Emotio-Intelligent: A New Adaptive Approach for Intelligent Evacuation in Crisis Situations.

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Abstract Addressing the critical challenge of evacuation, especially in crisis situations where uncontrollable emotions can significantly impact decision-making, is of paramount importance. In this article, the Emotional Intelligent Model is presented, a new approach that seamlessly integrates dynamic emotion recognition with an adapted intelligent evacuation strategy for crisis scenarios. Our methodology harnesses a combination of convolutional neural networks (CNN) for dynamic emotion sensing and long-term memory recurrent neural networks (LSTM) to provide decision support during evacuation. This paper provides an in-depth exploration of our system's architecture, encompassing the dynamic emotion recognition and personality profiling methods, as well as the adaptive evacuation strategy. During the experimental and validation phases, the MESA simulation platform was used. The results achieved confirm the effectiveness of the integrated emotional approach, which contributes to safer and smarter evacuation procedures in crisis situations.

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