Lyazat Naizabayeva
@iitu.edu.kz
Department of Information System
International Information Tehnology university
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
L. Naizabayeva, Ch.A. Nurzhanov, M.N. Satymbekov, and V.Zh. Elle
Elsevier BV
Lyazat Naizabayeva and Gulnara Zakirova
IEEE
This article is aimed at the selection of machine learning algorithms for the perception of elements in the composition of the soil. In our study, machine learning aims to track changes in the toxicity of contaminated soils in order to predict the level of human health hazard of a contaminated soil condition. For this, the following tasks were set: 1) collection and analysis of data on the types of soil toxicity; 2) study of a mathematical model of toxicity to assess the level of resistance; 3) testing the concentration of toxic elements in soils using four machine learning algorithms. Four machine learning algorithms were tested in the article to predict the content of heavy metals in the soil. The results show that the use of machine learning algorithms allows achieving a high degree of prediction of harmful elements concentrations in soils, which can be useful for making decisions on managing pollution sites. Further improvement of data accuracy and selection of the model and algorithm can also increase the accuracy of prediction. As a result of forecasting using the K-Nearest Neighbors algorithm, the accuracy of the model was achieved at the level of 68%, and using the Decision Tree - 69%. The results of the experiment make it possible to predict the measures of danger to human health from the state of contaminated soil. This will help reduce the risks of the relationship between soil pollution and human health. The obtained results can be useful for specialists in environmental protection, land resource management, and decision-making.
Bauyrzhan Omarov, Zhuldyz Sailaukyzy, Alfiya Bigaliyeva, Adilzhan Kereyev, Lyazat Naizabayeva, and Aigul Dautbayeva
Computers, Materials and Continua (Tech Science Press)
Chingiz Nurzhanov, Lyazat Naizabayeva, and Talgat Mazakov
IEEE
The article focuses on the use of climate data in modelling crop productivity and highlights the im-portance of continuous incoming meteorological infor-mation in predicting crop yields. The purpose of Article is to evaluate the challenges and potential of utilizing big data using climate data as an ex-ample for modelling crop productivity on contaminated sites with trace elements. The “MiscanCalc” and “Group Method of Data Han-dling” were developed to predict crop yields on soils con-taminated with toxic elements using meteorological data. These models evaluate the impact of climate data on bio-mass production, ripening and harvest periods, estimate future crop yields, and identify the predictors that have the greatest influence on these indicators.
Gulzat Turken, Lyazat Naizabayeva, Maxatbek Satymbekov, and Zukhra Abdiakhmetova
IEEE
The data warehouse is based on the development of relational databases, parallel processing and distributed processing technologies, as well as online analytical processing technologies. SAP BW is widely used in many enterprise data warehouses. It can issue reports of enterprises at the group level, manage the group's business operations, help find the laws and trends behind the performance of corporations and ultimately provide decision-making support for decision makers of enterprises. Therefore, how to use the SAP BW model architecture to better realize enterprise data warehouses is one of the hot topics in the data warehouse industry today. For a large corporation, its branches and subsidiaries have deployed multiple sets of enterprise data warehouses. At the same time, because there are multiple sets of data warehouse systems, when the data model needs to be modified at the group level, the data model of the branch also needs to be modified accordingly, and the flexibility of maintenance is poor. Finally, every time the corporation needs to upgrade the version of the data warehouse, it needs to maintain and upgrade the version of the data warehouse of each branch and the maintenance cost is high. Therefore, how to integrate multiple sets of data warehouses distributed in various subsidiaries into a corporation enterprise data warehouse by designing the model hierarchy, so as to ensure the reliability of data, enhance the flexibility of model modification and reduce the cost of data warehouse. This paper completes the design of an enterprise data warehouse of a large corporation, which can be used for the summary and collation of data from multiple branches such as finance and sales of a corporation, effectively improving the efficiency of data utilization and ensuring the authenticity of data. At the same time, through hierarchical design, the problems of poor flexibility in model modification and high system maintenance costs are solved.
