Vasyl Lytvyn

Work place: Department of Information Systems and Networks, Lviv Polytechnic National University, Lviv, 79012, Ukraine

E-mail: vasyl.v.lytvyn@lpnu.ua

Website: https://orcid.org/0000-0002-9676-0180

Research Interests: Models of Computation, Theory of Computation, Mathematics of Computing, Analysis of Algorithms, Computer Architecture and Organization, Computer systems and computational processes, Software Engineering, Software Development Process, Software Construction

Biography

Vasyl Volodymyrovych Lytvyn is the chair of Information Systems and Networks Department of the Institute of Computer Science and Information Technology of Lviv Polytechnic National University, Ukraine. He graduated from Ivan Franko National University of Lviv, Ukraine, in 1997. In 2012 Lytvyn received Doctor of Technical Sciences degree. His doctoral dissertation is devoted to building decision support systems based on the ontological approach. Lytvyn’s research interests include intelligent systems, machine learning, knowledge engineering and ontology construction. He published 170 scientific papers, 4 monographs and 5 textbooks. E-mail: vasyl.v.lytvyn@lpnu.ua.

Author Articles
Modelling of an Intelligent Geographic Information System for Population Migration Forecasting

By Dmytro Uhryn Yuriy Ushenko Vasyl Lytvyn Zhengbing Hu Olga Lozynska Victor Ilin Artur Hostiuk

DOI: https://doi.org/10.5815/ijmecs.2023.04.06, Pub. Date: 8 Aug. 2023

A generalized model of population migration is proposed. On its basis, models of the set of directions of population flows, the duration of migration, which is determined by its nature in time, type and form of migration, are developed. The model of indicators of actual migration (resettlement) is developed and their groups are divided. The results of population migration are described, characterized by a number of absolute and relative indicators for the purpose of regression analysis of data. To obtain the results of migration, the author takes into account the power of migration flows, which depend on the population of the territories between which the exchange takes place and on their location on the basis of the coefficients of the effectiveness of migration ties and the intensity of migration ties. The types of migration intensity coefficients depending on the properties are formed. The lightgbm algorithm for predicting population migration is implemented in the intelligent geographic information system. The migration forecasting system is also capable of predicting international migration or migration between different countries. The significance of conducting this survey lies in the increasing need for accurate and reliable migration forecasts. With globalization and the connectivity of nations, understanding and predicting migration patterns have become crucial for various domains, including social planning, resource allocation, and economic development. Through extensive experimentation and evaluation, developed migration forecasting system has demonstrated results of human migration based on machine learning algorithms. Performance metrics of migration flow forecasting models are investigated, which made it possible to present the results obtained from the evaluation of these models using various performance indicators, including the mean square error (MSE), root mean square error (RMSE) and R-squared (R2). The MSE and RMSE measure the root mean square difference between predicted and actual values, while the R2 represents the proportion of variance explained by the model.

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Information Technologies for Decision Support in Industry-Specific Geographic Information Systems based on Swarm Intelligence

By Vasyl Lytvyn Olga Lozynska Dmytro Uhryn Myroslava Vovk Yuriy Ushenko Zhengbing Hu

DOI: https://doi.org/10.5815/ijmecs.2023.02.06, Pub. Date: 8 Apr. 2023

A method of choosing swarm optimization algorithms and using swarm intelligence for solving a certain class of optimization tasks in industry-specific geographic information systems was developed considering the stationarity characteristic of such systems. The method consists of 8 stages. Classes of swarm algorithms were studied. It is shown which classes of swarm algorithms should be used depending on the stationarity, quasi-stationarity or dynamics of the task solved by an industry geographic information system. An information model of geodata that consists in a formalized combination of their spatial and attributive components, which allows considering the relational, semantic and frame models of knowledge representation of the attributive component, was developed. A method of choosing optimization methods designed to work as part of a decision support system within an industry-specific geographic information system was developed. It includes conceptual information modeling, optimization criteria selection, and objective function analysis and modeling. This method allows choosing the most suitable swarm optimization method (or a set of methods). 

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Structural Transformations of Incoming Signal by a Single Nonlinear Oscillatory Neuron or by an Artificial Nonlinear Neural Network

By Roman Peleshchak Vasyl Lytvyn Oksana Bihun Ivan Peleshchak

DOI: https://doi.org/10.5815/ijisa.2019.08.01, Pub. Date: 8 Aug. 2019

Structural transformations of incoming informational signal by a single nonlinear oscillatory neuron or an artificial nonlinear neural network are investigated. The neurons are modeled as threshold devices so that the artificial nonlinear neural network under consideration are systems of nonlinear van der Pol type oscillatory neurons. The neurons are coupled by synaptic weight coefficients to endow the systems with the configuration topology of a chain or a ring. It is shown that the morphology of the outgoing signal – with respect to the shape, amplitude and time dependence of the instantaneous frequency of the signal – at the output of such a neural network has a higher degree of stochasticity than the morphology of the signal at the output of a single neuron. We conclude that the process of coding by a single neuron or an entire chain-like or circular neural network may be considered in terms of frequency modulations, which are known in Physics as a way to transmit information. We conjecture that frequency modulations constitute one of the ways of coding of information by the neurons in these types of neural networks.

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Time Dependence of the Output Signal Morphology for Nonlinear Oscillator Neuron Based on Van der Pol Model

By Vasyl Lytvyn Victoria Vysotska Ivan Peleshchak Ihor Rishnyak Roman Peleshchak

DOI: https://doi.org/10.5815/ijisa.2018.04.02, Pub. Date: 8 Apr. 2018

Time-frequency and time dependence of the output signal morphology of nonlinear oscillator neuron based on Van der Pol model using analytical and numerical methods were investigated. Threshold effect neuron, when it is exposed to external non-stationary signals that vary in shape, frequency and amplitude was considered.

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