The paper focuses on the relationship between businesses and digital transformation, and how digital transformation has changed manufacturing in several ways. Aspects like Cloud Computing, vertical and horizontal integration, data communication, and the internet have contributed to sustainable manufacturing by decentralizing supply chains. In addition, digital transformation inventions such as predictive analysis and big data analytics have helped optimize sustainable manufacturing by reducing overproduction or underproduction through predicting customer demands. It integrates digital technology to enhance business operations, consumer engagement, supply chains, and coordination, the manufacturing process, energy conservation, efficiency, and environmental conservation and culture to satisfy business needs. Businesses’ failure to embrace digital transformation in this era contributes to their demise. This research paper will analyze and contrast several businesses and the extent of digital transformation’s influence on them during COVID-19. A two-stage study is conducted, the first stage assesses a chosen exemplary business success over three years. The second stage investigates the reasons for success, or otherwise, and the connection to digitalization in the business. Our outcomes suggest that digital transformation strongly influences firms’ effectiveness and survival from a technology-centric and business model standpoint. Some essential generic recommendations are suggested based on the results obtained.

In recent years, a lot of attention has been paid to using radiology imaging to automatically find COVID-19. (1) Background: There are now a number of computer-aided diagnostic schemes that help radiologists and doctors perform diagnostic COVID-19 tests quickly, accurately, and consistently. (2) Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia. First, a pre-processing method based on a Gaussian filter and logarithmic operator is applied to input chest X-ray (CXR) images to improve the poor-quality images by enhancing the contrast, reducing the noise, and smoothing the image. Second, robust features are extracted from each enhanced chest X-ray image using a Convolutional Neural Network (CNNs) transformer and an optimal collection of grey-level co-occurrence matrices (GLCM) that contain features such as contrast, correlation, entropy, and energy. Finally, based on extracted features from input images, a random forest machine learning classifier is used to classify images into three classes, such as COVID-19, pneumonia, or normal. The predicted output from the model is combined with Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation for diagnosis. (3) Results: Our work is evaluated using public datasets with three different train–test splits (70–30%, 80–20%, and 90–10%) and achieved an average accuracy, F1 score, recall, and precision of 97%, 96%, 96%, and 96%, respectively. A comparative study shows that our proposed method outperforms existing and similar work. The proposed approach can be utilised to screen COVID-19-infected patients effectively. (4) Conclusions: A comparative study with the existing methods is also performed. For performance evaluation, metrics such as accuracy, sensitivity, and F1-measure are calculated. The performance of the proposed method is better than that of the existing methodologies, and it can thus be used for the effective diagnosis of the disease.

The work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space based on a single low resolution depth image. The main challenge was to deal with a noisy and coarse representation of the human body, as observed by a depth sensor from a large distance, and to achieve high localization precision. The regression network was expected to reason about relations of body parts based on depth image, and to extract locations of joints, and provide coordinates defining the body pose. The method involved creation of a dataset with 200,000 realistic depth images of a 3D body model, then training and testing numerous architectures including feedforward multilayer perceptron network and deep convolutional neural networks. The results of training and evaluation are included and discussed. The most accurate DNN network was further trained and evaluated on an augmented depth images dataset. The achieved accuracy was similar to a reference Kinect algorithm results, with a great benefit of fast processing speed and significantly lower requirements on sensor resolution, as it used 100 times less pixels than Kinect depth sensor. The method was robust against sensor noise, allowing imprecision of depth measurements. Finally, our results were compared with VGG, MobileNet, and ResNet architectures.

Rough set-based approach to the classification of EEG signals of real and imaginary motion is presented. The pre-processing and signal parametrization procedures are described, the rough set theory is briefly introduced, and several classification scenarios and parameters selection methods are proposed. Classification results are provided and discussed with their potential utilization for multimedia applications controlled by the motion intent. Accuracy metrics of classification for real and imaginary motion obtained with different parameter sets are compared. Results of experiments employing recorded EEG signals are commented and further research directions are proposed.