CLUSTERING OF SCIENTISTS’ PUBLICATIONS, CONSIDERING FINDING SIMILARITIES IN ABSTRACTS AND TEXTS OF PUBLICATIONS BASED ON N-GRAM ANALYSIS AND IDENTIFYING POTENTIAL PROJECT GROUPS
// Scientific Journal of Astana IT University. V.16. - Nur-Sultan.November, 2023.-P. 94-107. - ISSN 2707-904X.

Авторы: Andrii Biloshchytsky, Olexandr Kuchansky*, Aidos Mukhatayev, Svitlana Biloshchytska, Yurii Andrashko, Sapar Toxanov, Adil Faizullin

Шифры: B 58

Аннотация: The article describes the solution to the problem of clustering scientists’ publications, taking into account the finding of similarities in the annotations and texts of these publications based on n-grams of analysis and cross-references, as well as the tasks of identifying potential project groups for the implementation of research and educational projects based on the results of clustering. The selection of scientific partners in the world practice is done without a comprehensive assessment of their activities. Most of the well-known indexes for evaluating the research activities of scientists need to consider information about citations fully. The methods developed in the study for evaluating the scientific activities of scientists and universities, as well as methods for selecting scientific partners for the implementation of educational and scientific projects on a scientific basis, allow us to organize the influential work of universities qualitatively. In the article, a probabilistic thematic model is constructed that allows the clustering of scientists’ publications in scientific fields, considering the citation network, which is an important step in solving the problem of identifying subject scientific spaces. As a result of constructing the model, the problem of increasing instability of clustering of the citation graph due to a decrease in the number of clusters has been solved. The main objective of this work is to address the challenge of selecting suitable partners for collaboration in scientific and educational projects. To achieve this, a method for choosing project executors has been developed, which employs fuzzy logical inference to harmonize expert opinions regarding candidate requirements. This approach helps facilitate the multi-criteria selection of potential partners for scientific and educational projects. In addition to the method, various software modules have been created as part of this research. These modules are designed for the automated collection of information on the publications and citation records of scientists through international scientometric databases. They also encompass a visualization module and a user interface that aids in evaluating the scientific activities of university teaching staff. Choosing partners for grants or strategic collaborations, especially in the context of a globalized and highly mobile scientific community, remains a pertinent issue. The approach described in this research involves clustering the scientific publications of potential project partners. Furthermore, it incorporates conducting comparative citation analyses of these publications and establishing proximity based on n-gram annotation analysis. These methods provide a scientific basis for making informed choices when selecting partners, which is crucial for initiating and advancing research projects. Consequently, the selection of partners for forming research project teams is an immediate and pressing task.

Maksat Kalimoldayev.
MODEL DEVELOPMENT AND CALCULATIONS FOR 35/10 KV ELECTRICAL SUBSTATIONS IN TURKESTAN REGION USING RASTRWIN3 PROGRAM
// Scientific Journal of Astana IT University. V.16. - Nur-Sultan.November, 2023.-P. 81-93. - ISSN 2707-904X.

Авторы: Maksat Kalimoldayev, Zhazira Shermantayeva

Шифры: K 17

Аннотация: The city of Turkestan, Kazakhstan is experiencing growth leading to an increased need for electricity. In order to meet this demand the city is upgrading its infrastructure specifically focusing on improving its 35/10 kV substations. Engineers are utilizing calculation software like RastrWin3 to design and analyze these substations. This software offers capabilities, for modeling substations. Using RastrWin3 the ability to import data from sources like drawings AutoCad, GIS maps and other relevant resources. This imported data serves as the foundation for constructing the substation model. Engineers can easily incorporate components such as transformers, feeders, circuit breakers and busbars into the model. Each element of the model can be assigned parameters like voltage, current, resistance and power to represent real world conditions. Additionally, load profiles can be generated for analysis purposes to capture fluctuations, in energy demands throughout the day and year. Numerical calculation software plays a role, in the design and analysis of substations. It provides engineers with a toolset to achieve the following objectives: 1. Construct models of substations. 2. Simulate the behavior of substations under operational conditions. 3. Resolve issues that may arise in electrical substations. 4. Enhance the design and optimization of substations. One notable software in this domain is RastrWin3 which offers capabilities for calculations and simulations related to electric substations. Engineers can utilize this program to evaluate power systems, in emergency and transient modes. Accounting for various factors such as non-linearity, power and reactive power losses, as well, as the influence of capacitive coupling. Various types of loads such, as consumer loads, substation auxiliary loads and loads from protection and automation devices are considered in the modeling process. The software RastrWin3 is utilized to design and analyze 35/10 kV substations, in Turkestan. This software assists in enhancing the precision of substation design reducing the time needed for designing and developing substations improving substation efficiency and lowering maintenance costs.

