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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Zhidekulova, Gulkiz
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Computer Science & IT Electrical & Electronics Engineering

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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.
Soil erosion analysis based on machine learning method Bolsynbek, Mukhammed; Abdikerimova, Gulzira; Serikbayeva, Sandugash; Batyrkhanov, Ardak; Shrymbay, Dana; Taszhurekova, Zhazira; Zhidekulova, Gulkiz; Shraimanova, Gulmira
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.v14i6.10452

Abstract

Soil erosion poses a serious environmental and agricultural threat that undermines land productivity, sustainability, and ecosystem stability. This study develops a robust machine learning framework for predicting and analyzing soil erosion across diverse landscapes by integrating advanced remote sensing data, climate indicators, and soil characteristics. Spectral indices such as the normalized difference vegetation index (NDVI), moisture stress index (MSI), and surface albedo were employed to assess vegetation condition, moisture levels, and surface reflectance. The proposed model, based on the extreme gradient boosting (XGBoost) algorithm, classifies erosion stages with up to 99% accuracy, ranging from healthy land to severely degraded areas. The methodology includes comprehensive feature engineering, dataset preprocessing, and model evaluation. Furthermore, a comparative analysis with traditional models (USLE and RUSLE) highlights the superior predictive performance of the proposed approach. The findings offer valuable insights for sensor-based monitoring systems and cloud-based decision-support tools, supporting sustainable land use management, erosion risk mitigation, and effective soil conservation strategies.
Co-Authors Abdikerimova, Gulzira Akhmetzhanova, Shynar Altynbekova, Zhanar Batyrkhanov, Ardak Bolsynbek, Mukhammed Borankulova, Gauhar Murzakhmetov, Aslanbek Sadirmekova, Zhanna Serikbayeva, Sandugash Shraimanova, Gulmira Shrymbay, Dana Taszhurekova, Zhazira Tulenbayev, Murat Tungatarova, Aigul Tussupov, Jamalbek