, testing, and deployment. These steps often require manual work and skilled developers, which makes the process slow and costly. It can also lead to mistakes and inconsistent results. With the introduction of Generative AI, things have started to improve. These systems can understand user input and generate code, documentation, and even design ideas. They help developers save time by assisting in coding and debugging tasks. However, these tools still have limitations. Most of them only focus on one part of development, like code suggestions or bug fixing, and do not provide a complete solution. Another issue is that developers still need to use multiple tools separately and connect them manually. This makes the workflow complicated and less efficient. Also, the code generated by AI tools often needs to be checked by humans to ensure it is correct, secure, and follows proper standards. Even though CI/CD pipelines help automate deployment, they mostly follow fixed rules and do not have intelligent decision-making capabilities. Similarly, testing tools can find errors but cannot automatically improve the quality of the code. Because of this, there is a need for a system that can intelligently manage all stages of development together. Today’s software systems are also becoming more complex, and there is a growing need to build applications quickly. Startups and companies want to turn their ideas into working products as fast as possible. However, depending on skilled developers and manual work often slows things down. This becomes even more difficult when dealing with large systems that require proper coordination and integration. To solve these problems, we introduce JellyfishAI, which is designed to automate the entire development process. Unlike existing tools, this system brings everything together in one place. It can understand user requirements, generate code, test it, check for errors, and deploy the application automatically. The system is built using multiple layers that include language processing, AI-based code generation, validation, and deployment. This ensures that the generated code is not only functional but also tested and ready to use. The system also improves over time using feedback and learning mechanisms. JellyfishAI also focuses on important factors like security, scalability, and maintainability. It includes features to detect vulnerabilities, manage dependencies, and improve performance, so that the final application meets industry standards. This system can change how software development is done. By automating repetitive tasks, developers can focus more on designing and solving problems instead of doing routine work. It also makes it possible for people without strong technical skills to build applications. In conclusion, AI has the potential to improve software development by making it faster, cheaper, and more efficient. However, to achieve full automation, we need systems that combine all stages of development in one place. JellyfishAI is an attempt to do that by providing a complete, end-to-end solution for building software automatically.

DOI: https://zenodo.org/records/20042299

Development of a Solar-Driven Self-Navigating Vacuum Robot: Design, Implementation, and Analysis

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/

A Machine Learning Approach For Sustainable Crop Yield Prediction Using Climatic And Soil Attributes

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

A Context-Aware And Personalized AI-Based Search Engine Using Large Language Models

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

Ann-Based Protection Coordination For Meshed Transmission Networks

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

Deep Learning – Driven Change Detection Framework For Pre And Post Flood Impact Analysis

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.

Design And Implementation Of A Real-Time Threat Detection Dashboard Using Open-Source Tools

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.

Leadfree Perovskite Solar Cell

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

Enhancing Speech Synthesis With Human-Like Emotional Intelligence For Natural And Expressive Communication

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

“Retrofitting Of Existing Vehicle For Converting To Electric Vehicle-BMS ”

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.

Multi-Class Brain Tumor Classifier: Ensemble Machine Learning

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.

Cognitive Dependency On Generative Ai Tools And Its Impact On Student Learning Behaviour

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

Vrikshveda: A Comprehensive Digital Library of Medicinal Plants Using Modern Technologies

Authors: Dr. Harikesh Singh, Sunny Kumar, Suryanshu Singh, Shivam Singh, Smit Verma

Abstract: It is a time marked by increasing environmental degradation and disconnect from traditional knowledge systems, and the preservation and dissemination of information of medicinal plants has become critically important. The Vrikshveda project represents an attempt in creating such a comprehensive digital library documenting India’s rich botanical heritage, emphasising on medicinal plants and their therapeutic applications. This research paper presents the development and implementation of Vrikshveda, a modern web and app platform designed to distill knowledge to society about diverse plant reserves, their medicinal properties, traditional uses, and build a community around it. The system combines botanical information, traditional knowledge, scientific research, 3D AI detection and social features to create an accessible repository that serves researchers, healthcare practitioners, students, and the general public. We use web and app technologies including React, Node.js, Kotlin and MongoDB, to make Vrikshveda provide an interactive platform where users can explore detailed information about medicinal plants, including their taxonomic classification, morphological characteristics, therapeutic applications, and other content around it. The project is an attempt to answer the urgent need for documentation of indigenous botanical knowledge before we may lose it to modernization, and simultaneously promoting awareness about using them for medical benefits. Through comprehensive data collection from genuine sources, field research, and collaboration with traditional healers and botanists, Vrikshveda aims to fill the gap between centuries of wisdom and contemporary scientific understanding.

Attitude Towards Digital Literacy Among Postgraduate Students In Higher Education

Authors: Sujash Kumar Mandal

Abstract: Our present world is the world of AI, Machine learning, quantum physics where study has been shifted from bookish knowledge to online learning. Every sector has been growing and growing up rapidly; especially in higher education it shifted to online learning or virtual mood. Many courses and exams are conducted through online and also certificate is provided digitally. The present study focuses on about the attitude of digital literacy among post graduate students. Hundred samples has taken for the study; a self-made Likert scale used for the data collection by the researcher and t-test, SD, Mean deviation also used for statistical treatment. The researcher finds that there is no significant difference among post graduate students towards digital literacy according to their race, gender, locality basis.

DOI: https://doi.org/10.5281/zenodo.20056572

How Artificial Intelligence Is Reshaping Climate Change Impacts

Authors: Piyush Dewangan, Shivam Vishwakarma, Nikhil Yadav, Prahlad Yadav, Himanshu Mokashe, Deepak Sahu

Abstract: Global climate change poses severe threats to agricultural and forested ecosystems that underpin terrestrial carbon balance, biodiversity, and food security. This paper presents a comprehensive investigation into how Artificial Intelligence (AI)—encompassing machine learning, convolutional neural networks (CNNs), long short-term memory (LSTM) networks, transformers, and generative adversarial networks (GANs)—is transforming climate change responses across agriculture and forestry. Drawing on peer-reviewed literature and documented case studies, we examine AI applications including precision irrigation, crop disease detection, yield forecasting, satellite-based deforestation monitoring, wildfire risk prediction, acoustic biodiversity surveillance, and hydrological flood modeling. A three-tiered analytical framework maps causal pathways from technological deployment to environmental, economic, and social outcomes, while critically addressing structural barriers including data scarcity, algorithmic bias, computational inequity, and governance deficits. Principal findings confirm that AI delivers measurable gains in climate mitigation and adaptation efficiency; however, transformative societal potential remains contingent on equitable data access, open-source computational infrastructure, and coherent multilateral policy frameworks.

DOI: https://doi.org/10.5281/zenodo.20060622

Top Management-Driven Quality Management: A Study Of Small And Large Foundries In India

Authors: Mahantesh M. Ganganallimath, Dr. K. Vizayakumar, Dr. Umesh M. Bhushi

Abstract: By providing cast components to the automobile, aerospace, railroad, construction, defence, and heavy engineering industries, the Indian foundry sector is essential to the manufacturing sector. Casting flaws, process unpredictability, material waste, high rejection rates, energy inefficiency, and growing international competitiveness are some of the industry’s major obstacles. In this regard, sustainable industrial growth now depends on quality assurance and quality-centric methods. The necessity of methodical quality assurance procedures, process control systems, and continuous improvement techniques in Indian foundries is examined in this study. The study highlights that quality-driven systems enhance customer satisfaction and product dependability while simultaneously lowering costs and promoting long-term competitiveness and environmental sustainability. The combination of Industry 4.0, automation, and statistical quality tools for stable growth is further supported by recent research on KPI-driven foundry quality systems and sustainable control models. An important part of the manufacturing sector, the Indian foundry industry greatly boosts employment and economic growth. This study looks into how top management influences quality management procedures in Indian foundries of different sizes. The study examines implementation difficulties, strategic quality efforts, and leadership commitment at various operational scales. The results show that whereas major foundries use organized quality management systems, small foundries encounter obstacles because of limited resources, ignorance, and opposition to change. The report suggests a framework to improve quality performance in the Indian foundry industry and emphasizes the necessity of a leadership-driven quality culture.

DOI: https://doi.org/10.5281/zenodo.20060718

AI-Based Approach For Smart Attendance System Using Face Recognition

Authors: P.Silpa Chaitanya, Ch. Lakshmi Mounika Priyadarsini, N. Sowmya, B. Pujitha, M. Lakshmi Triveni

Abstract: The rapid growth of Artificial Intelligence (AI) and machine learning has transformed automation in various fields, including education and corporate sectors. Traditional attendance systems that depend on manual entry or RFID cards are often time-consuming, inaccurate, and susceptible to proxy attendance. To address these issues, this paper proposes an AI-based smart attendance system that utilizes face recognition technology for real-time, contactless attendance marking. The system employs computer vision and deep learning models, particularly convolutional neural networks (CNN), to detect and recognize faces with high precision. Live camera input captures facial features, which are processed through image enhancement and feature extraction algorithms before being matched with a pre-trained dataset for identity verification. The system is scalable and capable of handling multiple users simultaneously. Experimental analysis shows that the model achieves over 95% accuracy under different lighting, facial poses, and occlusion conditions. This automated and secure approach reduces human intervention and offers a reliable, efficient, and intelligent alternative to traditional attendance methods.

DOI: https://doi.org/10.5281/zenodo.20065110

An IoT-Enabled Wearable Sensor Framework For Early Detection Of Cardiac Arrest

Authors: D Anveshini, K Chinna Reddemma, M Venkata Akshara, Ch Sai Samanvitha, Ch Sai sirisha

Abstract: Sudden cardiac arrest is a critical medical emergency that demands immediate recognition and timely intervention to improve a patient’s chances of survival. Although hospital-based cardiac monitoring systems are dependable, their high expense and lack of portability make them impractical for everyday personal monitoring. This work presents an Internet of Things (IoT)-based wearable framework that enables continuous and real-time cardiac health observation in non-clinical environments. The device integrates multiple biosensors including electrocardiogram (ECG), pulse oximeter, body temperature, and galvanic skin response (GSR) to acquire physiological data. The acquired data are uploaded to a cloud environment, where algorithms evaluate and categorize the user’s cardiac condition as normal, borderline, or severe. The system is linked to a companion mobile application that visualizes real-time readings and automatically issues alerts to caregivers and medical professionals when abnormalities are detected. Through the integration of wearable sensors, edge–cloud data analysis, and IoT communication, the proposed system delivers an economical approach for early cardiac distress prediction and prompt emergency support.

DOI: https://doi.org/10.5281/zenodo.20065529

Alzheimer Detection And Classification Using SVM

Authors: V Manogna, B Durga, P Sravani, N Yamuna, B Reshma

Abstract: Alzheimer’s disease (AD) is a progressive brain disorder that leads to memory loss and a gradual decline in thinking and reasoning abilities. One of the major challenges in dealing with Alzheimer’s is detecting it early and accurately using MRI brain scans. Traditional manual analysis of these scans can be slow, complex, and prone to human mistakes over the years, different machine learning (ML) models like Decision Tree, Random Forest, Logistic Regression, and K-Nearest Neighbors have been used to identify Alzheimer’s. However, these models often face issues such as overfitting, lower accuracy, and weak performance when dealing with complex and high-dimensional MRI data.to overcome these limitations, the proposed approach uses SVM model for detecting and classifying Alzheimer’s disease. The SVM model is well-suited for handling non-linear and complex data. It can effectively separate different disease categories by using advanced kernel functions and optimal hyperplane techniques. This leads to more precise and stable classification results, even with smaller datasets compared to existing ML models, the proposed SVM model achieves higher accuracy, sensitivity, and specificity, making it more dependable for automatic Alzheimer’s detection. It not only reduces errors but also helps in identifying the disease at an early stage, which is crucial for better treatment and patient care. With 98.16% classification accuracy, it outperforms current architectures significantly in the Alzheimer Detection and Classification Using SVM.

DOI: https://doi.org/10.5281/zenodo.20065684

Smart Driver Drowsiness Detection And Alert System Using Machine Learning And Iot

Authors: Sk.Firdaus Fathima, M.Anusha, S.Vennela, Sk.Sadiya Kousar, B.Gayathri

Abstract: Driver drowsiness is one of the major causes of road accidents globally, leading to serious injuries, deaths, and economic loss. To combat this, a real-time Driver Drowsiness Detection System has been implemented using machine learning algorithms combined with IoT hardware. The system tracks the driver’s eyes continuously through a real-time video feed obtained via a webcam. With the OpenCV and dlib libraries, the Eye Aspect Ratio (EAR) is computed to obtain a measurement of the degree of eye closure, which is a good predictor of drowsiness. Upon detection of prolonged eye closure, the system sends a serial communication command to an Arduino Uno microcontroller to activate a buzzer alarm and commence a progressive motor deceleration, mimicking a safe vehicle stop. This two-stage mechanism reduces the risk of accidents by giving both an initial warning and an automatic safety measure. Experimental data show that the designed system has a detection accuracy of 96.8% for different illumination conditions, with a response time of less than one second. The approach is cost-effective, non-invasive, and easily implementable on contemporary vehicles, ensuring it to be a promising solution for improving road safety.