Batyrbek Bakytkereiuly.
DEVELOPMENT OF A METHOD FOR COMBINING DATA IN ORDER TO PREVENT DUPLICATION OF RECORDS IN THE DATABASE OF THE INFORMATION SYSTEM FOR THE DEVELOPMENT OF METHODOLOGICAL COMPETENCE OF TEACHERS OF IT DISCIPLINES
// Scientific Journal of Astana IT University. V.16. - Nur-Sultan.November, 2023.-P. 69-80. - ISSN 2707-904X.

Авторы: Batyrbek Bakytkereiuly, Andrii Biloshchytsky, Dilara Abzhanova

Шифры: B 17

Аннотация: The article assesses the outcomes of digitization on higher education in Kazakhstan. Given current trends, there is a need to establish a dedicated focus on the ongoing professional growth of educators. A crucial measure in this regard involves shifting from disjointed learning approaches to implementing a cohesive system for the continuous professional development of teaching staff. It is crucial to highlight that in accordance with the Development Concept of Higher Education and Science in the Republic of Kazakhstan the duration of an individual’s active economic engagement has extended from 35-40 years to 50-60 years. This shift underscores the growing necessity for continuous learning throughout one’s life and underscores the significance of non-formal education. The educator plays a pivotal role in influencing the educational landscape and significantly contributes to the outcomes of socio-economic changes in Kazakhstan. In the current era of socio-economic and digital transformations the teacher’s proficiency has emerged as a crucial element that directly influences the quality of students’ education. The Ministry of Digital Development, Innovation, and Aerospace Industry of Kazakhstan has reported a yearly requirement for approximately 30,000 additional IT professionals. Consequently, the focus is on enhancing the methodological expertise of IT discipline educators, recognizing their pivotal role in influencing the quality of education and student achievements. Information systems aimed at enhancing this competence are emerging as a crucial element in elevating the overall effectiveness of education. The article presents the rationale for the need to create a single space for additional professional education of teachers of IT disciplines. It is considered as an effective mechanism for the implementation of the state educational policy and innovation strategy in the field of education. The aim of this initiative is to ensure that the human resource is ready to successfully achieve the country’s strategic goals in the context of digitalization processes. The article focuses on the development of a structural model of an information system aimed at improving the methodological competence of teachers of IT disciplines using the principles of continuing education. Based on a conceptual model with microservices subsystems, this methodology represents a comprehensive approach to the development of the competence of teachers of IT disciplines, integrating technological and methodological aspects. The development of a method for combining data in order to prevent duplication of records in the database of the information system is also considered. Methods of estimating the degree of similarity based on the types of attributes are proposed. The article extensively delineates the models, architecture, and components constituting the software implementation of the envisioned information system. It scrutinizes the accessibility of the employed data sources and formulates conclusions regarding the future potential for advancing the subject matter.

Kenzhegali Nurgaliyev.
AN ANALYSIS OF THE HETEROGENEOUS IOT DEVICE NETWORK INTERACTION IN A CYBER-PHYSICAL SYSTEM
// Scientific Journal of Astana IT University. V.16. - Nur-Sultan.November, 2023.-P. 53-68. - ISSN 2707-904X.

Авторы: Kenzhegali Nurgaliyev, Akylbek Tokhmetov, Liliya Tanchenko

Шифры: N 94

Аннотация: The article is devoted to the study of existing technologies regarding Internet of Things (IoT) device interaction in a heterogeneous network. Since each smart home appliance can be controlled by a customer who aims to find a cost-effective and easy-to-connect product for their own connected home, there are certain functional limitations for devices from distinct manufacturers that may decrease the intention to merge them all into a single network. A variety of proprietary protocols and communication standards embedded by vendors make their products unable to interact with other vendor devices if the connection standard used is not identical. Also, an IoT product design refers to its own functionality, mainly excluding the possibility of integration into other existing infrastructure. As IoT equipment emerges on the market, the complexity of its connection to a heterogeneous network corresponds to the firmware and the standard unification according to modern demands. It means that potential users might face the necessity of overcoming these issues to achieve high performance in terms of network interoperability. In general, an IoT gateway operating as a middleware might have the potential to enable a network with distinct communication models to operate without failure or data loss. This task requires the received data to be converted into the format in which the data is intended. This paper includes a comparative analysis of existing IoT device interaction standards, connection protocols, and data transfer technologies, evaluating their features for an effective adoption of the proposed network architecture, which can be used to improve the interoperability of heterogeneous IoT devices.

Mariya Yemelyanova.
APPLICATION OF MACHINE LEARNING FOR RECOGNIZING SURFACE WELDING DEFECTS IN VIDEO SEQUENCES
// Scientific Journal of Astana IT University. V.16. - Nur-Sultan.November, 2023.-P. 44-52. - ISSN 2707-904X.

