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.Real-time emotion detection based on facial expression is an innovative research field that has been applied in several areas, such as health, human–machine vision, and autonomous safety. Researchers in object detection are involved in developing methods to interpret, code facial expressions, and extract these features to be better predicted by machines. Furthermore, the success of deep learning with different architectures is exploited to achieve better performance. But these methods drastically fail in excessive sweating in different health conditions. We aim to create a dataset in different health conditions and detect facial emotion using the encoder and decoder-based deep learning methodology. The proposed architecture and the dataset present the progress made by comparing the other proposed methods and the quantitative and qualitative results obtained. The major benefit of our study is to enhance the emotion detection efficiency with other proposed methods and real-time applications for different health conditions. We propose the application of feature extraction of facial expressions with an end-to-end attention module-based fusion network for detecting different facial emotions (happy, angry, neutral, surprised, etc.) with an accuracy of 99.68%. The proposed system depends upon the human face; as we know, the face reflects human brain activities or emotions.

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Industrial sectors are reinventing in automation, stability, and robustness due to the rapid development of artificial intelligence technologies, resulting in significant increases in quality and production. Visual‐based sensor networks capture various views of the surrounding environment and are used to monitor industrial and transportation sectors. However, due to unclean suspended air particles that damage the whole monitoring and transportation systems, the visual quality of the images is degraded under adverse weather conditions. This research proposed a convolutional neural network‐based image dehazing and detection approach, called end to end dehaze and detection network (EDD‐N), for proper image visualization and detection. This network is trained on real‐time hazy images that are directly used to recover dehaze images without a transmission map. EDD‐N is robust, and accuracy is higher than any other proposed model. Finally, we conducted extensive experiments using real‐time foggy images. The quantitative and qualitative evaluations of the hazy dataset verify the proposed method's superiority over other dehazing methods. Moreover, the proposed method validated real‐time object detection tasks in adverse weather conditions and improved the intelligent transportation system.

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