Journal of Problems in Computer Science and Information Technologies
The "Journal of problems in computer science and information technologies" founded in 2022. The founder and publisher of the journal is the Al-Farabi Kazakh National University. The journal has an approved cover and title pages with an indication of the university, output numbers of the issue, ISSN, eISSN, editorial board, editorial policy, publication ethics and website. The journal is published 4 times a year. All articles of the journal registered in the CrossRef database and for each author's article assigned a DOI - a digital object identifier, which used to provide citation, reference and output to electronic documents. The editorial board of the journal includes leading Kazakhstani and foreign scientists. The journal follows the policy of information openness and accessibility of authors' publications, articles are posted on the journal's website in full-text access. Aims & Scopes The aim of the journal is to provide a forum for researchers and scientists to communicate their recent developments and to present their original results in various fields of computer science and information technologies. Objectives of the journal: Cover new scientific results of significant importance in the areas of computer science and information technologies; To provide international and interregional scientific coordinating functions in competent, high-quality and timely coverage of the main results of scientific works of the authors of the journal; To create an environment of openness and accessibility of wide coverage of the results of scientific works of young scientists applying for the defense of dissertations and obtaining academic degrees in scientific specialties of dissertation councils in various fields of computer science and information technologies; To form a constant steady interest among the scientific and scientific-pedagogical community, as well as among young and novice scientists in the journal, its growing demand in the professional circles of computer scientists and robotics of Kazakhstan, near and far abroad. The journal displays the results of current research in the following thematic areas: Artificial intelligence; Computer systems and networks; Database systems; Human computer interaction; Numerical analysis; Programming languages; Software engineering; Bioinformatics; Computational science, finance and engineering; Image and sound processing; Applied computer science; Computer graphics and visualization; Computer security and cryptography; Applied mathematics; Parallel and distributed computing.
KNOWLEDGE FUSION THROUGH DATALESS BERT PARAMETER MERGING VIA A REGRESSION-BASED AVERAGING
Recent progress in dataless knowledge fusion methods has focused on migrating knowledge from a teacher neural network to a student neural network without utilizing the teacher model's training data. As a result, fine-tuning pre-trained language models (PLM) has emerged as a simple method to improve the performance of Natural Language Processing (NLP) models in specific domains. These refined models are available as open source, but typically their training datasets are not, due to issues related to data privacy or intellectual property. This sets up an obstruction to combining knowledge from separate models to produce an enhanced single model. In this paper, we investigate the issue of combining separate models developed on distinct training datasets to create a unified model that performs on out-of-domain data for the student model. We suggest a dataless knowledge fusion technique that integrates models within their parameter space, directed by weights that reduce prediction discrepancies between the combined model and the separate models. Across a wide range of parameter configurations, our assessment indicates that the suggested approach substantially exceeds the performance of traditional baselines like Fisher-weighted averaging or model ensembling. Additionally, we observe that our approach serves as a viable alternative to multi-task learning, capable of maintaining and enhancing the individual models without requiring access to the training data. Our experiments confirm the dataless merging methods, integrating finely adjusted parameters to achieve robust multitask performance with negligible impact on class ratio degradation, approximately 2.1%.
Copyright (c) 2026 Journal of Problems in Computer Science and Information Technologies
APPLICATION OF MACHINE LEARNING METHODS FOR PHISHING ATTACK DETECTION: A COMPARATIVE ANALYSIS OF MODELS AND THEIR EFFECTIVENESS
Despite significant progress in combating, phishing continues to be one of the top cybersecurity risks, capitalizing on both technological gaps and human behavior. In this work, we propose a machine learning (ML) framework for creating phishing URL detector based on the LegitPhish labeled dataset that consists of 101,219 URLs and 17 engineered features for each URL. There is a particular focus on pre-modelling data diagnostics to resolve any data leakage, shortcuts and highly correlated variables that can drive the model results and cause artificial over-optimism. Six classification models were tested such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest, XGBoost, Multi-Layer Perceptron (MLP) and CNN-LSTM architecture. Seven informative features were selected for model development: After discarding two leakage-prone features has_ip_address, https_flag and eight redundant features. Experimental results revealed that the overall performance of the two models, Random Forest and XGBoost, were the best with an F1-score of 0.952 and an ROC-AUC value of 0.992 respectively. The precision score for the Random Forest model was 0.928 and the Recall score was 0.978, which provides a good balance between the detection of phishing and control of false positives. Despite the complexity of the CNN-LSTM model, it did not perform better than the ensemble-based models, while the MLP model proved competitive. The results confirm the significance of careful feature diagnostics and leakage prevention and show that well-tuned ensemble classifiers are able to deliver accurate, efficient, and viable results for the phishing URL detection task.
