Field Of Information Technology Research Articles

СУЧАСНИЙ СТАН, ОСОБЛИВОСТІ ТА ПЕРСПЕКТИВИ СПІВРОБІТНИЦТВА УКРАЇНИ З КРАЇНАМИ-УЧАСНИЦЯМИ АСЕАН

The purpose of the article is to analyze the main areas of foreign economic cooperation of Ukraine with ASEAN member countries and to determine the future prospects of their interaction. The article examines the trade and economic relations of Ukraine with ASEAN member countries during 2014-2022. An analysis of the volume of trade in goods between the parties is presented, which indicates a gradual increase in trade turnover from 2015 to 2021 and its further reduction in 2022 due to the start of a full-scale war The main categories of export and import of goods between Ukraine and ASEAN member countries in 2022 have been determined, which include grain crops, vegetable fats and oils, telecommunications equipment, metal products, etc. The geographical structure of trade operations was analyzed and the most popular export and import destinations for Ukraine, such as Malaysia and Vietnam, were determined. It is proven that, despite the gradual increase in trade volumes, the level of investment activity between the parties remains low, which is determined by a number of negative factors, including the war with Russia, corruption and the uncertainty of the legal environment in Ukraine. Positive factors that increase the investment attractiveness of Ukraine for the countries of Southeast Asia are considered, in particular, the geopolitical position and the presence of a highly qualified workforce. Based on the data of the National Bank of Ukraine, it was determined that the largest number of investments in Ukraine comes from Singapore, in particular, in enterprises in the fields of industry, trade and agriculture. The existence of significant potential for the development of trade and economic relations between Ukraine and ASEAN member countries has been proven. Ways of intensifying bilateral cooperation were proposed, such as the implementation of joint projects in the field of information technology and infrastructure, increasing the volume of Ukrainian exports and the creation of special economic zones. The need to sign trade agreements and expand business ties for the further development of cooperation between Ukraine and ASEAN member countries is highlighted.

Work in IT for women: The realities of Ukraine


 
 
 The article presents a study conducted on gender equality in Ukraine in the field of information technology. The problem of gender inequality still exists throughout the world. The purpose of the article is to study the working conditions of women in IT, to discuss the possible use of stereotypes, and to identify the reasons for women’s lack of interest in working in this field. Formally, there is no prohibition for women to work in the IT industry. However, there are a number of reasons in Ukraine that affect the possibilities of pursuing a career in this direction. The article analyzes the available statistical data and the results of a survey among representatives of the IT sector. It is worth noting the problem of data collection during the war in Ukraine. The importance of the sphere for the country’s economy as a whole and the relevance, demand for specialists, as well as the ratio of women and men employed in this area are considered. Global trends in gender distribution in the field of IT were analyzed. The main stereotypes and situations that constitute obstacles for women in this area are considered and the features inherent in Ukrainian society are also highlighted. Based on the statistical data of Ukraine the number and structure of female students, including foreigners, in recent years were analyzed. It was revealed that the structure of the gender ratio of men and women in education is changing with the formation of the labor market of Ukraine and the distribution by employment status. The ratio of working women and men is approximately the same, but the structure by employment status is different. In Ukraine the IT sector is largely based on individual entrepreneurs. Despite the martial law, the IT community is actively developing and new companies are being created. Training is actively carried out in paid and free courses. Surveys were conducted among students, and it was found that the beginning of a career in the field of information technology is already determined by certain stereotypes of society. Based on the conducted research, the structural gender relations in the field of IT and the main stereotypes that hinder the development of women in this area are analyzed. Recommendations for overcoming gender inequality in the field of IT in Ukraine are presented. Based on the information received, conclusions were drawn: the number of women and men studying technical and mathematical sciences in Ukraine is approximately the same. But in the future, in professional activities, women in many cases prefer to occupy weaker positions, leaving leading positions to men. The main reason for this dynamic is the generally accepted stereotypes and the actualization of the role of women in other areas.
 
