Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.

The modern industrial sector requires an intelligent fault diagnosis system to ensure reliable and safe processingsince traditional methods require expert diagnosis, which consumes time and requires labor. Furthermore, diagnosticresults are influenced by the expert’s expertise and in-depth knowledge of the machine. The objective of this paper isto solve the manual intervention problem and improve the fault diagnosis. We propose a novel two-stage unsupervisedlearning algorithm based on artificial intelligence (AI) that learns fault features efficiently from raw vibration signals.To accomplish the aforementioned goal, we encapsulate the two-stage learning technique such as sparse filtering andRectified Linear Unit (ReLU) regression function. As a first step, we used a two-layer neural network sparse filteringprocedure to extract vibration signals’ features. Based on vibration signals, ReLU regression determines the healthcondition of the machine in the second phase. ReLU is a linear function that improves the performance of neural network training. Here we utilized a sigmoid and softmax regression function to compare the performance of ReLU. Thesigmoid function works well for binary classification, whereas softmax works well for multiclass classification. A database of motor-bearing vibration signals containing signals about four different health conditions of machines, suchas Inner race faults (IF), Outer race faults (OF), and rolling faults (RF). The sparse filter is evaluated on different inputand output dimensions, which significantly increases the learning accuracy. We classified the health condition usingReLU and achieved 93.8% accuracy, which is higher than sigmoid and softmax. Through the two-step learning process, machine fault diagnosis is enhanced, as well as big data is effectively handled.

Deep learning is a subset of machine learning based on learning data representations, in contrast to task-specific algorithms. Deep learning models derive inspiration from how information is processed in the nervous system of the human body that consists of trillions of neurons communicating with each other. During a disaster, it is necessary to ensure that containment and rescue operations are conducted as quickly as possible with a primary focus on affected areas, as an improper organization might lead to wastage of resources such as money, materials, and time. To properly plan during disasters, satellite images of the affected location can be analyzed to identify the areas demanding immediate attention. A model can be designed using convolutional neural networks (CNNs) to help categorize the areas by the degree of destruction. To secure data fed into the model, a layer of security can be added between the input and output layers of the CNN. The model can be trained using old satellite images of the cities. New images fed into the model can be analyzed to obtain information on the level of devastation.