A BIBLIOMETRIC STUDY ON BLOCKCHAIN CONCEPT: A THEME ANALYSIS AND FUTURE DIRECTIONS FOR COMPUTER SCIENCE TRAINING

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

ON THE DEVELOPMENT OF MANAGEMENT MODELS FOR REGIONAL PROGRAMS OF ENVIRONMENTALLY SAFE OPERATION AT CRITICAL TRANSPORT INFRASTRUCTURE FACILITIES

The objects of the critical transport infrastructure are located in all regions of Ukraine, and the question of the safety of these objects is extremely relevant. A functional approach should be used to form an effective safety management unit for critical transport objects. Therefore, in order to achieve an acceptable level of safety of the critical transport infrastructure, it is necessary to have an effective mechanism for achieving this result, which can be achieved through the formation and efficient management of regional programs for the safe operation of critical transport infrastructure objects. Management models for regional safety programs at critical transport infrastructure facilities based on the existing approaches to construction of models of program and project management are proposed in the article. Critical transport infrastructure includes highways, state-owned transport enterprises, subway facilities, gas stations, bridges, sea and river ports, airports, and pipelines. These facilities are strategic for the state, and as a consequence, vulnerable, so they require special protection. To form an effective apparatus for environmental safety management in critical transport infrastructure facilities, the application of a program approach is proposed in the article. To assess the life cycle of regional environmental safety programs of critical transport infrastructure facilities based on the Deming cycle, a spiral model was developed, which is the environment for the operation of schematic, system, and service models of the environmental safety management program. Development of approaches to the management of regional programs for the environmentally safe operation of critical transport infrastructure facilities, based on the formation of strategic objectives and their decomposition, will be aimed not only at solving existing problems of critical transport infrastructure in the region but factors related to the occurrence of dangerous events for them and the elimination of the causes leading to these problems. A system model for managing regional safety programs for objects of critical transport infrastructure is proposed

INNOVATIVE DEVELOPMENT OF EDUCATIONAL SYSTEMS IN THE BANI ENVIRONMENT

The rapid advancement of technology and the ever-changing global landscape have presented unique challenges and opportunities for educational systems worldwide. The introduction of the BANI (Brittle, Anxious, Nonlinear, Incomprehensible) framework as a response to the volatile and unpredictable nature of contemporary environments has further emphasized the need for innovative approaches to education. This paper explores the innovative development of educational systems within the BANI environment, focusing on the integration of emerging technologies, pedagogical strategies, and learner-centred approaches. The paper begins by providing a comprehensive overview of the BANI framework and its implications for educational systems. It highlights the key characteristics of the BANI environment, including its inherent brittleness, anxiety-inducing nature, nonlinearity, and incomprehensibility. The formal model of interaction between projects and the BANI environment can assess innovation project value for future optimisation. Furthermore, it elucidates the potential consequences of neglecting to adapt educational systems to these volatile conditions, emphasizing the importance of innovation in education. Drawing upon recent research and theoretical frameworks, the paper explores various innovative approaches to educational development in the BANI environment. It discusses the integration of emerging technologies, such as artificial intelligence, virtual reality, and augmented reality, into teaching and learning processes. Moreover, it investigates the implementation of learner-centred development strategies that foster critical thinking, problem-solving skills, creativity, and adaptability. The paper addresses the role of educators and institutions in supporting innovative development within the BANI environment. It emphasizes the need for professional development programs that empower educators to leverage emerging technologies and implement learner-centred approaches effectively. Key management of innovative project principles in the BANI environment is defined in the paper. Additionally, it highlights the significance of collaboration among educational stakeholders, including policymakers, administrators, teachers, students, and parents, to foster an ecosystem that nurtures innovation in education. The paper discusses potential challenges and ethical considerations associated with the innovative development of educational systems in the BANI environment. It explores issues related to equity, privacy, data security, and the digital divide, emphasizing the importance of responsible and inclusive approaches to educational innovation. Contributes to the existing literature by providing insights and recommendations for the innovative development of educational systems within the BANI environment. By embracing emerging technologies, learner-centred pedagogies, and collaborative efforts, educational systems can better prepare learners to thrive in uncertain and rapidly changing contexts

