A terrifying spread of COVID-19 (which is also known as severe acute respiratory syndrome coronavirus 2 or SARS-COV-2) led scientists to conduct tremendous efforts to reduce the pandemic effects. COVID-19 has been announced pandemic discovered in 2019 and affected millions of people. Infected people may experience headache, body pain, and sometimes difficulty in breathing. For older people, the symptoms can get worse. Also, it can cause death because of the huge effect on some parts of the human body, particularly for those who have chronic diseases like diabetes. Machine learning algorithms are applied to patients diagnosed with Corona Virus to estimate the severity of the disease depending on their chronic diseases at an early stage. Chronic diseases could raise the severity of COVID-19 and that is what has been proved in this paper. This paper applies different machine learning techniques such as random forest, decision tree, linear regression, binary search, and k-nearest neighbor on Mexican patients’ dataset to find out the impact of lifelong illnesses on increasing the symptoms of the virus in the human body. Besides, the paper demonstrates that in some cases, especially for older people, the virus can cause inevitable death.

Introduction: The energy consumed for street lighting is a major expenditure in urban environments. According to the World Bank, it constitutes up to 65% of cities' electricity costs and 10% of their overall budgets. The demand for lighting is growing significantly due to rapid urbanization, thus eating up even more energy and money - unless smarter solutions are deployed to reduce costs. Method: In this paper, a model for street lighting was established, consisting of several lamp posts on both sides of the street. The model was the exact replica of the street lighing system inside the city of Kirkuk, Iraq. The number of objects passing along the street was monitored, both during and out of rush hours. This all was taken into account in the energy consumption calculation. The controller used for this model is Arduino UNO. The Arduino receives signals from 3 IR sensors, processes these signals, and then sends the action to the lamp posts. Fuzzy logic was applied in two cases: the first one is during the daylight, the second one is during the sunrise and the sunset, to control the intensity of the light of the lamp posts. Results: Both cases showed significant results regarding the reliability, efficiency, and countability of the system in decreasing the level of energy consumption. Conclusion: The system can be applicable for smart city projects. It is efficient, cost effective and shows reliable results in saving energy.

Introduction/purpose: The electroencephalography (EEG) signal has a great impact on the development of prosthetic arm control technology. EEG signals are used as the main tool in functional investigations of human motion. The study of controlling prosthetic arms using brain signals is still in its early stages. Brain wave-controlled prosthetic arms have attracted researchers' attention in the last few years. Methods: Several studies have been carried out to systematically review published articles as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used in the prosthetic arm and other technologies. Results: 175 articles were studied, compared, and filtered to only include the articles that have strong connections to the study. Conclusion: This study has three goals. The first one is to gather, summarize, and evaluate information from the studies published between 2011 and 2022. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control of prosthetic arms to all experts and scientists. The third goal is to recognize the gap in knowledge that demands further investigation and to recommend directions for future research in this area.

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Computer network is important for transferring data through different media to perform a high accuracy function. In this paper a computer network was designed, the network consists of several clients to communicate with each other subjectively to deliver the desired output to a certain point. The ethernet method was used and the base protocol is (IEEE803.2) which depends on the token ring principle in transferring data. The new addition to the network is to connect the network to the internet and control it remotely, where we can perform a test to the students through a site that is designed specifically for the tests. Theoretically, the test was done using Packet trainer software as well as an actual test was done for the students. The report was printed after the student finishes the exam. This network is the typical solution to transform into E-learning at universities and colleges.