Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

Computational Thinking (CT) is very useful in the process of solving everyday problems for undergraduates. In terms of content, computational thinking involves solving problems, studying data patterns, deconstructing problems using algorithms and procedures, doing simulations, computer modeling, and reasoning about abstract things. However, there is a lack of studies dealing with it and its skills that can be developed and utilized in the field of information and technology used in learning and teaching. The descriptive research method was used, and a test research tool was prepared to measure the level of (CT) consisting of (24) items of the type of multiple-choice to measure the level of "CT". The research study group consists of

The main work of this paper is devoted to a new technique of constructing approximated solutions for linear delay differential equations using the basis functions power series functions with the aid of Weighted residual methods (collocations method, Galerkin’s method and least square method).

Moment invariants have wide applications in image recognition since they were proposed.