APPLICATION INFORMATION MODELING AND MACHINE LEARNING ALGORITHM FOR CLASSIFICATION OF WASTE USING SUPPORT VECTOR MACHINE

The ecological state of the world is deteriorating for the worse every year. One of the main problems is inadequate waste disposal and inadequate sorting by waste type, which has led to inadequate treatment of bulk waste in landfills throughout the world. The issue of improper disposal of municipal solid waste (MSW) in Kazakhstan has been raised since 2013, to solve this problem, the first President of the Republic of Kazakhstan, Nursultan Abishevich Nazarbayev, issued a decree on the transition to a green economy. Under the leadership of the Ministry of Energy, it was planned to reduce the amount of inappropriate waste by 40% in the territory of Kazakhstan by 2030. There are a lot of problems in India like inadequate waste collection, transport, treatment, and disposal. Poorly recyclable garbage has a global impact, fouling oceans, obstructing sewers, and creating flooding, transferring infections, increasing respiratory problems due to burning, injuring animals that inadvertently consume waste, and affecting economic development. To classify garbage, researchers utilized a combination of mixed modeling and machine learning techniques. Using machine learning technology, the data obtained can be used to classify and redistribute garbage for any sector around the world

ПЛАНИРОВАНИЕ ЗНАНИЕВОГО КОНТЕНТА ОБРАЗОВАТЕЛЬНОЙ ПРОГРАММЫ С ИСПОЛЬЗОВАНИЕМ ОНТОЛОГИЧЕСКОГО ИНЖИНИРИНГА И ПРОЕКТНО-КОМПЕТЕНТНОСТНОГО ПОДХОДА

В статье рассматривается инновационная методика формирования знаниевых компонентов планируемого обучения, основанная на концепциях и механизмах онтологического инжиниринга, проектно-ориентированной технологии обучения и компетентностной модели выпускника. Показаны возможности образовательной среды, связанные, во-первых, с традиционным формирование знаниевого контента дисциплин учебного плана; во-вторых, в случае использования проектно-ориентированной технологии обучения, планировать знаниевый тренд и формировать знаниевый контент профилирующих и базовых дисциплин учебного плана специальности в соответствии с компетенциями компетентностных моделей этапов CDIO и, в-третьих, используя проектно-ориентированную технологию обучения и компетентностный подход, планировать знаниевый тренд и формировать знаниевый контент сценария индивидуальной траектории обучения. В этом случае, для конфигурирования сценария обучения используются знаниевые компоненты и параметры smart-контракта. На примере дисциплины «Технологии разработки распределенных приложений» и проекта «Банковская система типа клиент-сервер» приведены формализмы и концепции образовательной среды, связанные с формированием знаниевого контента данной дисциплины, в соответствии с компетентностными моделями этапов CDIO. Данная методика нашла свое отражение в образовательной среде, выполненной в виде web-приложения, и апробацию в учебном процессе на кафедре «Компьютерная и программная инженерия» университета «Туран»

METHOD FOR ANALYZING COMPLEX SYSTEMS ON THE EXAMPLE OF THE COMPETENCE MODEL OF ICB4.0 IPMA PROJECT MANAGERS

The article proposes to consider some of the results of the analysis of the internal relations of the structure of the model of individual competencies of project managers, proposed by the International Project Management Association (ICB IPMA). A method is proposed for analyzing such structures, which provides for a series of steps, starting with the formulation of the problem and identification of the system investigated for solving the problem, and ending with updating the idea of the structure of interactions of the elements of the system under consideration and setting a new problem (problem). The authors use an approach such as system engineering based on modeling, which assumes a plurality of representations (models) in the study of one system. In the article, the system under study is presented both in the form of a graph and in the form of an adjacency matrix, which makes it possible to use various methods of analysis and build various models on a common model of primary data. When presented in the form of a graph, an example of application for analysis of such software as yEd and Gephi is considered. When analyzing using matrix analysis, it is first of all proposed to use classical methods of analyzing such representations as Markov systems with discrete states. It is suggested to consider the representation in the form of a second-order adjacency matrix, presenting it in the form of a “system landscape” showing the number of “paths” of transitions from one state to another (connections between elements), including through the adjacent elements of the system. It is proposed to consider such a matrix as an analogue of a “decision matrix”, considering the full set of system elements both as a set of “strategies” and as a set of “reactions” to strategies, which allows applying the methods of analysis of such a matrix used in game theory (decision theory). The closeness between the conclusions obtained on the basis of the analysis of the set of visual representations proposed by the authors and also the analytical approach they use, using elements of Markov analysis and game theory, is shown

TRAFFIC SIGN RECOGNITION WITH CONVOLUTIONAL NEURAL NETWORK

Recognizing road signs is one of the most important steps drivers can take to help prevent 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 the 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, a convolutional neural network (CNN) was used for an autonomous traffic and road sign detection and recognition system. The proposed system works in real-time on the recognition of road signs images. 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

