3. Articles and Papers
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Item Open Access The effects of demographic factors on learners’ flow experience in gamified educational quizzes(Smart Learning Environments, 2025) Issabek A.; Oliveira W.; Hamari J.; Bogdanchikov A.In recent years, gamification gained widespread adoption in education aiming to increase students’ positive experiences (e.g., motivation, engagement, and flow state). However, the results of using gamification in education are still contradictory, challenging the community to comprehend the influence of individual factors on learners’ experiences within gamified educational systems. To tackle this challenge, this study explored how various demographic factors (i.e., gender, degree, individualism/collectivism, and masculinity/femininity) impact the flow experience of learners in a gamified educational quiz. A quantitative cross-cultural study involving 205 participants was conducted, utilizing partial least squares structural equation modeling to explore the influence of demographic factors on learners’ flow experience in the gamified educational quiz. The analysis revealed that age has a significative positive association with learners’ flow experience, while individualism has a negative association. These findings provide insights into educational technologies and gamification, offering a deeper understanding of how demographic factors shape learners’ flow experience in gamified educational environments.Item Open Access Removable Singularities of Harmonic Functions on Stratified Sets(MDPI, 2024) Dairbekov N.S.; Penkin O.M.; Savasteev D.V.There are deep historical connections between symmetry, harmonic functions, and stratified sets. In this article, we prove an analog of the removable singularity theorem for bounded harmonic functions on stratified sets. The harmonic functions are understood in the sense of the soft Laplacian. The result can become one of the main technical components for extending the well-known Poincaré–Perron’s method of proving the solvability of the Dirichlet problem for the soft Laplacian.Item Open Access MapReduce Solutions Classification by Their Implementation(The International Journal of Engineering Pedagogy (iJEP), 2023) Orynbekova K.; Bogdanchikov A.; Cankurt S.; Adamov A.; Kadyrov Sh.Distributed Systems are widely used in industrial projects and scientific research. The Apache Hadoop environment, which works on the MapReduce paradigm, lost popularity because new, modern tools were developed. For example, Apache Spark is preferred in some cases since it uses RAM resources to hold intermediate calculations; therefore, it works faster and is easier to use. In order to take full advantage of it, users must think about the MapReduce concept. In this paper, a usual solution and MapReduce solution of ten problems were compared by their pseudocodes and categorized into five groups. According to these groups’ descriptions and pseudocodes, readers can get a concept of MapReduce without taking specific courses. This paper proposes a five-category classification methodology to help distributed-system users learn the MapReduce paradigm fast. The proposed methodology is illustrated with ten tasks. Furthermore, statistical analysis is carried out to test if the proposed classification methodology affects learner performance. The results of this study indicate that the proposed model outperforms the traditional approach with statistical significance, as evidenced by a p-value of less than 0.05. The policy implication is that educational institutions and organizations could adopt the proposed classification methodology to help learners and employees acquire the necessary knowledge and skills to use distributed systems effectively.Item Open Access HARNACK’S INEQUALITY FOR HARMONIC FUNCTIONS ON STRATIFIED SETS(Siberian Mathematical Journal, 2023) Dairbekov N.S.; Penkin O.M.; Savasteev D.V.We prove Harnack’s inequality for nonnegative harmonic functions in the sense of the “soft” Laplacian on a stratified set with flat strata.Item Open Access Face extraction and recognition from public images using HIPI(ResearchGate, 2018) Bogdanchikov A.; Kariboz D.; Meraliyev M.Social networking services with public data are widely used nowadays. Billions of images uploaded to the internet each day over the world. This paper proposes the idea of a system which is currently being developed. The system collects images from public sources by some specific criteria, applies face detection and recognition algorithms on collected images, and provides the results in readable form. The Apache Hadoop library is used to increase performance of the system, and images are downloaded by the help of HIPI library. For testing purposes Labeled