DOI: https://doi.org/10.5281/zenodo.20066634

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Fertilizer Recommendation System Using SVM

Authors: Manogna.Velamakanni, DurgaBhavani.Makkena, Sindhu.Merugu, Harshika.Lekkala, Indira Lakshmi Borigorla

Abstract: Agriculture is very important for food security, but over 40% of farmers use too little or too much fertilizer, which causes low crop yield, soil damage, and financial loss. Smart recommendation systems can help farmers by giving accurate advice on the right type and amount of fertilizer. Many machine learning methods like Decision Trees, Random Forest, Gradient Boosting, and Neural Networks have been used for crop and fertilizer recommendations. However, these methods often need a lot of computing power, do not work well with small or noisy data, and can be hard for farmers to understand. To solve these problems, we propose a Support Vector Machine (SVM)-based fertilizer recommendation model. SVM works well with small and unbalanced datasets, reduces overfitting, and handles complex patterns while needing fewer resources, making it suitable for real farming. Using soil nutrient values and crop needs, the model gives reliable predictions. Tests show that the SVM model achieves 96.77% accuracy, making it effective for smart agriculture and proper fertilizer use.

DOI: https://doi.org/10.5281/zenodo.20066724

Detecting Falsified Resume Using Machine Learning

Authors: Badisa. Adhilakshmi, Mula Srilatha, Golla Manusri, Bodepudi Tejaswini, Daggubati Maneesha

Abstract: Faking resumes is one of the greatest challenges in the contemporary recruitment systems where most applicants tend to embellish or lie about their academic and professional experience or technical abilities in order to have an advantage in employment. The manual verification systems are tedious, time consuming and they are also subject to error, which makes them ineffective in large-scale hiring. Previous automated systems based on classical machine learning systems like Support Vector Machines (SVM) or Random Forest are only capable of dealing with structured data and do not effectively deal with unstructured, multilingual and complex resumes. The consequences of these limitations are low accuracy, low contextual knowledge and low scalability. To address these issues, in this paper, a hybrid AI-inspired resume verification system incorporating the methods of Natural Language Processing (NLP), deep learning, and classical machine learning will be suggested. The system preprocesses resumes of different types (PDF, DOCX, text) and finds significant data, including education, skills, and experience, and it describes it with contextual embeddings with Transformer-based models. Convolutional Neural Networks (CNNs) are used to capture local linguistic patterns whereas traditional ML models like Random Forest and Gradient Boosting are used to analyse engineered numerical features. An ensemble classifier is a stacked ensemble of these components that is used to give a final score of authenticity, or what percentage probability a resume is a fake resume. The experimental evidence shows that the hybrid model is much better in comparison to traditional methods, as the accuracy of the models is 85-95 with the greatest accuracy of the Transformer based model of 94, and better precision, recall, and F1-score. High-performance, scalable, and automated approach to resume fraud detection through the combination of NLP, deep learning, and classical ML will make recruitment processes more efficient, transparent, and more credible.

DOI: https://doi.org/10.5281/zenodo.20066837

Leveraging Ai And Blockchain To Enhance Cloud Storage Security

Authors: A Chenna Kesava Reddy, K Apurupa, K Akhila, N Abhinaya, N Trisha5

Abstract: Cloud storage has emerged as the backbone of modern digital ecosystems, enabling seamless data access, sharing, and collaboration across individuals, enterprises, and government organizations. However, the centralized nature of conventional cloud architectures makes them vulnerable to critical security challenges such as data breaches, manipulation, unauthorized access, and single-point failures. To address these issues, this study proposes a hybrid intelligent cloud security framework that integrates Artificial Intelligence (AI) and Blockchain technologies. Blockchain ensures decentralized trust through cryptographic immutability, distributed consensus, and smart contracts that automate data access and policy enforcement without third-party intervention. Simultaneously, AI specifically Long Short-Term Memory (LSTM) networks is employed for anomaly detection, analysing user activity logs and behavioural patterns to identify irregularities or potential intrusions in real time. The system dynamically adjusts resource allocation and access privileges based on AI-driven insights, enhancing operational efficiency and security adaptability. Experimental evaluation demonstrates that the model achieves high performance in terms of accuracy, precision, recall, F1-score, latency, and throughput, validating its robustness and scalability under varying network conditions. By combining AI’s predictive intelligence with blockchain’s decentralized integrity, the proposed approach delivers a secure, transparent, and self-optimizing cloud storage framework suitable for data-sensitive domains such as healthcare, finance, e-governance, and smart industries.

DOI: https://doi.org/10.5281/zenodo.20067047

Smart Health Surveillance System Using Iot Sensor

Authors: Ch Naga Lakshmi, B Tejaswini, B Anusha, R Nandini, N Tasleem

Abstract: The increasing incidence of chronic illnesses, including cardiovascular and respiratory disorders, has emphasized the importance of continuous health parameter monitoring. Conventional systems for vital sign assessment are primarily hospital-based, costly, and limited to periodic medical consultations, which may delay the detection of abnormal physiological variations. To overcome these limitations, this paper presents a Smart Health Surveillance System designed using Internet of Things (IoT) technology integrated with low-cost biomedical sensors. The proposed model employs a MAX30100 pulse oximeter to measure heart rate and blood oxygen saturation (SpO₂), a DS18B20 digital sensor to record body temperature, and a Node MCU ESP8266 microcontroller to process and transmit data. Measurement outputs are displayed on an LCD screen, while IoT functionality enables remote monitoring through wireless connectivity. Experimental evaluation demonstrates that the system achieves a heart rate accuracy of ±3 bpm, a SpO₂ accuracy of ±2%, and a temperature accuracy of ±0.5 °C when compared with standard medical devices. The prototype’s affordability, portability, and reliability make it an effective solution for continuous home-based health monitoring, telemedicine services, elderly care, and epidemic surveillance. Future work aims to integrate additional sensors—such as blood pressure and ECG modules—and to utilize cloud-driven analytics for predictive and preventive healthcare applications.

DOI: https://doi.org/10.5281/zenodo.20067343

Enhancing Healthcare with Edge AI in Medical Imaging- An Extensive Examination of Diagnostic Accuracy Treatment Decisions

Authors: Khushi Wadhwa, Rajat Takkar, Vaani, Kashish Sharma, Kartikay Singh Manhas

Abstract: In the world of medical imaging, Edge artificial intelligence (AI) is driving a revolution by enabling real-time analysis and diagnosis decision making. The aforementioned article examines the constantly developing subject of edge AI-powered healthcare imaging, describing the most recent ad-vancements, creations, and concepts that could transform a variety of medical fields by instantly interpreting medical images, which can be crucial in life-saving circumstances. The Edge AI can be used in remote clinics and other medical imaging situations. In rural diabetes camps, diabetic retinopathy can be diagnosed with Fundus cameras and point-of-care ultrasound without radiologists. In emergency situations, portable X-ray devices can diagnose fractures. The three main types of diagnos-tic procedures—imaging-based, pathology-driven, and protective diagnostic approaches—as well as the alterations and adaptations brought about by the application of Edge AI are also covered in this article. Using medical records raises several ethical issues because they are very sensitive documents. These challenges have also been discussed in this article. The necessity for further developments in Edge AI-based diagnostic techniques is also covered in the article. Additionally, there is a great deal of potential for the future in the creation of tools and techniques that are easy to use and incorporate into routine operations. The increasing usage of clinical decision support systems makes edge AI a promising topic in healthcare and diagnostics. Despite a number of obstacles to its application and adoption, the research concludes that Edge AI in healthcare has a promising future. However, in order to guarantee that facilities are available for this, a high degree of precision must be attained and patients must have better medical outcomes. The potential of AI to transform healthcare and enhance patient outcomes is also highlighted in this paper, with a focus on responsible implementation and ongoing assessment.

DOI: http://doi.org/

From Code To Compliance: Governing Artificial Intelligence Under The Digital Personal Data Protection Act, 2023

Authors: Ria Ranjan Kumar, Ms. Mitali Srivastava

Abstract: The growing adoption of Artificial Intelligence and its intervention in the legal sphere has been a welcome sight for everyone in the industry. From judicial institutions to corporations, AI has rapidly shown an overarching effect on judicial functioning. However, with this bargaining effect, what comes is the danger of Artificial intelligence. Due to the lack of regulatory mechanisms and compliance laws, a varied but unexpected stream of jeopardy is underway for all of us. This is somehow related to the growing correspondence being provided by human beings and the pervasive acceptance not just by the elite class of the world but across all classes. The only laws that are currently under the purview of this domain are the Digital Personal Data Protection Act, 2023, for privacy and personal data, the Information Technology Act, 2000 for cyber offences and intermediary liability, and the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021. With this dissertation, I shall try to lay down an intricate evaluation of the recent AI interception and reflect on the dire need to incorporate it with a proper legal regulatory system. Although the term “artificial intelligence” is not used in the DPDP act, one crucial word mentioned therein is “automated,” which refers to a digital procedure that can process data automatically. That is the closest the Act gets to discussing artificial intelligence. The act does not specifically address algorithmic bias, deepfakes, facial recognition, automated decision-making, AI explainability, or generative AI training data. There are a lot of inconsistencies in the law itself, and the dystopian tone of the act adds insult to injury.

DOI: https://doi.org/10.5281/zenodo.20069843

Microcontroller Based Automatic Power Factor Correction

Authors: Manas Kumar, Heera Sahu, Shivam Jaiswal, Amar Verma , Professor Pushpa Sahu

Abstract: With the mining industry moving from traditional manual methods to the advanced mechanised mining, the focus is also shifting to the energy efficiency of the equipment and system being employed. Most of the equipment used in mining like shovel, drill, elevator, continues miner, conveyor, pumps etc. runs on electricity. Electric energy being the only form of energy which can be easily converted to any other form plays a vital role for the growth of any industry. The Power Factor gives an idea about the efficiency of the system to do useful work out of the supplied electric power. A low value of power factor leads to increase is electric losses and also draws penalty by the utility. Significant savings in utility power costs can be realized by keeping up an average monthly power factor close to unity. To improve the power factor to desired level, reactive power compensators are used in the substations. The most common used device is capacitor bank which are switched on and off manually based on the requirement. If automatic switching can be employed for the correction devices, not only it will improve the response time but also removes any scope for error. The work carried out is concerned with developing power factor correction equipment based on embedded system which can automatically monitor the power factor in the mining electrical system and take care of the switching process to maintain a desired level of power factor which fulfils the standard norms. The Automatic Power Factor Correction (APFC) device developed is based on embedded system having 89S52 microcontroller at its core. The voltage and current signal from the system is sampled and taken as input to measure the power factor and if it falls short of the specified value by utility, then the device automatically switch on the capacitor banks to compensate for the reactive power. The number of capacitors switched on or off is decided by the microcontroller based on the system power factor and the targeted power factor. The measurement and monitoring of three different possible load types suggested that only the inductive loads required power factor correction. After employing the correction equipment the targeted power factor of 0.95 is achieved and the increase in power factor varied from 9% to 19% based on the combination of load. There is also a decrease of 1.7% in the total energy consumption due to reduction in load current. The economic analysis for power factor improvement considering the data from a local coal mine suggested the payback period to be around 9 months if the correction equipment is implemented.

DOI: https://zenodo.org/records/20070425

Exposure And Toxic Effects Of Chromium On Human Health: A Review

Authors: Aziz A. Isra, Chaturvedi Rachna, Prakash Jyoti

Abstract: Chromium (Cr) metal and Cr compounds are primarily used in applications like making stainless steel, polishing, and leather tanning. Chromium naturally occurs in air, water, rocks, and soil, via natural or anthropogenic sources. It exists in different oxidation states ranging from +6 to -2. The most stable forms are the trivalent Cr(III) and the hexavalent Cr (VI), which are interconvertible with each other. Chromium is an important trace element for human beings as it stimulates the breakdown of fatty acids and cholesterol. However, if exposed to a higher dose of chromium particles for a longer period, it can lead to human health toxicity and fatality. It is introduced into the environment through chemical and physical processes or even by biological transport systems in living organisms. Over the past decades, chromium contamination has become a significant threat with a negative influence on the environment, especially soil and water, and its accumulation affects human health, plant metabolism, and animal tissues. By gathering information from various published literature, we have highlighted the adversities caused by Chromium toxicity, for example, acute and chronic toxicity among human beings like carcinogenic potential, apoptosis, oxidative stress, and DNA adducts. This review focuses on the complex chemistry of chromium, its exposure routes, and hazardous effect of chromium on human health, and the mechanism of chromium toxicity upon entering the cells. Therefore, it is now important to investigate and develop various useful sustainable remediation strategies to balance and reduce the increased levels of chromium in the environment.