PPO-BASED PHYSICAL RESOURCE BLOCK SCHEDULING IN 5G NR: A REINFORCEMENT LEARNING APPROACH WITH 3GPP-COMPLIANT CHANNEL MODELING
This paper proposes a deep reinforcement learning approach to Physical Resource Block (PRB) scheduling in 5G New Radio networks. A Gymnasium-compatible simulation environment is developed on the 3GPP TR 38.901 UMa NLOS channel model with Rayleigh fading, serving as a reproducible experimental platform. A Proximal Policy Optimization (PPO) agent is trained with a composite reward function that jointly optimizes aggregate throughput and Jain's Fairness Index. The agent is evaluated over 100 episodes with five independent random seeds against three classical baselines — Round Robin, Proportional Fair, and Maximum CQI. The PPO agent achieves a mean throughput of 57.09 ± 1.04 Mbps with a Jain's Fairness Index of 0.358, surpassing Round Robin (32.37 Mbps, JFI 0.630) and Proportional Fair (35.31 Mbps, JFI 0.597) in throughput while exceeding Maximum CQI in fairness (JFI 0.100). A throughput-fairness tradeoff analysis confirms that the proposed agent occupies an operating point inaccessible to any single classical method. The composite reward function, which weights both objectives equally, enables learned adaptive behavior that balances spectral efficiency and user equity. The code and evaluation pipeline are fully open-source and reproducible using the Stable-Baselines3 library.
AI-DRIVEN TEXT AND AUDIO SYNTHESIS FOR LOW-RESOURCE LANGUAGE DATASETS
This paper presents an AI-driven methodology for constructing parallel text corpora and synthesizing parallel audio datasets for low-resource Turkic language pairs, namely Kazakh-Kyrgyz and Kazakh-Uzbek. The scarcity of high-quality linguistic and speech resources for these languages poses significant challenges for the development of neural machine translation and automatic speech recognition systems. The paper proposes and validates a methodology for parallel dataset construction driven by artificial intelligence, in which the selection of a translation system is guided by a set of criteria encompassing free accessibility, translation quality, and processing efficiency, assessed through experiments on a 2000-sentence test set. Based on this evaluation, large-scale parallel corpora for the Kazakh–Kyrgyz and Kazakh–Uzbek language pairs were generated using the selected AI system. A subsequent manual error analysis revealed that approximately 3.7% (Kazakh–Kyrgyz) and 4.3% (Kazakh–Uzbek) of the translations contained inaccuracies, indicating the need for post-editing and further model refinement. Audio synthesis experiments using MMS-TTS and TurkicTTS systems demonstrated that synthetic speech of near-natural quality can be generated for both languages, with NISQA scores reaching up to 4.44 for Uzbek. The findings confirm that the proposed methodology provides a practical and scalable foundation for expanding linguistic resources for low-resource Turkic languages and supporting further research in machine translation and speech processing.
COMPARATIVE ANALYSIS OF SEQUENCE-BASED AND GRAPH-BASED DEEP LEARNING FOR ANOMALY DETECTION IN MICROSERVICE TRACES
The transition from monolithic to microservice architectures has significantly increased the complexity of system observability. Distributed tracing has emerged as a vital tool for monitoring inter-service interactions, yet traditional anomaly detection methods often fail to capture the intricate structural relationships inherent in these systems. This article presents a comprehensive comparative analysis of sequence-based machine learning approaches, specifically Long Short-Term Memory (LSTM) networks, with Graph-based Deep Learning methods such as Graph Neural Networks (GNNs) for anomaly detection in microservice traces. We evaluate these paradigms across three critical dimensions: structural awareness, detection accuracy, and computational efficiency. Our analysis, drawn from recent frameworks including DeepTraLog and TraceVAE, demonstrates that graph-based models provide superior structural fidelity and achieve higher precision-recall metrics while maintaining scalability for high-dimensional telemetry data. The findings indicate that GNN-based approaches achieve F1-scores up to 0.954, significantly outperforming sequence-based alternatives which typically achieve F1-scores around 0.741. These results have important implications for the design of next-generation observability platforms for cloud-native applications.
INTEGRATING MACHINE LEARNING WITH OPEN-SOURCE 5G SA TESTBEDS FOR PERFORMANCE ANALYSIS AND KPI TIME SERIES MODELING
Open source 5G Standalone (SA) testbeds provide cost-effective environments for research and teaching, yet most existing implementations focus primarily on functional validation rather than leveraging machine learning for advanced network analytics. This study presents a comprehensive framework integrating SARIMAX, LSTM, and Transformer models with a fully operational 5G SA testbed combining Open5GS, srsRAN, MongoDB, and ZeroMQ-based RF emulation. The primary objective is to demonstrate predictive analytics capabilities for 5G network performance forecasting using real testbed-generated Key Performance Indicator (KPI) data. A comparative forecasting analysis was conducted using the three models trained on KPI datasets augmented through CTGAN synthetic data generation. Experimental validation confirmed reliable end-to-end 5G operation with synchronized configuration across PLMN, TAC, DNN, and security parameters. Under controlled single-UE, RF-free conditions, the testbed achieved ultra-low latency (1.34 ms RTT), near-gigabit throughput (847 Mbps downlink, 823 Mbps uplink), and rapid PDU session establishment (0.22 s). Performance profiling identified the User Plane Function (UPF) and database interactions as primary scaling bottlenecks. The machine learning evaluation revealed that while SARIMAX provides a reliable statistical baseline, neural network models achieve substantially higher forecasting accuracy for network KPIs. These results demonstrate the extensibility of open source 5G testbeds toward intelligent network management and predictive analytics applications.
