Volume 1 | Issue 2 | August 2026

Abstract: The "Coding Score Card Website" is an online platform designed to provide users with a centralized location to access and track their coding scores and rankings from various coding platforms, including Geeks for Geeks (GFG) , HackerRank , Hackrearth , Codeforce , Leetcode , Codechef etc.The website offers a user-friendly interface that presents coding scores in a visually appealing and understandable format, making it easier for users to access their performance and progress .Users can leverage their coding achievements and scores for resume building and career advancement in the tech industry. Resume Enhancement: Users can use their coding scores and achievements as part of their portfolio for job applications and career advancement in the tech industry. Keywords: Coding Score Card , Coding Scores , Quiz feature . The “Coding Score Card” System is a web based application. The main purpose of “Coding Score Card ” is to provide a convenient way for users to Watch Coding Score Of multiple Coding Practice websites Like GeeksGorGeeks, Hackerearth , HackerRank ,Codeforce , Leetcode etc.. This project also deals with use of web technology in the field of e-learning. Nowadays e-learning platform are encouraged as lot of manual work is not done and also it helps in saving time. People anywhere in the world with an internet connection can easily use these platforms.

Abstract: Agricultural insurance is a critical risk mitigation mechanism for farmers facing uncertainties caused by climate change, weather variability, crop diseases, and yield fluctuations. Despite large-scale government-backed initiatives such as the Pradhan Mantri Fasal Bima Yojana (PMFBY), conventional agricultural insurance systems continue to suffer from delayed claim settlements, subjective damage assessment, lack of transparency, and high administrative costs. This paper proposes a comprehensive and plagiarism-safe AI–Blockchain hybrid framework to automate agricultural insurance operations. Artificial intelligence models analyze multi-source data collected from IoT sensors, satellite imagery, and weather services to perform crop health monitoring, yield prediction, and quantitative risk scoring. Blockchain technology is employed to store insurance policies, ensure data immutability, and execute smart contracts for automated claim settlement. AI-generated risk scores act as trusted oracle inputs to trigger on-chain claim execution. The proposed framework improves efficiency, transparency, and trust, making it suitable for large-scale deployment in government and private agricultural insurance schemes.Agricultural insurance is a critical risk mitigation mechanism for farmers facing uncertainties caused by climate change, weather variability, crop diseases, and yield fluctuations. Despite large-scale government-backed initiatives such as the Pradhan Mantri Fasal Bima Yojana (PMFBY), conventional agricultural insurance systems continue to suffer from delayed claim settlements, subjective damage assessment, lack of transparency, and high administrative costs. This paper proposes a comprehensive and plagiarism-safe AI–Blockchain hybrid framework to automate agricultural insurance operations. Artificial intelligence models analyze multi-source data collected from IoT sensors, satellite imagery, and weather services to perform crop health monitoring, yield prediction, and quantitative risk scoring.

Abstract: Rapid urbanization has increased municipal waste generation. This study proposes an AI-driven smart waste management system using IoT sensors and machine learning to optimize waste collection routes, reduce operational costs, and improve recycling efficiency. A prototype was evaluated on simulated city data and achieved a 22% reduction in collection distance and an 18% improvement in bin overflow prediction accuracy. The results indicate that AI-enabled waste management can significantly improve operational efficiency while supporting sustainable urban development.Rapid urbanization has increased municipal waste generation. This study proposes an AI-driven smart waste management system using IoT sensors and machine learning to optimize waste collection routes, reduce operational costs, and improve recycling efficiency. A prototype was evaluated on simulated city data and achieved a 22% reduction in collection distance and an 18% improvement in bin overflow prediction accuracy. The results indicate that AI-enabled waste management can significantly improve operational efficiency while supporting sustainable urban development.Rapid urbanization has increased municipal waste generation. This study proposes an AI-driven smart waste management system using IoT sensors and machine learning to optimize waste collection routes, reduce operational costs, and improve recycling efficiency. A prototype was evaluated on simulated city data and achieved a 22% reduction in collection distance and an 18% improvement in bin overflow prediction accuracy. The results indicate that AI-enabled waste management can significantly improve operational efficiency while supporting sustainable urban development.

Abstract: Rapid urbanization has increased municipal waste generation. This study proposes an AI-driven smart waste management system using IoT sensors and machine learning to optimize waste collection routes, reduce operational costs, and improve recycling efficiency. A prototype was evaluated on simulated city data and achieved a 22% reduction in collection distance and an 18% improvement in bin overflow prediction accuracy. The results indicate that AI-enabled waste management can significantly improve operational efficiency while supporting sustainable urban development.Rapid urbanization has increased municipal waste generation. This study proposes an AI-driven smart waste management system using IoT sensors and machine learning to optimize waste collection routes, reduce operational costs, and improve recycling efficiency. A prototype was evaluated on simulated city data and achieved a 22% reduction in collection distance and an 18% improvement in bin overflow prediction accuracy. The results indicate that AI-enabled waste management can significantly improve operational efficiency while supporting sustainable urban development.Rapid urbanization has increased municipal waste generation. This study proposes an AI-driven smart waste management system using IoT sensors and machine learning to optimize waste collection routes, reduce operational costs, and improve recycling efficiency. A prototype was evaluated on simulated city data and achieved a 22% reduction in collection distance and an 18% improvement in bin overflow prediction accuracy. The results indicate that AI-enabled waste management can significantly improve operational efficiency while supporting sustainable urban development.