 

Visualizing entanglement in multiqubit systems

In the field of quantum information science and technology, the representation and visualization of quantum states and related processes are essential for both research and education. In this context, a focus lies especially on ensembles of few qubits. There exist many powerful representations for single-qubit and multiqubit systems, such as the famous Bloch sphere and generalizations. Here, we utilize the dimensional circle notation as a representation of such ensembles, adapting the so-called circle notation of qubits and the idea of representing the n-particle system in an n-dimensional space. We show that the mathematical conditions for separability lead to symmetry conditions of the quantum state visualized, offering a new perspective on entanglement in few-qubit systems and therefore on various quantum algorithms. In this way, dimensional notations promise significant potential for conveying nontrivial quantum entanglement properties and processes in few-qubit systems to a broader audience, and could enhance understanding of these concepts as a bridge between intuitive quantum insight and formal mathematical descriptions. Published by the American Physical Society 2024

Advancing Fruit Image Classification with State-of-the-Art Deep Learning Techniques

Fruit image classification technology using deep learning is making significant contributions in the agriculture and food retail sectors, promising to increase efficiency and productivity. However, there is an identified knowledge gap in dealing with the considerable variation in fruit appearance caused by factors such as type, size, color, and lighting conditions, as well as the precise identification of damage or disease. This research focuses on applying the developed Convolutional Neural Network architecture to fill this gap by using it in an extensive and diverse dataset, covering 67,692 image files categorized into 131 fruit classes. The training process showed substantial accuracy improvement, with training accuracy reaching 98.39% and validation accuracy at 90%, while training loss decreased to 0.0430 and validation loss to 0.2991. In the advanced stage of training, the training accuracy peaked at 99.43% in the 59th epoch with a shallow loss of 0.0251. However, the validation loss showed variation, indicating room for improvement in model generalization. The findings provide insight into the potential and challenges of applying Convolutional Neural Network models and fruit image classification with improved fruit sorting accuracy. Contribution to the literature in the field of information technology and agriculture by showing deep learning models can be improved to address the issue of fruit image variability.

Detecting Upcoming Patent Keywords by Predicting Keyword Trends Using Patent Keyword Network

In this ever-changing technological landscape, the ability to quickly predict technological trends becomes crucial for any company or institute engaged in informed decision-making and strategic planning. Data for predicting technological trends can come from various sources such as patent data, which is easily accessible to the public due to the nature of patents. This research is aimed at patent analysis, focusing on combining the keyword- based method, social network analysis (SNA) method, and neural network prediction to propose a feasible keyword trend prediction method based on patent analysis by targeting upcoming keyword trends. More specifically, we utilize Long Short-Term Memory (LSTM) to predict changes in keyword frequency using keyword centralities as input. To assess the effectiveness of the proposed method, we constructed the input dataset using the USPTO patent database in the Information and Communication Technology (ICT) field. We then experimented to compare the proposed method with the benchmark method. Furthermore, to counteract the unbalanced nature of patent data, the SMOGN method is introduced. The results demonstrate its potential for application in broader contexts.

Creating a model of a training task set for a multitask practical training in the courses “Informatics” and “Information technologies in professional activity”

According to the federal state educational standards, the curriculum for socio-economic and humanitarian students of secondary vocational education (SVE) and higher education (HE) includes disciplines that provide mastery of competencies in the field of information and communication technologies. Along with the course of informatics, they include disciplines of the block “Information technologies in professional activity”: “Information Technologies in Court Activity” (SVE), “Information Technologies in Professional Activity” (for several specialties of SVE), “Information and Communication Technologies in Judicial Activity” (HE), “Information Technologies in Management” (HE), “Information Technologies in Translation Studies” (HE) and several other disciplines.Based on the analysis of scientific literature and an array of manuals — workshops provided to students and teachers by leading Russian electronic library systems (ELS), as well as own pedagogical work in teaching informatics and the block of academic courses “Information Technologies in Professional Activity”, the author proposes a model of training tasks set to be solved during the study of these courses. The relevance of the article subject is conditioned by the necessity to effectively use the time allocated by the curricula of socio-economic and humanitarian training areas of vocational education and higher education for the study of informatics and information technologies in professional activities. The novelty of the results presented by the author is confirmed by the absence of articles and reports on the topic in the Russian Science Citation Index (RSCI) database

Secure Multiple-Image Transfer by Hybrid Chaos System: Encryption and Visually Meaningful Images