DOI: https://doi.org/10.5281/zenodo.20075414

Intelligent Sensing In Smart Homes: A Holistic Review Of IoT Architectures, AI-Driven Analytics, And Human-Centric Applications

Authors: Daniel Karikari Frempong, Mutala Nakpan Jentina, Hannah Owusu Ansah, Gabriel Oduro Asirifi

Abstract: The pivotal role of intelligent sensors in building and running smart homes is discussed in this literature review. First, we present a brief overview of smart homes and intelligent sensors, emphasizing the critical importance of this sophisticated technology used to transform ordinary homes into intelligent AI-controlled houses. The review then delves into the principles of several types of intelligent sensors, including energy, health and wellbeing, environmental, security, and appliance sensors. Besides playing a critical role in gathering data for personalized home automation services, this section touches upon their remarkable contribution to sustainable living, energy-saving, and human wellbeing. The review next examines key technologies and standards that enable seamless communication between devices, such as Matter, Wi-Fi, and Zigbee. This section also sheds light on how artificial intelligence and machine learning could change the paradigm of processing information collected by these intelligent sensors, leading to advanced predictive analysis and decision-making. Finally, we propose ways to address some challenges that impede the widespread application of intelligent sensors, such as interoperability, security, privacy concerns, and affordability. We also present promising avenues for future research on intelligent sensors for smart living, such as increased autonomy, advanced sensor miniaturization, and human-centric design.

DOI: https://doi.org/10.5281/zenodo.20080909

Predicting Coronary Heart Disease Risk With Machine Learning

Authors: Anshika Singh, Sneha Chhabra, Rajat Takkar, Harshwardhan Singh Thakur

Abstract: This study investigates the rising global disease burden, emphasizing the need for early detection to minimize mortality and healthcare costs. This article proposes a machine learning model for predicting disease risk from a dataset of 4240 patient records. Each record is characterized by 15 clinical and demographic attributes. This research paper employed five classifiers—Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Naive Bayes—to identify disease presence. Using hold-out validation, the models were evaluated, and Logistic Regression achieved the highest accuracy of approximately 84%, followed by Random Forest (~83.7%), SVM (~83.3%), and KNN (~82–83%). These results show the potential for early disease detection, enabling timely interventions. By integrating such models into practice, clinicians can maximize patient outcomes and reduce the disease burden globally. Future development includes expanding the dataset and adding an accessible interface for real-time analysis of disease risk.

DOI: https://doi.org/10.5281/zenodo.20226307

E-commerce Recommendation Systems Using Generative AI

Authors: Aniket Mishra, Ajinkya Bagal, Jayesh Jadhav, Rushikesh Nath

Abstract: This study examines the incorporation of generative artificial intelligence (Gen-AI) into e-commerce recommendation systems. Traditional approaches, such as collaborative filtering and content-based filtering, face challenges like sparse data, cold-start issues, and changing user preferences. Gen-AI models, especially transformer-based frameworks like GPT and diffusion models, provide innovative solutions for understanding and creating personalized content. This paper reviews the progression of recommendation systems, introduces generative models, and proposes a framework that integrates Gen-AI with current recommendation strategies to enhance accuracy, diversity, and contextual relevance.

DOI: Name : aniket mishra Contact No : +919518352808

Contour-Aware U-Net With Boundary Refinement For Precise Tumor Segmentation In MRI Scans

Authors: M.Indumathi, Uddandam Vinodkumar

Abstract: Tumor segmentation in Magnetic Resonance Imaging (MRI) plays an important role in diagnosis, treatment planning, and disease surveillance. But still there are many hurdles in the process because of low contrast tissues, unclear boundaries and high morphology variations. In this paper, we propose Contour-Aware U-Net (CAU-Net), which uses explicit contour refinement techniques along with multi-level feature fusion. Our framework includes three main components that are as follows: (1) Contour-Aware Decoder with Attention Fusion blocks for contour enhancement, (2) adversarial learning constraint for anatomically plausible results, and (3) combined hybrid loss function using cross entropy loss, dice loss, and sub-differentiable Hausdorff loss. Extensive experiments on tumor datasets have proven that our proposed approach outperforms existing approaches in terms of accuracy by producing Dice Similarity Coefficient score of 0.92 and reducing Hausdorff Distance by 38%. Our model performs exceptionally well in terms of boundary delineation that was the crucial requirement in clinical practice.

DOI: https://doi.org/10.5281/zenodo.20085607

Deep Learning-Based Cybersecurity Framework For Real-Time Threat Detection In Cloud Environment

Authors: Mani G

Abstract: The fast growth and acceptance of cloud computing technology have completely changed the IT infrastructure of organizations, but along with that transformation, there have been several emerging security concerns. These security concerns have become hard to detect using conventional security approaches, due to the complexity and the evolution of new cyber attacks. In this paper, a complete deep learning cybersecurity framework will be proposed, to detect any threats in real-time within cloud computing environments. The cybersecurity framework consists of several deep learning models. They include the TCN with an autoencoder to detect anomalies at 99% accuracy with a false positive rate of 2.2% based on CSE-CIC-IDS2018 dataset, a transformer with CNN to detect network intrusions with 99.12% accuracy, and a federated learning method for detecting attacks in distributed environment without violating any user’s privacy at 98.3% accuracy in 300 communication rounds.

DOI: https://doi.org/10.5281/zenodo.20085675

Real Time Traffic Flow Forecasting And Management System

Authors: Tejaswini Bagade, Preeti Wagh, Ms. Neeta Takawale

Abstract: This project focuses on the design and development of a Real-Time Traffic Flow Forecasting and Management System using machine learning and deep learning techniques. The system aims to predict traffic conditions accurately by analyzing real-time and historical traffic data collected from sensors, CCTV cameras, and GPS devices. Data preprocessing techniques are applied to remove noise and handle missing values for improved prediction accuracy. Advanced models such as LSTM, GRU, and CNN–LSTM are implemented to forecast traffic flow and support intelligent traffic management decisions. The proposed system helps reduce traffic congestion, improve road safety, optimize signal control, and enhance transportation efficiency through real-time monitoring and adaptive management strategies.

Food Spoilage Detection Using Arduino Uno

Authors: Nitin, Ishan Rana, Dr. Neha Gupta

Abstract: Food spoilage is a big concern affecting health, safety, and economy worldwide. This paper presents the design and implementation of a food spoilage detection system using an Arduino Uno microcontroller and MQ-135 gas sensor. The system detects gases such as ammonia, carbon dioxide, and volatile organic compounds (VOCs) released during food decomposition. Gas sensors provide a non-destructive and efficient way to monitor food quality by detecting chemical changes in the surrounding environment [1]. The MQ-135 sensor is mainly used due to its sensitivity to harmful gases associated with spoilage [2]. When the gas concentration exceeds a predefined threshold, the system alerts the user through an LED indicator in Arduino Board. The proposed system is cost-effective, portable, and easy to implement. It can be used in households, food storage facilities, and small-scale industries to ensure food safety and reduce wastage.

DOI: https://doi.org/10.5281/zenodo.20196415

Real Time Data Monitoring In Smart Grid

Authors: Sukanth Tumu, Balasubbareddy Mallala, Sudhakar Babu Thanikanti, U.Nikhil tej, B.Murali, N.Suresh

Abstract: The Real-Time Grid Monitoring System is an IoT-based project designed to continuously monitor and control electrical parameters in a power distribution setup. The system utilizes a NodeMCU (ESP8266) microcontroller for real-time data acquisition, processing, and wireless communication. A potentiometer is used to simulate and monitor voltage variations, while an LM324 operational amplifier serves as a crucial component for detecting short-circuit and open-circuit faults in the grid. In the event of such abnormalities, or when undervoltage conditions occur, a buzzer is activated to provide an immediate alert.The system incorporates two relays, enabling remote switching of connected loads through an IoT-based web interface, allowing users to manually control devices from anywhere using a smartphone or computer. Additionally, a 16×2 LCD display presents real-time voltage status, load condition, and fault information locally. This integration of hardware monitoring and IoT control ensures improved reliability, safety, and user convenience. The proposed system provides a cost-effective and scalable approach to enhance smart grid management, offering real-time visibility and quick response to faults. It demonstrates the potential of IoT in modern electrical systems by bridging automation, monitoring, and fault detection into a unified platform. Keywords: NodeMCU, RELAYS, BUZZER, LOADS, LCD, VOLTAGE MONITOR, LM324, OC, SC.

DOI:

Hashlytica – “A Web-Based Platform Using NLP And Machine Learning For Real-Time Social Insights And Engagement Optimisation”

Authors: Saanvi Anup K, Nandana D Nair, Shajahan Basheer, Suresha R

Abstract: In the digital age, social media platforms generate vast amounts of unstructured data that serve as a goldmine for businesses, marketers, and content creators. Identifying trending topics and understanding content engagement dynamics is critical for strategic decision- making. This report reviews 30 research papers focusing on social media analytics, ranging from big data architecture to advanced deep learning models. Based on this review, we propose a ‘Social Media Analyzer’ system designed to extract trending hashtags, perform sentiment analysis on user engagement, and provide actionable insights. We select “A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages” (Paper #13) as our base paper for its robust handling of informal text. The proposed work integrates Topic Modelling (LDA) with a Hybrid Deep Learning Classifier to predict content virality and audience sentiment.

DOI: https://doi.org/10.5281/zenodo.20092205

Al-Enabled Predictive Monitoring And Security Systems For Healthcare And Aviation

Authors: Aditi Nandiraju, Hunar D, Ashutosh,, Somraj, Janaki Kandasamy

Abstract: As critical infrastructure in aviation and healthcare becomes increasingly complex, traditional reactive strategies for maintenance and security are proving insufficient for handling dynamic real-world environments. This research examines the integration of AI-enabled predictive monitoring and security frameworks to create resilient, self-sustaining systems that can manage uncertainty with minimal human intervention. Central to this transition is the application of AI and machine learning models—such as XGBoost, CNNs, and LSTMs—to move from scheduled to proactive maintenance by accurately predicting the Remaining Useful Life (RUL) of aircraft engines and providing early warnings for cardiac events in healthcare. Simultaneously, the study prioritizes security by developing defense mechanisms against cyber-physical threats, including GPS spoofing, ADS-B vulnerabilities, and unauthorized network intrusions across both aviation and smart airport infrastructures. Despite these advancements, significant barriers remain, including high computational overhead, a lack of model interpretability (the “black box” problem), and a gap between simulation and real-world deployment. This work concludes that the future of dependable infrastructure lies in unified, lightweight, and explainable frameworks that allow systems to autonomously detect threats, recover from faults, and maintain themselves in unpredictable conditions.

DOI: https://doi.org/10.5281/zenodo.20093181

Retrofitting Of Existing Vehicle Into Electric Vehicle.pdf

Authors: Prof. K.S.Tamboli, Meher Karan Dnyandev, Gaiwad Nikhil Ganesh, Kate Dhruv Balsabheb

Abstract: The increasing demand for sustainable transportation and the need to reduce environmental pollution have accelerated the adoption of electric vehicles (EVs). However, the high cost of new EVs and the large number of existing internal combustion engine (ICE) vehicles present a significant challenge. Retrofitting of existing vehicles into electric vehicles has emerged as a practical and cost-effective solution to address this issue. This process involves replacing the conventional engine, fuel system, and exhaust components with an electric motor, battery pack, and motor controller. This paper focuses on the selection and integration of key components, particularly the electric motor and controller, which play a vital role in determining the performance, efficiency, and reliability of the converted vehicle. Various types of motors such as BLDC and induction motors are analyzed along with suitable controller strategies. The study also highlights design considerations, system integration challenges, and safety aspects involved in the for conversion process. Retrofitting not only reduces carbon emissions and fuel dependency but also extends the life of existing vehicles, making it an environmentally and economically viable solution. The proposed approach contributes to sustainable mobility while promoting innovation in electric vehicle technology.

DOI:

AIPhiShield: Client-Side Machine Learning For Real-Time Phishing URL And QR Code Threat Detection

Authors: Ramse Dhananjay Devdas, Pawar Gorakhnath Vishwanath, Prof. N. K. Patil

Abstract: Phishing attacks and malicious QR codes constitute two of the most prevalent vectors of cybercrime, accounting for billions of dollars in financial losses annually. Existing defences rely on server-dependent machine learning pipelines or easily bypassed keyword heuristics that produce unacceptable false-positive rates on legitimate sites. This paper presents AIPhiShield, a browser-native cybersecurity tool that replaces heuristic match-ing with a Logistic Regression classifier trained on 20 structural URL features using Python and scikit-learn, then exported as a 2.5 KB JSON weight file and executed entirely within the browser via a custom JavaScript inference engine. No URL is transmitted to any external server for machine learning scor-ing, preserving user privacy. Detection is augmented by cross-referencing the OpenPhish live phishing feed and a curated 52-entry compound-phrase blacklist. The integrated system ad-ditionally provides QR image scanning, live webcam QR scan-ning, an LLM-powered cybersecurity chatbot routed through a Flask proxy that conceals the API key from frontend code, voice input, and geolocation-enriched scan history. The trained model achieves 100% accuracy, precision, recall, and F1-score on a stratified 72-sample test set, with zero false positives and zero false negatives. Feature importance analysis identifies HTTPS usage, high-risk top-level domain, and raw IP address as the three strongest predictors.

