Volume 13 | Issue 7 |

 Abstract

This study has been undertaken to investigate the determinants of revenue generation and financial performance in the e-GramPanchayath system, using two analytical frameworks: the traditional Financial Ratio Analysis and an Econometric Model based on Arbitrage Pricing Theory (APT). To test the financial model, basic revenue indicators such as tax collections and service charges are used, while macroeconomic variables are applied in the APT framework. The macroeconomic variables include inflation, rural employment rate, government grants, and agricultural output. For this purpose, monthly time series data has been compiled from January 2015 to December 2020 from various Gram Panchayath records and government databases. The analytical framework includes both correlation analysis and regression modeling to identify the significant factors influencing revenue trends and financial sustainability in local governance systems.

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 Keywords

Digital Governance, E-Government, Rural Development, Transparency, Online Services

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The project ClickTalk Interface is a web-based application that takes human-computer interaction to the next level by combining voice commands and hand gestures. Built with Next.js and Tailwind CSS, it features tools like virtual mouse control, virtual volume control, speech-based commands, text-to-speech conversion. Using cutting-edge libraries like OpenCV, Media pipe, and speech recognition, the website makes tasks easier, boosts productivity, and improves accessibility. Its modular design helps users control their system in intuitive ways, all hands free.

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CLICKTALK INTERFACE

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As software development has advanced at a high speed with artificial intelligence (AI), AI-enabled code assistants are now indispensable assets to enhance the productivity of developers and the quality of code. But how can AI truly transform the way we write, debug, and optimize code? This paper presents an AI-facilitated VS Code extension, "AI Powered Pair Programming Assistant," which can function as a virtual coding companion through intelligent code recommendation, inline descriptions, test case generation, and feedback. By utilizing Gemini AI, our system combines cutting-edge code analysis and test case automation to accelerate the development process, minimize manual debugging efforts, and improve collaborative coding effectiveness. This integration tackles remote teams' challenges of time zone disparities and miscommunication through instant AI-backed support and suggestions. Through experimentation and evaluation, we explore the potential of AI-based assistants to maximize method generation, enhance test coverage, and facilitate smoother software development practices.

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SMART CODING PARTNER An AI-Powered Assistant for Better Code and Productivity

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 Abstract

This project develops an AI system to detect and classify diabetic eye diseases early using retinal images. It uses image enhancement and CNNs to extract key features. A hybrid model combining deep learning and traditional ML classifies disease types and stages. The system is trained on public datasets and considers patient info like age. It achieves high accuracy in identifying conditions like diabetic retinopathy and glaucoma. This tool helps doctors with faster, more accurate diagnosis and supports telemedicine use.

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 Keywords

Advanced Classification Technique for Diabetic Eye Disorders

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Blood donation systems in many regions continue to face significant challenges related to timely donor-recipient matching, efficient communication, and accurate prediction of blood demand. This paper presents PulseMatch, a smart, web-based blood donation platform designed to address these issues using cloud technologies and machine learning. Developed with a React.js frontend and Firebase backend, PulseMatch facilitates seamless interaction between hospitals and potential blood donors. The system enables real-time donor registration, request submission, and intelligent donor matching based on blood group, location, and availability. In addition, machine learning modules are proposed for fraud detection and blood shortage forecasting to improve reliability and response times. PulseMatch integrates a scalable architecture that supports live data synchronization, automated alerts, and future extensions including mobile compatibility and explainable AI. This paper details the system design, implementation workflow, and integration strategy for intelligent automation in blood donation, demonstrating the potential to modernize and optimize healthcare logistics through data-driven approaches.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blood Donation System, Smart Healthcare, Machine Learning, Firebase, React.js, Donor Matching, Fraud Detection, Shortage Prediction, Cloud-based Platform, Healthcare Automation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents the development of an AI-powered healthcare Chabot utilizing Retrieval-Augmented Generation (RAG) to provide accurate, reliable, and multilingual medical assistance. By integrating advanced natural language processing (NLP), image recognition, and speech processing, the Chabot offers personalized and context-aware health support, retrieving real-time information from open-access medical databases to enhance accuracy and reliability. Unlike traditional rule-based chatbots, which rely on predefined responses, our sys-tem dynamically generates answers using large language models (LLMs), ensuring adaptability to evolving medical knowledge. A key feature is its multimodal interaction, supporting multilingual voice conversations and computer vision for analyzing skin conditions, making healthcare assistance more inclusive. This enables users to engage via text, speech, or images, improving accessibility for non-native speakers and individuals with disabilities. Experimental results highlight improvements in response accuracy, efficiency, and user engagement, demonstrating the system's potential to bridge healthcare accessibility gaps.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Healthcare, Chatbot, Retrieval-Augmented Generation, Natural Language Processing, Computer Vision, Multilingual Support, Artificial Intelligence

