COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach’s effectiveness.

There is an exponential growth of text data over the internet, and it is expected to gain significant growth and attention in the coming years. Extracting meaningful insights from text data is crucially important as it offers value-added solutions to business organizations and end-users. Automatic text summarization (ATS) automates text summarization by reducing the initial size of the text without the loss of key information elements. In this article, we propose a novel text summarization algorithm for documents using Deep Learning Modifier Neural Network (DLMNN) classifier. It generates an informative summary of the documents based on the entropy values. The proposed DLMNN framework comprises six phases. In the initial phase, the input document is pre-processed. Subsequently, the features are extracted using pre-processed data. Next, the most appropriate features are selected using the improved fruit fly optimization algorithm (IFFOA). The entropy value for every chosen feature is computed. These values are then classified into two classes, (a) highest entropy values and (b) lowest entropy values. Finally, the class that holds the highest entropy values is chosen, representing the informative sentences that form the last summary. The results observed from the experiment indicate that the DLMNN classifier gives 81.56, 91.21, and 83.53 of sensitivity, accuracy, specificity, precision, and f-measure. Whereas the existing schemes such as ANN relatively provide lesser value in contrast to DLMNN.

This special issue entitled "cognitive machie intelligence for Cyber-Physical Systems" addresses under-researched and controversial topics on new emerging themes of cyber-physical systems (CPS).

Abstract The Model‐Driven Engineering paradigm is aimed at raising the abstraction level of Software Engineering approaches through the systematic use of models as primary artifacts, not only in software design and development, but also to understand, interact, configure, and modify the runtime behavior of software. It tries to overcome the wall between the documentation and the real state of the implementation. For that matter, our long‐term goal seeks to reach a higher degree of interoperability among available meta‐modeling technologies through bridges among technological spaces (TS bridges). The proposed system provides several ATL (ATLAS Transformation Language) transformations that enable the application of measuring operations over ATL transformation models and rules, and the generation of different complementary end‐user models, such as SVG charts and (X)HTML reports. For this work, we have evaluated a set of meta‐modeling TS bridges among UML, MOF, Ecore, KM3, and Microsoft DSL Tools. These results provide quantitative measurements of the declarative and imperative constructs of these transformations and relative quality factors as well. In addition to this, all the top‐level results extracted from the measurement of these TS bridges are merged into one unique model in order to assist in performing a comparative study among them. This comparative study suggests that it is feasible to apply automatic transformations over transformation models, i.e. meta‐transformations. In this regard, there are many open research trends towards complete management, validation, optimization, and inference of TS bridges between complementary meta‐modeling technologies. Copyright © 2010 John Wiley & Sons, Ltd.

Reflection is a powerful tool for the adaptation of applications at runtime. The modification of Web services is a task that entails the modification and compilation of the source code, as well as the deployment of the new version of the Web service in the application server. In this paper, we introduce RAWS (Reflective and Adaptable Web Service), a Web service design model based on a reflective architecture of two levels. RAWS allows both the dynamic modification of the definition and implementation structure of the Web service, and the dynamic modification of the Web service behavior in order to change the existing code or to add new functionalities. All these dynamic modifications are performed directly on the code during execution, with no need to have the Web service source code. RAWS improves Web services adaptability and maintainability, as well as the ability for authorized clients to remotely modify them.