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Lately, the COVID-19 pandemic is the first reason of deaths in people. Where the number of patients who have the same symptoms increased; however, the main causative agent separated and analysed. At the first, it called a novel coronavirus (2019-nCoV) then it was renamed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that cause a disease called COVID- 19. One of the symptoms of the disease is the difficulty breathing caused by lung damage that needs to be detected earlier as much as possible. Chest X-ray images are one of medical images, which can be read by physicians to detect COVID-19. In this paper, the suggested Schema of convolutional neural network (CNN) that can aid the doctors in hospital to improve the diagnosis of the five different classes (COVID-19, MERS SARS, ARDS and Normal). For evaluating testing set, the practical outcomes demonstrates the suggested Schema of CNN classifier with an accuracy of 98%.

The abilities of human brain to discover solutions for many problems is a great gift that motivate the scientists to develop the revolution of the artificial intelligence and using it in many areas. This paper proposed an intelligent chessboard which works in a way that similar to the human brain that predicts the next positions for any piece of the chess. MATLAB is used in the training of the artificial neural networks of the chessboard andimplemented on FPGA as a hardware part; the proposed system has many advantages such as the low cost of production with a high efficiency in the prediction of the position of any piece on the chessboard and the similarities to the human brain.