Faces in the Wild benchmark is used as a database for images containing over 13233 images of 5749 identities. Each image is jpg format in 250*250 resolutions. Results were more than good after testing the system with this database for face detection and recognition.Item Open Access Python to learn programming(ScieTech IOP Publishing, 2013) Bogdanchikov A.; Zhaparov M.; Suliyev R.Today we have a lot of programming languages that can realize our needs, but the most important question is how to teach programming to beginner students. In this paper we suggest using Python for this purpose, because it is a programming language that has neatly organized syntax and powerful tools to solve any task. Moreover it is very close to simple math thinking. Python is chosen as a primary programming language for freshmen in most of leading universities. Writing code in python is easy. In this paper we give some examples of program codes written in Java, C++ and Python language, and we make a comparison between them. Firstly, this paper proposes advantages of Python language in relation to C++ and JAVA. Then it shows the results of a comparison of short program codes written in three different languages, followed by a discussion on how students understand programming. Finally experimental results of students’ success in programming courses are shown.Item Open Access Classification of Scientific Documents in the Kazakh Language Using Deep Neural Networks and a Fusion of Images and Text(MDPI, 2022) Bogdanchikov A.; Ayazbayev D.; Varlamis I.The rapid development of natural language processing and deep learning techniques has boosted the performance of related algorithms in several linguistic and text mining tasks. Consequently, applications such as opinion mining, fake news detection or document classification that assign documents to predefined categories have significantly benefited from pre-trained language models, word or sentence embeddings, linguistic corpora, knowledge graphs and other resources that are in abundance for the more popular languages (e.g., English, Chinese, etc.). Less represented languages, such as the Kazakh language, balkan languages, etc., still lack the necessary linguistic resources and thus the performance of the respective methods is still low. In this work, we develop a model that classifies scientific papers written in the Kazakh language using both text and image information and demonstrate that this fusion of information can be beneficial for cases of languages that have limited resources for machine learning models’ training. With this fusion, we improve the classification accuracy by 4.4499% compared to the models that use only text or only image information. The successful use of the proposed method in scientific documents’ classification paves the way for more complex classification models and more application in other domains such as news classification, sentiment analysis, etc., in the Kazakh language.Item Open Access Application of TPB on the Saving Intention among the Students of Community Colleges: Moderating Effect of Mobile Applications(Global Business and Management Research: An International Journal, 2021) Nizar N.; Choban U.; Mohd H.A.The main objective of this study is to examine the saving intention among the Community College students from the Planned Behavior Theory (TPB) perspective and to investigate whether the proposed relationships are contingent upon their perceptions towards mobile applications. In order to achieve the objectives, a survey study was conducted among 117 community college students in Kedah, Selangor, Perak in Malaysia. PLS test results revealed that attitude, subjective norms, and perceived behavior control 'have' significantly influence students' intention to saving. However, mobile applications do not has moderate direct relationships. In conclusion, this study is significant as the understanding of saving behavior is essential not only for future retirement and investment plans of younger generations but also for their financial resilience when exposed to adverse shocks such as COVID-19.Item Open Access USING ANDROID TO IMPLEMENT INTELLIGENT TESTING SYSTEM(faculty of engineering and natural sciences, 2013) Amirgaliyev Y.N.; Kutlu A.; Bogdanchikov A.V.; Latuta K.N.; Suliyev R.N.The first part of the paper discusses the role of mobile applications in person’s life and proposes a method to apply testing system in Android device. The second part of the paper describes the algorithm used in the prototypes of the testing system. The third part explains details of each prototype and their key concepts. In conclusion the two prototypes are compared and advantages of each are highlightedItem Open Access DETECTING SOCIAL CONFLICTS IN KINDERGARTENS