An evolutionary Bi-LSTM-DQN framework for enhanced recognition and classification in rural information management - Taiping Deng; Xi He; Jiao Li; Feifei Ye; Jingyang Tang (PeerJ Computer Science)
As deep learning and reinforcement learning technologies advance, intelligent rural information management is transforming substantially. This article presents an innovative framework, the evolutionary bidirectional long short-term memory deep Q-network (EBLM-DQN), which integrates evolutionary algorithms, reinforcement learning, and bidirectional long short-term memory (Bi-LSTM) networks to significantly improve the accuracy and efficiency of rural information management, particularly for recognizing and classifying information relevant to farmers. The proposed framework begins with data preprocessing using disambiguation techniques and data complementation, followed by temporal feature extraction via a Bi-LSTM layer. It then employs a deep Q-network (DQN) to adjust and optimize weights dynamically. After feature extraction and weight optimization, evolutionary algorithms are used to select the optimal weights, enabling precise recognition and classification of conditions encountered by farmers seeking assistance. Experimental results indicate that the EBLM-DQN framework outperforms existing frameworks on public datasets and real-world applications, providing higher classification accuracy. This framework offers valuable technical support and a reference for future optimization and development of rural information management systems.