An optimized dense convolutional neural network (CNN) architecture (DenseNet) for corn leaf disease recognition and classification is proposed in this paper. Corn is one of the most cultivated grain throughout the world. Corn crops are highly susceptible to certain leaf diseases such as corn common rust, corn gray leaf spot, and northern corn leaf blight are very common. Symptoms of these leaf diseases are not differentiable in their nascent stages. Hence, the current research presents a solution through deep learning so that crop health can be monitored and, it will lead to an increase in the quantity as well as the quality of crop production. The proposed optimized DenseNet model has achieved an accuracy of 98.06%.Besides, it uses significantly lesser parameters as compared to the various existing CNN such as EfficientNet, VGG19Net, NASNet, and Xception Net. The performance of the optimized DenseNet model has been contrasted with the current CNN architectures by considering two (time and accuracy) quality measures. This study indicates that the performance of the optimized DenseNet model is close to that of the established CNN architectures with far fewer parameters and computation time.

In a WSN, sensor node plays a significant role. Working of sensor node depends upon its battery's life. Replacements of batteries are found infeasible once they are deployed in a remote or unattended area. Plethora of research had been conducted to address this challenge, but they suffer one or the other way. In this paper, a particle swarm optimization (PSO) algorithm integrated with an energy efficient clustering and sink mobility ((PSO-ECSM) is proposed to deal with both cluster head selection problem and sink mobility problem. Extensive computer simulations are conducted to determine the performance of the PSO-ECSM. Five factors such as residual energy, distance, node degree, average energy and energy consumption rate (ECR) are considered for CH selection. An optimum value of these factors is determined through PSO-ECSM algorithm. Further, PSO-ECSM addresses the concern of relaying the data traffic in a multi-hop network by introducing sink mobility. PSO-ECSM's performances are tested against the state-of-the-art algorithms considering five performance metrics (stability period, network, longevity, number of dead nodes against rounds, throughput and network's remaining energy). Statistical tests are conducted to determine the significance of the performance. Simulation results show that the PSO-ECSM improves stability period, half node dead, network lifetime and throughput vis-à-vis ICRPSO by 24.8%, 31.7%, 9.8 %, and 12.2%, respectively.

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The trending global pandemic of COVID-19 is the fastest ever impact which caused people worldwide by severe acute respiratory syndrome~(SARS)-driven coronavirus. However, several countries suffer from the shortage of test kits and high false negative rate in PCR test. Enhancing the chest X-ray or CT detection rate becomes critical. The patient triage is of utmost importance and the use of machine learning can drive the diagnosis of chest X-ray or CT image by identifying COVID-19 cases. To tackle this problem, we propose~COVIDPEN~-~a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. The proposed model is further interpolated by post-hoc analysis for the explainability of the predictions. The effectiveness of our proposed model is demonstrated on two systematic datasets of chest radiographs and computed tomography scans. Experimental results with several baseline comparisons show that our method is on par and confers clinically explicable instances, which are meant for healthcare providers.

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