USING DEEPLEARNING AND COMPUTER VISION(SDU University, 2025) Dina KengesbayEarly conflict detection in kindergartens plays a significant role in ensuring a harmonious learningatmosphere and in promoting the social growth of young children. While most previous works have onlyaddressed conflict detection through adults, in this paper, we specifically address conflict detection inkindergartens using deep learning, utilizing both spatial and temporal information to improve performance.The application of deep learning and computer vision in automatically detecting and analyzing earlyconflicts among young children is discussed in this paper. Using video footage, we leverage state-of-the-art RNNs and 3D CNNs for high-accuracy detection of conflict instances. Crucial visual cues—facialexpressions, gestures, poses, vocal tone, and movement—are examined for the extraction of tension oraggression signs. The model is evaluated on real kindergarten video data, with promising conflict detectionand classification results. The findings indicate the potential of AI-supported tools in assisting teachers inclass management, child behavior monitoring, early intervention mechanisms, and the fostering of a goodsocial environment.Item Open Access Development of method to analyzefactors of kidney disease by the use of fuzzy logic(SDU University, 2025) Assel Yembergenova; Azamat Serek; Bauyrzhan BerlikozhaThe study introduces a new strategy for the analysis of kidney disease parameters based on fuzzy logic.Fuzzy logic is a more accurate way to categorize clinical parameters than statistical analysis because thereis uncertainty and variability in medical data. The data is comprised of an extensive amount of clinicalparameters including age, blood pressure, specific gravity, albumin, sugar, random blood glucose, bloodurea, serum creatinine, sodium, potassium, hemoglobin, packed cell volume, white blood cell count, andred blood cell count.The methodology utilizes fuzzy logic centroid computation to categorize these parameters into low,medium, and high levels to provide a more dynamic and interpretable assessment of renal health. Fuzzymemberships give the current work the capability to discover intricate interrelationships between clinicalvariables, which may have been otherwise unattainable by conventional mean, median, and standarddeviation-based analyses.The findings confirm that fuzzy logic and conventional statistical methods enhance the comprehensionof kidney disease by incorporating intricate interactions between clinical variables. The method is employedto achieve more accurate prediction and diagnostic models, offering insight to be used in kidney diseaseassessment and medical decisions.Item Open Access A Survey on Multimodal Approaches for Lung Disease Diagnosis using Deep Learning(SDU University, 2025) Zhaniya MedeuovaLung disorders are a major global health issue. A quick and accurate diagnosis is essential for proper treatment. In order to increase diagnostic accuracy, recent multimodal techniques are gaining popularity. This study carried out a comprehensive analysis of research articles on multimodal approaches that were published between 2020 and 2024 in Scopus and Google Scholar. The results show that there is limited study on the multimodal approach and on a variety of lung disorders such as asthma, TB, pneumonia, and chronic obstructive pulmonary disease. Several studies concentrated mainly on the detection and binary classification of COVID-19. The field has several challenges, including limited datasets, high computing costs, difficulties in integrating multiple modalities, and lack of accessibility of the models. Future studies should look at a wider range of lung diseases, increase the accessibility of datasets, improve fusion methods for merging data from many sources, and create models that are easier to understand and use fewer resources. Resolving these issues will improve patient outcomes by advancing the real-world use of deep learning in medical diagnosis.Item Open Access Bias and Fairness in Automated Loan Approvals: A Systematic Review of Machine Learning Approaches(SDU University, 2025) Suraiyo Raziyeva; Meraryslan MeraliyevArtificial intelligence (AI) is increasingly transforming credit approval processes, enabling financial institutions to assess risk more efficiently and at greater scale. As these systems become more embedded in lending decisions, concerns around fairness, bias, and accountability have grown significantly. Many of these concerns stem from the use of historical data, proxy variables, and model optimization choices that can unintentionally reinforce existing social and economic inequalities. This work presents a systematic overview of the types and sources of bias in AI - driven loan approval systems and critically examines how machine learning techniques attempt to address them. It also highlights emerging solutions, including explainable AI, federated learning, human-in-the-loop frameworks, and intersectional fairness approaches. Despite ongoing advancements, unresolved challenges remain - particularly the need for dynamic fairness monitoring and for addressing intersectional biases affecting individuals from multiple marginalized groups. To bridge these gaps, the paper emphasizes the importance of interdisciplinary collaboration among AI developers, regulatory bodies, and social scientists. It advocates embedding fairness as a core design principle in the development and deployment of future AI systems. Overall, this study contributes to the growing effort to develop more transparent, inclusive, and socially responsible financial technologies.Item Open Access The effect of quarantine measures in COVID-19(Advances in Interdisciplinary Sciences, 2020) Yergesh D.; Kadyrov Sh.; Orynbassar A.We consider deterministic SEIQR epidemic model for novel coronavirus (COVID-19). In addition to the classical SIR model, it takes into account the exposed and quarantined states. The objective of the study is to estimate epidemiological parameters for COVID-19 in the United Kingdom and understand the effect of various quarantine measures. The basic reproduction number is estimated to be 3.622. The findings suggest that weaker quarantine measures may be insufficient to fight with the disease.Item Open Access Periodic solutions of a graphene based model in micro-electro-mechanical pull-in device(Applied and Computational Mechanics, 2017) Wei D.; Kadyrov Sh.; Kazbek Z.Phase plane analysis of the nonlinear spring-mass equation arising in modeling vibrations of a lumped mass attached to a graphene sheet with a fixed end is presented. The nonlinear lumped-mass model takes into account the nonlinear behavior of the graphene by including the third-order elastic stiffness constant and the nonlinear electrostatic force. Standard pull-in voltages are computed. Graphic phase diagrams are used to demonstrate the conclusions. The nonlinear wave forms and the associated resonance frequencies are computed and presented graphically to demonstrate the effects of the nonlinear stiffness constant comparing with the corresponding linear model. The existence of periodic solutions of the model is proved analytically for physically admissible periodic solutions, and conditions for bifurcation points on a parameter associated with the third-order elastic stiffness constant are determined.Item Open Access Integer Prime Factorization with Deep Learning(Vol 2 No 1 (2021): Advances in Interdisciplinary Sciences, 2021) Murat B.; Kadyrov Sh.; Tabarek R.Prime factor decomposition is a method that is used in number theory and in cryptography, as well. The security of the message depends on the difficulty of factorization. In other words, to hack the RSA system, factorization of N is needed, where N is a product of two prime (generally large) numbers. This paper analyzes the approaches which are already used to solve the problem, and proposes a new method which is expected to increase the efficiency of prime number factorization with the help of neural networks. The results in this paper can be used to develop and improve the security of cryptosystems.Item Open Access ENTROPY AND ESCAPE OF MASS FOR SL3(Z)\ SL3(R)(arXivLabs, 2010) Einsiedler M.; Kadyrov Sh.We study the relation between measure theoretic entropy and escape of mass for the case of a singular diagonal flow on the moduli space of three-dimensional unimodular lattices.Item Open Access ENTROPY AND ESCAPE OF MASS FOR HILBERT MODULAR SPACES(arXivLabs, 2011) Kadyrov Sh.We study the relation between metric entropy and escape of mass for the Hilbert modular spaces with the action of a diagonal element.Item Open Access DIOPHANTINE APPROXIMATION WITH RESTRICTED NUMERATORS AND DENOMINATORS ON SEMISIMPLE GROUPS(arXivLabs, 2014) Gorodnik A.; Kadyrov Sh.We consider the problem of Diophantine approximation on semisimple algebraic groups by rational points with restricted numerators and denominators and establish a quantitative approximation result for all real points in the group by rational points with a prescribed denominator and an almost prime numerator.Item Open Access EFFECTIVE EQUIDISTRIBUTION OF PERIODIC ORBITS FOR SUBSHIFTS OF FINITE TYPE(arXivLabs, 2016) Kadyrov Sh.We study equidistribution of certain subsets of periodic orbits for subshifts of finite type. Our results solely rely on the growth of these subsets. As a consequence, effective equidistribution results are obtained for both hyperbolic diffeomorphisms and expanding maps on compact manifolds.