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Coronavirus disease 2019 also known as COVID-19 has become a pandemic. The disease is caused by a beta coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The severity of the disease can be understood by the massive number of deaths and affected patients globally. If the diagnosis is fast-paced, the disease can be controlled in a better manner. Laboratory tests are available for diagnosis, but they are bounded by available testing kits and time. The use of radiological examinations that comprise Computed Tomography (CT) can be used for the diagnosis of the disease. Specifically, chest X-Ray images can be analysed to identify the presence of COVID-19 in a patient. In this paper, an automated method for the diagnosis of COVID-19 from the chest X-Ray images is proposed. The method presents an improved depthwise convolution neural network for analysing the chest X-Ray images. Wavelet decomposition is applied to integrate multiresolution analysis in the network. The frequency sub-bands obtained from the input images are fed in the network for identifying the disease.The network is designed to predict the class of the input image as normal, viral pneumonia, and COVID-19. The predicted output from the model is combined with Grad-CAM visualization for diagnosis. A comparative study with the existing methods is also performed. The metrics like accuracy, sensitivity, and F1-measure are calculated for performance evaluation. The performance of the proposed method is better than the existing methodologies and thus can be used for the effective diagnosis of the disease.

Vehicle detection has been the critical part of the traffic surveillance system for many years. However, vehicle detection method is still challenging. In this paper, differential morphology closing profile is used to extract the vehicle automatically from the traffic image. Along with closing profile, some addition operation has been applied as a part of the algorithm to get the high detection and quality rate. Result demonstrated that the novel method has an excellent detection and quality percentage. We also have compared our automated detection method with other traditional image processing based methods and the results indicate that our proposed method provides better results than traditional image processing based methods.

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Most ecological management applications use Wireless Sensor Networks (WSNs) to collect data regularly, with great temporal redundancy. As a result, a significant amount of energy is used transmitting redundant data, making it tremendously problematic to attain a satisfactory network lifetime, which is a bottleneck in enduring such environmental monitoring applications. A two-vector prediction model based on Normalized Quantile Regression (NQR) for Data Aggregation is proposed to proficiently accomplish energy reduction in synchronized data collecting cycles. The introduced NQR algorithm provides high-accuracy prediction. With accurate estimates, energy usage is reduced.Furthermore, it extends the network's lifetime. In intracluster transmissions, NQR uses a two-vector data-prediction algorithm to coordinate the anticipated sensor's reading and, as a result, minimize cumulative inefficiencies from unin-terrupted predictions. NQR algorithm can be integrated with both homogeneous and heterogeneous WSNs. When compared to existing methods, the suggested NQR methodology is shown to have high energy efficiency.The results show greater prediction accuracy, more positive predictions with high data quality, which help the network last longer.