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- W4289209780 abstract "Mental health disturbances are highly prevalent in so many medical cases like postpartum, cancer, etc., and significantly impact patients’ physical health. Often this disorder may result in undesirable consequences, like lunacy and suicide also. Hence, monitoring of mental conditions is highly recommended for such patients. In this regard, real-time facial expressions can help in signifying the behavioral psychology of patients. In this study, a dew computing-based real-time mental health evaluation framework using a lightweight convolutional neural network algorithm has been introduced. Contrary to standard lightweight convolutional neural networks (CNNs), the proposed one uses the bypass connection for better gradient propagation through the network, which results in better accuracy. The wild dataset FER-2013 and laboratory-controlled dataset CK+ have been utilized to obtain 68.93 and 96.12% accuracies for the experiment." @default.
- W4289209780 created "2022-08-01" @default.
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- W4289209780 date "2022-01-01" @default.
- W4289209780 modified "2023-10-16" @default.
- W4289209780 title "Dew Computing-Inspired Mental Health Monitoring System Framework Powered by a Lightweight CNN" @default.
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- W4289209780 doi "https://doi.org/10.1007/978-981-19-2177-3_31" @default.
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