Scientific Journal of Astana IT University

10

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

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.

11

Authors: Batyrbek Bakytkereiuly, Andrii Biloshchytskyi, Dilara Abzhanova

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.

13

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

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.

15

Authors: Kenzhegali Nurgaliyev, Akylbek Tokhmetov, Liliya Tanchenko

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.

9

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

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.Начало формы 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.

10

Authors: Balgaisha Mukanova

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.

13

Authors: Aigerim Mansurova, Aliya Nugumanova, Zhansaya Makhambetova

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.

13

Authors: Alua Myrzakerimova, Kateryna Kolesnikova, Mugulsum Nurmaganbetova

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.

13

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

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.

15

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

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.

14-21

Authors: V. Shevtsov, A. Ismailova, U. Aitimova, O. Khapilina

The pace at which scientific data is produced and disseminated has never been as high as it is currently. Modern sequencing technologies make it possible to obtain the genome of a specific organism in a few days, and the genome of a bacterial organism in less than a day, and therefore researchers from the field of life science are faced with a huge amount of data that needs to be analyzed. In this connection, various fields of science are converging with each other, giving rise to new disciplines. So, bioinformatics is one of these fields, it is a scientific discipline that has been actively developing over the past decades and uses IT tools and methods to solve problems related to the study of biological processes. In particular, a crucial role in the field of bioinformatics is played by the development of new algorithms, tools and the creation of new databases, as well as the integration of extremely large amounts of data. The rapid development of bioinformatics has made it possible to conduct modern biological research. Bioinformatics can help a biologist to extract valuable information from biological data by using tools to process them. Despite the fact that bioinformatics is a relatively new discipline, various web and computer tools already exist, most of which are freely available. This is a review article that provides an exhaustive overview of some of the tools for biological analysis available to a biologist, as well as describes the key role of information systems in this interdisciplinary field.

36-49

Authors: G. Azieva, A. Alimagambetova, U. Turusbekova

Kazakhstan is one of the few countries in the world rich in oil, deservedly called “black gold” because it is the most important source of energy. The relevance of the study of this paper is determined by the fact that the management of the oil industry affects not only the management process itself, but also the social aspects of the implementation of the development strategy of the state as a whole. It is necessary to identify aspects of management activity and define criteria by which it is possible to calculate the effectiveness of managerial decision-making in the analyzed industry. Agent models allow us to identify the main criteria for the effectiveness of managerial decision-making and optimize social and economic costs for their implementation within the framework of interdepartmental planning. The novelty of the research is determined by the fact that agent models are based not only on the associated parameters of the management process, but also affect the possibility of planning current activities for a long period. The article shows that the formation of agent models should affect both the aspect of the formation of matrices of complex managerial actions and calculations on the accounting of competencies in making managerial decisions. The practical significance of the study is determined by the fact that the development of complex models based on agent forms allows expanding the use of forms of control over the industry by the state and other stakeholders. The implementation of a matrix form of management is proposed, taking into account balanced industry indicators of management quality.

61-74

Authors: V. Yavorskiy, M. Yesmagambetova, S. Ussenov

The paper considers the features of the use of intelligent information technologies in the process of the emergency rescue service of the enterprise. To improve the efficiency of the rescue services, it is necessary to carry out strategic planning and management, which would be designed to prevent the occurrence of an emergency (emergency). Effective subsystems of emergency proactive planning should not only predict the occurrence of possible emergencies, but also provide for appropriate preventive measures, and emphasis should be placed on eliminating the underlying causes, not the emerging consequences. The change of modes in the event of an emergency can be effectively implemented through the deployment of a technologically secured situational center. Decisions in emergency situations are made in various operational situations, including crisis, and in extremely limited time. Nevertheless, they must be taken in a timely manner, be as reasonable as possible, and ensure the fullest and most effective use of available opportunities. Decision-making processes can be based on the personal experience of participants in the management process. As you carry out your professional activity, experience is formed, which subsequently allows you to perform some tasks much faster and more efficiently. To gain experience, it is proposed to use a knowledge management system based on ontology. In order to ensure maximum awareness of the management of the situation, it is proposed to use the expert system of the situation center. (ES SC) The ES of the situational center of the authorities can significantly improve the efficiency of management processes and provides information support for strategic and tactical management decision-making. The ES should implement tools for a comprehensive and operational assessment of the state of the management object and situational analysis of the identified problems.

