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@article{204446,
        author = {Mukthi K and Dr Nijaguna G S and Dr.krishna Kumar P R},
        title = {Adaptive Dynamic Task Scheduling and Priority Optimization System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2485-2491},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204446},
        abstract = {Efficient task management has become increasingly important in modern academic, professional, and personal environments due to the growing number of responsibilities individuals must handle daily. Traditional task management systems provide facilities for creating, updating, and tracking tasks; however, they largely depend on manual prioritization and user judgment. Such approaches often result in missed deadlines, poor productivity, and inefficient time management. To address these challenges, this paper presents an Adaptive Dynamic Task Scheduling and Priority Optimization System using Machine Learning. The proposed system integrates a Flask-based web application, SQLite database, and Random Forest Machine Learning algorithm to automatically predict task priorities based on parameters such as deadline proximity, task importance, category, and status. The system classifies tasks into High, Medium, and Low priority levels and provides intelligent scheduling assistance, overdue task detection, productivity analytics, and dashboard monitoring. Experimental evaluation demonstrates that the proposed system effectively improves task prioritization and supports better decision-making. The system reduces manual effort and enhances overall productivity through intelligent automation.},
        keywords = {Machine Learning, Task Scheduling, Random Forest, Flask, Productivity Management, Priority Prediction, Intelligent Scheduling.},
        month = {June},
        }