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Floods pose a significant threat to human life, infrastructure, and the economy. This paper presents Flood Sense, an advanced flood prediction system powered by machine learning. The system integrates historical meteorological data, real-time hydrological parameters, and satellite imagery to provide early warnings and risk assessments. Various machine learning techniques, including Decision Trees, Random Forest, and Artificial Neural Networks (ANN), are employed to enhance predictive accuracy. A web-based dashboard, built using Flask, enables real-time monitoring and alert dissemination. The goal of this system is to aid government agencies, disaster management teams, and local communities in making informed decisions to mitigate flood damage.

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 Keywords

Flood Prediction, Artificial Intelligence, Real- Time Monitoring, Disaster Management, Hydrological Analysis, Remote Sensing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents MindMate, an AI-powered mental health chatbot designed to offer accessible, anonymous support through interactive, voice-enabled conversations. Leveraging fine-tuned language models, LangChain, FAISS, and Streamlit, the chatbot delivers personalized guidance and retrieves relevant information in real time. It uses retrieval-augmented generation (RAG) to enhance accuracy and integrates validated psychological tools like PHQ-9 and GAD-7 for sentiment-aware responses. The chatbot is trained on a diverse dataset of mental health dialogues, FAQs, and synthetic conversations. Built with Hugging Face transformers and a FAISS-powered retrieval system, it dynamically adapts to user inputs. MindMate is accessible through a streamlined web interface, enabling users to seek help anytime, free from judgment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Mental health support, FAISS, LangChain, LLM, RAG, Sentiment analysis

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Early Identification of crop infections is a critical component of sustainable agriculture, as it is essential to maximize crop yields and minimize losses. Corn plants are susceptible to various infections that can have a substantial impact on crop production, including Northern Corn Leaf Blight, Common Rust, and Gray Leaf Spot. This research introduces a computational framework that incorporates artificial intelligence (AI) and employs deep learning techniques to identify diseases in maize plants while assessing their severity. The system will use high-resolution images that will be used to train a convolutional neural network (CNN) for robustly diagnosing disease. The system design incorporates a MongoDB database that will allow for efficient storage, retrieval, and management of disease-related data. The system will be able to provide growers with flexibility through real-time tracking and instant feedback to help growers make informed decisions to help control and prevent plant disease. The models are implemented utilizing TensorFlow and PyTorch, and are designed to be scalable and accurate. A well-organized interface will allow farmers and agronomists ease of access to the prediction process. The system design shows how automated disease detection can be combined with real-time information for smart farming. Future improvements will depend on improving the detection accuracy and later applying these models to more crops. This study reinforces sustainable agricultural practices and integrates AI-based precision farming systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Maize crop disease identification, AI-driven plant health monitoring, CNN-based disease classification, advanced deep learning in agriculture, intelligent farming systems, automated crop disease recognition, real-time agricultural diagnostics.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With the era of personalized entertainment, it is essential to get movie recommendations spot on with the user sentiments. Our paper introduces SentimentSync an artificial intelligence-based kannada movie recommendation system that relies on sentimental analysis of YouTube trailer comments and web scraping of dynamic movie listings of the Times of India in contrast to other traditional recommendation systems SentimentSync combines locally hosted sentimental analysis using NLTK's Vader with sophisticated web-scraping techniques through selenium the system generates aggregate sentiment scores for a user-queried film and a group of upcoming releases then rank-filter and returns recommendations based on similarity. An interactive flask web interface shows recommendations with an auto-generated explanation utilizing large language models (llms) through the langchain platform. Experimental outcome shows that our hybrid solution increases recommendation relevance as well as the ease of using an interactive web interface over having to use a paid APIs.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sentiment Analysis, Movie Recommendations, YouTube API, Selenium, Flask, Kannada Movies, Web Scraping, LangChain, NLTK, LLM

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Creative Commons Attribution 4.0 and The Open Definition