Predictive analytics driven time oriented prioritized task assistant under the framework of machine intelligence

Abstract

This paper presents a predictive analysis using computational tools, technologies, simulation and application to provide a prioritized tasks assistant for appropriate time management. The objective is to identify the tasks, design and implement the systems, test and refine it with appropriate prioritized structure using production rule of Machine Intelligence. The motivation behind this work is to create a time management system that is easy to use, flexible and customizable to execute tasks. The scope of this work is versatile including students, professionals and entrepreneurs. In the model formulation of the problem, the parameter such as difficulty level to keep track of tasks and deadlines, lack of flexibility and customization, time consuming manual tracking are taken into consideration. This is supplemented by a conducted market research to understand the needs and preferences of potential users of the system. In extension, surveys are conducted to collect feedback on system prototypes and improves system design. In the solution process, performance indicators such as managing tasks-deadlines-schedules, customizable options for different types of tasks-priority levels-alerts, develop automatic tracking and reporting features are considered. The primary dataset based on survey and popular dataset are used in developing the system with wireframing and time zone support. The incorporation of Voice assistant is also taken into consideration using Android extension.