The secure transmission of information is one of the most important topics in the field of information technology. Considering that images contain important visual information, it is crucial to create a safe platform for image transfer. One commonly employed tool to enhance the complexity and randomness in image encryption methods is the chaos system. The logistic and sine maps are utilized in encryption algorithms but these systems have some weaknesses, notably chaotic behavior in a confined area. In this study, to address these weaknesses, a hybrid system based on the Atangana–Baleanu fractional derivative is proposed. The various tests employed to evaluate the behavior of the new system, including the NIST test, histogram analysis, Lyapunov exponent calculation, and bifurcation diagram, demonstrate the efficiency of the proposed system. Furthermore, in comparison to the logistic and sine maps, the proposed hybrid exhibits chaotic behavior over a broader range. This system is utilized to establish a secure environment for the transmission of multiple images within an encryption algorithm, subsequently concealing them within a meaningful image. Various tools employed to assess the security of the proposed algorithm, including histogram analysis, NPCR, UACI, and correlation values, indicate that the proposed hybrid system has application value in encryption.

Educational models and technologies in the era of digital transformation

At present digital transformation of the society faces the problem of a shortage of a highly qualified personnel - specialists in the field of the latest information technologies, intellectual scientists with a high-quality fundamental education. The solution to this problem lies in the introduction of new educational models and technologies. The purpose of the research is to study the strategy for the formation of a new conceptual scheme of modern education. It has been emphasized that two leading educational models are currently being successfully implemented and complement each other: the classical model of «completed» education with the basic principle of «education once and for life», covering the need of the society for the generation and dissemination of knowledge, as well as the storage of cultural ideals, and the second model, called the «lifelong education model», which meets the principle of «lifelong education», corresponding to the rapid changes in technology and the nature of digital transformations of the society. The article pays special attention to new educational technologies such as: 1) analytical technologies, which are based on the analysis of big data from educational institutions in real time; 2) adaptive (or intellectual) learning, which is based on the principles of personalization of learning and building an individual educational trajectory; 3) Smart education, using learning tools in an interactive educational environment with elements of distance and mobile education. The methodological basis of the research is a systematic approach and general methods of scientific knowledge, such as analysis, synthesis, generalization. The relevance of the topic is associated with increased attention to the problem of matching the quality of the Russian education with the promising tasks of the digital transformation of the Russian society. Key conclusion. A high professional level of the personnel reserve, which will be able to practically implement state digitalization programs, is possible and is associated with the construction of a new concept of modern education.

РАЗВИТИЕ НАУЧНО-ТЕХНОЛОГИЧЕСКОГО ПОТЕНЦИАЛА РЕГИОНОВ ПРИВОЛЖСКОГО ФЕДЕРАЛЬНОГО ОКРУГА РОССИЙСКОЙ ФЕДЕРАЦИИ

Currently, in the context of active transformation of the global economy, innovation plays a key role in the development of regions. This is especially noticeable in industrialized countries, where the prospects for sustainable economic growth are closely linked to the integration of the latest scientific and technological achievements, including progress in the fields of information technology, biotechnology, nanotechnology, as well as the introduction of new materials and technologies aimed at saving resources and protecting the environment . The development of the scientific and technological potential of the regions of the Russian Federation is recognized as a priority task at the state level. Issues of developing and implementing strategies that can stimulate the scientific and technological development of the regional economy occupy a central place in discussions at the highest government level and in scientific circles. The Volga Federal District of Russia stands out among other regions due to its high scientific and technological activity. Analysis of the scientific and technological potential of a given region, taking into account its scientific, technological and economic characteristics, is an important task, which provides for the relevance of developing special territorial strategies and methods for assessing scientific and technological development. The object of the study is the scientific and technological potential of the Volga Federal District of the Russian Federation. The purpose of the study is to assess the state, problems and prospects for the development of the scientific and technological potential of the regions of the Volga Federal District. Research methods: statistical analysis, comparative analysis, documentary method, general scientific methods. The scientific novelty of this research includes the identification and analysis of key factors that influence scientific and technological potential, as well as determining the directions of its development in the regions of the Volga Federal District.

