Anis Yazidi

TL;DR: In this paper, a DL-based convolutional neural network (CNN) called DeprNet was proposed for classifying the EEG data of depressed and normal subjects, where the Patient Health Questionnaire 9 score was used for quantifying the level of depression.

TL;DR: In this article, a deep learning-based scheme is proposed for identifying the facial expression of a person, which consists of two parts: the former one finds out local features from face images using a local gravitational force descriptor, while, in the latter part, the descriptor is fed into a novel deep convolution neural network (DCNN) model.

TL;DR: This study proposes FER-net: a convolution neural network to distinguish FEs efficiently with the help of the softmax classifier and demonstrates that F ER-net is preeminent in comparison with twenty-one state-of-the-art methods.

TL;DR: A novel solution to the problem of identifying services of high quality using tools provided by Learning Automata (LA), which have proven properties capable of learning the optimal action when operating in unknown stochastic environments and is ideal for decentralized processing.

TL;DR: A survey is provided that describes some widely used crowd models and discusses their advantages and shortages from the angle of human-centered sensing, and reveals important research opportunities that may contribute to an improved and more robust emergency management.