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<span>Technological advancements are helping people with special needs overcome many communications’ obstacles. Deep learning and computer vision models are innovative leaps nowadays in facilitating unprecedented tasks in human interactions. The Arabic language is always a rich research area. In this paper, different deep learning models were applied to test the accuracy and efficiency obtained in automatic Arabic sign language recognition. In this paper, we provide a novel framework for the automatic detection of Arabic sign language, based on transfer learning applied on popular deep learning models for image processing. Specifically, by training AlexNet, VGGNet and GoogleNet/Inception models, along with testing the efficiency of shallow learning approaches based on support vector machine (SVM) and nearest neighbors algorithms as baselines. As a result, we propose a novel approach for the automatic recognition of Arabic alphabets in sign language based on VGGNet architecture which outperformed the other trained models. The proposed model is set to present promising results in recognizing Arabic sign language with an accuracy score of 97%. The suggested models are tested against a recent fully-labeled dataset of Arabic sign language images. The dataset contains 54,049 images, which is considered the first large and comprehensive real dataset of Arabic sign language to the furthest we know.</span>

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Multi-view fusion approaches have gained increasing interest in the last few years by researchers. This interest comes due to the many perspectives that datasets can be looked at and evaluated. One of the most urging areas that require constant leveraging with latest technologies and multi-perspective judgments, is the area of Psychology. In this paper, a novel multi-view fusion model using deep learning algorithms is presented to detect popular types of Personality Disorders among the Arab users of Twitter Platform in an expert driven fashion, based on the descriptions of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The work presented is the first of its kind with no publicly available datasets that report statements around Personality Disorders in the Arabic Language, thus we created AraPerson, a dataset which consists of 8000 textual tweets coupled with 8000 images that prescribe mental statuses for a total of 150 users collected with regular expressions generated under the supervision of domain experts. The dataset was fed into a baseline multi-view model by combining a CNN model with a Bi-LSTM model to detect two types of popular personality disorders by analyzing textual and visual posts on 150 users’ profiles. The experiments were followed with fusing DenseNet model with Bi-LSTM model, with experimenting the effect of using Concatenation, Addition, and Multiplication methods for vectors’ combination. The work presented in this paper is unprecedented, specifically in a controversial area such as personality disorders detection among Arab communities. The best reported accuracy is 87% which is very promising as the two types of personality disorders, which were investigated, are highly overlapping.