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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Bazarova, Madina
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Computer Science & IT Electrical & Electronics Engineering

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Using deep learning algorithms to classify crop diseases Murzabekova, Gulden; Glazyrina, Natalya; Nekessova, Anargul; Ismailova, Aisulu; Bazarova, Madina; Kashkimbayeva, Nurzhamal; Mukhametzhanova, Bigul; Aldashova, Madina
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.pp6737-6744

Abstract

The use of deep learning algorithms for the classification of crop diseases is one of the promising areas in agricultural technology. This is due to the need for rapid and accurate detection of plant diseases, which allows timely measures to be taken to treat them and prevent their spread. One of them is to increase productivity and maintain land quality through the timely detection of diseases and pests in agriculture and their elimination. Traditional classification methods in machine learning and algorithms in deep learning were compared to note the high accuracy in detecting pests and crop diseases. The advantages and disadvantages of each model considered during training were taken into account, and the Inception V3 algorithm was incorporated into the application. They can monitor the condition of crops on a daily basis with the help of new technology-applications on gadgets. Aerial photographs used by research institutes and agricultural grain centers do not show the changes that occur in agricultural grains, that is, diseases and pests. Therefore, the method proposed in this paper determines the types of diseases and pests of cereals through a mobile application and suggests ways to deal with them.
Analysis of the emotional coloring of text using machine and deep learning methods Abdykerimova, Lazzat; Abdikerimova, Gulzira; Konyrkhanova, Assem; Nurova, Gulsara; Bazarova, Madina; Bersugir, Mukhamedi; Kaldarova, Mira; Yerzhanova, Akbota
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.pp3055-3063

Abstract

The presented scientific article is a comprehensive study of machine learning and deep learning methods in the context of emotion recognition in text data. The main goal of the study is to conduct a comprehensive analysis and comparison of various machine learning and deep learning methods to classify emotions in text. During the work, special attention was paid to the analysis of traditional machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), and support vector machine (SVM), as well as the use of deep learning methods based on long short-term memory (LSTM). The experimental part of the study involves the analysis of different data sets covering a variety of text styles and contexts. The experimental results are analyzed in detail, identifying the advantages and limitations of each method. The article provides practical recommendations for choosing the optimal method depending on the specific tasks and context of the application. The data obtained is important for the development of intelligent systems that can effectively adapt to the emotional aspects of interaction with users. Overall, this work makes a significant contribution to the field of emotion recognition in text and provides a basis for further research in this area.
Ontological model of the process of intensification of teachers’ competencies Bazarova, Madina; Alibekkyzy, Karlygash; Adikanova, Saltanat; Bugubayeva, Alina; Zhomartkyzy, Gulnaz; Jaxalykova, Akmaral; Baidildina, Aizhan; Keribayeva, Talshyn
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp446-458

Abstract

Currently, there is a need to improve the education system and develop interdisciplinary research at all levels of education, from school to postgraduate education. The introduction of interdisciplinary connections contributes to the formation of a holistic understanding of natural phenomena and the connections between them. Thus, this knowledge becomes more meaningful and applicable in practice. This article proposes a conceptual model of the content of education in the form of a thesaurus and ontology. The use of these models will allow you to adaptively select and systematize educational information. The article also discusses the possibilities and experience of using ontological modeling and engineering for the conceptual description of school and higher education. In addition, the article discusses the development of an ontological model of the process of expanding teachers’ competencies with the integration of science, technology, engineering and mathematics (STEM) education. The use of ontological engineering methods will improve the quality of teacher education through the semantic description of knowledge in the subject area and the use of interdisciplinary and STEM approaches in the educational process. 
Co-Authors Abdikerimova, Gulzira Abdykerimova, Lazzat Adikanova, Saltanat Aldashova, Madina Alibekkyzy, Karlygash Baidildina, Aizhan Bersugir, Mukhamedi Bugubayeva, Alina Glazyrina, Natalya Ismailova, Aisulu Jaxalykova, Akmaral Kaldarova, Mira Kashkimbayeva, Nurzhamal Keribayeva, Talshyn Konyrkhanova, Assem Mukhametzhanova, Bigul Murzabekova, Gulden Nekessova, Anargul Nurova, Gulsara Yerzhanova, Akbota Zhomartkyzy, Gulnaz