Hospital Medical Behaviour Supervision and Operational Efficiency Evaluation Method based based on Big Data Platform

The application of cloud computing and big data core technologies and concepts to medical informatization can improve its flexibility and efficiency, and achieve the overall deployment and intensive management of the system. In the era of big data, the use of information management platform can optimize and standardize clinical diagnosis and treatment process, improve the quality and efficiency of diagnosis and treatment services, and improve the quality and level of scientific research, which meets the requirements of medical reform for fine management of hospitals and precise medical treatment in the era of evidence-based medicine. This paper introduces the development and application of big data analysis in the field of information technology. Through the research on the supervision of hospital medical service behavior, the top-level design is used to standardize the content of medical behavior supervision, and the effectiveness of supervision is discussed to achieve the purpose of reducing clinical paperwork. Based on the needs, the medical service behavior supervision system is proposed and constructed. Strengthen hospital medical behavior supervision through hospital big data analysis and knowledge base system support. This system can provide management with an effective monitoring and management tool, enabling them to promptly identify and solve problems that arise in medical services. By analyzing a large amount of medical data, hospitals can help predict the trend of disease occurrence in advance, thereby taking preventive and control measures in advance, and reducing the occurrence and spread of diseases. Through deep learning and analysis of patient data, personalized treatment plans can be provided for each patient to improve treatment effectiveness.

Component analysis for energy-efficient multimedia networks utilizing 5G radio access technologies

The object of the research is the energy-efficient multimedia networks based on 5G wireless access technologies. The research is aimed at developing energy-efficient multimedia networks based on 5G wireless access technologies, considering the increasing importance of information and communication technologies (ICT) in modern society. With the proliferation of wireless access networks and the advancement of fifth-generation mobile networks (5G), there is a need to assess and reduce the environmental impact of ICT. The article specifically focuses on the challenges related to energy consumption and CO2 emissions in radio access networks, highlighting the responsibility to balance technological advancements with environmental concerns. The paper examines various components and technologies necessary for enhancing energy efficiency in multimedia networks. It discusses the concept of multimedia, including digital storage, data processing, and interactive elements. Statistical data is provided to underscore the significant energy consumption and carbon footprint of the ICT industry, with an emphasis on radio access networks. Heterogeneous networks, non-orthogonal multiple access (NOMA) technologies, and multiple-input multiple-output (MIMO) technologies are identified as key components for achieving energy efficiency. The importance of reducing the distance between transmitters and receivers in heterogeneous networks is emphasized, as well as the use of energy-saving strategies such as putting small base stations into sleep mode during low network loads. Special attention is given to the role of green data centers in reducing CO2 emissions and optimizing the use of green energy in high-performance networks. Proposed methods include leveraging renewable energy sources, improving hardware energy efficiency, and implementing energy-efficient routing. The findings offer valuable insights for the development and implementation of energy-efficient multimedia networks, particularly in the context of 5G networks. The interdisciplinary approach advocated in the conclusion emphasizes the collective efforts needed to address environmental challenges in the field of information and communication technologies. The combination of these technologies ensures efficient resource utilization and reduced environmental impact compared to similar known approaches.

Finger Print Voting System

The field of biometrics, or the identification of people based on physiological or behavioral characteristics [6], has recently received a significant amount of attention in current research as a result of its applicability in the fields of information technology and security. With the increased usage of PIN numbers and passwords in everyday life, the vulnerabilities of these two technologies are becoming more apparent (e.g. password-cracking). Biometric authorization’s strengths are that it requires the user to be present and that it eliminates the hassles of passwords and PIN’s (or can be used in parallel with these for added security). Specifically, online identity verification, ATM machines, and building entrance authorization, are all areas where it is very useful to implement an automatic identification system.

An Enhanced Hybrid Deep Learning Model to Enhance Network Intrusion Detection Capabilities for Cybersecurity

Recently, we have noticed tremendous growth in the field of Information Technology. This increased growth has proliferated the use of new technologies and continued advancement of networking systems. These systems are widely adopted for real-time online and offline tasks. Due to this growth in information technology, maintaining security has gained huge attention as these systems are vulnerable to various attacks. In this context, an Intrusion Detection System (IDS) plays an important role in ensuring security by detecting and preventing suspicious activities within the network. However, as technology is overgrowing, malicious activities are also increasing. Moreover, legacy IDS methods cannot handle new threats, such as traditional signature-based methods requiring a predefined rule set to detect malicious activity. Also, several new methods have been proposed earlier to address security-related issues; however, the performance of these methods is limited due to poor attack detection accuracy and increased false positive rates. In this work, we propose and compare different deep-learning (DL) models that can be used to construct IDSs to provide network security. Details on convolutional neural networks (CNNs), Multilayer Perceptron (MLP), and long short-term memories (LSTMs) are introduced. A discussion of the outcomes achieved follows an assessment of the proposed DL model known as the FOA-CNN-LSTM technique. Comparisons are made between the suggested models and other machine-learning methods. This work presents a deep-learning approach based on hybrid CNN-LSTM with Fruit fly Optimization Algorithm (FOA) by ensemble techniques to distinguish between normal and abnormal behaviors.

