<span>An extensive review of the artificial neural network (ANN) is presented in this paper. Previous studies review the artificial neural network (ANN) based on the approaches (algorithms) used or based on the types of the artificial neural network (ANN). The presented paper reviews the ANN based on the goal of the ANN (methods, and layers), which become the main objective of this paper. As a famous artificial intelligent model, ANN mimics the human nervous system in handling the information transmited by different nodes (also known as neurons) in this model. These nodes are stacked in layers and work collectively to bring about solution to complex problems. Numerous structures exist for ANN and each of these structures is designed to addressa a specific task. Basically, the ANN architecture is comprised of 3 different layers wherein the first layer rpresents the input layer that consist of several input nodes that represent the input parameterfor the model. The hidden layer is te second layer and consists of a hidden layer of neurons. The neurons in this layer are directly connected to the neurons in the output layer. The third layer is the output layer which is the models’ response layer. The output layer neurons have the activation functions for the calculation of the ANN final output. The connection between the nodes in the ANN model is mediated by synaptic weights. This paper is a comprehensive study of ANN models and their layers.</span>

Due to the conditions in which countries experienced the outbreak of the Coronavirus and our problem in diagnosing the disease, some of them relied on swabs to know if a person was infected, and also their dependence on symptoms such as temperature, rapid heartbeat, pressure, coughing and other symptoms similar to the normal flu, but this method is failure sometimes, therefore it was the best way for early detection and diagnosis of cases of COVID- 19, as well as the accurate segregation of non-COVID-19 patients at cost and in the early stages of the disease, is a major difficulty in the current COVID-19 pandemic. Although widely used in diagnostic centres, radiation-based diagnostic techniques have drawbacks when it comes to disease newness. As a result, deep learning models are commonly used for X-ray interpretation by medical and computational researchers. Deep learning models can identify COVID-19, a critical task for treatment options based on diagnostic data these days. On the other hand, advances in artificial intelligence, machine learning, deep learning, and medical imaging methods enable outstanding performance, especially in detection, classification, and segmentation issues. These advances have allowed clinicians to more accurately monitor the human body, improving diagnosis and non-surgical patient examination. There are a variety of imaging methods that can be used to identify COVID-19, but we choose to use computerized tomography (CT) because it is the most commonly used. In addition, to detect COVID-19, we use a deep learning model based on a Convolutional Neural Network (CNN). Two samples of the tested data were used, where one of these data was collected from Al-Karkh Hospital in Baghdad, which consisted of 40 people, samples were taken according to their critical condition. The system was trained and tested on the basis of this dataset, where we used CNN three times, once to extract the feature and twice for the classification process. The results showed that the accuracy of the system reaches 100% because this system depends on the Bayes rule and it is not possible error.

<span>Recently, heat-related diseases like COVID19, Chickenpox, Typhoid, and others are increasing significantly; therefore, the need for portable devices to measures the heat of the human body accurately, quickly, easily with low cost has become very necessary to preserve the life of patients. For this reason, a smart system has been developed to monitor the patient's heat, in addition to temperature and humidity of the critical environment such as surgical operating rooms, patients’ isolation rooms and pharmacies, because it can help propagate infectious agents like viruses and bacteria. The proposed system divided into four parts: transmitted part (arduino, heat sensor, and hygrometer sensor), alarm part consists of lights and alarm bell, emergency part (doctors and nurses), and the medical application has been used as the last part. The application can be used only by authorized persons and through the accounts which are granted to them, in order to protect the data from sabotage and maintain the privacy and confidentiality of it.</span>