Авторы: Mariya Yemelyanova , Saule Smailova

Шифры: Y 41

Аннотация: The paper offers a solution to the problem of detecting and recognizing surface defects in welded joints that appear during tungsten inert gas welding of metal edges. This problem belongs to the machine vision. Welding of stainless-steel edges is carried out automatically on the pipe production line. Therefore, frames of video sequences are investigated. Images of some welding defects are shown in the paper. An algorithm proposed by the authors is used to detect welding defects in the video sequence frames, the efficiency of which has been confirmed experimentally. The problem solution of welding defects recognition is based on the use of traditional machine learning methods: support vector machine and artificial neural network. To build classification models, a labeled dataset containing automatically extracted texture features from the areas of welding defects detected in the video sequences was created. An analysis was performed to identify the strength of the correlation of texture features between each other and the dependent variable in the dataset for dimensionality reduction of the feature vector. The models were trained and tested on datasets with different numbers of features. The quality of the classification models was evaluated based on the accuracy metric values. The best results were achieved by the classifier built using the support vector machine with a chi-square kernel on a training sample with two features. The build models allow automatic recognition of such welding defects as lack of fusion and metal oxidation. The computational experiments with real video sequences obtained with a digital camera confirmed the possibility of using the proposed solution for recognizing surface welding defects in the process of manufacturing stainless steel pipes

Akylbek Tokhmetov.
DEVELOPMENT OF DAG BLOCKCHAIN MODEL
// Scientific Journal of Astana IT University. V.16. - Nur-Sultan.November, 2023.-P. 30-43. - ISSN 2707-904X.

Авторы: Akylbek Tokhmetov, Vyacheslav Lee, Liliya Tanchenko

Шифры: T 70

Аннотация: In this study the authors present an innovative approach to resolving scalability and efficiency challenges in blockchain technology through the integration of Directed Acyclic Graphs (DAGs). This approach helps to overcome the limitations of traditional blockchain systems, particularly in transaction processing. The classic blockchain has some problems as slow transaction processing and poor scalability. The authors offer Directed Acyclic Graph (DAG) as a scalable and energy-efficient alternative. The paper outlines the development of a DAG-based blockchain model, utilizing Python and Flask alongside the Ed25519 cryptographic curve. It conducts a comparative analysis of DAG with traditional consensus mechanisms like Proof of Work and Proof of Stake, underscoring the efficiency and security benefits of employment of DAG. The research methodology includes an extensive literature review and the construction of a practical model to demonstrate DAG’s applicability in blockchain networks. Particularly notable is the exploration of DAG’s potential in Internet of Things (IoT) ecosystems, addressing critical issues such as energy inefficiency and network communication challenges in existing consensus algorithms. The authors calculated the performance of the model and compared it with similar models on several evaluation criteria. The simulation results of our proposed model show an improvement in performance and security by minimizing end-to-end delay, time cost, energy consumption, and throughput. The model eliminates the limitations of classic blockchain systems, such as high latency and low scalability. It structures transactions and blocks as a DAG, which provides fast validation and high scalability without compromising security. The research demonstrates the transformative implications of DAG for advancing blockchain technology.

DEEP RECURRENT NEURAL NETWORKS IN ENERGY DEMAND FORECASTING: A CASE STUDY OF KAZAKHSTAN’S ELECTRICAL CONSUMPTION
// Scientific Journal of Astana IT University. V.16. - Nur-Sultan.November, 2023.-P. 16-29. - ISSN 2707-904X.

Авторы: Samat Kabdygali , Ruslan Omirgaliyev, Timur Tursynbayev, Korhan Kayisli, Nurkhat Zhakiyev*

Шифры: K 11

Аннотация: The critical transformation of the energy sector demands innovative approaches to ensure the reliability and efficiency of energy systems. In this pursuit, this study delved into the potential of Deep Recurrent Neural Networks (DRNNs) for forecasting energy demand, using a comprehensive dataset detailing Kazakhstan’s electrical consumption over a span of two years. Traditional statistical models have historically played a role in energy demand prediction, but the growing intricacy of the energy landscape calls for more advanced solutions. The paper presented a comparison of the DRNN with other traditional and machine learning models and highlighted the superior performance of DRNNs, especially in capturing complex temporal relationships. The energy sector is confronting unprecedented challenges due to population growth and the integration of diverse energy sources, leading to increased demand and system strains. Accurate energy demand prediction is essential for system reliability. Traditional models, though widely used, often overlook intricate variables like weather patterns and temporal factors. Through rigorous methodology, encompassing exploratory data analysis, feature engineering, and hyperparameter optimization, an optimized DRNN model was developed. The results demonstrated the DRNN’s exceptional capability in processing complex time-series data, as evidenced by its attainment of an R-squared value of 83.6%. Additionally, it achieved Mean Absolute Errors and Root Mean Squared Errors of less than 2%. However, there were noticeable deviations in some predictions, suggesting areas for refinement. This research underscores the significance of DRNNs in energy demand prediction, highlighting their advantages over traditional models while also noting the need for ongoing optimization. The findings underscore DRNN’s promise as a robust forecasting tool, pivotal for the energy sector’s future resilience and efficiency

DEVELOPMENT OF A MODEL FOR IMPLEMENTING A CASE METHOD FOR INTERACTIVE STUDY PROCESS MANAGEMENT MONITORING
// Scientific Journal of Astana IT University. V.16. - Nur-Sultan.November, 2023.-P. 5-15. - ISSN 2707-904X.

