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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 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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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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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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 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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 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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 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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 Keywords

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

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

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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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

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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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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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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

 Abstract

Cyber deception threats are becoming increasingly sophisticated, often evading traditional security measures such as firewalls and intrusion detection systems. Attackers can exploit unpatched systems or use advanced techniques to infiltrate networks. This paper introduces an AI-powered IP-based cyber deception system designed to confuse and deceive attackers using intelligent honeypots and anomaly detection. Our approach enhances threat intelligence and adapts dynamically to evolving threats.

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 Keywords

Component, formatting, style, styling, insert.

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The rapid advancement of deepfake generation techniques has created significant challenges in preserving the authenticity of digital media. This comprehensive survey examines the state-of-the- art in deepfake video detection, with a particular focus on hybrid Long Short-Term Memory (LSTM) models that combine spatial and temporal analysis capabilities. We analyze over 50 recent studies (2019-2024) to evaluate the effectiveness of various architectural approaches, including Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM), Three Dimensional Convolutional Neural Network- Long Short-Term Memory (3DCNN-LSTM), and attention-enhanced variants. The paper provides a detailed comparison of model performance across benchmark datasets such as FaceForensics++ and Celeb-DF, while discussing key evaluation metrics like AUC-ROC and F1-score that are critical for assessing detection reliability. We systematically identify current limitations in generalization capability, computational efficiency, and adversarial robustness that hinder real-world deployment. The survey concludes by outlining promising research directions, including multimodal fusion techniques, lightweight model architectures for edge deployment, and explainable AI approaches to enhance forensic credibility.

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Hybrid Long Short-Term Memory (LSTM), Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM), Three Dimensional Convolutional Neural Network- Long Short-Term Memory (3DCNN-LSTM), multimodal fusion techniques.

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Deepfake technology, driven by artificial intelligence, has developed rapidly over the past few years, raising issues of misinformation, privacy violations, and online security threats. This project is centered around creating a robust Deepfake Detection System based on machine learning methods to distinguish real media from the manipulated one. The system has a user authentication module for secure access via a login system. In addition, it incorporates an advanced deepfake detection algorithm that can scan images and videos to verify whether they are authentic. The detection model generates a fake accuracy percentage, reflecting how much media are likely manipulated. This measure adds transparency and gives users quantifiable feedback into possible deepfake risks. The system utilizes convolutional neural networks (CNNs) and deep learning to make high-precision identification of synthetic content. The technology can be applied to real-world scenarios such as media authentication, law enforcement, and social media surveillance, helping in the mitigation against misinformation. To make it scalable and efficient, the platform will be developed with an easy-to-use interface where individuals and organizations can upload and examine media easily.Through the creation of a correct and accessible detection system, we are moving closer to maintaining trust in digital content and preventing the risks involved in synthetic media manipulation.

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Deepfake Detection, Convolutional Neural Networks (CNNs), deep learning techniques, AI-driven cybersecurity

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 Abstract

This paper presents the design and implementation of a comprehensive web-based College ERP system aimed at enhancing the efficiency of academic and administrative operations in educational institutions. The system automates critical functions such as student enrolment, faculty management, attendance tracking, and examination processing, replacing traditional manual methods that often lead to inefficiencies and errors. Developed using modern web technologies, the solution ensures scalability, robust data security, and user-friendly access across various roles within the institution. It incorporates features such as role-based access control, a modular architecture, and real-time analytics to support data-driven decision-making and institutional transparency. By streamlining operations, reducing administrative workload, and improving communication among stakeholders, the system fosters a more organized and technology-driven educational environment. Furthermore, it is designed with future extensibility in mind, supporting cloud deployment and integration with advanced tools such as AI analytics and Learning Management Systems (LMS). This ERP system not only provides a practical approach to managing college operations efficiently but also serves as a foundational step toward ongoing innovation in educational technology.

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 Keywords

College ERP, Student Information System (SIS), Role-Based Access Control (RBAC), Attendance Management, Examination Management, Web-Based ERP System, Database Management, Cloud-Based Deployment & Data Security.

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In the rapidly evolving landscape of e-commerce and retail, the integration of predictive modelling, real-time data analytics, and artificial intelligence (AI) has significantly transformed pricing strategies, customer engagement, and operational efficiencies. This research investigates the implementation of predictive analytics techniques for new product pricing, the role of real-time data processing in enhancing business agility, and the transformative impact of AI in delivering personalized consumer experiences. Predictive modelling techniques leverage historical data, market trends, and consumer behavior to optimize pricing decisions, while real-time analytics architectures utilizing technologies like Apache Kafka and Apache Flink facilitate immediate insights into inventory management, customer preferences, and dynamic pricing.This paper presents a comprehensive analysis of how these technologies collectively empower businesses to achieve operational excellence, enhance customer satisfaction, and sustain competitiveness in the digital marketplace. Ethical considerations regarding data privacy and algorithmic fairness are also highlighted, ensuring responsible deployment of AI- drivennsolutions. The study ultimately emphasizes the critical role of data-driven, real-time, and AI- augmented approaches in shaping the future of e-commerce and retail industries.