Artificial Intelligence for a Sustainable Future: Smart Cities, Renewable Energy, Climate Monitoring, and Ethical Considerations

Authors: Ayura Ajinath Athare, Prof.Sweety Wanave-

Abstract: Abstract- Artificial Intelligence (AI) is rapidly transforming the global pursuit of sustainability by enabling intelligent, data-driven decision-making across urban development, renewable energy, and environmental monitoring. This paper explores AI’s applications in building smart cities, optimizing renewable energy systems, and advancing climate change monitoring, with a focus on India’s smart grid journey. Smart city initiatives integrate AI for traffic management, waste handling, and energy distribution, creating resource-efficient ecosystems. Renewable energy systems benefit from AI’s predictive analytics in demand forecasting, renewable integration, and energy storage, particularly relevant for India’s National Smart Grid Mission. AI also plays a pivotal role in climate monitoring by processing satellite imagery, IoT sensor data, and big data models to predict weather patterns, detect environmental degradation, and enable disaster preparedness. However, ethical concerns such as bias, transparency, privacy, and equitable access must be addressed to ensure inclusive adoption. A literature review of over 20 scholarly works and policy frameworks highlights current advancements, gaps, and future opportunities. The proposed framework integrates technical, ethical, and governance considerations for sustainable AI. By combining AI innovation with ethical governance, nations can accelerate progress toward the United Nations Sustainable Development Goals (SDGs) while ensuring fairness and resilience.

Reinforcement Learning For Intelligent Traffic Signal Control With Vehicle-Mounted IoT Sensors

Authors: Shubham Aher, Atharva Lambate

Abstract: Adaptive traffic signal control is an important requirement for reducing urban congestion and improving traffic flow in smart cities. Traditional fixed-time signal systems work on pre-defined schedules and cannot respond effectively to sudden changes in traffic demand, peak-hour congestion, road incidents, or uneven lane usage. This research paper presents an intelligent traffic signal control system that combines Reinforcement Learning (RL) with vehicle-mounted Internet of Things (IoT) sensors. In the proposed system, vehicles provide anonymized and aggregated traffic information such as position, speed, lane approach, queue formation, and movement direction. This information is collected by roadside aggregation units and used by reinforcement learning agents to dynamically select signal phases at intersections. The main objective of the system is to reduce average waiting time, queue length, unnecessary stops, vehicle idling, and unfair lane delays while maintaining data privacy. A multi-agent Advantage Actor-Critic based approach is considered for controlling multiple intersections, and other RL algorithms such as Q-learning, Deep Q-Network, and Proximal Policy Optimization may also be applied depending on the traffic environment. The system is evaluated through SUMO-based traffic simulation. The study shows that RL-based signal control can improve performance compared with fixed-time and threshold-based control methods, with preliminary simulation results indicating approximately 30% improvement in waiting time and queue length. The paper also discusses methodology, deployment process, scalability, communication challenges, privacy protection, limitations, and future scope of RL-IoT based intelligent traffic management.

DOI: https://doi.org/10.5281/zenodo.20095635

Edge-Optimized Lightweight MARN For Real-Time Diabetic Retinopathy Detection In Portable Screening Systems

Authors: Hanathika T, Gayatri K

Abstract: Diabetic retinopathy (DR) is a major cause of avoidable vision loss worldwide, and deep learning approaches have shown promising results on large-scale retinal image datasets such as EyePACS. However, many existing works mainly emphasize overall accuracy or referable DR detection, while giving less importance to factors like model reliability, interpretability, and performance on noisy real-world data. To address these limitations, this study presents a Multi-Attention Residual Network (MARN) built upon EfficientNet-B0 for simultaneous DR grading and referable DR classification using a resized version of the EyePACS Kaggle dataset. The proposed architecture integrates a residual fully connected head with dropout regularization and is trained using class-balanced sampling along with cross-entropy loss. The model is evaluated on both a five-class DR grading task and a clinically significant binary classification task (referable DR ≥ moderate versus non-referable DR). Experimental results on a subset of 6,081 images show that MARN improves five-class validation accuracy from 0.4618 to 0.4881 and increases the macro-F1 score from 0.4905 to 0.5195 when compared to a strong EfficientNet-B0 baseline. For referable DR detection, the model achieves an accuracy of 0.780, with sensitivity of 0.776 and specificity of 0.783, demonstrating a slight improvement in specificity while preserving high sensitivity. Further analysis indicates notable performance gains in Severe and Proliferative DR categories, with ROC-AUC scores of 0.865 and 0.915, respectively. In addition, Grad-CAM visualizations highlight that the model focuses on clinically relevant lesion regions, while t-SNE representations show improved clustering of advanced DR features. Overall, the proposed MARN framework delivers consistent improvements in classification performance, effective identification of vision-threatening DR, and enhanced interpretability, making it a reliable and explainable tool for clinical decision support rather than a purely black-box model.

Comparative Seasonal and Temporal Analysis of AQI in Noida and Agra

Authors: Harsh Vardhan, Aanurag Gangwar

Abstract: Air pollution continues to be one of the most important environmental problems in fast-growing parts of the Indo- Gangetic Plains (IGP), such as Agra and Noida, characterized by declining air quality levels. The current paper offers a comparative analysis of the Air Quality Index (AQI) temporal and seasonal trends in the cities of Agra and Noida based on the daily data from the Central Pollution Control Board (CPCB) for the period of 2021–2025. The descriptive statistical analysis demonstrates that AQI is higher and more variable in Noida than Agra, which implies that Noida is under higher levels of pollution. The time-series analysis reveals considerable AQI dynamics in the two cities, including peaks of the parameter under discussion during winter months. The results of the monthly and seasonal analysis also suggest strong seasonality, according to which AQI scores peak during winter and post-monsoon months, while monsoon months are associated with improved air quality. In terms of AQI categories, Noida witnesses more days classified as “Poor,” “Very Poor,” and “Severe,” while Agra shows a larger number of “Moderate” and “Satisfactory” days. Finally, the autocorrelation analysis demonstrates a high level of AQI dependence on the time dimension in both cities. These differences have been observed due to variations in the sources of emission, population density, pollution transport dynamics, and weather conditions. In conclusion, the analysis shows that Noida is more heavily and variably polluted than Agra. This study offers valuable guidance for designing air quality management plans specific to regions and helps frame effective measures for pollution-prone urban areas.

DOI: https://zenodo.org/records/20095989

Long-Term Analysis of Aerosol Loading and Particle Size Distribution over Western and Central India (2016–2025)

Authors: Ankita Tripathi, Anurag Gangwar

Abstract: This study investigates the spatial and temporal variability of aerosols over Western and Central India using satellite-derived Aerosol Optical Depth (AOD) and Ångström Exponent (AE) for the period 2016–2025. AOD provides information on aerosol loading, while AE is used to infer particle size distribution. The analysis was carried out at monthly, seasonal, and annual scales using a zonal approach to distinguish regional characteristics. The results reveal significant seasonal variation in aerosol properties. AOD shows maximum values during the pre-monsoon season, particularly over Western India (~0.47), attributed to enhanced dust activity and dry atmospheric conditions. In contrast, AOD decreases during the monsoon season due to wet scavenging processes. AE exhibits an opposite trend, with higher values during monsoon and post-monsoon seasons (up to ~1.63 in Central India), indicating the dominance of fine-mode aerosols from anthropogenic emissions and biomass burning. Monthly analysis further confirms this inverse relationship between AOD and AE, reflecting the transition from coarse to fine particles across seasons. Interannual analysis indicates relatively stable aerosol patterns with noticeable fluctuations, including a decline in AOD during 2020, likely associated with reduced anthropogenic activities. A clear regional contrast is observed, where Western India is dominated by coarse-mode dust aerosols (high AOD, low AE), while Central India shows a higher influence of fine-mode anthropogenic aerosols (moderate AOD, high AE). Overall, the combined assessment of AOD and AE provides critical insights into aerosol behavior, sources, and seasonal dynamics. The findings are relevant for improving air quality management, understanding aerosol–climate interactions, and supporting environmental policy development in India

DOI: https://zenodo.org/records/20096175

Spatio-Temporal and Seasonal Analysis of Crop Residue Burning in Punjab and Haryana Using Satellite-Derived Fire Count Data

Authors: Sonal Saral, Anurag Gangwar

Abstract: Agricultural residue burning is a major contributor to seasonal air pollution in northwestern India, significantly affecting air quality across the Indo-Gangetic Plain. This study presents a comprehensive Spatio-temporal and seasonal analysis of crop residue burning in Punjab and Haryana during 2021–2025 using satellite-derived fire count data (MODIS and VIIRS), with a focus on pre-monsoon (Rabi: April–May) and post-monsoon (Kharif: October–November) periods. The results indicate that post-monsoon burning dominates total fire activity, accounting for approximately 70–75% of annual fire counts, with Punjab alone contributing more than 80% of regional fire events. Peak Kharif fire activity exceeded 170,000 events in Punjab, while Haryana recorded comparatively lower counts (~21,000 events). In contrast, Rabi burning remained relatively stable, averaging ~85,000–90,000 fires in Punjab and ~25,000–26,000 fires in Haryana. Temporal trends reveal a substantial decline in Kharif fire counts, with reductions of nearly 90–94% between 2021 and 2025, indicating the effectiveness of policy interventions and residue management technologies. However, Rabi burning exhibited limited reduction, highlighting a critical gap in mitigation strategies. Spatial analysis shows dense clustering of fires in central and northwestern Punjab, whereas Haryana exhibits more dispersed burning patterns. The strong seasonal concentration and magnitude of fire activity confirm that biomass burning remains a dominant driver of particulate pollution. These findings emphasize the need for crop-specific, season-targeted mitigation strategies to achieve sustained improvements in regional air quality.

DOI: https://zenodo.org/records/20096389

Impact of Personalization Algorithms on Consumer Decision Fatigue and Purchase Decision-Making in Digital Commerce Contexts

Authors: Nimisa Bhagchandani

Abstract: The rapid growth of digital commerce and the increasing use of artificial intelligence have significantly changed the way consumers make purchase decisions online. One of the most common applications of AI in this space is the use of personalization algorithms, which provide users with tailored product recommendations based on their preferences and past behaviour. While these systems are designed to improve convenience and enhance user experience, they may also create unintended challenges for consumers. This study examines the impact of personalization algorithms on consumer decision fatigue and purchase decision-making in digital commerce contexts. The research focuses on understanding whether personalized recommendations simplify the decision-making process or contribute to cognitive overload. Decision fatigue is considered as a key factor that may influence how consumers respond to multiple product options and recommendations. The findings of the study are expected to provide insights into how personalization influences consumer behaviour beyond its intended benefits. It highlights the need for digital platforms to balance personalization with user comfort and cognitive ease. The study contributes to a better understanding of the psychological effects of personalization and its role in shaping online consumer decision-making.

DOI: https://zenodo.org/records/20099925

Netflix Clone Page

Authors: Harini . K, Assistant Professor S.Janani

Abstract: The rapid growth of Over-The-Top (OTT) platforms has fundamentally changed the way users consume multimedia content on the internet. Streaming services like Netflix have set a highstandard for user experience, interface design and content discovery. This project, titled “Netflix Clone – Web Application Using HTML, CSS, JavaScript, React and Vite” . The backend is built using Node.js, providing RESTful APIs to handle user interactions and data management. PostgreSQL is used as the relational database for storing user data, movie details, and other application information., aims to design and implement a responsive and interactive web interface that emulates the core look and feel of the Netflix platform using modern front-end technologies. The primary objective of this project is to build a single-page application (SPA) that allows users to browse a catalogue of movies and series, view categorized lists such as “Popular”, “Trending” and “Top Rated”, and navigate to detailed information pages for each title. The user interface isimplemented using HTML5 for structure, CSS3 for styling, and JavaScript for dynamic behavior. React is used as the front-end library to efficiently manage UI components and state, whileserves as a fast and optimized build tool and development server. The system implements CRUD (Create, Read, Update, Delete) operations for managing users, movies, and preferences. The application is developed using Visual Studio Code and follows modern web development practices. Together, these tools provide a modern, modular and scalable environment for building the Netflix clone. The project follows a structured approach including requirement analysis, interface design, component design, implementation, and basic testing. Emphasis is placed on responsive design to ensure that the application works across desktops, laptops and mobile devices. Features such as reusable React components, props and state, routing (if used), and API-like data retrieval from static JSON or mock data are incorporated to simulate real-world application behaviours

DOI: http://doi.org/

Black Spot Accident Prediction Using Machine Learning And GIS

Authors: Priyanka N Godiyal, Rutuja Amrale, Revati Ma’am, Archana Ma’am.