Авторы: Gulnaz Zunimova, Gulzhan Soltan, Dmitry Likhacheuski, Aisulu Ismailova, Nazym Issayeva

Шифры: Z 93

Аннотация: The importance of this research topic lies in the need to gather, store, analyze and disseminate accurate information on the management status of educational institutions to guarantee the provision of quality educational services. This is particularly crucial in the current trend towards digitalizing educational work and utilizing the internet to facilitate practical management tasks. The study aims to construct a case method model to supervise educational process management outcomes. For that purpose, the study scrutinises the implementation methodologies of existing case models to monitor and analyse the situation without human intervention. Also, the investigation entails creating an implementation algorithm and a simulation model of an interactive case method to monitor educational activities. The formalized logical-semantic apparatus is used to address the research problems. The algorithm development and simulation modeling enabled correlation of the obtained answer with the assigned task, automating the results output. Generating answers in the case method involves considering rules based on the base word, answer keywords, and constraints (true/ false). These elements are part of the thesaurus specific to the survey domain and are included in the test’s base vocabulary. To test an answer against a question, each inductive step is viewed through a logical formula. Logical statements such as conjunction, disjunction, logical negation, implication, and equivalence are represented by formulas. This method enables the evaluation of the respondent’s answer based on the nodes within the statement tree integrated into the test. The progression from the initial word to the node-connection generates an automated assessment of individual educational process management standards. This study enables the enhancement of automated monitoring capabilities for the University’s educational process results. The research suggests the potential for developing models and algorithms to form question sets and enable individualized assessments based on the respondent’s performance. As a consequence, the devised algorithm and simulation model for the interactive case approach are showcased. During the testing, the key words and example words were compared with the responses of the participants and specific results were obtained. Additional case vocabulary terms need to be added to address the limitations encountered during the model testing and the testing period.

METHODS OF NAVIGATING ALGORITHMIC COMPLEXITY: BIG-OH AND SMALL-OH NOTATIONS
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 160-183. - ISSN 2707-904X.

Авторы: Zhanar Bimurat , Yekaterina Kim, Rauza Ismailova, Bimurat Sagindykov

Шифры: B 58

Аннотация: Dealing with agriculture, it is valuable to know an amount of moisture in a soil and to know how to forecast the stored soil moisture within particular period. Forecasting the stored soil moisture works for planning an extent and structure of crop production areas and adjustment of plant-growing programs. Having known about an amount of moisture in one-meter soil and the depth of precipitation in a vegetation season shall help farmers to determine a seeding time, type of fertilizers depending on soil quality and to work out an irrigation schedule as well. In this regard, over the last few years some vigorous activities applied to machine training methods of the weather forecast have been launched in the world. The goal of present research is to develop an artificial neuron network which shall afford an opportunity to figure out a stored soil moisture prior to outgoing to winter in a short-term. North Kazakhstan Region agrometeorological measuring stations for the period from 2012 to 2022 were used in the course of the neuron network training. The Levenberg-Marquardt algorithm aimed at non-linear regression models optimization was chosen for network training. The algorithm includes sequential approximation of initial parameter values to a local optimum. The mean squared error (MSE) function and the correlation coefficient ensure accuracy and precision of forecasts. As a result, 7 neural networks under MATLAB environment using the Levenberg-Marquardt algorithm, with different input and output data, and with different number of learning iterations came to realization. Following analysis of the results, the choice was fallen on the ANN9 best network offering minimum error function and actual data maximum correlation. The neural network obtained fits for use to make efficient decisions in the North Kazakhstan region agricultural sector in the short term.

NEURAL NETWORK MODEL OF SOIL MOISTURE FORECAST NORTH KAZAKHSTAN REGION
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 149-159. - ISSN 2707-904X.

Авторы: Aigul Mimenbayeva , Anargul Shaushenova , Sandugash Bekenova, Maral Ongarbayeva, Lyazzat Zhumalieva, Zhanar Altynbekova , Ardak Nurpeisova

Шифры: M 72

Аннотация: Dealing with agriculture, it is valuable to know an amount of moisture in a soil and to know how to forecast the stored soil moisture within particular period. Forecasting the stored soil moisture works for planning an extent and structure of crop production areas and adjustment of plant-growing programs. Having known about an amount of moisture in one-meter soil and the depth of precipitation in a vegetation season shall help farmers to determine a seeding time, type of fertilizers depending on soil quality and to work out an irrigation schedule as well. In this regard, over the last few years some vigorous activities applied to machine training methods of the weather forecast have been launched in the world. The goal of present research is to develop an artificial neuron network which shall afford an opportunity to figure out a stored soil moisture prior to outgoing to winter in a short-term. North Kazakhstan Region agrometeorological measuring stations for the period from 2012 to 2022 were used in the course of the neuron network training. The Levenberg-Marquardt algorithm aimed at non-linear regression models optimization was chosen for network training. The algorithm includes sequential approximation of initial parameter values to a local optimum. The mean squared error (MSE) function and the correlation coefficient ensure accuracy and precision of forecasts. As a result, 7 neural networks under MATLAB environment using the Levenberg-Marquardt algorithm, with different input and output data, and with different number of learning iterations came to realization. Following analysis of the results, the choice was fallen on the ANN9 best network offering minimum error function and actual data maximum correlation. The neural network obtained fits for use to make efficient decisions in the North Kazakhstan region agricultural sector in the short term.

