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Background: Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of infection caused by a coronavirus in the respiratory tract can vary from mild to lethal. A serious illness can be developed in old people and those with underlying medical problems like diabetes, cardiovascular disease, cancer, and chronic respiratory disease. For the diagnosis of the coronavirus disease, due to the growing number of cases, a limited number of test kits for COVID-19 are available in the hospitals. Hence, it is important to implement an automated system as an immediate alternative diagnostic option to pause the spread of COVID-19 in the population. Objective: This paper proposes a deep learning model for classification of coronavirus infected patient detection using chest X-ray radiographs. Methods: A fully connected convolutional neural network model is developed to classify healthy and diseased X-ray radiographs. The proposed neural network model consists of seven convolutional layers with rectified linear unit, softmax (last layer) activation functions and max pooling layers which were trained using the publicly available COVID-19 dataset. Results and Conclusion: For validation of the proposed model, the publicly available chest X-ray radiograph dataset consisting COVID-19 and normal patient’s images were used. Considering the performance of the results that are evaluated based on various evaluation metrics such as precision, recall, MSE, RMSE & accuracy, it is seen that the accuracy of the proposed CNN model is 98.07%.

Demand for the high performance computing for industry, education and research is continuously motivating the application development industry to manoeuvre the existing and the upcoming applications towards the cloud. The deployment or the migration of the new or existing application on cloud data centres demands greater skills for development and maintenance of the applications. The biggest challenges for the data centre service providers are to balance the bottleneck of performance and cost. Many application or service owners have demanded for higher performance at a higher cost. Nevertheless, the traditional customers base have attracted towards cloud computing due to the low cost and higher performances. Thus for the data centre service providers it is the challenge to make the highest performance available towards the customers in the least cost. In order to make this challenge possible, the data centre service providers deploy various load balancing strategies for the most effective use. A number of research attempts are made towards achieving the best possible load balancing strategy by number of parallel research attempts. Many parallel research attempts have demonstrated the adaptive, strategic and just in time scheduling and load balancing algorithms with notable reduction in time for scheduling. Nonetheless, the previous works are always outperformed by the new algorithms proposed by other research works. In the recent time, the use of genetic algorithms and genetic optimization algorithms has demonstrated higher performances. However, the uses of biogenetic algorithms have the chance to improve the performance further as always. Thus, this work proposes an additional optimization using proposed bio genetic optimization for effective balancing the data centre tasks. The proposed method demonstrates the 4% higher performance compared to the existing methods for a less loaded data centre and 16% improvements for a highly loaded data centre.