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Today's teenagers are dealing with anxiety and stress. Anxiety, depression, and suicide rates have increased in recent years because of increased social rivalry. The research is focused on detecting anxiety in students due to exam pressure to reduce the potential harm to a person's wellness. Research is performed on databases for anxious states based on psychological stimulation (DASPS) and our own database. The measured signal is divided into sub bands that correspond to the electroencephalogram (EEG) rhythms using the Butterworth sixth-order order filter. In higher dimensional space, the nonlinearities of each sub-band signal are analyzed using higher order statistics third-order cumulants (TOC). We have classified stress and anxiety using the support vector machine (SVM), K-nearest neighbor (K-NN), and deep learning bidirectional long short-term memory (BiLSTM) network. In comparison to previous techniques, the proposed system's performance using BiLSTM is quite good. The best accuracy in this analysis was 87% on the DASPS database and 98% on the own database. Finally, subjects with high stress levels had more gamma activity than subjects with little stress. This could be an important attribute in the classification of stress. [ABSTRACT FROM AUTHOR]

Face recognition from low quality videos is one of the major challenges prevailing in video surveillance system. Several works have been contributed towards the face recognition, but have suffered due to the fact that the low quality videos have face part with low resolution. Also, using traditional feature extraction schemes make the recognition process to be tough. This paper introduces a novel feature descriptor and classification scheme for recognizing the face from low quality videos. Here, the video frames are sequentially provided to the Viola–Jones algorithm for detecting the face part, and the quality of the face part is improved by applying bi-cubic interpolation based super resolution scheme. Now, the features of enhanced video frame are extracted using proposed local direction feature descriptor, namely scattering wavelet-based local directional pattern (SW-LDP). Then, the extracted features are fed as the input to the actor critic neural network, where training is done using the newly developed fractional calculus based krill–lion (fractional KL) algorithm. The proposed fractional KL-ACNN algorithm is experimented using the standard FAMED database. From the analysis, it is evident that the proposed classifier achieved low FAR of 3.89%, and low FRR of 4.04%, and high accuracy value of 95%, respectively. [ABSTRACT FROM AUTHOR]