Abstract: Road traffic accidents are a leading cause of mortality worldwide, with India recording over 1.5 lakh fatalities annually. Identifying ‘black spots’ — specific road segments with disproportionately high accident frequency — is critical for targeted infrastructure intervention. Traditional methods of black spot identification rely on statistical thresholds applied to historical data, which are often reactive and location-agnostic. This paper proposes an integrated framework combining Machine Learning (ML) and Geographic Information Systems (GIS) for predictive black spot detection. We review and compare ML algorithms including Random Forest, XGBoost, Support Vector Machines (SVM), and Deep Neural Networks applied to multi-source data comprising accident records, road geometry, traffic volume, and environmental factors. Spatial analysis techniques such as Kernel Density Estimation (KDE) and spatial autocorrelation are used for feature engineering. Results show that ensemble methods achieve accuracy above 90%, with XGBoost yielding the highest AUC-ROC of 0.94. GIS-integrated output maps provide actionable, zone- specific risk rankings to support road safety planning.

DOI: https://doi.org/10.5281/zenodo.20118287

Hybrid Transformer-LSTM Framework For Temporal Representation Learning And Longitudinal Risk Prediction In Clinical Time-series

Authors: Abdullahi Idris, Aminu A. Abdullahi, Jamilu Awwalu, Abdullahi Uwaisu Muhammad

Abstract: Clinical time-series data are inherently complex, characterized by temporal dependences, irregular sampling and missing observations making accurate longitudinal risk prediction a challenging task. The study presents a novel hybrid Transformer framework for temporal representation learning and longitudinal risk prediction in clinical time-series that integrates the strengths of self-attention mechanism of Transformers to capture long-range interactions across time steps with the LSTM networks in modeling short-term temporal dependencies. A fusion module is introduced to adaptively combine representations from both components, enabling robust learning from irregular and partially observed clinical data. The experimental results demonstrate that the hybrid transformer framework effectively categorized patients into high-risk and low-risk categories based on their attributes. The training results indicate that the model performed well, with an accuracy of 98.6%, a sensitivity of 96.2% and a specificity of 97.8%. The model correctly identified 11 out of 18 high-risk patients and 16 out of 22 low-risk patients, with apparent errors of 38.9% and 27.3% respectively. These findings indicate that the hybrid Transformer framework can successfully learn patterns associated with cardiovascular risk from training data. Similarly, the test results confirm the model’s ability to predict previously unseen data. The model correctly categorized 9 out of 12 high-risk cases and 6 out of 8 low-risk cases, resulting an overall accuracy of 91.2%, sensitivity of 89.3% and specificity of 92.0% with a 25% apparent error in both cases.

DOI: https://doi.org/10.5281/zenodo.20118703

Design And Simulation Of 1 KW Permanent Magnet Synchronous Wind Generator Using Skewed And Unskewed Rotor

Authors: M.R.Manas, Dr. Umakanta Choudhury

Abstract: This study provides an in-depth analysis of the electromagnetic comparative assessment of the unskewed and skewed rotors for a 1 kW, three-phase, inner-rotor permanent magnet synchronous generator intended for small-scale direct-drive wind power applications. The generator has 36 stator slots and 12 rotor poles, with a 220 mm outer diameter of the stator and a stack length of 60 mm. The unskewed generator uses a ring magnet rotor design and features a gap size of 2.0 mm, while the skewed rotor design uses a block magnet rotor with a linear step of 10 degrees in three stages, with the air gap size of 1.5 mm. Performance criteria used for the finite-element-based simulations using Altair FluxMotor include the following: cogging torque, back-EMF waveform quality, losses, torque ripple, voltage, and efficiency, combined with thermal analysis. The reduction of the peak-to-peak cogging torque of the skewed rotor reaches 84.5phase back-EMF decreases by 67requirements of IEEE 519 regarding harmonics. Both the unskewed and skewed rotors show comparable efficiency at the same operating point (19 N·m and 500 rpm): 95.34full-load efficiency of the unskewed rotor (92.87the corresponding efficiency of the skewed rotor (91.96.

DOI: https://doi.org/10.5281/zenodo.20120373

AI-Powered Car Marketplace

Authors: Tanu Yadav, Neelam Sahu, Deepak Sahu

Abstract: The rapid expansion of the pre-owned automobile industry has increased the demand for reliable and intelligent digital platforms for vehicle trading. Traditional used-car marketplaces often face challenges such as lack of transparency, inefficient search mechanisms, inconsistent pricing, and fraudulent listings, which reduce user trust and overall customer satisfaction. This research proposes an AI- powered car marketplace designed to improve the process of buying, selling, and exchanging second- hand vehicles through intelligent automation and secure digital infrastructure. The proposed system integrates advanced technologies including intelligent search optimization, personalized recommendation systems, automated listing moderation, and secure authentication mechanisms to enhance platform reliability and usability. The platform provides users with detailed vehicle listings, filtering and comparison features, responsive communication channels, and mobile-friendly accessibility to simplify customer interaction and decision-making. The backend architecture is developed to support scalable data management and efficient transaction handling using modern web technologies. Artificial Intelligence modules are incorporated to improve recommendation accuracy, optimize search relevance, and identify suspicious or duplicate listings. Experimental evaluation indicates that the proposed system improves search efficiency, recommendation precision, and operational transparency compared to conventional online used-car trading systems. The research demonstrates how AI-driven digital marketplaces can enhance trust, user engagement, and efficiency within the pre-owned vehicle industry while providing a scalable solution suitable for modern automotive e-commerce applications.

Corrosion Detection and Monitoring System: Yolo Based Real Time Deep Learning Framework

Authors: Mr. Prajwal Narayan Chaudhary, Mr. Pranav Prasad Kulkarni, Mr. Chetan Ashok Bhalekar, Mr. Aditya Ganesh Gunjal, Professor Kalyani Zirpe

Abstract: Corrosion is a significant cause of damage in industrial infrastructure, transportation systems, marine equipment, pipelines, and metal parts. Traditional methods for inspecting corrosion mainly rely on manual observation and regular maintenance. These processes are time-consuming, labor intensive, and are subjective, which can lead to human error. Delays in spotting corrosion can lead to serious structural failures, higher maintenance costs, operational downtime, and safety risks. To address these issues, this paper introduces a real-time AI-based Corrosion Detection and Monitoring System. This system uses the YOLOv5 deep learning framework along with a modern web-based structure. The new system combines computer vision, deep learning, and web technologies to automate the detection of corrosion and assess its severity. It uses the YOLOv5s object detection model to find corrosion areas in uploaded images and live camera feeds. A React.js frontend offers an engaging and responsive user interface. Meanwhile, a FastAPI backend handles image processing, runs the necessary calculations, and communicates results. The system evaluates detected corrosion areas using bounding box calculations to estimate the amount of corrosion and categorize its severity as mild, moderate, or severe. It also features graphical visualizations, historical tracking, and repair suggestions to support preventive maintenance. This framework provides nearly real-time detection with higher accuracy and less reliance on manual inspection. Its modular and scalable design allows it to be used in various industries, including maritime, civil infrastructure, manufacturing, automotive, and aviation. Tests show that the system successfully identifies corrosion under different environmental conditions while maintaining good computational performance. This solution represents a cost- effective and smart way to monitor structural health and perform predictive maintenance.

DOI: https://zenodo.org/records/20121779

Predective Maintenance Of Induction Motor Using Machine Learning

Authors: Prof. G. R. Padule, Shweta Anil Bhosale, Dnyaneshwari Ravikant Patil, Vrushali Vishal Zambare

Abstract: Induction motors are vital components in industrial and commercial systems, where unexpected failures can lead to costly downtime and reduced productivity. Traditional maintenance strategies such as corrective and preventive maintenance are often inefficient, either reacting too late or performing unnecessary servicing. Predictive maintenance, powered by machine learning (ML) techniques, offers a smarter approach by forecasting motor health conditions based on real-time data analysis. This review paper presents an overview of recent advancements in predictive maintenance for induction motors using ML algorithms. Various techniques such as support vector machines (SVM), artificial neural networks (ANN), random forests, and deep learning models are discussed for fault detection, diagnosis, and remaining useful life (RUL) estimation. The paper also highlights the importance of feature extraction from vibration, current, and temperature signals, as well as the integration of Internet of Things (IoT) and cloud computing for real-time monitoring. Comparative analysis of different ML approaches is provided to identify their strengths, limitations, and potential for industrial application. Finally, the review outlines current challenges and future research directions for developing efficient, scalable, and interpretable predictive maintenance frameworks for induction motors.

Ai-Powered Analysis For Detecting Sleep Irregularities Through Deep Learning Models

Authors: R.Renuka, Dr.S.Mohana

Abstract: Typically, sleep disorders like insomnia, sleep apnea, and narcolepsy may not receive appropriate diagnosis until serious physical and mental health issues develop. Traditional techniques, though effective, involve polysomnography, which is not only labor-intensive and time-consuming but also demands special clinical conditions. Hence, this study aims to develop a framework that relies on AI techniques to utilize a hybrid model of Deep Learning techniques, including Convolutional Neural Networks (CNN) and Long Short- Term Memory (LSTM), to process EEG signals to identify sleep disorders. The CNN model can automatically identify spatial features in the raw signals, and the LSTM model can identify temporal dependencies in the signals to correctly classify Awake, REM, and NREM stages. Preprocessing techniques have been employed to clean and normalize the signals. The system, trained and validated using standardized data sets like PhysioNet, exhibits robustness and generalization in dealing with different patterns of sleep. It can also be used to analyze new EEG signals in real-time, detect abnormal sleep patterns, and predict the occurrence of sleep disorders. This intelligent system can greatly improve the efficiency of diagnosis and reduce the need to rely on manual diagnosis. It can also prove to be a cost-effective solution.

DOI: https://doi.org/10.5281/zenodo.20132129

Experimental Investigation of Mechanical Properties in Dissimilar Al-Cu Joints Using Friction Stir Welding

Authors: Miss Gaikwad Janhvi Anurath, Miss. Kadam Vaishnavi Raju, Mr. Chinmay Shinde, Mr. Narayanpure Sujal, Prof. Dr.Ashish Kumar

Abstract: Friction Stir Welding (FSW) is an advanced solid-state joining technique used for welding similar and dissimilar metals without melting the base materials. In this project, an experimental investigation has been carried out to study the mechanical properties of dissimilar joints between Aluminium Alloy AA6061 and Copper (ETP Copper) using the Friction Stir Welding process. The purpose of this study is to evaluate the effect of welding parameters on the strength and quality of the welded joints. The welding experiments were performed using a carbide conical ball nose tool under different process conditions such as rotational speed, welding speed, and plunge depth. Proper fixture arrangements and clamping systems were used to obtain defect-free joints. AA6061 aluminium and copper were selected due to their wide applications in aerospace, automobile, marine, electrical, and heat transfer industries where light weight materials with high thermal and electrical conductivity are required. After the welding process, the joints were examined through visual inspection and tested for various mechanical properties including tensile strength, hardness, and microstructural characteristics. The experimental results showed that welding parameters greatly affect heat generation, material flow, and intermetallic compound formation at the weld interface. Optimized welding conditions produced sound joints with better tensile strength and uniform hardness distribution. The investigation concludes that Friction Stir Welding is an efficient and economical process for joining dissimilar aluminium-copper materials with fewer defects and Improved mechanical properties compared to conventional fusion welding methods. The results of this project can be useful for industrial applications requiring strong, lightweight, and conductive dissimilar metal joints.

DOI: https://doi.org/10.5281/zenodo.20133147

Temporal Assessment Of River Water Quality During Maha Kumbh 2025 In The Prayagraj Sangam Region Using CPCB Monitoring Data

Authors: Saurabh Singh

Abstract: The present study investigates the temporal variation of river water quality during Maha Kumbh 2025 in the Prayagraj Sangam region using Central Pollution Control Board (CPCB) monitoring observations. The assessment was carried out using major physicochemical and biological parameters including turbidity, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), pH, and fecal coliform. Temporal trend analysis and statistical interpretation were performed to evaluate the impact of large-scale pilgrimage activities on river water quality. The results indicated noticeable fluctuations in turbidity, COD, BOD, and fecal coliform concentrations during major bathing events, suggesting enhanced anthropogenic influence and sediment disturbance in the river system. Dissolved oxygen remained relatively stable throughout the monitoring period, while pH values stayed within acceptable environmental limits. Correlation analysis revealed positive relationships among turbidity, COD, and fecal coliform, indicating combined effects of organic and microbial contamination during intensive bathing periods. The study highlights the significance of continuous water quality monitoring during mass religious gatherings for sustainable river management and environmental protection.

DOI: http://doi.org/10.5281/zenodo.20133734

Development Of A Spreadsheet–Based Tool For Simple Design Of Rcc Structural Elements

Authors: Jeevanantham D, Nithya M

Abstract: In the modern construction field, many advanced software tools are available for structural design, but they are often complex and not easily accessible for all users. Microsoft Excel plays a major role in engineering calculations because it is simple, flexible, and capable of mapping data efficiently without errors. In this study, a spreadsheet-based tool is developed for the design of reinforced cement concrete (RCC) elements such as slab, beam, column, and footing. The design follows a load transfer path from slab to footing. The spreadsheet is created based on IS 456:2000 provisions, where only selected input cells are editable and all other cells are pre-formulated. The tool helps to determine whether the structure is safe or unsafe and provides reinforcement details. This method is useful for basic design understanding and preliminary structural verification.

