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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Murzakhmetov, Aslanbek
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Computer Science & IT Electrical & Electronics Engineering

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Approach to automating the construction and completion of ontologies in a scientific subject field Sadirmekova, Zhanna; Murzakhmetov, Aslanbek; Abduvalova, Ainur; Altynbekova, Zhanar; Makhatova, Valentina; Akhmetzhanova, Shynar; Tasbolatuly, Nurbolat; Serikbayeva, Sandugash
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3064-3072

Abstract

Domain ontologies facilitate the organization, sharing, and reuse of subject areas. Building a software ontology is labor-intensive and time-consuming. In the process of obtaining a software ontology, it is required to analyze a huge number of scientific publications relevant to the software being modeled. The process of ontology replenishing with information from a huge number of scientific publications can be facilitated and accelerated through the use of lexical-syntactic patterns of ontological design. In this paper, we consider the possibility of automated construction of scientific subject area ontologies based on a heterogeneous patterns system of ontological design. This system includes ontological design patterns and is intended for ontology developers. System also includes automatically built lexical and syntactic patterns, which help to automatic replenishment of the ontology with information extracted from natural language texts.
Ontology engineering of automatic text processing methods Sadirmekova, Zhanna; Tussupov, Jamalbek; Murzakhmetov, Aslanbek; Zhidekulova, Gulkiz; Tungatarova, Aigul; Tulenbayev, Murat; Akhmetzhanova, Shynar; Altynbekova, Zhanar; Borankulova, Gauhar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6620-6628

Abstract

Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.
Memory management principle for dynamic isolation in agent-based epidemic modeling Murzakhmetov, Aslanbek; Borankulova, Gaukhar
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10538

Abstract

This paper presents a new epidemiological modeling approach that adapts the working set (WS) concept from computer memory management to the dynamics of infectious diseases. Traditional compartmental models provide valuable insights but are limited in their ability to capture dynamic isolation and heterogeneous contact patterns. In contrast, the WS model conceptualizes a time-varying subset of agents actively participating in social interactions, allowing for dynamic adjustments to the rate of infection and the explicit identification of superspreaders. By incorporating isolation states for both susceptible and infected individuals, the model more realistically captures quarantine and targeted interventions. Including an incubation period reduces epidemic peaks by nearly 40% and delays them by more than three weeks, providing critical time for public health response. Within the WS model, moderate isolation reduces peak infection rates by more than three times compared to uncontrolled scenarios, while high isolation almost completely prevents large-scale spread. These results highlight the model's ability to estimate the intensity and timing of interventions with greater accuracy than traditional models. By integrating the time window parameter and computer resource management principles, the adapted WS model represents a robust and adaptable tool for analyzing epidemic dynamics. The results highlight its potential for advancing epidemic modeling and supporting real-time public health decision-making.
Co-Authors Abduvalova, Ainur Akhmetzhanova, Shynar Altynbekova, Zhanar Borankulova, Gauhar Borankulova, Gaukhar Makhatova, Valentina Sadirmekova, Zhanna Serikbayeva, Sandugash Tasbolatuly, Nurbolat Tulenbayev, Murat Tungatarova, Aigul Tussupov, Jamalbek Zhidekulova, Gulkiz