THE TASK OF CHOOSING PARTNERS FOR THE ORGANIZATION OF COOPERATION IN THE FRAMEWORK OF SCIENTIFIC AND EDUCATIONAL PROJECTS
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 139-148. - ISSN 2707-904X.

Авторы: Andrii Biloshchytskyi, Olexandr Kuchansky, Aidos Mukhatayev, Yurii Andrashko, Sapar Toxanov, Adil Faizullin

Шифры: B 58

Аннотация: The primary objective of this article is to establish a set of fundamental criteria for the selection of scientific partners for collaborative research efforts. Achieving this objective entails addressing the challenge of identifying criteria that are both objective and universally applicable, capable of encompassing various fields of scientific research, such as natural sciences, technical sciences, and economic sciences, among others. One such criterion, applicable to scientists, may involve assessing their publication activity within specific research areas that align with the objectives of the relevant scientific research or international projects. In the contemporary landscape of scientific research, there is a growing urgency to enhance the effectiveness of research endeavors and to foster efficient collaboration within scientific communities. This is particularly vital for organizations oriented towards project-based research.In the formation of research project teams, a conventional approach is to select partners from the pool of scientists possessing the requisite qualifications and experience in the execution of such projects. A widely accepted yardstick for evaluating the outcomes of scientists’ research endeavors is the citation metrics associated with their publications. Typically, these metrics take the form of scalar values. While this approach offers several advantages, it is not without its limitations. One notable drawback is the potential loss of information when converting raw data into scalar metrics, and the existence of certain edge cases where the parameter remains unchanged despite variations in the number of citations and publications. Hence, it is pertinent to explore the development of new methodologies or modifications to existing ones that can effectively evaluate the results of scientists’ research activities while mitigating these limitations. The article describes the criteria for the search and selection of partners for joint scientific research. This will make it possible to effectively form teams for narrowly focused scientific research or international collaboration projects in interdisciplinary scientific projects such as the European Horizon Program or educational projects such as the Erasmus plus program. Also, the proposed solution will allow the formation of small teams for joint scientific publications. It is imperative to acknowledge that the process of partner selection is predominantly driven by a consideration of the knowledge, whether it be novel or foundational, possessed by prospective partners who are entrusted with the execution of a project. It becomes crucial to delineate the specific criteria governing partner selection which can vary contingent upon factors such as the typology of partners, the nature of project tasks, the depth of knowledge possessed, and related contextual variables. A vital underpinning for the formation of project consortia is the mathematical conundrum of choice which furnishes a formal rationale for the judicious selection of a particular partner.

SYSTEMATIC DATA PROCUREMENT IN AN OWL-EMBEDDED INFORMATION AND ANALYTICAL FRAMEWORK FOR THE MONITORING OF WATER RESOURCES IN THE ILE-BALKHASH BASIN
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 125-138. - ISSN 2707-904X.

Авторы: Meiran Zhiyenbayev, Assel Ospan, Nadezhda Kunicina, Madina Mansurova, Roman Titkov

Шифры: Z 62

Аннотация: The world is facing an escalating water shortage crisis, with dire consequences for ecosystems, human health, and socio-economic development. This article explores the multifaceted nature of the water shortage problem of Ile-Balkhash basin that falls under the jurisdiction of the Balkhash-Alakol Republic of Kazakhstan, its underlying causes, and the complex web of challenges it presents. The predicament in the Ile-Balkhash basin is a complex interplay of various factors. Climate change has led to erratic precipitation patterns, exacerbating the problem. The simultaneous rise in population places additional stress on the already limited water resources. Moreover, inefficient water management practices have perpetuated the issue, hindering the equitable distribution of water. The challenge of conducting a comprehensive basin analysis is a formidable task due to the numerous variables and indicators involved. It demands an enormous amount of time and effort. To address this issue, a web application framework integrated with the Web Ontology Language database, allowing for the execution of advanced queries to extract valuable insights from various objects and indicators, has been developed. The database underpinning this system is meticulously compiled, drawing upon data from the Hydrological monitoring of water bodies and the National Hydrometeorological Service of the Republic of Kazakhstan. These sources provide critical data that forms the bedrock of the analysis. Recognizing the importance of data storage and management in this endeavor, integrated components have been established. These components play a pivotal role in structuring the diverse data sources and maintaining their currency. The water shortage issue in the Ile-Balkhash basin serves as a stark reminder of the urgency with which such crises must be addressed. The tools and methods offer hope and underscore global water management sustainability

MATHEMATICAL FRAMEWORK FORMULATION AND IMPLEMENTATION FOR HYPERSPECTRAL AEROSPACE IMAGES PROCESSING
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 111-124. - ISSN 2707-904X.

