EVOLVING PARADIGMS IN CRIME PREDICTION: NEURAL ARCHITECTURES AND ETHICAL CONSIDERATIONS IN URBAN SAFETY
The rapid urbanization of the past decade has transformed crime from a local policing challenge into a complex, dynamic system driven by socio-economic, spatial, and temporal forces. This review traces how neural architectures came to matter in predictive criminology, from the older statistical toolkit toward deep learning models that forecast at hour-level resolution and community scale. Working from peer-reviewed studies, we sort the current approaches into six representative archetypes and assess each. The verdict is mixed. Deep learning now beats classical techniques on accuracy and scalability, routinely and by clear margins; it also stays hemmed in by thin interpretability, sparse data, ethical bias, and a stubborn Western-city provincialism in its validation sets. Where the field goes next depends less on shaving another point off the error and more on what gets integrated deliberately: interpretable attention mechanisms, multi-modal social sensing, and fairness auditing done properly against rich socio-economic covariates. Trust is the binding constraint on deployment. Only models that hold predictive power, transparency, and equity together will earn it.
Copyright (c) 2026 Р. Ережепов, М.Ж. Калдарова (Автор)
FORECASTING RENEWABLE ENERGY GENERATION: COMPARISON OF ARIMA, XGBOOST, AND A DEEP-LEARNING-INSPIRED PROXY MODEL
Accurate short-term forecasting of renewable energy generation is important for power-system operation, balancing, and energy planning. This paper compares three forecasting approaches: the classical ARIMA model, the XGBoost gradient-boosting algorithm, and a simplified window-based proxy model inspired by the logic of LSTM sequence modelling. The study is designed as an undergraduate research paper and uses synthetic hourly data for 2023. The dataset imitates typical daily, seasonal, and stochastic patterns of solar and wind generation. The models were trained on the first eleven months and evaluated on the final thirty-day hold-out period. For solar generation, the proxy model achieved the lowest RMSE (21.50 MW), while XGBoost produced a competitive result (23.44 MW). For wind generation, both approaches showed similar performance, which reflects the stochastic nature of wind power. The study also demonstrates that MAPE may be unstable when generation values are zero or close to zero; therefore, RMSE and MAE are recommended as the main comparison metrics.
renewable energy, solar generation, wind generation, time series, ARIMA, XGBoost, LSTM-proxy, machine learning, RMSE, MAE, MAPE.
Copyright (c) 2026 С. Тургын, Г.Ж. Таганова (Автор)
IMPROVING APPROACHES TO IDENTIFYING PROBLEM BUSINESS PROCESSES BASED ON THE BAYESIAN METHOD
In modern organizations, the complication of business processes and an increase in the amount of data make it difficult to make managerial decisions. Especially timely and accurate identification of problematic business processes is an important condition for improving the efficiency of the organization. Traditional deterministic and heuristic approaches do not show sufficient effectiveness in the face of uncertainty. In this article, the possibility of using the Bayesian probability method to improve approaches to identifying problem business processes is studied. The Bayesian method allows you to assess the problem probability of processes by combining a priori knowledge and observation data. The results of the study indicate a high level of flexibility and interpretation of the proposed approach. The proposed approach was tested on the basis of synthetic data and the ability to distinguish between problematic and normal business processes was evaluated. In the course of the study, a quantitative ranking of the risk level of processes was carried out by calculating aposteriory probabilities. The results obtained showed that the Bayesian model works stably in conditions of uncertainty compared to traditional methods. In addition, the interpretation of the results of the model allows us to support the management decision-making process. The presented method is suitable for practical application in systems for monitoring business processes and optimizing them.
Copyright (c) 2026 А. С. Шаяхметова, Н. Тасболатұлы, Н.Е. Досанов, М. Отман, Н. Темир (Автор)
EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) FOR INTERPRETING STUDENT PERFORMANCE PREDICTION IN STEM AND COMPUTER SCIENCE: A SYSTEMATIC LITERATURE REVIEW
Educational Data Mining (EDM) enables the early identification of learning difficulties through the prediction of students' academic performance. However, machine learning models often operate as "black boxes," making their decision-making processes difficult to interpret. Explainable Artificial Intelligence (XAI) addresses this challenge by providing insights into why these models generate specific predictions. This article presents a systematic literature review of research published over the past five years concerning the application of Explainable Artificial Intelligence (XAI) to predict student performance in computer science and STEM education. The findings indicate that the most frequently used features were students' behavioral data and academic records, while the primary prediction tasks focused on grade prediction and identifying students at risk of academic failure. The study also analyzed the application domains of XAI, identifying global feature importance analysis, the explanation of individual predictions, and support for pedagogical interventions as the most prevalent areas. Furthermore, SHapley Additive exPlanations (SHAP) emerged as the most widely used XAI method, applied predominantly for global model interpretation, with limited use for explaining individual predictions. Finally, a significant research gap was identified regarding the use of Explainable Artificial Intelligence (XAI) for course optimization, customized visualizations, and the generation of personalized recommendations. Addressing these gaps could enable educators to provide data-driven, personalized support to improve individual student outcomes.
