COVID-19, one of the most highly transmissible diseases in the twenty-first century, has had a profound impact on global lifestyles. Recently, the medical industry has increasingly relied on machine learning, which shows promise in anticipating the presence of COVID-19. By using machine learning techniques, test result turnaround time can be accelerated, and medical personnel can promptly attend to patients' needs. These algorithms analyze various attributes to classify COVID patients and predict their likelihood of contracting the disease. This study aims to utilize X-ray images processed by machine learning algorithms to predict the occurrence of COVID-19 and enhance its detection rate. The paper outlines two strategies employing machine learning techniques: one for predicting the likelihood of infection and the other for identifying positive cases. Different machine learning algorithms, such as decision trees, logistic regression, support vector machines, naive Bayes, and artificial neural networks, were employed. The simulation results reveal that the artificial neural networks model outperforms other methods in terms of accuracy rate.

Wireless sensor network (WSN) is a kind of network specifically suitable for place where infrastructure and resources are playing a vital role. Moreover, nodes in a WSN are autonomous in nature. WSNs can be able to solve various real-time problems and issues like smart healthcare, smart office, smart energy, smart home, etc. As energy becomes one of the scarce supplies for this kind of network, attacks against authentication help to validate the legitimacy of sensor nodes become foremost important. Such attacks exhaust the power of nodes that are currently connected to a WSN, thereby reducing their lifetime. In this article, a zonal node authentication technique as well as optimal data access scheduling that renders data deliverance with improved quality of service and network lifetime is proposed. The results obtained from simulation for diverse WSN topologies accentuate the claim of our method over the existing solutions and demonstrate to be efficient in discovering legitimate sensor nodes with the optimal workload. Besides improved network lifetime, efficiency, and throughput, the proposed method also reinforces the security measures of the WSN by integrating node authentication.