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@article{182648,
        author = {Anila C Biju and Annapoorna K K and Emlin Maria Roy and Jasmin T Jose},
        title = {Resource Recovery Optimization And Crisis Response Using Machine Learning And Big Data Tools},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {2834-2841},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182648},
        abstract = {The Resource Recovery Optimization and Crisis Response Using Machine Learning and Big Data Tools project leverages advanced machine learning algorithms such as en-semble learning algorithms and big data analytics to enhance disaster and pandemic management. It focuses on improving disaster prediction. In pandemic scenarios, machine learning is utilized for predictive modeling, outbreak monitoring, and disease diagnosis. The project trains models using historical and real-time data to improve prediction accuracy and resource allocation efficiency. In addition, it addresses key challenges, including data reliability, infrastructure costs, and real-time processing constraints. By exploring synergies between machine learning and big data analytics, this initiative seeks to develop a centralized platform that supports both government and social efforts in effective crisis management and resource recovery.},
        keywords = {Support Vector Machine, Random Forest Algorithm, Voting Classifier algorithm},
        month = {July},
        }