Explainable Artificial Intelligence, XAI, Educational Data Mining, EDM, Performance Prediction, Computer Science Education, STEM Education, Systematic Literature Review.
Copyright (c) 2026 А.А. Алибай, Г.К. Таганова (Автор)
CONTROL METHODS FOR PRACTICAL TRACKING OF NONLINEAR SYSTEMS UNDER NON-STRICT CONDITIONS
For high-order nonlinear systems, especially in the presence of uncertainties and time-varying delays, achieving practical output tracking is a challenging scientific problem. This paper proposes control methods operating under non-strict conditions. During the study, state- and output-feedback controllers were synthesized based on the homogeneous domination approach, the power integrator technique, and Lyapunov-Krasovskii functionals. The proposed approach enables the construction of control laws while taking into account parameter uncertainties and time delays in the system. The obtained results theoretically prove that all states of the closed-loop system are uniformly bounded and that the tracking error converges to a predefined accuracy region after a finite time. Compared with existing results, the proposed method is characterized by relaxed assumptions on the system and applicability to high-order nonlinearities. In particular, it is highly effective in ensuring stability and achieving accurate tracking under parametric uncertainties and time-varying delays. Moreover, the theoretical results demonstrate that the proposed approach can be applied to various engineering systems, including robotics, automated control systems, and energy processes.
Copyright (c) 2026 А.К. Ерденова, Н. Тасболатулы (Автор)
EVENT RECOMMENDATION UNDER EXTREME COLD-START CONDITIONS: THE VALUE OF SCHEDULE, WEATHER AND CALENDAR CONTEXT (BASED ON DATA FROM A KAZAKHSTANI TICKETING PLATFORM)
This article addresses the problem of personalized recommendation of cultural and entertainment events under the extreme cold-start conditions typical of online ticketing platforms. The study is based on real anonymized data from a national ticketing platform of Kazakhstan, covering 12,715 interactions of 10,657 users with 2,074 events in four cities of the country from March 2023 to February 2024. The dataset is characterized by a median of one interaction per user, an 83 % share of cold-start users in the holdout month, and about 50 % of purchases falling on premiere events with no sales history. Within a unified protocol of strict temporal validation on six monthly folds, 13 algorithms were compared: from basic popularity models to a hybrid gradient-boosted ranker LightGBM (LambdaRank) enriched with weather, calendar and schedule features and rank blending via reciprocal rank fusion (RRF). Statistical significance was assessed using a paired bootstrap procedure (5,000 replicates, 95 % confidence intervals). It was found that knowledge of the next month’s event schedule provides the largest and statistically significant quality gain (ΔMAP@10 = +0.0084; p < 0.0001), exceeding the effect of any refinement of the ranking algorithm itself; the best configuration achieves MAP@10 = 0.0578, which is 17 % above the strongest non-personalized baseline. The weather context demonstrates a gain at the boundary of statistical significance (p = 0.053), while calendar features alone yield no significant effect. The results substantiate the priority of integrating schedule data into production recommendation pipelines of event platforms.
recommender systems, cold start, learning to rank, gradient boosting, LambdaRank, context-aware recommendation, event platforms, MAP@10.
