Computer modeling is designed to predict the behavior of complex systems by solving the corresponding mathematical equations of the physical process. The simulation requires a huge …
In this paper, we use the method of stochastization of one-step processes to demonstrate the advantages of a stochastic representation of the system. To achieve this goal, the …
The purpose of this work is to study the capabilities of the Julia language for numerical modeling of stochastic systems. As a stochastic system we consider the model of …
Recently, the place of the main programming language for scientific and engineering computations has been little by little taken by Julia. Some users want to work completely within …
In research problems that involve the use of numerical methods for solving systems of ordinary differential equations (ODEs), it is often required to select the most efficient …
This work is devoted to studying the possibilities of hybrid modeling of communication network algorithms in the Julia programming language. A simulation of the system, which …
The history of using machine learning algorithms to analyze statistical models is quite long. The development of computer technology has given these algorithms a new breath. …
It is known that modeling consists of a few stages. Analytic calculations are made first and afterwards, they are converted into numerical ones. It is a fairly complicated task, …
In this paper the computer paralinguistic model for emotions recognition based on deep neural networks is proposed. The main stages of its construction were studied and relevant …
Imbalance of the classes, characterized by a disproportional ratio of observations in each class, is one of the significant problems in machine learning. Class imbalances can be …
This work is devoted to the study of the capabilities of the Modelica and Julia programming languages for the implementation of a continuously discrete paradigm in modeling hybrid …
In this paper, two approaches (combinatorial and operatorial) to the stochastization of the one-step processes are discussed for the closed and open version of the Lotka–Volterra …
This paper examines the problem of detecting skin malignancies, in particular, melanoma, from the analysis of dermoscopic images using deep learning methods. For this purpose, a …
The paper investigates the problem of recognizing human emotions by voice using deep learning methods. Deep convolutional neural networks and recurrent neural networks with …
A practical and simple stable method for calculating Fourier integrals is proposed, effective both at low and at high frequencies. An approach based on the fruitful idea of Levin, …
This paper has a practical aim. For a long time, implementations of pseudorandom number generators in standard libraries of programming languages had poor quality. The situation …
Computer algebra systems are complex software systems that cover a wide range of scientific and practical problems. However, the absolute coverage cannot be achieved. Often, it is …
This article presents a numerical method to determine the approximate solutions of the Lienard equations. It is assumed that the second-order nonlinear Linard differential …
According to the modest opinion of the authors, data science, machine learning, neural networks are extremely relevant areas of science. Around them there was an expectation that …