NONLINEAR DIMENSIONALITY REDUCTION FOR LOOKALIKE AUDIENCE DETECTION USING MANIFOLD LEARNING AND AUTOENCODER-BASED REPRESENTATIONS
Identifying users with similar behavioral characteristics is a critical task in modern targeted advertising and customer analytics systems. High-dimensional tabular datasets describing user activity often contain complex nonlinear relationships that cannot be effectively captured by traditional linear dimensionality reduction techniques. This study investigates representation learning approaches for constructing scalable look-alike audience detection systems using large-scale telecommunications data. Classical dimensionality reduction techniques, including Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE), are first analyzed as baseline methods for exploring the structure of high-dimensional data. While PCA performs linear projections that preserve global variance and t-SNE reveals local neighborhood structures through nonlinear embedding, these methods are primarily designed for visualization and exploratory analysis and do not provide scalable parametric mappings for new data samples. To address these limitations, a representation learning framework based on autoencoders is proposed for generating compact latent embeddings of users. The model is trained on a large-scale anonymized telecommunications dataset containing behavioral, demographic, device-related, and service usage attributes. Embeddings are learned for multiple feature entities and concatenated into a unified user representation that integrates heterogeneous behavioral information. User similarity is then computed using cosine similarity in the latent space, enabling efficient identification of look-alike audiences. The proposed system is evaluated using clustering metrics and multiple independent validation tasks with external target variables to ensure unbiased performance estimation. Experimental results demonstrate that autoencoder-based embeddings produce a more structured latent space and improve both similarity-based retrieval and downstream classification performance compared to classical dimensionality reduction techniques. The findings highlight the effectiveness of deep representation learning for high-dimensional tabular data in real-world recommendation and targeted advertising systems.
ENHANCING WIND SPEED FORECASTING WITH LARGE LANGUAGE MODELS: A CASE STUDY OF KAZAKHSTAN
Accurate wind speed forecasting plays an important role in the planning, operation, and control optimization of modern wind energy systems. Kazakhstan, the country with the largest wind energy potential in Central Asia, is facing significant challenges due to the highly variable, nonlinear, and non-stationary nature of wind. This study proposes a wind-speed forecasting framework based on frozen pre-trained Transformer backbones (BERT/BART) with lightweight projection-based adaptation for time-series inputs. The study employs frozen pre-trained Transformer backbones with self-attention and lightweight projection-based adaptation of Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional and Auto-Regressive Transformers (BART) as the core of the model. In this retrospective analysis, hourly wind speed data from five representative cities across Kazakhstan are used to evaluate simulated operational forecast performance at seven time steps: 1, 3, 6, 36, 72, 144, and 432 hours. The LLMs were compared with AutoRegressive Integrated Moving Average (ARIMA), Segmented Recurrent Neural Network (SegRNN), and Patch Time Series Transformer (PatchTST) models, and the results showed superior accuracy and higher stability in long-term forecasting. These results support the potential of pre-trained Transformer backbones as effective sequence models for wind-speed forecasting under diverse climatic conditions.
ALGORITHMIC APPROACH TO OPTIMAL RESOURCE CONTROL IN AN OPEN ECONOMY MODEL
The dynamic development of the global economy and the impact of external factors create challenges for ensuring sustainable growth, making effective resource management an urgent task. This paper addresses the problem of optimal resource management in an open economy model with constraints on labor distribution, investment, and foreign trade balances. To solve this problem, a numerical implementation algorithm based on a modified Lagrange multipliers method is proposed. The developed algorithm enables obtaining a numerical solution to the optimal control problem (OCP) and makes the model applicable for practical use. The conducted computational experiments confirmed the effectiveness of the approach: the constructed state and control trajectories demonstrate the achievement of a stable equilibrium state of the system while satisfying all imposed constraints. The practical value of the study lies in the fact that the algorithm provides the possibility of implementing numerical solutions to OCPs and using them for analyzing and forecasting economic development under resource limitations.