Copyright (c) 2026 Б. Б. Маратов, Ж. Ламашева (Автор)
AI-POWERED ANALYSIS OF FIREWALL SYSTEMS FOR CORPORATE NETWORK PROTECTION
Modern cyber threats are characterized by a high degree of complexity and variability, which makes the task of protecting corporate networks from confidential data leakage especially relevant. Traditional approaches to information security are losing their effectiveness when faced with attacks based on anomalous user behavior and the use of covert communication channels. In this regard, the role of next-generation firewalls (NGFW) and intrusion detection and prevention systems (IDS/IPS) is increasing. This study is aimed at exploring the possibilities of integrating NGFW and IDS/IPS with machine learning technologies to enable intelligent analysis of network traffic. As part of the practical component, a module was developed in the Python programming language, based on the Random Forest algorithm, which provides automatic threat classification. The module was tested to see how well it works when people try to attack it. This was done to find out if it is really useful in the world. This work is, about keeping information safe and secure using artificial intelligence. We looked at the problems we are facing now with keeping data safe and we tried to come up with some solutions.
NGFW, IDS/IPS, Random Forest, data protection, artificial intelligence, cybersecurity
Copyright (c) 2026 Г.З. Зиятбекова, Wojcik Waldemar, Д. Жарқынұлы, Р.Ы. Ықласұлы (Автор)
COMPARATIVE ANALYSIS OF 3D MAPPING ALGORITHMS FOR ENVIRONMENTAL EXPLORATION ON GRAPH BASIS
In a modern topology many robots already use the existed map structures, but developing an autonomous system in a previously unknown environment with minimal data and a limited field of view has not yet been definitively explored. Many features of the cities or some area create a closed route, which robots may not leave without human intervention. This paper compares the proposed hybrid graph-construction system with existing algorithms, RRT* and D*Lite, for navigation in an unknown environment; deterministic paths are used in open terrain for obstacle avoidance. The developed algorithm will create a topographic graph that creates nodes for the future graph, which will then create loops. Once the loop detected the algorithm will use these nodes to exit the loop for further exploration of the terrain. The goal of this paper is to analyze how the graph construction algorithm performs in a dynamic environment, compared to RRT* and D*Lite, where objects are static and dynamic. To achieve the goal, the following objectives will be performed: constructing a 2D map with random objects and destinations, training neural agents to detect possible nodes and exit them, and constructing an optimal path from point to point using minimal computation in a sufficiently open area. The three methods were simultaneously simulated in 40 different situations with different initial parameters. The hybrid path construction method achieved the goal in half of all tests, where on average they approached the goal by 27.7%. The highest result was shown by RRT* at 37.3%, only it took an average of 1409 nodes to achieve the goal, which is twice as much as the proposed method in the work. D*Lite completed the training with a score of 3% of the distance traveled, as closed loops were created with complex calculations and heavy traffic.
Copyright (c) 2026 Т.К. Жукабаева, А.A. Жулумбетов (Автор)
EVALUATING THE EFFECTIVENESS AND PROSPECTS OF IMPLEMENTING TASK MANAGEMENT TOOLS TO INCREASE THE PRODUCTIVITY OF SMALL TEAMS
In the context of digital transformation and the spread of remote work, task management is becoming an important factor in the productivity of small teams. Today, there are many digital tools for managing projects and tasks. However, their diversity and differences in functionality create the problem of choosing the most effective solutions for small teams. Insufficient adaptation of tools to the specifics of the work of small teams can lead to a decrease in the efficiency of interaction, fragmentation of work processes and irrational use of time. The purpose of the study is to evaluate the effectiveness of using task management tools in small teams. It is important to identify some of the reasons that may affect the success of their implementation in work, as well as their further application in work. This article analyzes some scientific journals and publications that relate to the organization of a team in companies or small groups of people. We've looked at different approaches to how tasks are managed. Agile methodologies, modern platforms for project management and systems that track working hours, were most often used. Today, these tools are used in most teams to properly organize people, as well as monitor the completion of tasks. The analysis conducted in this article shows that the use of task management platforms has a positive effect on the effectiveness of teams in various fields of activity. They make it much easier to track and coordinate work with tasks and their effectiveness. However, there are some problems at first. With a high number of people in the team, or incompetence using new technologies can lead to inefficient use of these tools. The practical significance of the research lies in the formation of recommendations on the selection and implementation of task management tools that can increase teamwork productivity, optimize resource allocation and shorten project deadlines. The results obtained can be useful for managers of small teams and startups. They can also be used in the work of digital agencies and individual entrepreneurs. In addition, these findings can be used by specialists who work in distributed or remote teams.
task management, small teams, productivity, digital tools, task managers, Agile approaches, Scrum, process automation, planning tools, collaboration, efficiency.