MATHEMATICAL AND COMPUTER MODELS OF THE COVID-19 EPIDEMIC

The COVID-19 epidemic has gone down in history as an emergency of international importance. Currently, the number of people infected with coronavirus around the world continues to grow, and modeling such a complex system as the spread of infection is one of the most pressing problems. Various models are used to understand the progress of the COVID-19 coronavirus epidemic and to plan effective control strategies. Such models require the use of advanced computing, such as artificial intelligence, machine learning, cloud computing, and edge computing. This article uses the SIR mathematical model, which is often used and simple to model the prevalence of COVID-19 infection. The SIR model can provide a theoretical basis for studying the prevalence of the COVID-19 virus in a specific population and an understanding of the temporal evolution of the virus. One of the main advantages of this model is the ease of adjusting the sampling parameters as the study scale increases and the most appropriate graphs between the data and the resulting assumptions. Computer models based on the mathematical SIR model of the spread of the COVID-19 epidemic make it possible to estimate the number of possible deaths in the future. In addition, on the basis of the proposed models, it will be possible to assess the effectiveness of measures taken to prevent infection by comparing published data with forecasts. Computer models in Python are created on the basis of the proposed mathematical apparatus of SIR. The following libraries were added in the Python high-level programming language for the numerical solution of the system of differential equations for the SIR model: NumPy, Matplotlib PyPlot and the Integrate module from the SciPy library

APPLYING MACHINE LEARNING FOR ANALYSIS AND FORECASTING OF AGRICULTURAL CROP YIELDS

Analysis and improvement of crop productivity is one of the most important areas in precision agriculture in the world, including Kazakhstan. In the context of Kazakhstan, agriculture plays a pivotal role in the economy and sustenance of its population. Accurate forecasting of agricultural yields, therefore, becomes paramount in ensuring food security, optimizing resource utilization, and planning for adverse climatic conditions. In-depth analysis and high-quality forecasts can be achieved using machine learning tools. This paper embarks on a critical journey to unravel the intricate relationship between weather conditions and agricultural outputs. Utilizing extensive datasets covering a period from 1990 to 2023, the project aims to deploy advanced data analytics and machine learning techniques to enhance the accuracy and predictability of agricultural yield forecasts. At the heart of this endeavor lies the challenge of integrating and analyzing two distinct types of datasets: historical agricultural yield data and detailed daily weather records of North Kazakhstan for 1990-2023. The intricate task involves not only understanding the patterns within each dataset but also deciphering the complex interactions between them. Our primary objective is to develop models that can accurately predict crop yields based on various weather parameters, a crucial aspect for effective agricultural planning and resource allocation. Using the capabilities of statistical and mathematical analysis in machine learning, a Time series analysis of the main weather factors supposedly affecting crop yields was carried out and a correlation matrix between the factors and crops was demonstrated and analyzed. The study evaluated regression metrics such as Root Mean Squared Error (RMSE) and R2 for Random Forest, Decision Tree, Support Vector Machine (SVM) algorithms. The results indicated that Random Forest generally outperformed the Decision Tree and SVM in terms of predictive accuracy for potato yield forecasting in North Kazakhstan Region. Random Forest Regressor showed the best performance with an R2 =0.97865. The RMSE values ranged from 0.25 to 0.46, indicating relatively low error rates, and the R2 values were generally positive, indicating a good fit of the model to the data. This paper seeks to address these needs by providing insights and predictive models that can guide farmers, policymakers, and stakeholders in making informed decisions

FEASIBILITY ANALYSIS OF AIR FLOATING DESIGN FOR ELECTRICITY GENERATION

In the past several decades, there were presented different innovative technologies rather than traditional wind turbines for renewable energy that uses wind kinetic energy and remains in the air through aerodynamic forces. Unlike wind turbines with towers, their systems operate in a flight, and they are connected to a foundation by a cable that either transmits the energy generated at the airfoil or transmits mechanical energy to the ground. Nowadays, there are several existing and developing technologies; however, each of them has limitations and challenges. This work will present an analysis of air floating design for electricity generation at high altitudes. It is a tethered wind turbine with a Balloon system, which has a simple controlling system, relatively higher efficiency, and low-cost technology. The concept of the design is to model the electricity generation device powered by clean renewable energy, mainly wind power. Base on the concept of kite or helium balloon to provide enough buoyancy to keep the device working at certain altitude. To increase the energy conversion efficiency and the feasibility of the device, it is mostly used in the country, open area. Despite high efficiency which needs further investigation, the designed device is moveable, pollution free and little space consumed