Hussein Al Osman

Neural Network,Convolutional Neural Network,Deep Learning,Deep Neural Network,Long Short-term Memory,Training Set,Anomaly Detection,Anomaly Detection Methods,Bipolar Disorder,Classification Accuracy,Data Augmentation,Deep Network,Emotion Categories,Emotion Recognition,Emotional Dimensions,Emotional States,Ensemble Method,Generative Adversarial Networks,Multilayer Perceptron,Neural Architecture Search,Support Vector Machine,Test Dataset,Unsupervised Learning,2D Convolutional Layers,Annotated Dataset,Anomaly Score,Artificial Neural Network,Audio-visual Recordings,Autoencoder,Average Accuracy,Average Probability,Average Training,Balanced Accuracy,Base Learners,Bayesian Neural Network,Bidirectional Long Short-term Memory,Binary Datasets,Bipolar Disorder Patients,Control Scenario,Convolutional Neural Network Layers,Convolutional Neural Network Structure,Convolutional Recurrent Neural Network,Data Generation,Diffusivity Values,Distraction Detection,EEG Channels,EEG Data,EEG Features,EEG Signals,EEG-based Emotion Recognition,