85-94

Authors: A. Mukasheva, D. Yedilkhan, M. Aldiyar

The article is devoted to the description of the method of multidimensional database, which is an effective method of data storage, which allows analyzing data qualitatively, and most importantly in a short time. The article discusses the capabilities of multidimensional databases, in particular, multidimensional OLAP (On-Line Analytical Processing) cubes when analyzing large amounts of data. Provides an overview and features of a multidimensional database and discusses the steps you need to take with a multidimensional database to understand the structure and capabilities of an OLAP cube. To create a knowledge base, it describes the steps you can take to create and execute a multidimensional database that you can collect from various sources, save to a database, and then prepare a report using OLAP analysis. Various information system data processing technologies such as OLTP and OLAP were considered. The algorithm of the data storage process for analysis purposes was studied. A model of a multidimensional database in the form of a three-dimensional cube was presented. Examples of analysis and ways of obtaining information from the data cube were also given. The use of a multidimensional database in higher education institutions as a simple and effective method of data storage is considered. There are also illustrations of the structure of a higher educational institution to see the bulkiness of information, and what kind of information database operates in the educational institution.

14-23

Authors: Sharipa Temirgaziyeva, Batyrkhan Omarov

Road sign recognition is one of the most important steps drivers can take to avoid dangerous roads or accidents. The purpose of the research work is to develop a recognition system, increasing the classification accuracy of the model, using deep learning methods of Road sign recognition system for drivers in real time on the road. Stages of road sign image classification were carried out, and other authors’ solutions were analyzed. In addition, in this work, the convolutional neural network (CNN) method with the help of deep learning was used to create a system for identifying and recognizing road signs. The proposed road sign recognition system can work in real time to recognize the image of road signs. In this paper, a model is trained using deep learning of 43 different road signs using existing datasets and collected local road signs. A traffic sign detection and recognition system is presented using an 8-layer convolutional neural network, which acquires different functions by training different types of traffic signs. In previous studies, models were trained using simple machine learning algorithms, but the relevance of this study is that a CNN model was trained for a classification task based on convolutional neural networks using deep learning. As a result of the study, classification accuracy of 95% was obtained using deep learning methods. As a novelty of the work, it is possible to note the diversity of the convolutional network methods used to increase the efficiency of the used data set and model training algorithms, the variety of received road signs and algorithms for its recognition, as well as the achievement of a high accuracy rate. This allowed the system to overcome the limited accuracy and performance issues caused by environmental factors, and to be more versatile and accurate than most modern systems.

34-44

Authors: Arailym Tleubayeva, Assar Maidanov, Arina Kantayeva

Traditional street lighting systems receive data about daylight levels and adjust the lighting. However, in such conditions, energy consumption increases since the sensors of such systems receive data on only one indicator, which is daylight. Therefore, a suitable automated intelligent lighting system model is needed. Intelligent lighting systems can adjust the brightness of the light not only based on natural data but also based on the movement of vehicles and people. This paper describes the development, implementation, and testing of a smart lighting system model to increase energy efficiency and high reliability. This system is controlled by a microcontroller programmed to control the lighting and receive data from sensors for processing with good efficiency. Distributed sensors record environmental conditions such as daylight and traffic. Photoresistors change resistance in daylight to light up the streets at night. The HC-SR501 infrared motion sensor detects objects emitting infrared radiation (heat) in the controlled motion zone and sends a signal to the microcontroller. The intelligent lighting system uses LEDs, which consume less energy and achieve high efficiency. Calculations show that the efficiency of using these lamps is almost 70%, compared to what is used in conventional street lighting systems.