Авторы: Assiya Sarinova, Alexandr Neftissov , Leyla Rzayeva, Lalita Kirichenko, Sanzhar Kusdavletov, Ilyas Kazambayev

Шифры: S 22

Аннотация: This paper proposes a preprocessing algorithm for aerospace hyperspectral images based on a mathematical apparatus effectively applied in pre-compression transformation problems. In particular, several methods have been analyzed for hyperspectral image (signal) preprocessing from the point of view of digital signal processing algorithms. These mathematical methods are used for problems of filtering signals from noise of different natures and for compression and restoration of signals after their transmission through communication channels. The results of comparative analysis of preparatory processing of lossy compression algorithms based on wavelet analysis, discrete and orthogonal transforms are also given, demonstrating minimization of loss level of reconstructed decoded images. The performance of the proposed preprocessing algorithms with quality metrics is presented to evaluate the quality of the reconstructed hyperspectral aerospace images. The results of this study can be applied and used in the tasks of special processing of hyperspectral images, as well as fundamental knowledge of mathematical apparatuses of the proposed orthogonal preprocessing, considering the specificity of the data which is very important in obtaining images ready for compression for the subsequent identification of objects of the Earth’s surface and using such mathematical transformations at the hyperspectral image preprocessing stage before compression provides efficient archiving of the obtained data, while reducing the communication channel load. Through the use of quality metrics of the reconstructed images, the preprocessing algorithm provides an understanding of the threshold of the peak signal-to-noise ratio value and the efficiency of its application to calculate and minimize the loss rate

DEVELOPMENT OF A LINEAR REGRESSION MODEL BASED ON VEGETATION INDICES OF AGRICULTURAL CROPS
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 101-110. - ISSN 2707-904X.

Авторы: Aigul Mimenbayeva, Azat Yessen, Aiman Nurbekova, Raya Suleimenova, Tleugaisha Ospanova, Akmaral Kasymova, Rozamgul Niyazova

Шифры: M 72

Аннотация: The article is devoted to the study of vegetation indices for assessing the productivity of agricultural crops of the North Kazakhstan Agricultural Experimental Station (NKAES) LLP. The research was carried out using a modern software package for processing satellite images, EOS Land Viewer. The work used images from the Landsat 8 (USA) and Sentitel 2 (European Space Agency) spacecraft. Digitized Earth remote sensing data for the last 3 years are presented, showing changes in the amount of moisture reserves on the territory of NKAES LLP. Time series of distribution of the studied coefficients were constructed according to different phases of active vegetation biomass in the study area. The resulting time series made it possible to identify annually repeating patterns, a linear trend of increasing and decreasing NDWI and NDVI on the territory of the NKAES LLP.Review of studies over the past 5 years, published in highly rated foreign journals, on various vegetation indices, including indices designed to assess moisture content in vegetation and soil. It is noted that the first normalized water index, NDWI, using the SWIR infrared channel, unlike the widely used NDVI vegetation index, actually penetrates 80% of the atmosphere. Analysis of the obtained NDWI allowed us to identify dry, moderately dry and fairly humid periods on the territory of the NKAES LPP from 2020 to 2023. Based on the research carried out, the feasibility of using normalized difference water indicators and normalized vegetation indices for further use in forecasting yields in the conditions of the North Kazakhstan region is substantiated. Next, using vegetation indices and additional agrometeorological factors, a linear model for predicting crop yields was developed. The coefficient of determination of the resulting model is 0.90 which indicates that the selected trend line reliably approximates the process under study.

GESTURE RECOGNITION OF MACHINE LEARNING AND CONVOLUTIONAL NEURAL NETWORK METHODS FOR KAZAKH SIGN LANGUAGE
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 85-100. - ISSN 2707-904X.

Авторы: Samat Mukhanov*, Raissa Uskenbayeva , Young Im Cho , Kabyl Dauren, Les Nurzhan, Maqsat Amangeldi

Шифры: M 93

Аннотация: Recently, there has been a growing interest in machine learning and neural networks among the public, largely due to advancements in technology which have led to improved methods of computer recognition of objects, sounds, texts, and other data types. As a result, human-computer interactions are becoming more natural and comprehensible to the average person. The progress in computer vision has enabled the use of increasingly sophisticated models for object recognition in images and videos, which can also be applied to recognize hand gestures. In this research, popular hand gesture recognition models, such as the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) were examined. These models vary in their approaches, processing time, and training data size. The important feature of this research work is the use of various machine learning algorithms and methods such as CNN, LSTM, and SVM. Experiments showed different results when training neural networks for sign language recognition in the Kazakh sign language based on the dactyl alphabet. This article provides a detailed description of each method, their respective purposes, and effectiveness in terms of performance and training. Numerous experimental results were recorded in a table, demonstrating the accuracy of recognizing each gesture. Additionally, specific hand gestures were isolated for testing in front of the camera to recognize the gesture and display the result on the screen. An important feature was the use of mathematical formulas and functions to explain the working principle of the machine learning algorithm, as well as the logical scheme and structure of the LSTM algorithm

Alua Myrzakerimova.
A MATHEMATICAL MODEL FOR AN AUTOMATED SYSTEM OF MEDICAL DIAGNOSTICS
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 71-84. - ISSN 2707-904X.