LOW-COST NIGHT VISION CAMERA BASED ON A MODIFIED CONSUMER WEBCAM AND LIGHTWEIGHT REAL-TIME ENHANCEMENT
Low-light perception is a core requirement for robotics, inspection, and wearable situational awareness, yet reliable night- vision solutions often require expensive sensors or computation- ally heavy processing. This manuscript presents (i) a low-cost near- infrared (NIR) night-vision camera system with real-time enhancement and mixed-reality visualization, and (ii) a controlled benchmarking protocol that compares multiple sensor modalities and classical en- hancement pipelines on a Linux embedded platform. To prioritize real-time feasibility, we deliberately avoid neural networks and other AI/ML models, and instead benchmark lightweight classical methods: CLAHE, bilateral filtering + CLAHE, Non-Local Means (NLM) + CLAHE, Retinex SSR, Retinex SSR with percentile normalization, and two proposed pipelines (a CLAHE-based pipeline and a Retinex integrated variant). Experiments were conducted in a fully dark room using identical illumination conditions and multiple observation distances. We report runtime (ms/frame, FPS) and no- reference quality metrics (entropy, edge strength, Laplacian variance, RMS contrast, mean intensity) to accommodate the absence of ground-truth references. A representative run shows that the proposed CLAHE-based pipeline achieves 246.5 FPS at 640×480 while substantially improving objective no-reference metrics relative to raw frames, whereas Retinex with percentile normalization yields higher contrast/entropy but at 12.7 FPS. Hardware benchmarking indicates that thermal imaging provides the clearest visibility but at high cost/power, Kinect requires stronger illumination, and event-based sensing offers extremely low latency but requires temporal modulation to observe static targets.
APPLYING SELF-PLAY REINFORCEMENT LEARNING TO KAZAKH NATIONAL BOARD GAMES: A CASE STUDY ON TOGYZKUMALAK
The paper presents the results of a study on training agents for competitive board games, using the Kazakh national Togyzkumalak game, a zero--sum game, as a case study. As demonstrated by Alpha Zero, it is possible to train a champion without human expert knowledge or supervised learning. However, each game has unique characteristics that require specific research to develop an excellent player. In this study, two approaches were used to train agents: model-free and model-based. The self-play technique was used effectively during the training. As a result, the agents developed the ability to play Togyzkumalak at a competitive level.
DEVELOPMENT OF THE RETRIEVAL-AUGMENTED GENERATION (RAG) SYSTEM FOR THE KAZAKH LANGUAGE USING HYBRID INFORMATION METHODS
Abstract. This study presents the development and experimental evaluation of the Retrieval-Augmented Generation (RAG) system for the Kazakh language with an emphasis on comparative analysis of information retrieval methods. The main purpose of the work was to test the hypothesis of the superiority of a hybrid approach combining statistical (BM25) and semantic (vector search) methods over individual approaches. Based on the corpus of legal documents of the Republic of Kazakhstan, 270 experiments were conducted using three data extraction methods combined with six modern large language models (LLM). The results demonstrate that the hybrid method achieves the highest accuracy (82.2%), statistically significantly surpassing vector search by 3.3% (p < 0.01) and BM25 by 5.5% (p < 0.001). All three methods showed accuracy above 75%, which confirms the high efficiency of RAG systems for the Kazakh language. The analysis also revealed that hybrid search provides the greatest stability of results when working with different language models. This study makes a significant contribution to the development of RAG systems for languages with limited resources, offering an empirically based methodology to improve the accuracy and reliability of response generation.
HADOOP, MULTI-AGENT SYSTEMS AND MACHINE LEARNING: EXPLORING SCALABILITY, FAULT TOLERANCE AND WORKLOAD DISTRIBUTION BEHAVIORS
Paper reports experimental results comparing several machine learning techniques performance when distributing massively parallel computation to a set of interconnected machines. Computational resources are intentionally heterogenous to simulate real ad-hoc network environment and provide realistic setting test results. Namely Round Robin, Q-Learning and Least Loaded algorithms-based solutions are examined for their scalability, fault tolerance and workload distribution behaviors. The novelty of the paper is a set of empirical set of coefficients and bottlenecks for each implementation that is free of infrastructure specifics or error and exemption handling tools for future considerations by engineering professionals and scholars.
THREE-DIMENSIONAL FRACTAL GEOMETRY MODELING AND DIGITAL HOLOGRAPHY BASED ON R-FUNCTIONS
This paper is devoted to modern research in the field of digital modeling of complex-shaped geometric objects and the determination of their optical properties, which currently represent one of the most relevant challenges in contemporary science. In particular, the problem of realistic representation of three-dimensional objects with fractal geometry and their holographic reconstruction in a full 3D format is of significant scientific and practical importance for such fields as industry, medicine, engineering, architecture, materials science, virtual reality (VR), and digital art. Fractal structures possess a number of unique properties, including self-similarity, unlimited detail, and high spatial complexity, which makes them an effective mathematical basis for modeling natural objects such as plants, vascular systems, bone tissues, crystalline structures, and surface reliefs. At the same time, the geometric representation of fractal forms using classical methods is challenging, and their mathematical modeling requires the application of high-precision and formally rigorous techniques. At present, the mathematical description of fractal objects is often based on statistical, stochastic, or iterative algorithms. However, such approaches are generally characterized by insufficient analytical rigor and smoothness, blurred boundaries, and the lack of a holistic spatial description. In this regard, there arises a need to develop methods for modeling complex fractal forms based on strict analytical expressions, in particular using the R-functions apparatus. An additional challenging task is the reconstruction of holographic images of the modeled fractal objects, which requires high-precision optical modeling. The application of holographic technologies based on the principles of interference and diffraction makes it possible to adequately reproduce the spatial and structural features of fractal objects in a digital environment.