55-64

Authors: Dinara Kaibassova, Margulan Nurtay, Ardak Tau, Mira Kissina

Due to the popularity of the easiest way to obtain personal information among attackers, phishing detection is becoming a popular area for research aimed at countering the implementation of such attacks. Malicious website detection is essential to prevent the spread of malware and protect end users from victims. Unfortunately, malicious URL detection still needs to be better understood due to a lack of features and inaccurate classification. Possible sources were examined in order to investigate the subject. Based on the collected information from previous studies, this study is devoted to solving the problem of detecting phishing websites using Ensemble Learning. The aim of the work is to choose the most optimal algorithm for classifying phishing websites using gradient boosting algorithms. AdaBoost, CatBoost, and Gradient Boosting Classifier were chosen as Ensemble Learning algorithms and were used to improve the efficiency of classifiers. Practical studies of the parameters of each algorithm for finding the optimal classification model are given. Research and experiments were carried out on a dataset containing information extracted from the contents of a URL: main URL, domain, directory, and file. A thorough Exploratory Data Analysis (EDA) was carried out, as a result of which the main dependencies and patterns of determining phishing resources were identified using correlation analysis. ROC AUC Score was chosen as an evaluation metric for the algorithms. The best result for predicting phishing websites was demonstrated by the AdaBoost Classifier algorithm, with an average ROC AUC score of 99%. The results of the experiments were illustrated in the form of graphs and tables.

81-88

Authors: Ardabek Khompysh, Nursulu Kapalova, Kunbolat Algazy, Kairat Sakan

This article presents one of the main transformations of symmetric block ciphers used to protect confidential information, a new method for obtaining a non-linear S block, and an analysis of the results obtained. The S-box obtained by this method can be used as a nonlinear transformation in block cipher algorithms to protect confidential data transmitted over an open channel. In most well-known works in the field of analysis and synthesis of modern block symmetric ciphers, S-box is used as a mathematical apparatus for cryptographic Boolean functions. In this case, each S-box is represented by a set of composite Boolean functions whose properties characterize the efficiency of the nonlinear substitution node. Substitution nodes for modern symmetric primitives, including key unfolding functions, are usually implemented as replacement tables. Considering that in most modern block symmetric ciphers for introducing round keys, the encryption algorithm uses a linear operation (bitwise addition modulo 2), S-blocks are the only elements responsible for the cryptographic stability of block encryption algorithms. The required number of rounds of block symmetric ciphers is selected taking into account the results of the cryptographic analysis performed, provided that the properties of S-boxes are specified. As the main criteria and performance indicators, the balance and nonlinearity of composite Boolean functions are used; strict avalanche criterion (SAC), propagation criterion; algebraic degree; the value of the autocorrelation function. In this article, a study was made of the nonlinearity and strict avalanche criterion (SAC) of the S-box used in the block symmetric encryption algorithm. The results of the study were compared with the S-boxes of modern cryptographic algorithms and showed good results.

101-112

Authors: Alua Myrzakerimova, Katerina Kolesnikova, Mugulsum Nurmaganbetova

Today, the importance of information support for various medical technologies has increased significantly. The use of modern information technologies is becoming a critical factor in the development of most branches of knowledge and areas of practice, so the development and implementation of information systems is an urgent task. The clinical decision support system provides clinicians and stakeholders with individualized patient assessments and recommendations to assist in the clinical decision-making process. Knowledge-based information systems are widely used in medicine around the world. Modern technical capabilities make it possible to reach a qualitatively new level of presentation of the course of the disease, namely, based on appropriate mathematical models, to model the typical development of the pathological process in a particular disease, to speed up the process of diagnosing and receiving recommendations on treatment protocols. In Kazakhstan, there is no variety of decision support systems in medicine, especially in the process of diagnosing diseases. The purpose of this study is to develop the structure of an automated system for diagnosing diseases. The Unified Modeling Language (UML) is used as a design tool. The structure of the automated system is presented; the main components and key terms are considered. A mathematical model of diagnostics is shown (in the example of diseases of intestinal and pancreatic insufficiency) based on decision-making methods with fuzzy initial data. A diagram of the data flows of the system is presented. Thus, the paper proposes the structure of an automated system that will contribute to high-quality diagnostics due to an effective method of system organization and the use of fuzzy set theory methods.