Gulbakyt Sembina, Karina Mayandinova, Lyazat Naizabayeva, and Saule Sagnayeva
Private Company Technology Center
One of the most important tasks of improving the information technology infrastructure of an enterprise is to increase the efficiency of the incident management system. The relevance of this study lies in the fact that at present the work of the technical support service in the conditions of a large flow of applications accelerates violation of the deadlines for resolution established by the business. It, in turn, leads to downtime of information systems and financial losses of the enterprise. This article analyzes the feasibility of introducing a third line of technical support to increase the proportion of incidents resolved within the framework of the Service Level Agreement adopted at the enterprise. A comparative analysis of the widely used two-level model with the proposed three-level model in this work is considered, using business process model notation. The effectiveness of the model is confirmed by automated computations using metrics, by calculating the rate and satisfaction coefficients within the framework of two and three levels of the model and then comparing these indicators to establish patterns. Thus, it is possible to track how successfully and timely incidents of information systems are resolved, which in turn directly reflects the availability and correct functioning of systems and the entire company. The company's practical losses due to system downtime were calculated, as well as the resulting financial losses before and after the adopting of the three-level system, taking into account the associated costs to identify if the initiation of the model is justified and profitable. Thus, the proposed model can be adopted by organizations in order to improve the quality of services provided by the IT department, to reduce the effect and impact of incidents on the performance and availability of systems that affect the formation of financial statements
Azat Kabdullin, Maxat Kabdullin, and Lyazat Naizabayeva
IEEE
Coronary heart disease is a disease that causes the death of most people in the worldwide. According to the WHO, 9.43 million people died from ischemic stroke in 2018.Residents of some CIS countries (Belarus, Kazakhstan, Kyrgyzstan, Russia and Ukraine) have a higher cardiovascular mortality rate at the age of 55-59 than the French at the age of 75-79. Premature mortality rates in women in European countries are lower than in men, but have similar dynamics [12], [13].The constant increase in the amount of information in cardiology makes the development of new methods for data analysis urgent. Using existing risk assessment approaches, it is impossible to predict about half of the episodes of acute coronary syndrome. Big data machine learning can lead to better diagnostic and treatment outcomes at a lower cost. The inductive approach allows you to identify patterns arising from data analysis and develop algorithms that can learn on their own. Although machine learning models for cardiovascular risk assessment are superior to traditional methods, to date, no large-scale machine learning studies have been conducted to prove a predictive role in the general population using routine clinical data. In addition, there is no clear recommendation which of the algorithms will work better in a given situation. The use of an empirical approach when choosing a machine learning method and the principle of "black box" make it difficult to conduct large-scale studies and implement machine learning methods in clinical practice. This work is devoted to the study of the optimization of neural networks using a correlation analysis of signs to predict the possibility of coronary heart disease. As a result of the analysis of literary sources, the shortcomings of previously developed software tools were identified, seriously limiting the use of these programs in the medical field.
C. Nurzhanov, V. Pidlisnyuk, L. Naizabayeva, and M. Satymbekov
Birlesik Dunya Yenilik Arastirma ve Yayincilik Merkezi
The general purpose of this study is to conduct a content study on ‘computer’ and ‘educational technologies’ research and trends between 2016 and 2020. The topics were evaluated according to years, universities of the authors, citations, keywords, document type, source, sponsors and publication languages. The articles examined in the research include keywords related to ‘computer’ and ‘instructional technologies’ between 2016 and 2020; 1,798 articles obtained by scanning the Scopus database according to the title, keywords and summary of the articles were examined. When the results of the study were examined, it was concluded that English, which is the universal language, is very common; the researches are mostly published as ‘conference papers’ and the most used keyword in the study is ‘Computer Science’. In addition, it has been concluded that computer science is the basis of educational technologies in recent years. Similar content analysis studies may be recommended for other software used in computer training.
 
 Keywords: Computer, educational technology, computer science, content analysis, trends.
Mohamed Ahmed Hamada and Lyazat Naizabayeva
IEEE
Nowadays, the business market is more complicated and comprises many challenges; it became more competitive and surrounded by high-risk patterns. Seeking for new technologies and adopting innovation is becoming an important and crucial issue to eliminate the complexity of the decision-making process and failure probability. Decision support system (DSS) is a computerized system that encompasses mathematical and analytical models, knowledge base and a user interface to help managers for making better decisions. This research aims to develop a decision support system based on K-means clustering algorithm to detect the optimal store location through social network events. Also, this research explains how to extract data from one social network channel "Instagram" using the "Octoparse API" as a web data extraction tool. K-means algorithm identifies k- number of centroids, and allocates every data point to the nearest cluster. As a result, we analyzed 12754 posts started on the 1st of January 2019. Cleaned data are transformed using Minimax and K-means algorithms. As an output, we have got json format data file with centres which are placed on the map to provide a better understanding. The Result of this research is a visualized map pointed with places to define the optimal location of a specific store at the selected region. The practical value of this DSS tool is to help users to make a more valuable and accurate decision which lead to a decrease in the probability of ineffective business decision and minimize the business losses.