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Predictive Modelling, Real-Time Data Analytics, Artificial Intelligence, Dynamic Pricing, Customer Personalization, E-Commerce.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The calculation of the Air Quality Index (AQI) in India differs greatly from global norms due to regional characteristics such as geographical diversity, seasonal fluctuations, and pollution sources. While most countries use consistent techniques that emphasize pollutants such as PM 2.5, NO2, and O3, India's methodology favors PM 10 and PM 2.5 due to high dust levels, industrial emissions, and biomass combustion. The AQI calculation for India includes adaptive seasonal modifiers to account for crop burning, festivities like Diwali, and climatic conditions such as monsoons and winter inversion. Additionally, regional weightage variables are added depending on local pollution sources, which improves accuracy. Unlike worldwide models, which rely mainly on static pollution criteria, India's model makes dynamic modifications to account for real-time environmental and demographic conditions. This approach provides a more relevant and accurate representation of air quality, catering to India's unique climatic, industrial, and cultural conditions. In addition, we present a detailed investigation of chemical processes and how their various quantities influence the toxicity of the compounds produced. We investigate the significance of five key gases. We assess the adverse effects of the produced items utilizing data from internet sources and a variety of calculation and visualization methodologies. The evaluation is based on established threshold values for all gases involved.

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Air pollution, Pollution Control Board, Pollutant data analysis, Predictive modelling, Random Forest ML algorithm, User Friendly website, Data visualization.

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The AI-Powered Afforestation Planner project aims to address the growing issue of air pollution through strategic afforestation. By leveraging advanced remote sensing and machine learning techniques, the project identifies barren land areas suitable for tree planting to improve air quality. The study focuses on the Kanakapura Taluk in Ramanagara District, where land classification is performed using Google Earth Engine (GEE) with manually provided training samples. These samples were used to classify the region into urban areas, water bodies, vegetation, and barren lands using the Random Forest algorithm. The project fetches real-time Air Quality Index (AQI) data to assess pollution levels and recommends the optimal number and species of trees for planting. The final output is a web application that provides users with land classification results, barren land area calculations, and tree species recommendations tailored to improving air quality based on AQI levels. The web-based approach ensures accessibility for end users, offering an interactive tool for better environmental decision-making.

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Afforestation, Land Classification, Google Earth Engine (GEE), Random Forest, Air Quality Index (AQI)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As virtual statistics turns into an increasing number of popular ensuring strong safety for sensitive files is critical this studies introduces a complicated protection framework that mixes biometric watermarking with device mastering to establish a tamper-resistant and adaptive protection system by way of encoding intricate iris and fingerprint patterns the usage of a custom designed rubiks cube encryption algorithm the method creates a comfy embedded watermark that is tremendously proof against manipulation in parallel convolutional neural networks CNNs examine and authenticate biometric statistics permitting real-time detection of spoofing tries and unauthorized changes the adaptive gaining knowledge of functionality of CNNs lets in the system to refine its detection accuracy through the years strengthening its resilience against rising threats this precise integration of encryption and shrewd pattern recognition gives extensive improvements in file security with ability packages in sectors which include healthcare finance and authorities wherein records integrity and authentication are paramount.

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Biometric Watermarking, Document Security, Rubik Encryption, Convolutional Neural Networks (CNN), Machine Learning, Iris and Fingerprint Fusion, Zero-bit Watermarking, Authentication, Spoofing Detection, Fraud Detection.

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Creative Commons Attribution 4.0 and The Open Definition

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The AI Based Crop Recommendation System in Karnataka is an online platform designed to assist farmers in making optimal decisions regarding crop cultivation. It incorporates features such as analysis of soil health, weather forecasting, and market price predictions. Farmers can also access information on suitable crop varieties, irrigation management, and pest control methods through the system. By using this AI-driven system, farmers can improve crop yields, reduce input costs, and enhance overall agricultural productivity. This digital solution streamlines agricultural decision-making and supports sustainable farming practices in Karnataka by providing farmers with essential information and recommendations.

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 Keywords

artificial intelligence, crop recommendation, agriculture, Karnataka, precision farming, sustainable agriculture.

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Creative Commons Attribution 4.0 and The Open Definition

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The threat of ransomware is considerable in cybersecurity risk and often goes undetected by traditional signature-based detection approaches. In this paper, we present a deep learning-based behavioral analysis framework supporting pro-active detection and disruption of ransomware. Rather than depending on signatures, the framework analyzes system-level activities, such as file encryption, abnormal access, and process relations. The framework utilizes Long Short- Term Memory (LSTM) networks to analyze temporal activities and Recurrent Neural Networks (RNNs) to extract features, enabling real-time identification of ransomware. Our system detects anomalies present in suspicious behavioral patterns, it provides warnings to the administrators, and automatically either quarantines files or isolates from the network. By using deep learning, our framework detects better and has fewer false positives compared to traditional methods. This study demonstrates the potential for deep learning for analyzing behavior for ransomware protection purposes, giving us a strong and adaptive means of defending against evolving cybersecurity threats.

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 Keywords

Ransomware Detection, Deep Learning, Behavioral Analysis, Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs).

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