Coronavirus Disease 2019 (COVID-19) is a high death rate respiratory condition that requires easy-to-reach markers for prediction. The electrocardiograph (ECG) alterations that may occur after COVID-19 hospitalization have not been fully studied yet. COVID-19 also affects heart function, which can be seen on an ECG. As a result, ECG can be used to detect virus-infected individuals. The database consists of ECG images. In this scenario, a convolution neural network (CNN) is utilized to classify COVID-19 ECG. The model is made up of eight layers, including a convolution layer, a max-pooling layer and a dense layer. The ECG image is fed into a CNN model, which classifies the COVID-19 ECG. The model provides us with 98.11% accuracy, 98.6% sensitivity and 96.40% specificity. Although 100.00% of the categorization of normal images and COVID-19 ECGs were not accurately determined by the proposed CNN model, this is the first CNN model to categorize ECG images into normal and COVID-19 classes from the ECG database and provide additional diagnostic to medical experts. © 2021 ASSA.

This paper concerns robust synchronization and parameter identification for nonlinear gyroscope systems. Gyros are widely utilized in navigational applications where synchronization plays a vital role. A system of nonlinear dynamical equations with some parameters presents a model of gyro systems. The parameters of gyro can vary in time, which can lead to desynchronization of the gyro systems. In this paper, we assume that a gyro system has bounded time-varying unknown parameters and the synchronization problem is considered in two situations. First, the synchronization of two gyroscopes with identical dynamical model and, second, the synchronization of a gyroscope with the Rossler system. The Lyapunov stability theory with control terms is employed to cope with the problem. Also, the identification of time-varying unknown parameters is the side goal of the paper. The proposed scheme synchronizes chaotic nonlinear systems in both situations appropriately. In addition, the slave parameters converge to the nominal values of master parameters despite uncertainty. Simulation results illustrate the superiority of the proposed method.

In this paper we study the controllability problem in a Banach space for various classes of functional inclusions with causal operators with an infinite delay, and impulse effects. Basing on the topological degree theory for condensing multimaps, we prove a global theorem on the existence of trajectories for systems governed by functional inclusions. As an application, we obtain generalizations of existence theorems for the controllability problem for a semilinear first order functional differential inclusions of this type and a semilinear functional differential inclusions of a fractional order 0 < q < 1.

We consider equations defined by continuous mappings acting between finite-dimensional real vector spaces. It is assumed that the mappings are differentiable in the first variable. A regularity condition for this type of equations is obtained. It is shown that the regularity assumption implies the existence of solutions to the considered equations. Systems of two equations defined by continuous mappings acting between finite-dimensional real vector spaces are considered. It is assumed that the first mapping is differentiable in the first variable and the second mapping is differentiable in the second variable. A regularity condition for this type of systems is obtained. It is shown that the regularity assumption implies the existence of solutions to the considered system. The proofs of the main results of the paper are based on Brouwer's fixed point theorem and global implicit function theorem.

Currently, due to the successful use of neural network technologies for analyzing data of various formats, the range of problems that can be solved using mathematical modeling methods has significantly expanded. The paper deals with the topical task of analyzing speech perception by social media data and assessing the level of conflictogenity, social approval / stress of the residents regarding urban planning projects. The purpose of the paper is to develop and show on a practical example the effectiveness of the integration of neural network and mathematical models for solving such tasks. Mathematical models are built using the methods of mathematical statistics and topological data analysis.

In civil aviation, many tasks require the use of modern technologies, in particular artificial intelligence technologies. The features of the crew and dispatcher decision-support tasks that can be solved based on deep neural networks are considered. The features of the intelligent crew interface for improving interaction with the operational decision support system are considered. Optimization of air traffic management by using genetic algorithms is illustrated by the example of two tasks. A special feature of the genetic algorithm for solving the problem of optimizing the sequence and landing times of arriving aircraft is a non-standard crossing operator. A special feature of the genetic algorithm for solving the problem of constructing conflict-free trajectories of a given length at low altitude in conditions of complex terrain is the method of forming the objective function. In conclusion, there is a list of problems that need to be solved for the wider use of artificial intelligence in civil aviation.

We consider the model of the production side of the Russian economy. This model is derived as the solution of the nonlinear dynamic optimization problem of the macroeconomic agent we call Producer. This agent maximizes his discounted profit flow under technologic, demographic and financial constraints. We use the method of relaxation of complementary slackness conditions to transform the model to the more regular one and evaluate its parameters on the Russian macroeconomic data. We show that this model can successfully replicate the large set of Russian macroeconomic indicators such as gross domestic product, loans of producers, volume of fixed assets etc.

This paper considers the problems of adopting digital technologies in decision-making. Two types of digital decision technologies are described, namely, the direct (or traditional) technology with human decision-making based on computer-aided advisor systems and also the inverse technology with independent computer decision-making in which a human merely monitors the decision process. These technologies are compared with each other, and some applications are discussed.

Algebraic methods of processing data obtained by control and measurement systems to achieve angular superresolution are presented. The efficiency of using the methods in the formation of approximate images of objects at low signal-to-noise ratios is shown. The results of numerical experiments demonstrate the possibility of obtaining images with a resolution exceeding the Rayleigh criterion by 3-10 times. The robustness of the solutions obtained by the methods of algebraic exceeds many well-known approaches. The relative simplicity of the presented methods allows the use of inexpensive computing devices and perform real-time measurement processing.

The COVID-19 pandemic situation keeps on ruining and affecting the wellbeing and prosperity of the worldwide population and due to this situation, the doctors around the world are working restlessly, as the coronavirus is increasing exponentially and the situation for testing has become quite a problematic and with restricted testing units, it’s impossible for every patient to be tested with available facilities. Effective screening of infected patients through chest X-ray images is a critical step in combating COVID-19. With the help of deep learning techniques, it is possible to train various radiology images and detect COVID-19. The dataset used in our research work is gathered from different sources and a specific new dataset is generated. The proposed methodology implemented is beneficial to the medical practitioner for the diagnosis of coronavirus infected patients where predictions can be done automated using deep learning. The deep learning algorithms that are used to predict the COVID with the help of chest X-ray images are evaluated for their prediction based on performance metrics such as accuracy, precision, Recall, and F1-score. In this work, the proposed model has used deep learning techniques for COVID-19 prediction and the results have shown superior performance in prediction of COVID-19. © 2021, International Institute for General Systems Studies. All rights reserved.