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A fault in a power system is an anomalous state that must be recognized as soon as feasible. To minimize the repercussions of the fault, such as damages occurred to the device, loss of tangible assets and loss of human resources, it is critical to notice the problem promptly. In a power distribution system, there are several approaches for detecting different types of faults. In this paper, a neoteric approach using Bayesian optimized Convolutional Neural Network is used to detect and classify different symmetrical as well as unsymmetrical faults in power distribution systems. The effectiveness of the proposed CNN model is validated for an IEEE 13 bus radial distribution system grid modeled (and simulated) in PSCAD. Time series of the measured 3-phase fault currents (for eleven different categories of faults) are used to create training & testing data. This data has been imported in MATLAB software to develop a CNN classifier (whose hyper-parameters are optimized by using a Bayesian optimizer) for faults in power distribution system, under distinct fault situations by varying fault resistance, faulty node and fault inception angles. Findings of simulation clearly indicate that proposed model has very high categorizing accuracy and is superior and competitive to other techniques available in literature. [ABSTRACT FROM AUTHOR]

In December of 2019, a novel coronavirus (COVID-19) appeared in Wuhan city, China and has been reported in many countries with millions of people infected within only four months. Chest computed Tomography (CT) has proven to be a useful supplement to reverse transcription polymerase chain reaction (RT-PCR) and has been shown to have high sensitivity to diagnose this condition. Therefore, radiological examinations are becoming crucial in early examination of COVID-19 infection. Currently, CT findings have already been suggested as an important evidence for scientific examination of COVID-19 in Hubei, China. However, classification of patient from chest CT images is not an easy task. Therefore, in this paper, a deep bidirectional long short-term memory network with mixture density network (DBM) model is proposed. To tune the hyperparameters of the DBM model, a Memetic Adaptive Differential Evolution (MADE) algorithm is used. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) images datasets. Comparative analysis reveals that the proposed MADE-DBM model outperforms the competitive COVID-19 classification approaches in terms of various performance metrics. Therefore, the proposed MADE-DBM model can be used in real-time COVID-19 classification systems. [ABSTRACT FROM AUTHOR]

Multispectral LiDAR (Light Detection And Ranging) is characterized of the completeness and consistency of its spectrum and spatial geometric data, which provides a new data source for land-cover classification. In recent years, the convolutional neural network (CNN), compared with traditional machine learning methods, has made a series of breakthroughs in image classification, object detection, and image semantic segmentation due to its stronger feature learning and feature expression abilities. However, traditional CNN models suffer from some issues, such as a large number of layers, leading to higher computational cost. To address this problem, we propose a CNN-based multi-spectral LiDAR land-cover classification framework and analyze its optimal parameters to improve classification accuracy. This framework starts with the preprocessing of multi-spectral 3D LiDAR data into 2D images. Next, a CNN model is constructed with seven fundamental functional layers, and its hyper-parameters are comprehensively discussed and optimized. The constructed CNN model with the optimized hyper-parameters was tested on the Titan multi-spectral LiDAR data, which include three wavelengths of 532 nm, 1064 nm, and 1550 nm. Extensive experiments demonstrated that the constructed CNN with the optimized hyper-parameters is feasible for multi-spectral LiDAR land-cover classification tasks. Compared with the classical CNN models (i.e., AlexNet, VGG16 and ResNet50) and our previous studies, our constructed CNN model with the optimized hyper-parameters is superior in computational performance and classification accuracies. [ABSTRACT FROM AUTHOR]