Авторы: Alua Myrzakerimova, Kateryna Kolesnikova, Mugulsum Nurmaganbetova

Шифры: M 99

Аннотация: One of the primary focuses of the Republic of Kazakhstan concerning sustainable and stable improvements in the well-being of its population is the advancement of the healthcare sector. A mathematical model for an automated medical diagnostics system integrates machine learning algorithms, statistical models, and decision trees to analyze patient data and facilitate accurate diagnoses. This model enables healthcare professionals to enhance the efficiency and reliability of medical diagnostics by leveraging advanced computational techniques. These distinguishing features can be incorporated by developing a mathematical model for diagnosing diseases, enabling precise identification, and guiding appropriate treatment strategies. Machine learning algorithms play a crucial role in automated systems for medical diagnostics. An ensemble of multiple algorithms, such as combining decision trees with gradient boosting or using a combination of neural networks and traditional machine learning, can yield improved diagnostic accuracy and robustness. Predicting the progression of diseases is a crucial aspect of healthcare, enabling personalized interventions and improved patient outcomes. A mathematical approach can facilitate this prediction by monitoring changes in diagnostic results aligned with the severity of symptoms, which inherently vary over the observation period. By employing mathematical modeling techniques, healthcare professionals gain valuable insights into disease progression, supporting informed decision-making and tailored treatments. In conclusion, developing a mathematical model for an automated medical diagnostics system, incorporating machine learning algorithms, statistical models, and decision trees, significantly contributes to healthcare. These models enhance the accuracy, efficiency, and personalization of medical diagnoses. Additionally, mathematical models aid in the differential diagnosis of challenging conditions and provide predictions regarding disease progression, ultimately benefiting patient care and treatment outcomes

MATHEMATICAL, SOFTWARE AND HARDWARE SUPPORT OF THE CONCEPTUAL MODEL OF THE INFORMATION SYSTEM OF PRECISION AGRICULTURE
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 55-70. - ISSN 2707-904X.

Авторы: Alexandr Neftissov, Andrii Biloshchytsky, Sapar Toxanov, Sapar Toxanov, Saken Ordabayev, Olexandr Kuchansky, Yurii Andrashko, Vladimir Vatskel

Шифры: N 34

Аннотация: This study analyzes the current situation of application of precision farming technologies and solutions by agricultural enterprises of the Republic of Kazakhstan. The main players and used solutions have been identified. The statistics of application, as well as the potential of use is examined. Within the framework of the analysis of the applied solutions the advantages and disadvantages of competitors in the market were determined. It was defined that the applied systems provide the possibility of remote management, but EGISTIC is more focused on the management of all processes of the farm, including the warehouse, while John Deere is focused on the management and analytics of agricultural machinery. EGISTIC offers features for warehousing and inventory planning, something not found in the base version of John Deere Operations Center. John Deere focuses on data sharing which can be important for large farms or groups of farmers. EGISTIC makes extensive use of satellite imagery to analyze field conditions which can be a great asset for identifying problem areas and planning interventions. Depending on the specific needs and priorities of an agribusiness, one system may be preferable to another. If machinery management is the main focus, John Deere might be the best choice. If in-depth analysis of field conditions and inventory control is important, EGISTIC may be more appropriate. By analyzing, the directions for research are highlighted. A conceptual model of information system for precision farming is developed. Hardware for realization of the conceptual model is possible on the basis of universal programmable logic controller of modular architecture being developed. Within the limits of the given research the conceptual model of the universal programmable logic controller of modular architecture and the structural model of the software of the universal programmable logic controller of modular architecture have been developed. The interaction with the conceptual adaptive model of information and communication system is also considered. This paper analyzes the key principles and functions of both the universal programmable logic controller and the information and communication system, as well as their possible integration within a single concept.

Dinara Zhaisanova.
A BIBLIOMETRIC STUDY ON BLOCKCHAIN CONCEPT: A THEME ANALYSIS AND FUTURE DIRECTIONS FOR COMPUTER SCIENCE TRAINING
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 41-54. - ISSN 2707-904X.

