ArticlePeerJ. Computer science2025
An evolutionary Bi-LSTM-DQN framework for enhanced recognition and classification in rural information management.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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