Complex modeling of complicated processes: analytical model parameters adaptation in solving qualimetric problems

The relevance of the study is due to the fact that despite the sustainable development of methods and technologies for creating models of various classes and purposes, the tasks of managing their quality (the so-called inverse problems of theoretical qualimetry) are often neglected. For example, in the field of information technology, much more attention is paid to assessing the quality of software products, which is reflected in the corresponding standards. These tasks become especially relevant when models reflect a complex process (CP), and such processes may be unique, and obtaining complete objective data on such CPs prior to modeling is difficult or impossible (accidents and disasters models, military operations, natural disasters, and others like that). The article’s main purpose is to consider the issues of solving the inverse qualimetric problem by parametric adaptation of analytical models to unpredictable possible changes in the complex processes’ nature. The solution to this problem is based on the combined (analytical and simulation) modeling of unique processes. Theoretical qualimetry involves a quantitative assessment of any product's qualitative properties; to ensure the adequacy level of analytical models (based on the results of their parametric adaptation), it is planned to evaluate with the use of well-known metrology principles regarding relative measurement deviations. In this case, detailed simulation modelling results are used as a reference for combined modelling. The complexity of the processes conditiond by the expediency of their polymodel description in order to mutually compensate for the objective deficiencies of heterogeneous models, as well as the use of simulation capabilities to adjust the parameters of analytical models in cases where the use of the latter is due to strict time constraints on obtaining calculation results and developing control actions based onthem. A variant of the formalized statement of the problem of parametric adaptation of analytical models is presented. A presented example that considers the adaptation of parameters of the Lanchestertype analytical model conducted on simulation data. The adaptation of parameters could be carried out in different ways, and the expediency of choosing a certain one is determined by the assessment of mutual deviations of the results of heterogeneous modeling. The results of the calculations witnesses that parametric adaptation can be an effective element in solving one of the key tasks of model qualimetry, namely, ensuring their required qualitative properties. The prospect of further research is to consider the issues of structural adaptation of simulation models to the given conditions for displaying complex processes.

Artificial Intelligence and Psyche: The Trickster and Our Moral Compass

This article explores the mercurial nature of Artificial Intelligence (AI) as a reflection of Jung’s concept of the trickster archetype, delving into how AI epitomizes at once the brightest aspirations and the darkest inclinations of the human psyche. Through personal experiences shared by the author from his decades of working in the field of information technology and as viewed through a Jungian lens, this discussion illuminates the psychological underpinnings of our collective engagement with AI technologies, arguing that AI presents ethical and moral conundrums that must be navigated cautiously by the humans who use it.

NEED ANALYSIS OF MOBILE LEARNING AT JUNIOR HIGH SCHOOL IN INDONESIA: A BLIOMETRIC STUDY

This research examines the need for mobile learning at Indonesia's junior high school. Mobile learning is a learning approach that uses mobile devices and digital technology to support the learning process. This research aims to understand research trends related to mobile learning and the need for mobile learning in Indonesian junior high schools. This research uses bibliometric analysis to evaluate mobile learning research trends in Indonesian junior high schools. Bibliometric analysis involves collecting and analyzing relevant bibliographic data or scientific publications. The research uses four application tools. First, Publish or Perish was used to search for scientific articles by keyword, resulting in 752 articles. Then, through Mendeley, filtering was carried out based on specific criteria, resulting in 561 articles. Next, Covidence filtered articles in detail, resulting in 158 relevant articles. Finally,VOS viewer is used for data visualization with co-occurrences. The results of the bibliometric analysis show that research on mobile learning in Indonesian junior high schools has increased significantly since 2020. Various disciplines are involved in mobile learning research, primarily focusing on mathematics, science, and language education. Apart from that, the field of information technology also has a vital role in developing mobile learning applications and infrastructure. This research provides an objective view of research trends and mobile learning needs in Indonesian middle schools, provides direction for further research, and supports the development of more innovative and inclusive education in the future.

