Scientific Journal of Astana IT University
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DETERMINATION OF THE RELIABILITY OF AIR POLLUTION MEASUREMENT DATA BASED ON VEHICULAR EMISSION RECOGNIZED AS CONCOMITANT IN ASTANA
Get PDFAir pollution is a primary global concern due to its adverse effects on human health and the environment. Accurate air pollution measurement is crucial for developing effective control strategies and evaluating their impact. In Astana, vehicular emissions are recognized as a significant contributor to air pollution. This study aims to determine the reliability of air pollution measurement data in Astana by examining the impact of vehicular emissions on air quality to establish the accuracy of air pollution measurement methods in the city. The study will use a combination of monitoring and modeling techniques to quantify the contribution of vehicular emissions to air pollution in Astana. The monitoring component will involve the deployment of air quality monitoring stations throughout the city to measure levels of pollutants such as particulate matter (PM). The modeling component will use air dispersion models to simulate the dispersal of pollutants from vehicular emissions and predict their concentration levels in different parts of the city. The results of this research will provide insights into the effectiveness of existing air pollution control strategies and may inform future efforts to improve air quality in Astana. Based on the collected data for a certain period, a comparative table is built describing the difference between the actual data from the collection points of air emission indicators. The article will likely interest researchers and policymakers concerned with air pollution and its effects on human health and the environment. The critical point of the article is that air quality is affected not only by transport but also by other emissions
A MATHEMATICAL MODEL FOR AN AUTOMATED SYSTEM OF MEDICAL DIAGNOSTICS
Get PDFOne 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.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.In diagnosing diseases, the differential diagnosis between primary biliary cirrhosis of the liver and active hepatitis with cholestatic syndrome poses a challenge due to their overlapping symptoms. Both conditions exhibit jaundice, pruritus, fatigue, and hepatomegaly. However, distinguishing features based on clinical and laboratory observations can aid in accurate differentiation between the two states. These distinguishing features can be incorporated by developing a mathematical model for diagnosing diseases, enabling precise identification and guiding appropriate treatment strategies.
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
Get PDFThis 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
CLUSTERING OF SCIENTISTS' PUBLICATIONS, CONSIDERING FINDING SIMILARITIES IN ABSTRACTS AND TEXTS OF PUBLICATIONS BASED ON N-GRAM ANALYSIS AND IDENTIFYING POTENTIAL PROJECT GROUPS
Get PDFThe 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
MATHEMATICAL MODELING OF WATER MOVEMENT DURING A DAM BREAK USING THE VOF METHOD
Get PDFRiver valleys in mountainous areas are often subject to heavy rains and melting glaciers, resulting in the risk of mudflows and the destruction of hydraulic protective structures. In order to minimize the potential risk and negative outcomes of a disaster, both on an individual and environmental scale, it is crucial to possess essential information. This includes understanding the timing, location, and extent of flooding, as well as comprehending the force of water flow impact on protective structures. In the research, the numerical process of the movement of the water flow caused by the breakthrough of the dam is investigated. A two-dimensional numerical model of water flow during a dam break was constructed using the VOF method to describe the described process. With the help of the VOF method, the movement of the water surface is captured, while maintaining the law of conservation of mass. The mathematical model consists of Reynolds-averaged incompressible Navier-Stokes equations and includes the interphase equation. The turbulent k-e model was used to close the system of equations. The numerical algorithm used is PISO (Pressure-Implicit with Splitting of Operators). The obtained numerical results agree with the experimental data, indicating the developed algorithm’s reliability and accuracy. The results are presented as comparative graphs and images showing the contour of the free surface movement along the experimental reservoir. A numerical model that has been tested in this way can provide significant support in preventing the devastating consequences of a dam break and providing timely assistance during the evacuation of the population
DEVELOPMENT OF A QUESTION ANSWERING CHATBOT FOR BLOCKCHAIN DOMAIN
Get PDFLarge 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
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
Get PDFThe 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.
APPLYING MACHINE LEARNING TO IDENTIFY COUNTERFEIT FOODS
Get PDFCurrently, the shelves of shops and supermarkets are filled with food that people consume daily, with many products coming from abroad. However, are all these products useful for the human body, and do they meet the standards? In this article, we will talk about how to identify low-quality products using modern machine learning. Recognition and classification of images and text based on machine learning can be a key technology in the fight against low[1]quality food. Automatic image and text recognition and classification of product information enable end customers to identify counterfeit products accurately and quickly by comparing them to trained templates. However, it is clear that this does not apply to all food processing enterprises. In food production, low-quality and non-standard products are used to reduce the cost of the product. Manufacturers can change their products by replacing higher quality products with lower quality ones. They may use confusing terms on the label to mislead you. When buying and serving counterfeit products, consumers suffer in different ways. First, they may not be getting the nutrients they need, adulterated foods may not be safe for their health, and may also be an economic loss for consumers. We evaluate the technical feasibility of the components of this food fraud detection architecture using a real-world scenario, including machine learning models to distinguish multiple products from each other. It allows you to control the circulation of food products at the state level, thereby protecting the end consumer from purchasing low-quality and potentially dangerous goods. In this article, we used the MobileNetV2 model and multiclass classification and evaluated the model we received from different angles
DEPENDENCE OF COMPETITIVENESS ON THE LEVEL OF BUSINESS CONFIDENCE OF THE ENTERPRISE
Get PDFThe article deals with the issue of ensuring the competitiveness of construction contractors depending on the level of business confidence, which is esteemed as the amount paid on schedule construction contracts. To improve the competitiveness of enterprises the authors propose indicators to identify the existing potential for efficiency enhancement and ensuring competitiveness depending on the level of business confidence of the enterprise. Indicators of competitiveness of construction companies are determined by fuzzy sets, including pricing policy, the efficiency of fixed assets and the level of diversification. The main direct and indirect factors of competitiveness of the contracting enterprise are outlined
USING IT TOOLS IN SOCIOLOGICAL SURVEYS FOR DETERMINING THE SOCIAL PROFILE OF ASTANA IT UNIVERSITY STUDENTS
Get PDFThe article gives an overview of Astana IT University's (AITU) experience in using IT tools in sociological study to determine the social profile of an AITU student based on the data of sociological surveys. The purpose of the study is aimed at summarizing the experience how IT tools can be used in gathering and processing data and survey result analysis. In this article the features of various software and hardware tools, the process of applying information technologies at different stages of sociological research that affect the effectiveness of sociological research are discussed. The aim of the study is to determine the effectiveness of various software (from word processors to statistical packages) and hardware (from cell phones to modern computers) tools at different stages of conducting a sociological survey.
The most common problems the sociologist may run into when looking for a survey tool are technical, design, legal, and expertise one as well as problems with length, survey fatigue and interpreting respondents’ mood. It is highly likely in the case of long, confusing, or complicated surveys the questions can be skipped or responded mindlessly by respondents and be resulting in confusing results and inaccurate responses. Long surveys more than 10-15 minutes can course respondents mind fatigue which can lead and result the dishonest responses and survey dropout. The respondents can be tired with especially open-ended questions which require careful and deep thinking. The problem of selecting adaptive tools for conducting sociological research for obtaining the most reliable and high-quality information lies in the lack of a universal IT tool for implementing sociological research at its all stages.
The model of the university efficiency promotion for sustainable development is presented to compare the progress of university efficiency in learner recruiting, hiring of local and international staff, the number of staff participated in the program of professional development, the number of laboratories and IT technologies, the range of national and international partners, etc. Stages of a Data-driven decision-making approach in university efficiency promotion are presented in detail