Application of Convolutional Long Short-Term Memory Network Model in Laboratory Violation Behavior Detection

This study proposes a method based on the CLSTM model for detecting misconduct among laboratory personnel. The model integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Initially, the CNN perceives image features and performs feature extraction through convolutional and pooling operations. These extracted features are subsequently fed into the LSTM network, which leverages its memory capability to capture and understand patterns of misconduct in the temporal sequence. This structure enables the CLSTM model not only to effectively process and analyze complex image data but also to strike a balance between long-term memory and short-term variations, thereby enhancing the accuracy and robustness of misconduct detection. The model enhances the representation capability of image features through the convolutional network, further optimizing the LSTM model's performance in computational efficiency and training effectiveness. Compared to traditional LSTM models, CLSTM demonstrates faster convergence during training. To validate the model's effectiveness, experiments were conducted on two different datasets. The results indicate that the proposed CLSTM model significantly outperforms traditional LSTM methods, achieving performance improvements ranging from 7% to 11%. These findings underscore the superiority of the CLSTM model in laboratory personnel misconduct detection tasks.