COMPUTATIONAL MODEL OF KAZAKH VOWEL–CONSONANT HARMONY
This study presents a computational model of morphological generation based on the agglutinative nature of the Kazakh language and vowel–consonant harmony. In this work, we model the Kazakh vowel–consonant harmony system, which governs allomorphic selection of suffixes based on both vowel and consonant properties. Using a rule based approach, the model generates verb and noun forms across various grammatical categories (tense, aspect, case, possession). Experimental results demonstrate the model’s high efficiency and full compliance of the generated word forms with vowel harmony rules. Specifically, thousands of combinations were created for derivative words (DTL), possession (POSS1, POSS2), and case (CASE) categories. This research makes a significant contribution to the development of natural language processing (NLP) morphological analysis and automatic translation systems for the Kazakh language.
HYBRID 3D-AWARE FACE CLUSTERING VIA DEEP EMBEDDINGS AND GEOMETRIC DESCRIPTORS
This paper presents a 3D-aware face clustering methodology that robustly groups unlabeled face images by identity under challenging conditions of pose variation, facial expression, and partial occlusion. The proposed approach integrates 2D deep embeddings with 3D geometric features extracted from reconstructed facial meshes, leveraging both photometric and structural information. Preprocessing includes grayscale normalization, landmark-based alignment, and contrast enhancement. 3D face models are generated using a 3D Morphable Model (3DMM) and optionally refined through neural rendering to improve shape fidelity. From these reconstructions, we extract interpretable 3D descriptors-PCA shape coefficients, geodesic distances, and curvature histograms - that complement embeddings from ArcFace and FaceNet. Clustering is performed using a two-stage hybrid algorithm: DBSCAN for outlier removal followed by K-Means++ with a fused distance metric combining cosine and Mahalanobis distances. Experimental results demonstrate that the proposed method significantly outperforms 2D-only and 3D-only baselines in terms of Silhouette Score, Adjusted Rand Index (ARI), and Purity. The findings confirm that fusing 2D and 3D modalities yields semantically consistent and pose-invariant identity clusters, establishing a strong foundation for face analysis in unconstrained environments.
PRE-FILTERING OF GRID MAPS WITH SKELETONIZATION AND MORPHOLOGY METHOD FOR EFFICIENT PATH PLANNING FOR MOBILE ROBOTICS
SLAM-based occupancy grid maps are widely used for indoor mobile robots, but their noise, jagged obstacle borders, and spurious narrow gaps can mislead the classical grid-based path planners, e.g. A* algorithm, causing unstable routes, edge-hugging behavior, and increased computation. This manuscript proposes a lightweight pipeline to improve global path planning robustness and speed in warehouse-like environments. The approach first converts the SLAM map into a conservative binary representation and applies morphological refinement to remove small artifacts and smooth free-space connectivity. Next, the free space is skeletonized and transformed into a graph that captures the topology of navigable corridors. Global path planning is then performed using a hybrid strategy: a local grid search connects the start and goal regions to the skeleton, while the main route is computed on the skeleton graph. Experiments in two warehouse scenarios show that the proposed method substantially reduces node expansions and path planning time compared to full-grid A* algorithm, while producing more physically plausible paths that avoid false passages in noisy maps. These results indicate that morphological and skeleton-based global path planning can serve as a practical solution to enhance efficiency for robust navigation in realistic SLAM based path planning.
COMPUTATIONAL SIMULATION OF INSTITUTIONAL-INVESTMENT DYNAMICS USING PANEL VECTOR AUTOREGRESSION
This study develops a computational framework for simulating dynamic interactions between institutional quality indicators and foreign direct investment using a panel Vector Autoregression model applied to a multi-country dataset. The work emphasizes the algorithmic structure of the modeling pipeline, including preprocessing of heterogeneous panel time series, numerical stationarity diagnostics and cointegration testing. Impulse-response simulations are used to examine system behavior following institutional shocks, illustrating the dynamic propagation of disturbances in a high-dimensional environment. Although the empirical application concerns institutional governance, the contribution of this study lies primarily in its computational architecture, numerical considerations, and reproducible design of a data-driven simulation environment. The presented framework demonstrates how computational finance and applied computer science can integrate econometric modeling to analyze complex, interdependent systems.
Copyright (c) 2025 Journal of Problems in Computer Science and Information Technologies
UNDERSTANDING DIGITAL TRANSFORMATION AND AGENTIC SYSTEM THROUGH BIBLIOMETRICS: DEVELOPING AN AGENTIC MODEL OF HUMAN–AI INTERACTION
This study conducts a comprehensive bibliometric analysis of research on digital transformation within the social sciences from 1997 to 2024 and integrated these findings to develop an Agentic Model of Human–AI Interaction. Drawing on 389 articles indexed in the Web of Science database, the analysis examines publication dynamics, influential authors and institutions, citation structures, and thematic research clusters using RStudio-based bibliometric tools. Results reveal that digital transformation has evolved into a central driver of societal, economic, and cultural change, with research output peaking in 2021–2022. Prominent themes include artificial intelligence, innovation processes, digital media technologies, and the societal implications of emerging technologies. Beyond mapping the knowledge landscape, the study proposes a conceptual agentic system model that explains how AI agents process, interpret, and operationalize human queries through structured stages of planning, retrieval, reasoning, and response generation. This integration of bibliometric insights with system conceptualization contributes to a deeper understanding of the evolving human–AI relationship and highlights key gaps and future research opportunities in the study of agentic systems within digital transformation.
