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.

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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.

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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

 

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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

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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/

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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

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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

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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.

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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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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.

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