Impact Of Ethanol Exposure On Hepatic Function And Oxidative Stress Biomarkers In Albino Rats

Authors: Timothy O. Oni, Iboyi Nathaniel Onuche, Dokubo Chinweike Unoma, Odo Vincentmary C, Kene-Okonkwo Adachukwu, Iboko Ifeoma Juliet

Abstract: Alcohol (ethanol) is a hepatotoxic agent that induces oxidative stress and disrupts liver function. This study evaluated the effects of three commercial ethanol brands (Seaman, Chelsea, and Lords) at high (3 ml/kg/day) and low (1.5 ml/kg/day) doses on liver function and antioxidant status in Wistar rats. Thirty-five rats (150–160 g) were divided into seven groups (n = 5) and treated for two weeks. Liver enzymes (ALT, AST), protein profile, bilirubin, and oxidative stress markers (SOD, CAT, LDH, MDA) were analyzed using standard methods. Control values were ALT = 24.26 U/L and AST = 138.23 U/L. High-dose Seaman produced the greatest hepatotoxicity (ALT = 39.33 U/L; AST = 203.53 U/L), followed by Lords (ALT = 32.23 U/L; AST = 156.33 U/L), while Chelsea reduced AST (101.66 U/L). Albumin decreased markedly in Seaman high-dose (1.23 g/dL vs. 2.22 g/dL control). Oxidative markers showed SOD = 27.01 ×10⁻⁶ U/mL and CAT = 88.02 U/mL in controls. Lords low-dose caused severe depletion (SOD = 6.63 ×10⁻⁶ U/mL), while Chelsea high-dose increased antioxidant activity (SOD = 43.11 ×10⁻⁶ U/mL; CAT = 122.73 U/mL) but elevated LDH (489.75 μg/mL). Ethanol induced dose- and brand-dependent hepatotoxicity. Seaman was most hepatotoxic, Lords caused greatest oxidative depletion, while Chelsea elicited adaptive antioxidant responses with cellular damage.

DOI: http://doi.org/10.5281/zenodo.20134138

Drug Addiction, Trafficking National and International Legal Perspective

Authors: Bobelya A

Abstract: Drug trafficking and drug addiction represent major global challenges affecting public health, social stability, and national security. Drug trafficking refers to the illegal production, transportation, and distribution of narcotic drugs and psychotropic substances across national and international borders. This illicit trade contributes significantly to the spread of drug addiction, organized crime, corruption, and violence. Drug addiction, on the other hand, is a chronic disorder characterized by compulsive drug use despite harmful consequences, leading to severe physical, psychological, and social problems. At the international level, several conventions and organizations have been established to control drug trafficking and abuse. Key legal frameworks include the Single Convention on Narcotic Drugs, 1961, the Convention on Psychotropic Substances, 1971, and the United Nations Convention Against Illicit Traffic in Narcotic Drugs and Psychotropic Substances, 1988. These conventions aim to regulate the production, distribution, and use of narcotic substances and encourage international cooperation in combating drug-related crimes. Organizations such as the United Nations Office on Drugs and Crime play a crucial role in monitoring global drug trends and assisting countries in implementing drug control policies. At the national level, many countries have enacted strict legislation to prevent drug trafficking and substance abuse. In India, the primary law governing drug control is the Narcotic Drugs and Psychotropic Substances Act, 1985, which criminalizes the manufacture, possession, sale, purchase, and transportation of narcotic drugs without authorization. The Act also provides stringent penalties for offenders and empowers enforcement agencies to investigate and prosecute drug-related crimes. This study highlights the relationship between drug trafficking and addiction while examining the legal frameworks designed to control these issues at both international and national levels. It emphasizes the need for stronger cooperation among governments, effective law enforcement, public awareness, and rehabilitation programs to address the growing problem of drug abuse and trafficking worldwide.

DOI: http://doi.org/10.5281/zenodo.20134480

Evaluation Of Leadership Behaviours Exhibited By Project Managers On Building Construction Projects In The Federal Capital Territory, Abuja. Nigeria.

Authors: Adebiyi Adeniyi Mayowa

Abstract: Effective leadership behaviour is critical for the successful delivery of building construction projects, particularly in complex and stakeholder-intensive environments such as the Federal Capital Territory, Abuja. This study evaluates the leadership behaviours demonstrated by project managers on building construction projects in Abuja, aiming to determine the prevalence of negative leadership traits in project practice. A quantitative research design was employed, utilizing structured questionnaires distributed to built environment professionals. Descriptive statistics, including frequency, percentage, mean score, and standard deviation, were used for data analysis. The study achieved a response rate of 84%, with 185 valid responses analyzed. The results indicate that the most prominent negative leadership behaviour was insensitivity to team members’ behaviour (Mean = 3.2083), followed by poor communication behaviour (Mean = 2.8375) and assuming little or no responsibility (Mean = 2.5875), all rated at a neutral level. Other behaviours, such as inadequate planning and organizing (Mean = 2.4042), highly autocratic behaviour (Mean = 2.4000), overdependence (Mean = 2.3750), inability to select competent personnel (Mean = 2.3208), high reliance on subordinates (Mean = 2.1583), and nonchalant attitude (Mean = 2.1292), were rated lower and considered less prevalent. The average total mean score of 2.4912 suggests general disagreement that these negative leadership behaviours are commonly exhibited by project managers, indicating that project managers in the study area generally display acceptable and positive leadership practices. The study recommends ongoing leadership development through training in communication, emotional intelligence, interpersonal relations, and team management, as well as the promotion of participative leadership styles, enhanced accountability systems, and mentorship for emerging project managers. Strengthening leadership capacity among project managers is identified as essential for improving team coordination, stakeholder satisfaction, and project delivery performance in Nigeria’s construction industry.

DOI: https://doi.org/10.5281/zenodo.20135601

Adaptation of Artificial Intelligence in Key Indian Industries: Education, Healthcare, Agriculture, Finance, and Content Creation – Trends, Tools, and Transformative Impacts

Authors: Chaudhari Bhushan Ramesh, Seema S Bonde, Chaudhari Pranav Sunil, S G Patil, Sharma Nibha Rajdev, S M Pawar

Abstract: Artificial intelligence was once was an idea on paper but in past few year it has moved in real-world use and is changing the landscape by enhancing efficiency innovation and accessibility. This paper is a study of ai adoption in various industries in India. The research paper is a combination of various research papers, industrial report, and survey-based reports on different sectors like agriculture, content-creation, finance, education, healthcare in India focusing on adoption trends, tools, and transformative impacts. The research adopts a qualitative secondary research methodology. The study integrate insight from the different reports and the finding reveal that the ai adoption in finance and content-creation sector is high, while in education and healthcare sector is moderate, and in agriculture sector is low due to lack of awareness.AI applications exhibit measurable benefits including cost reduction, productivity improvement and enhanced decision-making. Despite the benefits there are challenges that cannot go unnoticed like data privacy, skill gap, and lag of infrastructure. The research study concludes that AI holds the transformative capability for development of India.

DOI: https://doi.org/10.5281/zenodo.20137352

Efficient Rsa Prime Generation Using Vedic Mathematics Divisibility Ruless

Authors: Deepak kumar

Abstract: I wondered if ancient Vedic mathematics could speed up modern RSA cryptography. RSA needs large prime numbers, but finding them takes time. Vedic divisibility rules (mod 3, 7, 11 flags) reject 90% of wrong candidates instantly. My tests show 4.3x speedup for 120-bit primes. This bridges 5000-year-old Indian math with 21st-century security.

DOI: http://doi.org/10.5281/zenodo.20151255

Twitter Sentiment Analysis Using Hybrid CNN-BiGRU Deep Learning Model

Authors: Shubham Rathod, Dr.Rajeshwari Kannan

Abstract: Social media platforms such as Twitter generate huge textual data every day, making sentiment analysis an important research area in Natural Language Processing (NLP). As there are many tweets that are noisy and reqired some contextual background . So the Research proposes the hybrid architecture of CNN and BiGRU so solve such issue. The Dataset which has been used is Sentiment 140 which contains 1.6 million entries.The methodology used is preprocessing , tokenization , CNN feature extraction and BiGRU contextual learning.The result shows the final accuracy using such architecture comes out to be 77.13%.Along with that there is use of flask web applicaton to create an interface.

DOI:

Modeling Of Reverse Osmosis Water Desalination Powered By Photovoltaic Solar Energy Using MATLAB/Simulink: Case Study Of Port Sudan, Sudan

Authors: Mohamed Eltom Musa, Emad Saad Saied, Mohamed Yagoub Adam

Abstract: Water scarcity in arid coastal regions has shifted from a resource limitation to a structural constraint on development. Port Sudan represents a critical case where high-salinity seawater coexists with abundant solar energy potential. This study develops an integrated MATLAB/Simulink model of a photovoltaic-powered seawater reverse osmosis (PV-SWRO) desalination system to evaluate performance under real Red Sea conditions. The model combines osmotic pressure calculations, hydraulic energy requirements, and photovoltaic power generation into a unified framework. Under a salinity of approximately 40 ppt, osmotic pressure reaches ~34 bar, requiring an operating pressure near 60 bar. For a plant capacity of 10,000 m³/day, the system requires 35,000 kWh/day, supplied by a PV array of approximately 7.8 MW. Results show that energy demand increases nonlinearly with salinity, while PV integration significantly reduces operational costs. The levelized cost of water (LCOW) is estimated at 0.66 USD/m³, confirming economic feasibility. The study demonstrates that PV-powered SWRO desalination is a technically viable and sustainable solution for water-scarce coastal regions.

DOI: https://doi.org/10.5281/zenodo.20137941

Design Of A Seawater Desalination System Powered By Photovoltaic Cells In Port Sudan, Sudan

Authors: Mohamed Eltom Musa, Emad Saad Saied, Mohamed Yagoub Adam

Abstract: Freshwater scarcity in coastal regions such as Port Sudan has become a persistent constraint driven by population growth, climate variability, and infrastructure limitations. While seawater desalination offers a technically viable solution, its implementation is often constrained by high energy requirements and operational costs. This study presents the complete engineering design and techno-economic evaluation of a seawater reverse osmosis (SWRO) desalination system powered by photovoltaic (PV) energy under Red Sea conditions. The system is designed for a production capacity of 10,000 m³/day, incorporating detailed modeling of osmotic pressure, mass balance, hydraulic energy requirements, and photovoltaic power generation. The elevated salinity of the Red Sea (~40 ppt) results in an osmotic pressure of approximately 33.9 bar, requiring an operating pressure near 60 bar. The total energy demand is estimated at 12.78 GWh annually, supplied by a 7.8 MW photovoltaic system based on local solar irradiance data. Economic analysis indicates a capital cost of approximately 22 million USD and a levelized cost of water (LCOW) of 0.66 USD/m³, with a positive net present value over the project lifetime. In addition, the system achieves a significant reduction in carbon emissions compared to conventional fossil-fuel-based desalination. The results confirm that photovoltaic-powered desalination provides a technically feasible and economically sustainable solution for water supply in arid coastal environments.

DOI: http://doi.org/10.5281/zenodo.20151229

The Use Of Artificial Intelligence In The Modern Healthcare System

Authors: Keshav Sharma

Abstract: Artificial Intelligence (AI) has become a groundbreaking phenomenon in the modern healthcare system as it allows conducting sophisticated data analysis, predictive modeling, and intelligent decision support. The world is gradually moving towards the adoption of AI technologies in all healthcare facilities to improve the precision of diagnosis, the ease of treatment regimen, the efficiency of work, and the price of healthcare. The methods of diagnosis and treatment of diseases in the healthcare sector are changing with AI-based systems, beginning with the interpretation of medical images up to personalized medicine and robotic surgery. Nevertheless, even though it may have some advantages, the introduction of AI into the health care industry also creates strong doubts regarding the privacy of its data, algorithm bias, transparency, and ethical accountability. This research paper will discuss the use of artificial intelligence in the modern healthcare system through the analysis of its use, advantages, drawbacks, and ethical concerns. The paper discusses the role of machine learning algorithms and deep learning models in detecting disease, patient monitoring, and management in healthcare. Additionally, the paper also critically assesses the issues like data reliability, non-interpretability of AI models, and regulatory issues. The results have underscored that although AI can achieve great success in enhancing healthcare delivery, its use should be supported by effective governance policies and ethical considerations to achieve safe and fair use. The study ends with the recommendation that healthcare authorities, computer scientists, and policy makers need to engage in interdisciplinary collaboration to optimize the advantages of AI and reduce the risks, which may arise.

Plant Disease Detection Using ESP-32 With Machine Learning Model

Authors: Harshith S, Nikhilesh G, Raghunandan S, Shashank D S, Mrs. Shwetha S K

Abstract: Crop illnesses remain one of the major sources of farm losses worldwide, and identifying them at an early stage can greatly improve yield protection. Many farmers still depend on manually walking across their fields and inspecting each plant, a process that consumes significant time and can be inconsistent. This study presents a low-cost detection system we developed using an ESP32-CAM to capture images of leaves and transmit them wirelessly to a cloud-based machine learning model. The system analyses each image to determine if the leaf is healthy or affected by a particular disease, and the outcome appears immediately on a web interface accessible to farmers via their phones. Our aim was to design an affordable and easy-to-use solution so that even smallholder farmers without technical expertise can operate it with ease and confidence in their daily farming activities without needing additional support.