CONCEPTUAL MODEL OF THE QADAM DIGITAL PLATFORM AS A UNIFIED DIGITAL ECOSYSTEM FOR SMES
The digital transformation of small and medium-sized businesses (SMEs) is a key factor in sustainable economic growth, increased competitiveness, and the integration of national economies into global value chains. In the developing platform economy, digital platforms act not only as technological solutions but also as institutional coordination mechanisms, facilitating interactions between businesses, government, financial institutions, and consumers. This article examines the conceptual model of the QADAM digital platform, focused on SMEs, as a unified digital ecosystem. The platform integrates a B2B portal, an AI assistant for businesses, a digital commercial platform, P2P interactions, and a set of business tools, creating an end-to-end chain of digital services—from business registration and support to management and commercial decision-making. The objective of this study is to develop and validate the conceptual model of the QADAM digital platform, including a business model, decision-making model, and monetization model, as well as to analyze its potential for SME development.
MASS-CONSERVING PHYSICS-INFORMED AUGMENTATION AND FOURIER FEATURE NETWORKS FOR SMALL-DATA PREDICTION OF MOLYBDENITE (MO₂S) LEACHING KINETICS
Molybdenum remains a strategic metal for advanced steels and catalysis, while environmental and energy pressures are accelerating interest in hydrometallurgical leaching routes for molybdenite (MoS₂). Predicting leaching kinetics is difficult because the process is highly nonlinear and strongly influenced by reagent chemistry and gas–liquid conditions, yet experimental datasets in metallurgical laboratories are often extremely small. This manuscript develops a hybrid, data-efficient machine-learning approach designed specifically for small-data settings. The method combines physics-informed data augmentation that enforces strict mass conservation with a Fourier Feature Network intended to reduce spectral bias and better capture sharp kinetic transitions. Using only six experimental measurements, the resulting model achieves high predictive accuracy on held-out data (R² = 0.9793, MAE = 1.61%) and maintains stable generalization without evidence of train–test divergence. The study concludes that physically admissible augmentation coupled with Fourier-enriched representations can produce reliable kinetic surrogates from minimal data, supporting in-silico screening and optimization of leaching conditions for process design and control.
SMART BUILDING CLIMATE CONTROL: MACHINE LEARNING APPROACH FOR INDIVIDUAL THERMAL PREFERENCE PREDICTION
Modern building management systems rely on uniform climate settings that fail to accommodate individual occupant preferences, resulting in energy waste and reduced comfort satisfaction. This study presents a data-driven approach for personalized thermal comfort prediction using machine learning algorithms integrated with multimodal sensor networks. We developed and evaluated three classification models (Random Forest, XGBoost, and Artificial Neural Network) using environmental parameters (air temperature, humidity, CO₂ concentration) and physiological measurements (heart rate variability, blood pressure, oxygen saturation) collected from controlled experiments with eight participants under various thermal conditions. The optimized Random Forest model achieved 95% accuracy in predicting seven-level thermal sensation votes using only ten key features identified through SHAP analysis. Indoor air temperature emerged as the dominant predictor, while physiological parameters provided complementary information for personalized comfort assessment. The proposed system demonstrates significant potential for integration into smart building automation, enabling dynamic climate control that adapts to individual preferences while optimizing energy consumption. Implementation of such personalized HVAC systems could reduce energy usage by up to 20% compared to conventional static temperature control, while simultaneously improving occupant satisfaction and productivity in commercial buildings.
COMPARATIVE ANALYSIS OF PHYSICS-INFORMED AND CONVENTIONAL LSTM AND RNN MODELS FOR TEMPERATURE FORECASTING USING ERA5 REANALYSIS DATA
Climate change is one of the most serious modern problems affecting the Earth's atmosphere, as it causes a range of harmful effects worldwide. Due to the uneven nature of climate data, forecasting climate change is a challenging task today. Many previous studies in climate and machine learning have used recurrent neural networks (RNNs) and long short-term memory (LSTM) models to predict climate trends. Although these models are effective at identifying long-term trends in data, they often fail to satisfy physical laws such as energy conservation, mass balance, and thermodynamic principles. In this research, the aim was to develop physics-informed RNN and physics-informed LSTM models and compare their effectiveness with standard RNN and LSTM models. The study utilized data on air temperature at 2 meters above the surface for the cities of Astana, Almaty, and Shymkent for model training, validation, and testing. According to the results, physics-informed models achieved the lowest root mean square errors in Almaty (3.52 °C) and Shymkent (3.80 °C). RNN and LSTM models performed better in Astana (RMSE = 5.44 and 5.47 °C), where seasonal changes are relatively abrupt. These findings demonstrate that PINNs give more physically consistent forecasts for moderate climates, while conventional recurrent models remain more effective for locales with highly variable conditions.