THE IMPORTANCE OF ATHLETICS TEACHERS' USE OF EDUCATIONAL TECHNOLOGY

In the field of education, information and communication technologies and multimedia capabilities have become extremely widespread and have become available to almost all schools at certainrates. However, sometimes there are technical difficulties, sometimes it is not possible to fully use educational technologies and multimedia tools in physical education classes, for example, the service received at that time is insufficient or the desired result is not achieved. In this study, athletics teachers were asked how often they use instructional technology and multimedia tools, and the reasons, if any, for their insufficient use of these tools.

Preface

2023 The International Conference on Electronic, Eelectrical and Information Technology (EEIT 2023), the Online International Conference to be held on November 24-26, 2023. Aiming at providing an excellent international academic forum for all the researchers and practitioners, the conference enjoys a wide spread participation among all over the universities and research institutes.It was provide opportunities for the delegates to exchange new ideas and application experiences, to establish business or research relations and to find global partners for future collaboration. Papers and topic concentrates on related fields of Electronic, Eelectrical and Information Technology are presented.EEIT2023 proceeding tends to collect the most up-to-date, comprehensive, and worldwide state-of-art knowledge on Electronic, Eelectrical and Information Technology and other related topics. All the accepted papers have been submitted to strict peer-review by 2-3 international expert referees, and selected based on originality, significance and clarity for the purpose of the conference. The conference program is extremely rich, profound and featuring high-impact presentations of selected papers and additional late-breaking contributions. I sincerely hope that the conference would not only show the participants a broad overview of the latest research results on related fields, but also provide them with a significant platform for academic connection and exchange. The Technical Program Committee members have been working very hard to meet the deadline of review. The final conference received over 64 submissions, and 36 papers have been accepted.I would like to express our sincere gratitude to all the keynote speakers, editors, TPC members and organizers for their hard work, precious time and endeavor preparing for the conference. Our deepest thanks also go to the volunteers and staffs for their long-hours work and generosity they’ve given to the conference. The last but not least, we would like to thank each and every of the authors, speakers and participants for their great contributions to the success of EEIT2023.The Organizing Committee of EEIT2023List of Committee is available in this pdf.

Digital technologies in the financial sector of Kazakhstan

Digital systems play an important role in the activities of various fields, including the tasks of the financial sector. Simultaneously with the innovative development of the banking system in the field of information technology, in order to further improve the processes of providing services, significant steps are being taken towards improving the quality of services provided, as well as in the organization of business processes of second-tier banks. The authors investigate the impact of large financial institutions on the level of digital technology development: Kaspi Bank and Freedom Bank. The object of the article is to study the principles and models of action on the basis of which these digital ecosystems work. The ways of their development and functioning in the activities of two commercial banks are being investigated. This research is relevant due to the fact that in the context of the introduction of new technologies, for example: digitalization, as well as, in general, the limits and boundaries of services offered by the largest market players are being overcome, which creates the potential to accelerate the development of a competitive and secure digital financial market of a new generation. In this article, the authors define the concept of the ecosystem of banks and analyze large Kazakhstani financial institutions based on their financial services. Based on the data analysis, conclusions are drawn and the main conclusion is proposed, which determines the main direction of development for the development of financial digital ecosystems. Currently, two types of ecosystems are actively developing in Kazakhstan. In the first of them, the bank is the core of the ecosystem of non-financial services, in which it acts as a settlement operator. In the second case, a large non-financial company begins to build its own ecosystem of lifestyle services and eventually thinks about creating its own settlement center. At the same time, the digital ecosystem is definitely not limited only to its own services, but also integrates with partner solutions. At the same time, the ecosystem assumes, first of all, the transformation of human resources, their involvement in the process.

THE POSSIBILITIES OF USING BIG DATA TECHNOLOGIES IN SOLVING PROBLEMS OF PROCESSING DATA ON ATMOSPHERIC AIR POLLUTION

The main objective of the article is to substantiate the possibility of using Big Data technologies in the field of atmospheric air monitoring. In the form of a diagram, a model for processing big data obtained from measuring meteorological gas analysis stations using the PySpark library for further experimental studies is presented. The factors accompanying the use of Big Data in the field of atmospheric air monitoring are derived, and the performance of the Pandas and PySpark libraries is compared. The obtained results will allow us to further rely on the derived factors and use the most optimal data processing technologies to build predictive machine learning models in the field of analyzing the level of atmospheric air pollution. Consistent use of big data and machine learning methods will ensure clean and healthy air for future generations through more effective predictive analytics. This article is valuable for students and specialists in the field of information technology, in particular, in the field of data processing and machine learning.