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The COVID-19 coronavirus illness is caused by a newly discovered species of coronavirus known as SARS-CoV-2. Since COVID-19 has now expanded across many nations, the World Health Organization (WHO) has designated it a pandemic. Reverse transcription-polymerase chain reaction (RT-PCR) is often used to screen samples of patients showing signs of COVID-19; however, this method is more expensive and takes at least 24 hours to get a positive or negative response. Thus, an immediate and precise method of diagnosis is needed. In this paper, chest X-rays will be utilized through a deep neural network (DNN), based on a convolutional neural network (CNN), to detect COVID-19 infection. Based on their X-rays, those with COVID-19 indications may be categorized as clean, infected with COVID-19 or suffering from pneumonia, according to the suggested CNN network. Sample pieces from every group are used in experiments, and categorization is performed by a CNN. While experimenting, the CNN-derived features were able to generate the maximum training accuracy of 94.82% and validation accuracy of 94.87%, The F1-scores were 97%, 90% and 96%, in clearly categorizing patients afflicted by COVID-19, normal and having pneumonia, respectively. Meanwhile, the recalls are 95%, 91% and 96% for COVID-19, normal and pneumonia, respectively.

The promising services offered by cloud computing environments have led to huge amount of data that need to be processed and stored. Wireless cloud networks rely on Transmission Control Protocol/Internet Protocol (TCP/IP) for reliable transfer of data traffic between the cloud end-users and servers and vise-versa. Even though TCP has been successful for several applications, it, however, does not perform well in wireless cloud environments. The many-to-one communication pattern used in such environments with such huge amount of data resulted in TCP incast problem. Transmission Control Protocol incast problem happens in cluster based storage workloads where a lot of end-users communicate simultaneously to a server in the cloud through a bottleneck router, creating buffers overflows which lead to high packet loss. This paper presents an empirical study on TCP incast in current wireless cloud networks and how it is caused. It evaluates TCP-Vegas and TCP-Sack to examine their behaviors and suitability for short-lived connections in terms of queue occupancy level, packet drops, throughput, link utilization and bandwidth unfairness between the TCP connections. It was found that both protocols suffer from high packet loss and link underutilization with comparable throughput.

In this study paper, the feasibility of constructing a complete smart system for anticipating electrical power consumption is created, as electricity's market share is expected to expand over the future decades. Smart grids and smart meters will help utility companies and their customers soon. New services and businesses in energy management need software development and data analytics skills. New services and enterprises are competitive. The project's electricity consumers are categorized by their hourly power usage percentage. This classification was done using data mining (five algorithms in specific) and data analysis theory. This division aims to help each group minimize energy use and expenditures, encourage energy-saving activities, and promote consumer involvement by giving tailored guidance. The intended segmentation is done through an iterative process using a computer classification computation, post-analysis, and data mining with visualization and statistical methodologies.