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An Explainable Temporal Vision Framework is proposed to simultaneously detect silent depression and mental fatigue using facial micro-expressions and eye behavior, and deep learning-based architectures like CNN, LSTM, CNN-LSTM hybrid, GNN, and transformer models can achieve a significant boost in behavioral analysis performance.
The LSTM model’s ability to capture complex temporal dependencies in water usage data demonstrates its suitability for real-world forecasting applications and it is proposed that the integration of Internet of Things (IoT) technologies can further enhance the model's responsiveness and predictive capabilities.