Information Systems, Multidisciplinary, Computer Science, Artificial Intelligence
6
Scopus Publications
Scopus Publications
Clustering based Medical Image Segmentation: A Study on MRI Scans of Brain Tumors Aigul B. Mimenbayeva, Aliya A. Aruova, Gulmira K. Bekmagambetova, Rozamgul S. Niyazova, Rakhila D. Turebayeva, Akgul A. Naizagarayeva, Ainur F. Tursumbayeva Icaai 2024 Conference Proceedings of the 2024 8th International Conference on Advances in Artificial Intelligence, 2025 This study investigates the application of Hierarchical clustering for image segmentation, with a focus on its efficacy in analyzing medical images, particularly MRI scans of brain tumors. Image segmentation plays a pivotal role in computer vision, facilitating various applications across industries. Leveraging a systematic approach, we conduct a comprehensive review of recent literature on machine learning algorithms for image segmentation. Subsequently, utilizing a dataset comprising MRI images with and without tumors, we preprocess and analyze the data using the Histogram of Oriented Gradients (HOG) technique to extract pertinent features. These features serve as input for the Hierarchical clustering algorithm to partition the images into distinct regions of interest. For each row of vectors, the Jensen-Shenton distance was calculated. The resulting symmetric matrices are distances among the corresponding vectors, quantifying dissimilarity in cluster analysis. Our findings underscore the effectiveness of Hierarchical clustering in clustering medical images, with potential implications for advancing computational analysis in healthcare and related domains.
Neural Network with Fine-Tuned BERT for IELTS Writing Evaluation Dias Ilyas, Aigul Mimenbayeva, Almagul Kadirbayeva Sist 2025 2025 IEEE 5th International Conference on Smart Information Systems and Technologies Conference Proceedings, 2025 In recent years, advancements in Natural Language Processing (NLP) have paved the way for automated systems that can efficiently evaluate written texts, offering significant improvements over traditional human grading methods. One critical area where these systems are being applied is in the evaluation of IELTS writing tasks. This paper presents a novel approach to automating IELTS writing evaluation by utilizing neural network architecture with the BERT tokenizer. We explore how pre-trained transformer models, particularly BERT, can be fine-tuned for multi-dimensional essay scoring, addressing various facets such as coherence, lexical resources, grammatical accuracy, and task response. The proposed method leverages BERT's ability to understand contextual relationships within text, allowing for a more nuanced and detailed evaluation compared to conventional machine learning techniques. The model is trained on a dataset consisting of IELTS Writing Task 2 responses, and its performance is measured against standard scoring criteria. Preliminary results indicate that the use of the BERT tokenizer significantly improves the model’s ability to assess essay quality, achieving high correlation with human-assigned scores. This work contributes to the growing field of automated essay scoring (AES), offering a robust framework that can be applied to large-scale language proficiency testing systems like IELTS, ultimately enhancing the efficiency and accuracy of language assessments.
Analysis of Machine Learning Algorithms for Forecasting Harvest Data in Kazakhstan Gaukhar Toktagulova, Aigul Mimenbayeva Sist 2024 2024 IEEE 4th International Conference on Smart Information Systems and Technologies Proceedings, 2024 This study presents a comparative analysis of machine learning algorithms for forecasting harvest data in Kazakhstan. Leveraging extensive datasets spanning over three decades, our research evaluates the performance and predictive accuracy of various algorithms in forecasting potato yields—the predominant crop in the region. Through empirical analysis, Linear Regression, Lasso Regression, and Ridge Regression emerge as top-performing models, exhibiting R^2 scores exceeding 0.95. These linear models effectively capture the complex relationship between weather conditions and agricultural outcomes, offering valuable insights for agricultural planning and decision-making. Additionally, Gradient Boosting and ElasticNet algorithms demonstrate competitive performance, highlighting the potential of ensemble learning techniques in agricultural forecasting. Conversely, Support Vector Regression (SVR) exhibits poor performance in this context, emphasizing the importance of selecting appropriate algorithms tailored to the specific characteristics of agricultural datasets. Overall, our findings underscore the significance of employing advanced machine learning techniques to enhance the accuracy and reliability of harvest forecasts, thereby empowering stakeholders in the agricultural sector to make informed decisions and ensure food security in Kazakhstan.