DOI: http://doi.org/10.5281/zenodo.20153156

Effect Of Surface Roughness On Characteristics Of Magnetic Fluid Based Squeeze Film Between Porous Annular Discs

Authors: Pragnesh L Thakkar, H C Patel

Abstract: An endeavor has been made to check and investigate the impact of surface roughness on the characteristics of squeeze film between porous annular discs is bestowed in presence of magnetic fluid. The involved Reynolds equation is solved with suitable boundary conditions and expressions for pressure and load carrying capacity are obtained. The expressions are obtained numerically and results are bestowed graphically. It is found that the load carrying capacity increase with increasing magnetization. The impact becomes more sharp when mean (-ve) is involved. In addition, standard deviation and aspect ratio decrease the load carrying capacity this negative effect is going to be minimized by the magnetic fluid lubricant in the case of negatively skewed roughness. Moreover, the investigation makes it clear that a performance of a bearing system is going to be enhanced by choosing suitable values of magnetization parameter and aspect ratio.

DOI: http://doi.org/10.5281/zenodo.20153697

Military Aircraft Detection Using AI And Machine Learning Based On YOLOv5

Authors: Gaikwad Komal Vitthal, Shaikh Javed Ahmad, Shaikh Aslam Amir

Abstract: The detection and Classification of military aircraft play a crucial role in modern defence and surveillance systems. Traditional radar based approaches are often limited by high cost, environmental constraint, and reduced effectiveness against stealth aircraft. This paper presents a deep learning based approach for automatic military aircraft detection using the YOLOv5 object detection framework. The model is trained on publicly available framework. Experimental results demonstrate that the proposed system successfully detects aircraft such as F-35 and F-16 with confidence score of 0.94 and 0.80, respectively, while achieving an inference speed of approximately 6ms per image. The system provides high accuracy,robustness, and real time capability, Making it suitable for defence surveillance applications.

DOI: https://doi.org/10.5281/zenodo.20155776

A Content-Based Movie Recommendation System Using Machine Learning Techniques

Authors: Nishant Singh, Sudhanshu Kumar, Shushant Mani Tripathi, Manisha Pundir

Abstract: With the rapid growth of digital streaming platforms, users are exposed to a vast amount of movie content, making it difficult to identify relevant choices. This paper presents a Content-Based Movie Recommendation System that suggests movies based on their inherent features such as genre, cast, and keywords. The proposed system utilizes Machine Learning techniques, including TF-IDF (Term Frequency–Inverse Document Frequency) or Count Vectorization for feature extraction and Cosine Similarity for measuring similarity between movies. Unlike collaborative filtering methods, the system does not rely on user interaction data, thereby effectively addressing the cold start problem for new users. The model processes a structured movie dataset, converts textual data into numerical vectors, and generates recommendations based on similarity scores. The system is implemented using Python and deployed using Streamlit, providing an interactive and user-friendly interface. Experimental results demonstrate that the proposed system can efficiently generate accurate and relevant movie recommendations in real time. This approach highlights the effectiveness of content-based filtering techniques in enhancing user experience and improving content discovery in modern digital platforms.

Performance Optimization Versus Employee Psychological Erosion

Authors: Ms. Sanika Sachin Jadhav

Abstract: The growing use of algorithmic management systems in pharmaceutical organisations has changed how employees are supervised, evaluated, and directed. Instead of relying on human managers, these systems use automated data collection and continuous monitoring to govern how employees work. This study looks at both sides of this shift, the operational benefits it produces and the psychological harm it causes. A cross sectional survey was conducted with 250 pharmaceutical professionals, comprising 125 Sales Representatives and 125 Quality Control Analysts. The study measured technostress, psychological contract breach, and perceived algorithmic opacity. Results showed that AI supervision increased output by 18.4% but also led to a 22% rise in technostress scores and a 128% jump in turnover intention among algorithmically managed workers. A strong negative correlation of r = 0.74 (p < 0.01) between algorithmic opacity and organisational trust confirms that lack of transparency is a key mechanism through which algorithmic management damages the employee organisation relationship. Based on these findings, this paper proposes a Human Centric Algorithmic Framework that incorporates Human in the Loop design as a practical governance solution.

IoT-Based Hospital Automation And Patient Monitoring System

Authors: Ms. Arati H. Kunjir, Mr. S.P. Shinde

Abstract: The rapid growth of the Internet of Things (IoT) has significantly transformed the healthcare sector by enabling real-time monitoring, automation, and intelligent decision-making. Traditional hospital systems often rely on manual monitoring and limited automation, which can lead to delayed responses and inefficiencies in patient care. This paper presents an IoT-based hospital automation and patient monitoring system that continuously monitors vital health parameters such as temperature, heart rate, oxygen saturation (SpO₂), and environmental conditions. The system integrates smart sensors, microcontrollers, and cloud platforms to collect, process, and transmit data in real time. Medical staff can access patient data remotely through a web or mobile interface, enabling timely intervention and improved healthcare management. The proposed system enhances patient safety, reduces workload on medical staff, and improves the overall efficiency of hospital operations.

DOI: https://doi.org/10.5281/zenodo.20157156

Biogeochemical Cycles Of Carbon, Sulphur And Oxygen

Authors: Seema Kumari, Dr. Mukta Jain

Abstract: Biogeochemical cycles represent natural routes through which vital chemical elements circulate among the atmosphere, hydrosphere, lithosphere, and biosphere. These cycles are crucial for preserving ecological equilibrium and supporting life on our planet. Among the significant biogeochemical cycles, carbon, Sulphur, and oxygen cycles are essential in regulating environmental processes and aiding living organisms. The carbon cycle encompasses the transfer of carbon through photosynthesis, respiration, decomposition, and combustion, thus sustaining atmospheric carbon dioxide levels. The Sulphur cycle involves the transit of sulphur compounds through rocks, soil, water, atmosphere, and organisms via weathering, volcanic activities, microbial decomposition, and industrial emissions. The oxygen cycle is intricately linked to the carbon cycle, where oxygen is generated during photosynthesis and utilized in respiration, oxidation, and combustion processes. These interrelated cycles facilitate nutrient recycling, energy transfer, and the maintenance of ecosystem stability. Human activities such as deforestation, industrialization, mining, fossil fuel combustion, and environmental pollution have disrupted the natural equilibrium of these cycles, resulting in climate change, acid rain, global warming, ozone depletion, and ecological imbalance. Consequently, comprehending the operation and importance of carbon, Sulphur, and oxygen cycles is vital for environmental conservation, sustainable resource management, and safeguarding life on Earth.

DOI: https://doi.org/10.5281/zenodo.20157119

“The Evolving Role Of AI In Personalized Learning: A Review Of Adaptive Educational System

Authors: Mr. Aditya.P.pol, Mr. Chinamy.C.kulkarni, Mr.Siddharth.D.Dethe

Abstract: Artificial Intelligence (AI) has become a major force in transforming modern education by making learning more personalized, interactive, and efficient. Traditional teaching methods often follow a uniform approach where every learner receives the same content regardless of individual learning speed, interests, or understanding levels. AI-driven adaptive educational systems attempt to solve this issue by analyzing student behavior, learning patterns, and academic performance to provide customized learning experiences. This research paper explores the growing role of AI in personalized learning and examines how adaptive educational systems improve student engagement, learning outcomes, and instructional efficiency. The study also reviews important technologies such as machine learning, natural language processing, reinforcement learning, and learning analytics used in adaptive learning platforms. In addition, the paper highlights the challenges related to data privacy, fairness, ethical concerns, and implementation barriers. The findings indicate that AI-powered educational systems significantly improve learner performance and engagement when compared with traditional learning environments. The study concludes by discussing future opportunities for AI-enhanced education and the importance of combining technology with effective pedagogical practices.

DOI: https://doi.org/10.5281/zenodo.20157460

Number Plate Recognition Using Machine Learning

Authors: Mulay tanuja suresh, Dr.N.A.Doshi, Shaikh Aslam Amir

Abstract: Number plate recognition is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. The system can be implemented on the entrance for security control of a highly restricted area like military zones or area around top government offices e.g. Parliament, Supreme Court etc. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is then converted into grayscale. The number plate is then extracted. Then, using KNN (K- Nearest Neighbours) algorithm is used to recognize the digits and the alphabets. This data can be used to find vehicle’s owner, place of registration, address, etc. The system is implemented using Python, and its performance is tested on real images. It is observed from the experiment that the developed system successfully detects and recognize the vehicle number plate on real images.

DOI: https://doi.org/10.5281/zenodo.20176415

Comparative Process Design and Modeled Performance of a Small-Scale Bioethanol Production System Using Agricultural Residues

Authors: Samriddha Sharma, Om Prakash Sondhiya

Abstract: The increasing environmental and economic concerns associated with fossil-fuel dependency have intensified global interest in renewable transportation fuels. Among alternative biofuels, bioethanol has emerged as one of the most commercially viable and widely adopted options because it can be produced from renewable biomass resources and integrated into existing fuel infrastructures. This study presents a comparative process-design assessment of a compact bioethanol production system utilizing three abundant lignocellulosic agricultural residues: rice straw, sugarcane bagasse, and corn stover. A literature-informed process model was developed for a small-scale educational bioethanol unit comprising feedstock preparation, dilute-acid pretreatment, enzymatic hydrolysis, yeast fermentation, and reflux-assisted distillation. The investigation evaluates the influence of biomass composition on fermentable sugar recovery, ethanol yield, process efficiency, and energy demand. The modeled analysis indicates that sugarcane bagasse demonstrates the most favorable conversion performance under the selected operating assumptions, yielding approximately 74 g/L fermentable sugars and 34.5 g/L ethanol prior to separation. Corn stover exhibited intermediate performance, whereas rice straw produced comparatively lower ethanol concentrations because of its elevated ash and silica content, which reduce carbohydrate accessibility during pretreatment. The results further reveal that pretreatment and distillation account for the majority of the process energy requirement, highlighting the importance of heat integration, solids management, and process optimization in improving system efficiency. The study concludes that a modular small-scale bioethanol system can serve as an effective educational and research platform for demonstrating biomass-to-fuel conversion technologies. Furthermore, transparent presentation of modeled assumptions and calculation procedures strengthens the academic reliability of design-stage biofuel studies intended for instructional and comparative analysis.

Temporal And Seasonal Assessment of Turbidity and Chlorophyll-A In River Ganga Using Sentinel-2 Satellite Imagery and Google Earth Engine

Authors: Swati Singh

Abstract: The River Ganga, one of India’s most significant rivers, plays a major role in domestic, agricultural, industrial, ecological, and religious activities in Northern India. However, over the past few decades, its water quality has significantly declined due to increasing urbanization, industrial discharge, untreated sewage, and agricultural runoff. This study performs a temporal and seasonal assessment of turbidity and chlorophyll-a between 2019 and 2024 using Sentinel-2 satellite imagery and Google Earth Engine (GEE). The research covers the entire stretch of the Ganga from Uttarakhand to West Bengal and analyzes four seasons (pre-monsoon, monsoon, post-monsoon, and winter). Sentinel-2 imagery was processed using cloud-based geospatial analysis techniques. Results show that turbidity increases during the monsoon due to sediment transport, while chlorophyll-a is found to be higher in urban areas like Kanpur and Varanasi due to nutrient enrichment. This study proves that remote sensing techniques are an effective and cost-effective tool for large-scale river management.

DOI: https://doi.org/10.5281/zenodo.20176566

Smart Industrial Safety Wearable Devices Using Artificial Intelligence For Proactive Risk Prevention And Worker Protection: A Comprehensive Literature Review

Authors: Sahil Arun Bodke, Devika Deepak More, Samruddhi Mahendra Pansare, Prof. P. A. Mande, Prof. Bangar A.P., Prof. Bhosale S.B.

Abstract: Industrial workplaces continue to pose significant hazards to workers, including toxic gas exposure, thermal stress, mechanical injuries, and fatigue-related accidents. Conventional safety systems have largely remained reactive, responding to incidents after they occur rather than preventing them proactively. The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and advanced wearable sensor technologies has opened transformative opportunities for proactive occupational safety. This paper presents a comprehensive literature review of existing research on AI-integrated industrial safety wearable devices, covering sensor technologies, machine learning algorithms, edge computing strategies, cloud-based analytics, and alert mechanisms. We synthesize findings from over 25 peer-reviewed studies published in IEEE, Springer, and Web of Science indexed journals between 2019 and 2025. Key research gaps identified include the lack of multi-modal sensor fusion with real-time edge AI, insufficient datasets for industrial fatigue prediction, limited ergonomic wearable designs for harsh environments, and the absence of Explainable AI (XAI) in safety-critical decision making. Based on the review, we propose an integrated four-layer system architecture combining physiological and environmental sensing, edge-level AI inference, MQTT-based cloud communication, and a multi-level alert mechanism.

