Time Management Recommendations with Bayesian Neural Networks: An Intelligent Assistant Approach
Abstract
Time management is an important aspect of our daily lives and this research aims at creating an intelligent assistant that will be able to support an individual in managing their time appropriately using application of the Bayesian Neural Networks (BNNs). Many of the conventional time management strategies are posed to be ineffective since they cannot be changed depending on the unique person and situations in a similar way personal organization systems cannot be changed to suit the unique person and situations. Therefore, the use of Bayesian neural networks (BNNs) to perform inference makes them ideal for this purpose given that BNNs use probability and Bayesian inference to work under uncertainty. The involved process involved utilization of effective data gathering methodologies to obtain comprehensive information on users’ activities, preferences, and productivity to develop the BNN model. The plan is that the latter was integrated into the context of a mobile application where, similar to an intelligent agent of an organism, it adapts in real time and suggests the most efficient ways of planning the time. A number of user tests pointed at level 3: Most of the users reported high levels of satisfaction and ranked productivity gains throughout the different activities under study. Escalating accuracy was also affirmed by the result analyses owing to the continual refining of the model in subsequent phases. This work demonstrates the possibility of the proposed BNN approach to change time management approaches and present a more effective approach of time management in line with the need of the user. As such, future work will comprise a continuation of the model’s development with the addition of new functions that will complement the existing features and make the tool even more useful and appealing to potential users.