SIMULATION MODELING OF DATA INTEGRITY VIOLATIONS IN INTELLIGENT SOCIAL SYSTEMS
The article examines the issues of ensuring the integrity and confidentiality of electronic data through the use of post-quantum cryptographic algorithms Kyber and Dilithium. The rapid development of quantum computing threatens the security of traditional cryptographic systems (RSA, ECC), making the protection of digital infrastructures—such as government platforms, financial services, educational ecosystems, and intelligent social systems—an urgent priority. The Kyber algorithm (for encryption and key exchange) and Dilithium algorithm (for digital signatures) are part of the post-quantum cryptography standards recommended by NIST. Their implementation significantly enhances resistance to quantum attacks, ensures the integrity of electronic transactions, and provides reliable authentication within digital ecosystems. Particular attention is paid to the integration of post-quantum algorithms into trusted electronic systems, including e-voting, state registries, digital document management, and cloud storage infrastructures. Architectures based on the combination of Kyber and Dilithium guarantee not only protection from external interference but also verifiability, authenticity, and consistency of data throughout its entire life cycle. Thus, the application of post-quantum cryptographic algorithms represents a crucial step in the development of secure digital ecosystems and the formation of a new paradigm of digital trust
IMPLEMENTATION OF A REPRODUCIBLE 5G STANDALONE TESTBED USING OPEN-SOURCE COMPONENTS
Deploying a 5G Standalone (SA) network is often constrained by cost, complexity, and limited access to RF hardware. This paper describes a practical, software-defined 5G SA implementation assembled entirely from open-source components Open5GS for the 5G Core, srsRAN for gNB and UE, MongoDB for subscriber data, and ZeroMQ for RF emulation to enable end-to-end connectivity without radios. The blueprint details service initialization order, subscriber provisioning, and configuration alignment across PLMN, TAC, DNN, and key material, followed by validation of UE registration, PDU session establishment, and user-plane data transfer. On the reference setup, the system achieves low round-trip latency (≈1.34 ms), high throughput (≈847 Mbps TCP downlink and ≈823 Mbps uplink), and rapid PDU session setup (≈0.22 s), supporting repeatable functional and performance testing in a laboratory environment. The described approach lowers the barrier to 5G experimentation for teaching, prototyping, and research while providing a reproducible path from basic bring-up to performance evaluation. Limitations include the use of emulated RF and a single-cell scenario; nevertheless, the workflow can be extended to over-the-air SDR tests, mobility, and slicing when needed.
INTELLIGENT SYSTEM FOR AUTOMATIC DETECTION AND SCORING OF SHOOTING TARGETS BASED ON COMPUTER VISION AND MICROCONTROLLER TECHNOLOGIES
This paper presents an intelligent system for the automatic detection and scoring of shooting targets based on the Raspberry Pi 3 microcontroller platform and computer vision technologies. The objective of the study is to develop an autonomous and highly accurate yet low-cost complex capable of recording and analyzing shooting results without human intervention. The system integrates mechatronic and algorithmic components, including Nema 17 stepper motors, color sensors, a webcam, and a server-side image processing module, forming a unified cyber-physical architecture. The algorithmic core is based on geometric calibration using homography, adaptive illumination equalization via CLAHE, and a radial precision evaluation model. To detect bullet holes, a modified YOLOv8-Nano neural network architecture was employed, optimized for recognizing low-contrast circular targets. Experimental results confirmed the high accuracy and robustness of the proposed approach: under stable lighting conditions, the system achieved a spatial recognition precision of ±2 mm with a response time below 0.2 seconds. The training and validation curves of the model demonstrate smooth convergence and stable generalization, confirming the correctness of the architectural modifications and the optimization of the loss function. The scientific novelty of this work lies in the integration of a mechatronic framework and deep-learning algorithms into a unified real-time system that enables automatic target replacement, image processing, and result visualization through a web interface. The practical significance is in the potential application of the system in sports schools, mechatronics laboratories, training centers, and research test ranges requiring accurate and autonomous shooting evaluation. Future work will focus on extending system capabilities through the integration of advanced neural network algorithms (YOLOv8, Detectron2), cloud-based technologies, and automatic camera stabilization, further improving accuracy and autonomy while maintaining low implementation cost.