DOI: https://doi.org/10.5281/zenodo.20176697

Artificial Intelligence And Machine Learning In Bioethanol Production: Advancing Efficiency, Sustainability, And Process Optimization

Authors: Shubhangi Baghel, Om Prakash Sondhiya

Abstract: Bioethanol has emerged as one of the most promising renewable energy sources for reducing greenhouse gas emissions and decreasing dependence on fossil fuels. However, conventional bioethanol production systems face significant challenges, including low conversion efficiency, process instability, high operational costs, and limitations in feedstock utilization. Recent developments in artificial intelligence (AI) and machine learning (ML) have introduced advanced computational approaches capable of transforming industrial bioethanol production through predictive analytics, process automation, and intelligent optimization. This paper examines the role of AI and ML technologies in enhancing fermentation efficiency, optimizing biomass pretreatment, predicting ethanol yield, and improving overall sustainability in bioethanol production systems. The study also discusses key machine learning algorithms, including artificial neural networks, support vector machines, random forests, and deep learning frameworks, alongside their industrial applications. Furthermore, the paper evaluates challenges associated with data quality, computational complexity, scalability, and ethical considerations. The findings indicate that AI-driven systems significantly improve process accuracy, reduce waste generation, and enhance economic feasibility. Future research directions involving digital twins, autonomous biorefineries, and explainable AI are also explored.

Production And Performance Evaluation Of Bioethanol Fuel From Rice Husk Waste

Authors: Vivek Mishra, Om Prakash Sondhiya

Abstract: Rice husk is a common lignocellulosic agricultural by-product produced in huge amounts across the world, with nearly 150 million tons generated every year. This work examines the preparation and assessment of bioethanol obtained from rice husk waste as an eco-friendly second-generation biofuel. The rice husk was collected, dried, powdered, and treated with 4% NaOH at 90°C for 2 h, followed by steam explosion at 121°C for 30 min to remove lignin and hemicellulose components. Enzymatic saccharification was carried out using cellulase (30 FPU/g) and xylanase (10 FPU/g) at pH 5.0 and 50°C for 72 h, producing 68.4 g/L reducing sugars. Fermentation was performed with Saccharomyces cerevisiae (MTCC 178) at 32°C for 96 h and resulted in 32.6 g/L bioethanol with 95.2% fermentation efficiency. The produced bioethanol was purified through double distillation and molecular sieve dehydration to reach 99.5% purity, and the product was analysed using GC-MS, FTIR, and NMR techniques. The physicochemical parameters, including density (789 kg/m³), calorific value (26.8 MJ/kg), and octane number (108), matched ASTM D4806 requirements. Engine testing on a 4-stroke, single-cylinder SI engine (5.2 kW, 1500 rpm) with E10, E20, E50, and E85 blends revealed that E20 decreased CO emissions by 38% and HC emissions by 32% relative to gasoline, with only a 3.5% decline in brake thermal efficiency. CFD analysis using ANSYS Fluent confirmed the experimental findings with an error lower than 6%. The results demonstrate that rice husk can serve as an effective feedstock for large-scale bioethanol manufacturing while supporting waste utilisation and renewable energy production.

Toxic gas sensor and temperature monitoring in industries using Internet of things (IOT)

Authors: Ms.Pharande Harshada Sudhir, Dr.Dhaigude.N.B

Abstract: In working environment, the toxic gas leakage accidents are the main reason for workers health and also causes death. The Toxic gas can be detected and monitored by recent technologies using Internet of things. This project is mainly used to reduce the industrial accidents and hazardous. This process is monitored by Internet of things. Arduino Micro controller board is connected with gas sensor, Flame sensor and Temperature senor. The alert message is display by LCD through Arduino. The alert signal arises when the gas level increases above the normal gas level. This can be done by internet receiver channel. The sensor will receive the information about the gas level and it is stored in internet. This will used for analyzing and processing the safety regulations in industrial environment.

DOI: https://doi.org/10.5281/zenodo.20177739

Intelligent Human Resource Management Systems: A Framework For AI-Driven Organizational Excellence

Authors: Dr. Jermiah Anand Jupalli, Dr. Kiran Koduru

Abstract: The rapid evolution of artificial intelligence (AI) and digital transformation has significantly influenced the domain of human resource management (HRM), enabling the development of intelligent and data-driven systems. This paper proposes an Intelligent Human Resource Management System (IHRMS) framework designed to enhance organizational efficiency and decision-making through AI-driven analytics, automation, and predictive modeling. The study integrates multiple HR functions, including recruitment, performance evaluation, employee engagement, and attrition prediction, into a unified intelligent system. A synthetic dataset is utilized to evaluate the performance of the proposed model, and comparative analysis is conducted with traditional machine learning approaches such as Support Vector Machine and Decision Tree. The results demonstrate that the proposed IHRMS model achieves higher accuracy, improved prediction consistency, and better decision support capabilities. Furthermore, the study addresses ethical considerations such as fairness, transparency, and data privacy in AI-based HR systems. The findings indicate that intelligent HR systems can significantly contribute to organizational excellence by improving workforce management, enhancing employee experience, and enabling strategic decision-making.

DOI: http://doi.org/10.5281/zenodo.20179023

Strategic Campaign Restructuring and Multi-Level Segmentation

Authors: Akashdeep Singh, Ansh Gupta, Anmol Goyal

Abstract: This research paper presents a comprehensive analytical study on strategic campaign restructuring and multi-level segmentation within a revenue intelligence ecosystem. The research was conducted during an industry-integrated business analytics engagement at Reviniti, a revenue intelligence platform developed by 1DigitalStack. The study investigates KPI-driven dashboard optimization, marketing attribution analysis, campaign ROI evaluation, cohort analysis methodologies, data validation procedures, and automated reporting frameworks. The implementation integrated Microsoft Excel, Google Sheets, Metabase, and the Reviniti platform to support analytical processing, visualization, and stakeholder reporting. The project identified major gaps in last-click attribution models and introduced structured multi-touch attribution methodologies for improved revenue allocation. Significant business outcomes included an 18% reduction in cost per acquisition, a 40% increase in dashboard adoption among non-technical stakeholders, a 31% improvement in lead-to-close ratio, and an over 80% reduction in reporting cycle duration. The paper demonstrates the practical significance of structured business intelligence systems, dashboard-centric architectures, and KPI-driven decision-making frameworks in optimizing marketing performance and operational efficiency.

DOI: https://doi.org/10.5281/zenodo.20179426

Reinforcement Learning-Driven AI Control for PMSM with Field-Oriented Control

Authors: Tejaswini Taware

Abstract: This seminar work presents a reinforcement learn-ing based field-oriented control strategy for Permanent Magnet Synchronous Motor (PMSM) drives. A Twin Delayed Deep Deterministic Policy Gradient (TD3) agent is used to replace the conventional PI current controller in the dq-axis current loop. The controller is validated using a 10 s staircase per-unit speed profile with repeated acceleration and braking transitions. The obtained results show fast tracking, low overshoot, stable dq current regulation, and improved robustness for practical intelligent drive applications.

DOI: https://doi.org/10.5281/zenodo.20180838

A Study On The Impact Of Digital Music Streaming Platforms On Listeners

Authors: Sagar C Saner, Shivam Dubey, Mrs.Supriya kamareddy

Abstract: The rapid growth of digital music streaming platforms has transformed the way individuals access, consume, and discover music. This study examines the impact of digital music streaming platforms on listeners’ music consumption behavior in comparison with traditional music formats such as CDs and cassettes. The research specifically investigates changes in listening behavior, the influence of personalized recommendation systems, the role of streaming platforms in music discovery, and listeners’ perceptions toward AI-generated music. A descriptive cross-sectional research design was adopted, and primary data were collected through a structured questionnaire from 102 respondents using a convenience sampling technique. Data analysis was conducted using descriptive statistics and Chi-square tests with the help of SPSS. The findings indicate that digital music streaming platforms have significantly influenced music consumption behavior by providing greater convenience, wider music accessibility, and increased listening time. Personalized recommendation systems and algorithm-based playlists were found to strongly influence listeners’ music preferences and choices. Streaming platforms also emerged as powerful tools for discovering new songs, artists, and music genres. However, respondents showed only moderate acceptance toward AI-generated music, suggesting cautious openness toward this emerging technology. Statistical analysis further revealed that demographic variables such as education, occupation, and gender showed selective influence on certain aspects of music behavior, whereas age had no significant impact on perceptions of AI-generated music. Overall, the study concludes that digital music streaming platforms have become a dominant force shaping modern music consumption patterns and listener experiences.

A Review-Based Comparative Study of Metaheuristic Techniques for Optimal Power Flow Optimization

Authors: Madhusudan Kumar, Abhinav Kumar Singh, Prof. Vishal Mehtre

Abstract: Optimal Power Flow (OPF) is a crucial problem in power systems that involves generating power at minimum cost while ensuring safe and feasible operation. This problem is normally solved by using mathematical approaches but they are not always effective due to the problem’s complexity. In this context, researchers have started to apply smart, nature-inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). These techniques help find better solutions, even for complex problems. In this paper, we analyse these algorithms in terms of speed, accuracy, computational effort and robustness. After analysing results of various research papers, we find what algorithm works best under various power system conditions.

DOI: https://doi.org/10.5281/zenodo.20198493

AI-Food Expiry Tracker And Smart Recipe Suggestion

Authors: Gunjan and Rishita Vohra, Ms. Preeti Kumari

Abstract: The AI-Based Food Expiry Tracker and Smart Recipe Suggestion System is a web-based platform developed tointelligentlymonitorfooditems,predictexpirydates,andsuggestsuitablerecipesbasedonavailableingredients.Thesystemoperates through three major components — user, business, and admin — each offering specializedfunctionalities for foodtracking,recipegeneration,andsystemmanagement.Fooditemscanbeaddedmanuallyorthroughsmartlogging,andusersreceive timely notifications before items expire. The integrated AI module enhances usability by analyzing ingredientcombinations and recommending region-specific Indianrecipes to prevent wastage. For efficient and reliable performance, thesystemarchitectureincorporatestechnologiessuchasPHP, Laravel, HTML, CSS, JavaScript, MySQL, andPythonforAIandmachinelearningintegration.Thisprojectpresentsascalableandsustainablemodelthataddressesthegrowingissueoffoodwastage by promoting intelligent kitchen management and mindful consumption.

Design And Estimation Of Wifi Park Using Tekla Structure

Authors: M.Sakthivel, A.P.Aakashram, V.Balaji, R.Sabarinathan

Abstract: Design and estimation of a Wi-Fi park using Tekla Structures software focuses on creating a smart and sustainable public space equipped with wireless connectivity and recreational facilities. The design process ensures structural stability, durability, and safety, while also meeting functional and aesthetic needs of modern urban environments. Using Tekla Structures, accurate 3D modeling, detailing, and clash detection are performed to achieve precision in elements such as foundations, columns, pergolas, lighting poles, benches, and landscape structures. The software supports material optimization, which helps minimize wastage and control project cost effectively. Consideration of wind, live, and environmental loads ensures that the design is reliable under varying conditions. The estimation stage includes preparation of a detailed Bill of Materials (BOM), cost estimation, and fabrication details, enabling smooth project planning and execution. This integrated methodology enhances construction accuracy, improves efficiency, and reduces human error compared to traditional design methods, resulting in a cost-effective, innovative, and structurally sound Wi-Fi park for contemporary use. The project involves designing a smart and sustainable Wi-Fi park using Tekla Structures for accurate 3D modeling and detailing. It ensures structural stability, safety, and cost efficiency through material optimization and load considerations. The estimation process includes BOM preparation and cost analysis for effective project execution.

DOI: https://doi.org/10.5281/zenodo.20383969

Design Of Structural Truss And Fabrication Modeling In Tekla

Authors: S.Gowtham, N. Nithish, A.M. Surya, S.Maari

Abstract: This project focuses on the modeling and detailing of a structural truss using Tekla Structures software, aiming to understand the complete process of planning, modeling, and preparing fabrication drawings within one integrated platform. Tekla Structures is used to create an accurate 3D model of truss components, including members, gusset plates, and connections, ensuring that every part is represented correctly. From this detailed model, general arrangement (GA) drawings and fabrication drawings are automatically generated, which help in construction and workshop manufacturing. The use of Tekla Structures improves precision, minimizes human errors, and saves time when compared to traditional manual drafting methods. The software allows easy modification and updates, which makes it convenient when any design changes occur during the project. All modeling and detailing work in this project follows relevant Indian Standard codes to maintain the structural safety, stability, and reliability of the truss. The project also highlights how Tekla integrates design, detailing, and fabrication in a single workflow, allowing engineers, fabricators, and contractors to collaborate effectively. This integration not only enhances productivity but also improves communication between different project teams. Through this project, it becomes clear that Tekla Structures is a highly effective tool for truss modeling and detailing, as it provides a realistic 3D visualization and ensures that fabrication drawings are accurate and ready for use in construction. Overall, the project demonstrates the practical benefits of using Tekla Structures in modern structural engineering, especially for improving accuracy.

DOI: https://doi.org/10.5281/zenodo.20384080