Developing countries have had numerous obstacles in diagnosing the COVID-19 worldwide pandemic since its emergence. One of the most important ways to control the spread of this disease begins with early detection, which allows that isolation and treatment could perhaps be started. According to recent results, chest X-ray scans provide important information about the onset of the infection, and this information may be evaluated so that diagnosis and treatment can begin sooner. This is where artificial intelligence collides with skilled clinicians’ diagnostic abilities. The suggested study’s goal is to make a contribution to battling the worldwide epidemic by using a simple convolutional neural network (CNN) model to construct an automated image analysis framework for recognizing COVID-19 afflicted chest X-ray data. To improve classification accuracy, fully connected layers of simple CNN were replaced by the efficient extreme gradient boosting (XGBoost) classifier, which is used to categorize extracted features by the convolutional layers. Additionally, a hybrid version of the arithmetic optimization algorithm (AOA), which is also developed to facilitate proposed research, is used to tune XGBoost hyperparameters for COVID-19 chest X-ray images. Reported experimental data showed that this approach outperforms other state-of-the-art methods, including other cutting-edge metaheuristics algorithms, that were tested in the same framework. For validation purposes, a balanced X-ray images dataset with 12,000 observations, belonging to normal, COVID-19 and viral pneumonia classes, was used. The proposed method, where XGBoost was tuned by introduced hybrid AOA, showed superior performance, achieving a classification accuracy of approximately 99.39% and weighted average precision, recall and F1-score of 0.993889, 0.993887 and 0.993887, respectively.

Rapid developments in Internet of Things (IoT) systems have led to a wide integration of such systems into everyday life. Systems for active real-time monitoring are especially useful in areas where rapid action can have a significant impact on outcomes such as healthcare. However, a major challenge persists within IoT that limit wider integration. Sustainable healthcare supported by the IoT must provide organized healthcare to the population, without compromising the environment. Security plays a major role in the sustainability of IoT systems, therefore detecting and taking timely action is one step in overcoming the sustainability challenges. This work tackles security challenges head-on through the use of machine learning algorithms optimized via a modified Firefly algorithm for detecting security issues in IoT devices used for Healthcare 4.0. Metaheuristic solutions have contributed to sustainability in various areas as they can solve nondeterministic polynomial time-hard problem (NP-hard) problems in realistic time and with accuracy which are paramount for sustainable systems in any sector and especially in healthcare. Experiments on a synthetic dataset generated by an advanced configuration tool for IoT structures are performed. Also, multiple well-known machine learning models were used and optimized by introducing modified firefly metaheuristics. The best models have been subjected to SHapley Additive exPlanations (SHAP) analysis to determine the factors that contribute to occurring issues. Conclusions from all the performed testing and comparisons indicate significant improvements in the formulated problem.

Courses in Computer Architecture and Organization are regularly included in Computer Engineering curricula. These courses are usually organized in such a way that students obtain not only a purely theoretical experience, but also a practical understanding of the topics lectured. This practical work is usually done in a laboratory using simulators of computer systems. Since the open literature contains a variety of simulators being used for such purposes, this paper attempts to give a survey of simulators suitable for teaching courses in computer architecture and organization, to establish the evaluation criteria and to evaluate selected simulators according to these criteria.

This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).