Leveraging Attention and Conv-LSTM Fusion for Enhanced Management Performance and Efficiency

The development of risk management practices is becoming an increasingly popular topic among both enterprises and government agencies. Conventional risk management practice aims to lessen the possibility of undesirable outcomes. Value generation and performance management are at the heart of alternative organizational processes. The proposed system is based on the premise that risk management should be an integral part of these procedures. While preprocess, extract features, and train the model, remember the sequence of importance. Data preprocessing includes tasks such as vertical normalization and white noise removal. The feature extraction process makes use of a correlation-based feature selection strategy. After features are extracted, unified A-Conv-LSTM models must be trained. The proposed method achieves better results than two cutting-edge methods, CNN and LSTM. There was a 97.44% improvement in accuracy after using the approach.