122-138

Authors: Serik Omirbayev, Aidos Mukhatayev, Andrii Biloshchytskyi, Sapar Toxanov, Adil Faizullin, Svitlana Biloshchytska

The article considers the creation of structural model for the system of development of methodological competence of IT-discipline teachers based on continuing education with the description of the structure of microservices subsystems. To implement the proposed conceptual model the paper proposes a software architecture based on the use of classical service-oriented design pattern “Model-View Separation” (separation of model objects from the graphical interface objects with the user) and provides for interaction with the database, the composition of its main packages is detailed. Logical and physical service-oriented models of basic classes of objects of subject area and auxiliary objects (services) have been developed. The article also proposes a relational database structure based on the use of metadata tables, directories, and operational data in a relational database management system environment. Methods and mechanisms for data protection and replication are proposed and justified, considering the specifics of system operation in higher education institutions. The article aims to develop insurgents and mechanisms of creation of information technology improvement of the system of higher education and creation and operation of integrated flexible software tools to support the mixed system of organization of educational process, as well as increasing the effectiveness of the introduction of a mixed system for the organization of the educational process on the basis of the use of the latest technical means of training and documentation, ensuring free access of students to teaching and methodological resources, сontent and criteria for the assessment of knowledge in disciplines and modules, data recording, and partial compensation for the increased workload of teachers and staff through the automation of assessment, registration and assessment processes. A task-oriented model for managing technical problems in the implementation of the information system for the development of teachers’ methodological competence is also proposed, and a conceptual framework for managing these problems has been developed from the perspective of considering projects as a set of objects and subjects. Technical problems in projects are shown to be obstacles to moving in the right direction, created by random events, uncertainties, and changes. A mathematical model of the change in resistance value caused by technical problems has been developed and methods to counteract these problems have been proposed.

14-23

Authors: Gulnara Bektemyssova, Abdul Rahim Bin Ahmad, Sharafat Mirzakulova, Zhanar Ibraeva

The variety of communication services and the growing number of different sensors with the appearance of IoT (Internet of Things) technology generate significantly different types of network traffic. This implies that the structure of network traffic will be heterogeneous, which requires deep analysis to find the internal features underlying the data. A common model for analyzing the processes of a multiservice network is a model based on time series. Numerous empirical data studies indicate that the packet intensity time series do not belong to the general aggregates of a normal distribution. The problem of predicting network traffic is still relevant due to managing information that flows into a heterogeneous network. In this work, the authors studied the time series for stationarity in order to select an appropriate forecasting model. A visual assessment of the series assumed non-stationarity. The Augmented Dickey-Fuller Test is applied, and the measured network traffic is predicted using the ARIMA (Auto-Regressive Integrated Moving Average) statistical method. Results were obtained using the Econometric Modeler Matlab (R2021b) application. The results of the autocorrelation function (ACF) and partial ACF are analyzed, with the help of which the ARIMA model is optimized. As a result of the study, a software algorithm for the ARIMA (0,2,1) model was developed.