CVD PREDICTION FROM HRV DERIVED FROM WEARABLE PPG
Cardiovascular disease is the leading global cause of death; ischemic heart disease (IHD) is its most common and lethal form, motivating scalable, non-invasive screening. We tested whether a single 60-minute photoplethysmography (PPG) recording from the Zhurek fingertip wearable can distinguish healthy autonomic control from IHD-related dysregulation. Agreement with a three-lead Holter reference was clinically acceptable (HR −0.601 bpm; SDNN +33.1 ms; RMSSD −4.8 ms). Forty hour-long sessions were analyzed (20 healthy, 18–22 years; 20 angiography-confirmed IHD) using eight HRV/demographic features. Mann–Whitney tests showed significant differences for SDNN, LF, HF, Max_HR, BMI, and age (p<0.05), and a two-component PCA (49.5% variance) separated cohorts without labels. SHAP for a CatBoost model highlighted LF and age as strongest positive contributors and HF as protective. Thus, one-hour PPG preserves diagnostically useful autonomic signatures, enabling ~24× shorter monitoring than Holter and supporting scalable ambulatory IHD risk stratification.
RESNET EMBEDDING-BASED PIPELINE FOR TRANSPARENT DIAGNOSIS OF PULMONARY EMPHYSEMA ON LOW-DOSE CT
This study presents a methodology for the automated detection and quantification of pulmonary emphysema from low-dose chest computed tomography (CT) scans. As a morphological subtype of chronic obstructive pulmonary disease (COPD), emphysema can be accurately assessed on CT imaging. Our approach utilizes a pre-trained ResNet152 model to extract high-dimensional feature embeddings (2048 dimensions) from mid-lung patches. Patients were automatically categorized based on the percentage of low attenuation areas (LAA%) below –950 Hounsfield units (HU), a standard measure for emphysema severity. The extracted feature embeddings were subsequently analyzed using statistical methods and logistic regression to identify key discriminative and interpretable features. A logistic regression model, trained on the top 20 most salient features, achieved a high level of performance, with an Area Under the Curve (AUC) of 0.94 and an Average Precision (AP) of 0.87 on a balanced dataset of 90 subjects. Furthermore, the selected features exhibited a strong correlation with LAA%, demonstrating their utility for regression-based severity assessment. The findings confirm the viability of using pre-trained deep embeddings for transparent and reproducible emphysema screening. This method avoids the need for extensive end-to-end model retraining, making it highly adaptable for integration into existing clinical CT analysis workflows. Keywords: Emphysema, low-dose CT, deep learning, ResNet, feature embeddings, explainable AI, COPD.
APPLICATION OF RULE-BASED METHOD FOR AUTOMATIC EXTRACTION OF TAGS FROM COLUMN-STYLE PDF-DOCUMENTS
This study presents a rule-based hybrid pipeline for the automated extraction of structured metadata from PDF versions of Kazakh-language newspaper articles, focusing on the national newspaper Egemen Qazaqstan. The primary goal is to support the development of a machine-readable knowledge base for future use in training large language models (LLMs) and building an AI-powered assistant for data journalism in Kazakhstan. The pipeline integrates three open-source parsers – pdfminer.six, PyMuPDF, and pdfplumber – to extract key elements such as title, author, date, abstract, text, journal name, and category. To evaluate extraction quality, we compared the results of the automated parser against manually annotated reference files across three real-world issues of the newspaper. The evaluation employed three complementary metrics: Precision, Textual Semantic Similarity (TSS), and Holistic Precision, which jointly assess both exact and semantic matches. The experimental results show that most tags – especially structured fields like date, journal, and category – achieved perfect Holistic Precision (1.00), while more variable fields like title still scored above 0.85. The validated pipeline was then applied to the full corpus of 2,140 newspaper PDFs published between 2017 and March 2025, successfully converting 159,135 articles into structured JSON format. This enriched corpus serves as a foundational knowledge base for Kazakh-language AI systems in journalism and media analysis.
INFORMATION SYSTEM FOR METALLURGICAL PROCESS ANALYSIS AND OPTIMIZATION
The metallurgical industry faces increasing challenges in reconciling production efficiency with environmental compliance while managing heterogeneous data streams across complex processing operations. Traditional approaches to metallurgical process analysis rely on manual calculations and isolated software tools, limiting operational efficiency and introducing potential errors in critical decision-making processes. This paper presents the design and implementation of a comprehensive web-based information system specifically developed for integrated metallurgical process analysis and optimization. The system architecture employs a modular design incorporating three specialized computational modules: pyrometallurgical calculations for ore-to-metal conversions, hydrometallurgical process modeling for extraction operations, and auxiliary process calculators for specialized applications. The platform integrates Django-based backend processing with responsive frontend interfaces, supporting multi-user access, comprehensive data validation, and seamless integration with existing plant information systems. Implementation includes predictive analytics capabilities utilizing machine learning algorithm for forward process prediction and optimization. System validation demonstrates robust performance with processing times ranging from 0.6 to 3.4 seconds across different computational modules and operational success rates exceeding 98.7% for all core functions. The platform supports multiple data input formats including manual entry and Excel file processing, with comprehensive export capabilities (JSON, CSV, Excel) enabling integration with downstream analysis tools. Performance evaluation indicates the system successfully addresses key industrial requirements for accuracy, reliability, and scalability in metallurgical process analysis applications. The developed architecture provides a practical framework for implementing digital transformation initiatives in metallurgical operations while maintaining computational precision required for critical industrial applications