An Artificial Neural Network Model to Forecast Grain Crops in North-Kazakstan Agricultural Experimental Station Aigul Mimenbayeva, Maylen Omirtay, Rozamgul Niyazova, Gulmira Bekmagambetova, Raya Suleimenova, Ainur Tursumbayeva Ciees 2023 IEEE International Conference on Communications Information Electronic and Energy Systems, 2023 The paper analyzes and predicts the yield of grain crops of LLP “North-Kazakhstan Agricultural Experimental Station” for the last 30 years. The class of artificial neural networks-multilayer perceptron (MLP) was used as a research tool. The process of training of a model of a neural network consisting of 30 networks was performed on the basis of 70% training and 30% control samples. Based on the analysis of residuals histogram and scatter plot of the target and output function, the best neural network models MLP-1-7-1 (p=0.92), MLP-1-8-1 (p=0.97), MLP-1-5-1 (p=0.82) were selected. Next, using the best MLP 1-8-1 network, the model was tested the given data. Using time series projection, a graph was illustrated and a table of predicted grain yield values for the coming years was tabulated. The absolute error showed the high accuracy of the obtained best MLP 1-8-1 network for forecasting the amount of grain crop yield. The obtained model of artificial network can be applied in research and monitoring of agricultural production development.
DETERMINATION OF THE NUMBER OF CLUSTERS OF NORMALIZED VEGETATION INDICES USING THE K-MEANS ALGORITHM Aigul Mimenbayeva, Samat Artykbayev, Raya Suleimenova, Gulnar Abdygalikova, Akgul Naizagarayeva, Aisulu Ismailova Eastern European Journal of Enterprise Technologies, 2023 The process of clustering of normalized vegetation indices in five regions with a total area of 2565 hectares of the North Kazakhstan region was studied. A methodological approach to organizing the clustering process is proposed using the vegetation indices NDVI, MSAVI, ReCI, NDWI and NDRE, taking into account individual characteristics in the three main phases of spring wheat development As a result of the research, vegetation indices were grouped into 3 classes using the k-means clustering method. The first cluster contained vegetation indices whose maximum values occupied about 33.98% of the total area of the study area. It was found that NDVImax located in the first cluster was positively correlated with soil-corrected vegetation indices MSAVI and crop moisture indicators NDMI (R2=0.92). The second cluster is characterized by minimum values of NDVImax coefficients at the germination, tillering and ripening phases (from 0.53 to 0.55). The lowest values of vegetation indices occupied 35.9 % in the germination phase, 37.9 % in the tillering phase, and 40.1 % of the field from the total area. The third cluster is characterized by average values of vegetation indices in all three phases. A correlation matrix was also constructed to assess the closeness of the relationship between actual yield and NDVI vegetation indices. The maximum coefficient was obtained at the germination phase, R=0.94 with a minimum significance coefficient p=0.018. The approach used in this study can be useful in the analysis of satellite data, as it can improve the sensitivity of the constellation procedure. From a practical point of view, the results obtained make it possible to assess the condition of agricultural crops in the early stages of the growing season, which makes it possible to improve their productivity based on the results of cluster analysis
A review of free resources for processing and analyzing geospatial data Aigul Mimenbayeva, Tamara Zhukabayeva ACM International Conference Proceeding Series, 2020 The article is devoted to most popular free online resources according to Earth Observing System research, that can be used to solve individual analytical problems in the study of geospatial images. The functions of such services as Earth Explorer, Land Viewer, EO Browser, Sentinel Playground, Copernicus, INPE were analyzed, and 1488 hectares of meadows were surveyed in Pavlodar region, Irtysh area on EOS Land Viewer and EOS Crop Monitoring platforms.
