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Abstract-An important problem in teaching courses in computer architecture and organization is to find a way to help students to make a cognitive leap from the blackboard description of a computer system to its utilization as a programmable device. Computer simulators developed to tackle this problem vary in scope, target architecture, user interface, and support for distance learning. Usually, they include the processor only, lacking the whole-system perspective. The existing simulators mainly focus on the programmer's view of the machine and do not provide the designer's perspective. This paper presents an educational computer system and its Web-based simulator, designed to help teaching and learning computer architecture and organization courses. The educational computer system is designed to cover a broad spectrum of topics taught in lower division courses. It offers a unique environment that exposes students to both the programmer and the designer's perspective of the computer system. The Web-based simulator features an interactive animation of program execution and allows students to navigate through different levels of the educational computer system's hierarchy-starting from the top level with block representation down to the implementation level with standard sequential and combinational logic blocks.

Developing countries have had numerous obstacles in diagnosing the COVID-19 worldwide pandemic since its emergence. One of the most important ways to control the spread of this disease begins with early detection, which allows that isolation and treatment could perhaps be started. According to recent results, chest X-ray scans provide important information about the onset of the infection, and this information may be evaluated so that diagnosis and treatment can begin sooner. This is where artificial intelligence collides with skilled clinicians’ diagnostic abilities. The suggested study’s goal is to make a contribution to battling the worldwide epidemic by using a simple convolutional neural network (CNN) model to construct an automated image analysis framework for recognizing COVID-19 afflicted chest X-ray data. To improve classification accuracy, fully connected layers of simple CNN were replaced by the efficient extreme gradient boosting (XGBoost) classifier, which is used to categorize extracted features by the convolutional layers. Additionally, a hybrid version of the arithmetic optimization algorithm (AOA), which is also developed to facilitate proposed research, is used to tune XGBoost hyperparameters for COVID-19 chest X-ray images. Reported experimental data showed that this approach outperforms other state-of-the-art methods, including other cutting-edge metaheuristics algorithms, that were tested in the same framework. For validation purposes, a balanced X-ray images dataset with 12,000 observations, belonging to normal, COVID-19 and viral pneumonia classes, was used. The proposed method, where XGBoost was tuned by introduced hybrid AOA, showed superior performance, achieving a classification accuracy of approximately 99.39% and weighted average precision, recall and F1-score of 0.993889, 0.993887 and 0.993887, respectively.

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