36-47

Authors: D. Muratuly, N.F. Denissova, I.V. Krak

This article discusses the current problem of identifying violations during distance learning during the final certification. The coronavirus (COVID-19) pandemic has served as a stimulus for innovation in the field of education in all countries, including Kazakhstan. Innovative approaches are being taken to ensure the continuity of education and training. Thanks to the rapid response measures taken by governments and partners around the world to ensure the smooth learning process. The ongoing digital transformation of an educational institution requires appropriate information content, suitable methodological models, effective teaching methods and a supportive learning environment. The solution to one of the urgent tasks is to ensure the quality and reliability of assessing the knowledge of students by introducing an online proctoring system. The primary task of the online proctoring system is to recognize faces and identify abnormal behavior of students. The basis for obtaining data is the unified information educational environment of the D. Serikbayev East Kazakhstan Technical University is represented by the SPORTAL hardware and software system, which is an integration of two powerful subsystems: a Web application - the Dales of Knowledges educational portal and the SPORTAL information and software complex. The main theoretical results obtained are aimed at solving practical problems and are being introduced into the educational environment of D. Serikbayev East Kazakhstan Technical University to increase the degree of confidence in the results of students’ knowledge in distance learning using an online proctoring system. The article presents the results of studies of one of the Viola-Jones face detection methods, commonly known as Haar cascades. During the study, a technology for identifying faces and detecting violations in real time was developed. Domestic and foreign scientists who have made a significant contribution to the development of methods for processing facial images are noted.

61-73

Authors: K. Asmaganbetova, Zhenis Otarbay, Almaz Turginbekov

The efficacy of the organization and corporate strategy are substantially impacted by enterprise resource planning (ERP) technologies. However, it might be difficult to manage and apply the anticipated benefits of ERP. Workarounds further complicate outdated and evolving business procedures, which impedes the ongoing development of ERP. As a result, reaping the rewards is sometimes challenging. The complexity of realizing advantages also rises when subsidiaries are not able to discover new benefits on their own, even though benefits may differ from subsidiary to subsidiary. ERP systems, which integrate, synchronize, and centralize corporate data, are frequently viewed as a key resource for businesses that thrive in a fastevolving global market. The selection of ERP systems is a crucial and challenging strategic choice because of the high cost of purchasing, installation, and implementation as well as the variety of offers. Because there are many different tangible and intangible requirements. This paper presents data on the development of innovative digital technologies in enterprise management. The main theoretical concepts of innovative digital technologies are described, as well as automated control systems and methods of their development are studied. This paper presents a model of innovation management in an industrial enterprise, an analysis of the ICT market, and developed recommendations on the methodology for implementing an ERP (Enterprise Resource Planning) system.

87-117

Authors: A. Biloshchytskyi, A. Kuchansky, S. Biloshchytska, Y. Andrashko, S. Toxanov, A. Faizullin

The article discusses the creation of an information-analytical system for evaluating the scientific performance of structural units of universities and research institutes based on the approach of constructing complex integral evaluation. A model of information technology for evaluating the results of scientific activity is proposed, consisting of four modules: an information collection module, an information storage module, an analytical module, and a module for user interaction and data visualization. The modular structure of the technology will allow expanding and modifying the capabilities of each of the modules independently of the others, as well as increasing the stability and flexibility of the technology. The implementation of this system is performed using microservices technology. A conceptual model of the information system and a structural model of the functioning of the information collection module, as well as a structural model of the information system database, are proposed. It is shown that most of the well-known indices for evaluating the performance of subjects of scientific activity, for example, h-index, g-index, e-index, I-10 index, etc., do not fully take into account information about citation. Therefore, the method for calculating the evaluation of scientific research activities of scientists was proposed, which does not lose information about any citation of the author and publication. This method determines the scalar evaluation of the results of scientific activity, and it is based on determining a few coefficients. The coefficients define one scientist’s citation in the publications of other scientists. As a result, assessment is obtained by solving a system of linear algebraic equations that are constructed based on calculated coefficients. Most of the known evaluation approaches have their own calculation features and disadvantages, which are associated with the loss of some information. Therefore, it is not recommended to give preference to one of them. For the purposes of a comprehensive assessment of the productivity of research activities of scientists, the authors proposed a method of vector evaluation of the results and the construction of the integral assessment. This method is based on the construction of vectors and scalar estimates for each scientist in a multidimensional metric space. The dimensionality of the space is determined by the number of calculated scalar estimates. The method is also based on the construction of an ideal point, which consists of scalar estimates that are the best in terms of achieving maximum performance. The assessment of each subject of scientific activity is calculated as the metric distance from the ideal point to the vector of scalar estimates of this subject of scientific activity.