The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.
This literature presents an improved maximum power point tracking (MPPT) controller based on radial basis function neural network (RBFNN) control strategy to extract optimal power for wind power generation system. The proposed RBFNN controller is trained online using gradient descent algorithm and its network learning rate modification is carried out by the modified particle swarm optimization algorithm. The proposed MPPT controller uses optimal torque control methodology to extract optimal power available in the wind by upholding the generated torque at an optimal level. The most promising aspects of the proposed controller are that it not only extracts maximum available power from wind, but it also rapidly responses to the change in wind speeds and maintains converter with negligible converter losses. To evaluate the performance of the proposed MPSO-RBFNN-based MPPT controller, an extensive simulation study and experimental analysis is performed. The attained results confirm the enhanced performance of the proposed MPPT controller.
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