AI Health Task Management System

Abstract

The \"challenge control machine with AI\" is an revolutionary answer designed to streamline and decorate the performance of undertaking management for people and companies. In brand new fast-paced world, powerful time management isvital, and this device targets to empower customers to prioritize responsibilities intelligently. The machine incorporates an AI-primarily based mechanism that takes into account different factors, including closing dates, significance, and person-described urgency tiers. Leveraging system mastering, the AI model learns from ancient undertaking statistics to predict and assign priorities to new obligations routinely. This ensures that customers can focus at the most vital and time-touchy sports, optimizing productivity and minimizing the danger of missed closing dates.

Introduction

The project focuses on developing an AI-driven task management system designed to improve productivity by intelligently prioritizing tasks based on urgency, importance, deadlines, and workload. Traditional task management methods lack the adaptability and intelligence required to handle dynamic, multitasking environments, especially for students and professionals facing complex schedules.

The system leverages advanced machine learning algorithms—such as Random Forest for priority prediction, Linear Regression for deadline estimation, and K-Nearest Neighbors for personalized task recommendations through collaborative filtering. It integrates historical user data, feedback loops, and time-series forecasting to continually adapt and optimize task prioritization.

The methodology includes data collection, feature engineering, machine learning model integration, user interface design, security measures, and deployment. The AI system aims to reduce stress, improve time management, and increase overall productivity by offering personalized, real-time task prioritization and recommendations.

Conclusion

The venture, an AI-prioritized task management machine, has effectively addressed the project of improving time performance and challenge prioritization for users. the combination of AI algorithms, specifically the Random forest version, has confirmed its effectiveness in predicting mission priorities based on closing dates and urgency ranges. The empirical look at discovered promising effects in phrases of undertaking prioritization accuracy and system response time. users stated tremendous feedback, indicating satisfaction with the machine\'s capability. Comparative analyses against traditional challenge managementstructures showcased the specific benefits of incorporating AI prioritization. User adoption quotes and engagement metrics validated a positive reception, with customers actively utilising the machine for assignment control. The machine\'s scalability become examined, ensuring its overall performance remains strong beneath varying consumer and challenge hundreds. protection audits confirmed the machine\'s integrity in handling person records, prioritizing the safety of touchy facts. Addressing mentioned insects and non-stop improvement based totally on consumer comments had been imperative to refining the machine. The three algorithms used in the task includes: Linear Regression, Random forest set of rules, KNN algorithm

References

[1] Reference: \"Integrating AI into undertaking control\" by way of Smith et al. (magazine of synthetic Intelligence, 2020) [2] \"A Comparative analysis of assignment Prioritization Algorithms\" by way of Johnson and Lee (conference on device mastering, 2019) [3] \"improving task Prioritization thru Collaborative Filtering\" via Chen et al. (global convention on Human-laptop interaction, 2021) [4] \"Predicting task deadlines: A Time-collection Forecasting method\" via Wang and Kim (journal of device mastering research, 2018) [5] Pressure checking out an AI primarily based web carrier: A Case observe writer: IEEEAnand Chakravarty [6] Predicting “maintenance priority” with AI writer: IEEE Ömer Yi?it Astepe; Ali Seymen Alkara [7] Control tools for real options techniques and challenge analysis T.S. Durrani; S.M. Forbes; A.T. McKinven IEMC\'01 proceedings. trade management and the new industrial Revolution. IEMC-2001 (Cat. No.01CH37286) yr: 2001 | convention Paper | publisher: IEEE [8] A take a look at on project control machine N S Jyothi; A Parkavi 2016 worldwide conference on research Advances in included Navigation systems (RAINS) yr: 2016 | convention Paper | publisher: IEEE

Copyright

Copyright © 2025 Prof. Rohan B. Kokate, Prof. Sudhanshu Tripathi, Ayush Meshram. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.