Turken Gulzat, Naizabayeva Lyazat, Vladimir Siladi, Sembina Gulbakyt, and Satymbekov Maksatbek
Czech Technical University in Prague - Central Library
This paper effectively uses the data mining and optimization methods to investigate a classification based on decision trees algorithm, then optimizes by the method of grid search and cross-validation, which improves the prediction accuracy of the decision tree model for the PCs sales in practical application and solves insufficient training data, high computational cost, and low prediction accuracy. The main goal of the article is to predict PC sales using machine learning tools caused by various types of operating system factors in practical applications. This article proposes a combined predictive research model that fully reveals the benefits of optimization and neural networks, and also has a very accurate fit and forecasting accuracy. The proposed predictive model is implemented in the data science software platform RapidMiner. A decision tree model is executed, then the model’s prediction capacity is evaluated and tested. Grid search optimizer is used to automatically build the final model using the best-optimized parameter for training the classifier. The paper combines grid the grid search and cross-validation to optimize the parameters of the decision tree to improve the classification prediction accuracy of the decision tree model. This article combines neural networks with optimization methods to establish a prediction model for laptop sales. This model gives full play to the advantages of optimization and neural networks and has very good fitting capabilities and prediction accuracy. Besides, the neural network for the prediction model has strong dynamic analysis capabilities. Once there are new observations, it can continue to be added to the modeling, which has high adaptability. The Neural Network algorithm has the highest accuracy of the predicted PC sales by evaluating the results of the five kinds of algorithms. The result for prediction accuracy shows the highest performance.
M.N. Satymbekov, I.T. Pak, L. Naizabayeva, and Ch.A. Nurzhanov
Walter de Gruyter GmbH
AbstractIn this study the work presents the system designed for automated load balancing of the contributor by analysing the load of compute nodes and the subsequent migration of virtual machines from loaded nodes to less loaded ones. This system increases the performance of cluster nodes and helps in the timely processing of data. A grid system balances the work of cluster nodes the relevance of the system is the award of multi-agent balancing for the solution of such problems.
Maksat N. Kalimoldayev, Vladimir Siladi, Maksat N. Satymbekov, and Lyazat Naizabayeva
IEEE
Paper presents results of a practical experiment that was conducted in order to pursuit main objective — design and implement novel iterative MapReduce framework based on Hadoop technology. To study the effects of implementation parallel scientific applications, we deployed. Despite the fact that they did not show better performance than the same algorithm implemented in MPI, we conducted experiments to solve problems iterative computations using the Hadoop architecture. As a result of the experiments, the fail-safe behavior of Hadoop technology was revealed in the solutions of complex hadacies.
Naizabayeva Lyazat, Nurzhanov Chingiz, Orazbekov Zhassulan, and Tleuberdiyeva Gulnara
Walter de Gruyter GmbH
Abstract In this article a software implementation of the environmental monitoring is developed and presented, which is responsible for receive, store, process and analysis of data. For logical database design system Computer- Aided Software Engineering (CASE) technology, the AllFusion ERwin Data Modeler was selected. To develop corporate Oracle database management system used. The database contains a set of objects, which store all the primary and additional service information, as well as a set of software modules of business logic. The developed information system makes it possible to find optimal solutions for clean and disposal of the contaminated areas. There are advantages of created databases on the areas to be remediated, such as the analysis of remediation made by using plants.
Kanat Amirtayev, Akzhan Ibadullayeva, Lyazat Naizabayeva, and Zhazira Shermentayeva
IEEE
The numerical algorithm and the set of application programs allow taking into account all the factors influencing the considered rod, i.e. the axial force of temperature, heat flow, heat exchange and partial heat isolation were developed.
Lyazzat Atymtayeva, Assel Akzhalova, Kanat Kozhakhmet, and Lyazat Naizabayeva
IEEE
This article provides an overview and analysis of technologies for creating a hybrid intelligent systems for information security auditing and management, based on international standards of information security and scientific approaches to the management of information security (linguistic approach, heuristic expert evaluation, etc.)
N.I. Nugumanov, L. Naizabayeva, and D. Nurlanov
IEEE
This paper shows results of development of one of the key modules, Requisition Log, which allows to automate receipt of the orders from clients and processing thereof. The follows tools have been used: Enterprise Architect Unified Modeling Language (eaUML) for logical development of logistical applications, toolboxes of MS SQL Server in design of the physical database, the Borland Delphi environment to create the client application.