Abstract Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN)
algorithm known for its ability to effectively analyze and process sequential data with long …

Abstract Deep Reinforcement Learning (DRL) has gained significant adoption in diverse
fields and applications, mainly due to its proficiency in resolving complicated decision …

In response to the growing number of diabetes cases worldwide, Our study addresses the
escalating issue of diabetic eye disease (DED), a significant contributor to vision loss …

Background Smart grids, characterized by their ability to integrate renewable energy
sources and manage the dynamic balance between supply and demand, require …

The low stability and robustness have been significantly hindering the practical applications
of Deep Reinforcement Learning (DRL)-based controllers in industrial processes. In this …

This study demonstrates application of Deep Deterministic Policy Gradient (DDPG)-based
algorithm to provide comprehensive and flexible plans for reservoir operation planning of …

The significant fluctuations in the liquid level of the two-stage tandem separators on FPSOs
result in low oil and gas separation efficiency. To address this issue, an improved adaptive …

Traditional production scheduling and resource allocation methods often struggle to adapt to
changing conditions in manufacturing environments. To address this challenge, this study …

To handle incomplete and indeterminate data, neutrosophic logic/set/probability was
recognized. The neutrosophic falsehood, truth, and indeterminacy modules show symmetry …