AI Driven Task Management and Voice Integration

Prioritization among tasks is the most basic building block of effective project and workflow management, especially when the number of tasks is large and there are few marginal resources available. This paper presents a hybrid model implemented based on neural networks for task-priority forecasting and considering textual and numerical attributes. In particular, the model uses NL processing methodologies to process task descriptions. Auxiliary metadata-significance, projected duration, workload, and optimal working hours-are processed by special-ized numerical input layers. All parts are put together in a systematic architecture, within which the model can decide that a task is of "High" or "Normal" priority along with an appropriate score for confidence. Preprocessing of the training dataset adds multiple features to make it comprehensive, which consequently enhances the robustness and the scalability of the model. The developed model relies on the binary cross-entropy loss function evaluation in terms of accuracy on unseen data, and hence it results in a system that provides dependable predictions as an effective instrument for automated task management in dynamic settings of organizations.