Learning Based Method
Learning-based methods are revolutionizing diverse fields by leveraging data-driven approaches to solve complex problems previously tackled by rule-based or heuristic techniques. Current research emphasizes the development and refinement of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning (RL) algorithms, to improve accuracy, robustness, and efficiency across applications. These advancements are significantly impacting various sectors, from optimizing logistics and resource allocation to enhancing medical diagnosis and improving user experiences in interactive systems.
Papers
Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration
Ali Kazemi Arani, Triet Huynh Minh Le, Mansooreh Zahedi, M. Ali Babar
Edge-DIRECT: A Deep Reinforcement Learning-based Method for Solving Heterogeneous Electric Vehicle Routing Problem with Time Window Constraints
Arash Mozhdehi, Mahdi Mohammadizadeh, Xin Wang