Авторы: Dinara Zhaisanova, Madina Mansurova

Шифры: Z 62

Аннотация: This paper aims to study the blockchain concept domain in the computer science field due to bibliometric study. Authors employed bibliometric and network analysis techniques to analyze existing literature. In total, 719 articles in the period of 2019 to August 2023 from the Web of Science (WOS) database were analyzed after applying search string, and criteria for inclusion and exclusion. Initial data screening involved the extraction of fundamental information, followed by data analysis based on co-occurrence, bibliographic coupling, and citation using special program software VOSviewer and R program. research areas “compute science” and “engineering”. In addition to that, VOSviewer and R-based tools illustrate the application of text mining involves utilizing computational techniques to extract, analyze, and represent the key concepts and relationships within the field of blockchain technology. Data analysis primarily involved co-occurrence analysis, bibliographic coupling, co-authorship examination, citation analysis, and co-citation analysis. In the context of a blockchain concept thematic analysis, was applied clustering by coupling. Furthermore, it was conducted the thematic analysis to scrutinize the content of prior studies in the computer science field using clustering by coupling. Ranking of the authors, organizations, and countries was applied according to total link strength metric which was used to quantify the overall strength of connections between nodes within a network. Besides, citation analysis has also been conducted to assess the articles’ ranking, considering both worldwide and localized citations. Bibliometric results indicate blockchain concepts within such thematic frameworks as access control scheme, identity management system, supply chain management, artificial intelligence integration, blockchain technology applications, and blockchain smart contract

Aigerim Mansurova.
DEVELOPMENT OF A QUESTION ANSWERING CHATBOT FOR BLOCKCHAIN DOMAIN
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P. 27-40. - ISSN 2707-904X.

Авторы: Aigerim Mansurova, Aliya Nugumanova, Zhansaya Makhambetova

Шифры: M 26

Аннотация: Large Language Models (LLMs), such as ChatGPT, have transformed the field of natural language processing with their capacity for language comprehension and generation of human-like, fluent responses for many downstream tasks. Despite their impressive capabilities, they often fall short in domain-specific and knowledge-intensive domains due to a lack of access to relevant data. Moreover, most state-of-art LLMs lack transparency as they are often accessible only through APIs. Furthermore, their application in critical real-world scenarios is hindered by their proclivity to produce hallucinated information and inability to leverage external knowledge sources. To address these limitations, we propose an innovative system that enhances LLMs by integrating them with an external knowledge management module. The system allows LLMs to utilize data stored in vector databases, providing them with relevant information for their responses. Additionally, it enables them to retrieve information from the Internet, further broadening their knowledge base. The research approach circumvents the need to retrain LLMs, which can be a resource-intensive process. Instead, it focuses on making more efficient use of existing models. Preliminary results indicate that the system holds promise for improving the performance of LLMs in domain-specific and knowledge-intensive tasks. By equipping LLMs with real-time access to external data, it is possible to harness their language generation capabilities more effectively, without the need to continually strive for larger models

Balgaisha Mukanova.
SIMPLIFIED ADAPTIVE TRIANGULATION OF THE CONTACT BOUNDARIES OF THE DAM MODEL
// Scientific Journal of Astana IT University. V.15. - Nur-Sultan.September, 2023.-P.16-26. - ISSN 2707-904X.

Авторы: Balgaisha Mukanova

Шифры: M 93

Аннотация: To numerically solve the system of integral equations, it is customary to establish a discrete grid within each integration area. In the context of 3D modeling, these areas correspond to surfaces situated in space. The standard discretization technique employed for the computational domain is triangulation. This study addresses the integral equation system pertinent to the electrical tomography of dams. The structural model encompasses an embankment dam, the upstream and downstream water bodies, the dam’s base, and a potential leakage region on the upstream side. An alternative configuration may be encountered in specific scenarios with no water downstream. Consequently, the model may incorporate up to nine distinct contact boundaries. Accordingly, the system of integral equations comprises an equivalent number of equations. Effectively resolving this system through numerical methods necessitates applying triangulation techniques to these diverse surfaces. While mathematical packages like Matlab offer triangulation functions, they may not fully address the specific demands of the problem. Additionally, the grid resolution should be heightened in proximity to key elements such as the sounding line, the supply electrode, and the various contact lines within the medium. These considerations transform the triangulation task into a distinct subtask within the numerical simulation of the resistivity tomography problem. In this paper, we provide our specific approach to this problem. The simplification of the triangulation algorithm is rooted in the predominant utilization of the two-dimensional geometric properties inherent to the object under study. For most contact boundaries, the triangulation is constructed layer by layer with a gradual modulation in triangle dimensions as one progresses from one layer to the next, orthogonal to the axis of the dam. Concerning the surface corresponding to the leakage area, cylindrical coordinates are used for surface parameterization. This approach enables partitioning the surface into discrete strata, facilitating a systematic, layer-by-layer grid construction. Additionally, points at the intersections of contact boundaries are integrated into the pre-existing triangulation by applying a standard function within the Matlab package. In the future, the mathematical modeling based on the Integral Equation Method with adaptive discretization will help incorporate real-time computations into information systems related to monitoring hydraulic structures.