Copyright (c) 2025 А.К. Коротков, М.Ж. Қалдарова, Д.А. Кузин (Автор)
DEVELOPMENT OF A TASK MANAGEMENT TOOL PROTOTYPE FOR SMALL TEAMS: APPROACHES AND TECHNOLOGIES
Effective task management is a critical success factor for small teams, but market analysis shows that most corporate solutions are overly functional and not adapted to the specific needs of small teams. It is often difficult to implement such systems. In environments where simplicity and speed of work are a priority, the difficulties for implementation are high complexity and lack of work with new technologies. This article will consider an approach to developing a task management tool. It will have an intuitive interface for employee coordination and task allocation. For development, we have considered many approaches and identified those that are suitable for our tool. Much attention was paid to testing the techniques in real conditions. For small teams, such conditions as communication between employees, the current distribution of tasks, as well as transparency of processes were considered. The User Experience was also analyzed, which was focused on several groups with different responsibilities. The architecture of the project was built on a modern stack. Technologies such as JavaScript were used to work with Backend or server logic. For the visual component, HTML was used to create the frame side and Tailwind CSS to overlay a beautiful visual component on the frame. Grafana was also used to build metrics and dashboards. The practical significance of the work is confirmed by the results of testing a functioning prototype in a real small team. The conducted validation demonstrated a reduction in operating costs for administration, a 25% reduction in time spent on setting and monitoring tasks, as well as achieving full transparency of work processes thanks to the implemented visual analytics system.
Copyright (c) 2026 А.К. Коротков, Б.Ж. Ергеш (Автор)
ГИБРИДНАЯ РЕКОМЕНДАТЕЛЬНАЯ МОДЕЛЬ ДЛЯ РАСПРЕДЕЛЕНИЯ СТУДЕНТОВ МЕЖДУ НАУЧНЫМИ НАСТАВНИКАМИ
В данной статье рассматривается проблема распределения студентов и научных руководителей в высшем образовании путем предложения гибридной рекомендательной системы, основанной на искусственном интеллекте и обработке естественного языка (NLP). Для преодоления семантических и когнитивных барьеров между исследовательскими предложениями студентов и компетенциями научных руководителей система использует многоязычную архитектуру Sentence-BERT (SBERT) для генерации плотных векторных представлений, обеспечивая высокоточное семантическое сопоставление на английском, русском и казахском языках. Кроме того, модель интегрирует модифицированный алгоритм оптимизации Гейла-Шепли для обеспечения справедливого распределения академической нагрузки и предотвращения выгорания научных руководителей, успешно уменьшая дисбаланс рабочей нагрузки. Предложенная микросервисная архитектура также включает в себя объяснимый искусственный интеллект (XAI) для обеспечения прозрачных обоснований сопоставления и динамический механизм пороговой обработки для обработки аномальных или узкоспециализированных запросов типа «холодный старт». В конечном итоге, этот подход, основанный на данных, повышает качество академического наставничества, снижает академические трения и предлагает масштабируемое ИТ-решение, готовое к бесшовной интеграции в современные университетские системы управления обучением (LMS).
Copyright (c) 2025 С.Ж. Советов, А.С. Аканова, Н.С. Ермекова (Автор)
INTERPRETABLE AI FOR CYBER THREAT DETECTION IN SMART SYSTEMS
The rapid development of intelligent technologies, IoT infrastructures, and cloud services has led to an increasingly complex cybersecurity landscape. Because intelligent systems generate massive volumes of heterogeneous streaming data in real time, the challenges of accurately detecting threats in the cyber environment have increased significantly. Traditional intrusion detection systems used to identify cyber threats typically rely on static attack signatures and, therefore, have limited ability to detect new forms of cyber threats. This study presents an interpretable artificial intelligence framework for real-time cyber threat detection in streaming intelligent systems using machine learning models, combined with explainable AI methods such as SHAP and LIME to enhance detection capabilities. Security-related data streams and other sources were analyzed to identify potential anomalies and cyberattacks. Based on experimental evaluation, the proposed model, based on combined methodologies, such as hybrid explainable AI model, demonstrated superior performance compared to each of the individual machine learning models across several evaluation metrics, including accuracy, precision, recall, and F1 score. Overall, this work demonstrates how to leverage machine learning and explainable AI methods to improve trust, transparency, and the practical applicability of cybersecurity monitoring solutions in dynamic, intelligent environments.
Copyright (c) 2025 A.A. Aбдукаримова, Р.А. Исмаилова, А.М. Джумагалиева, В.Б. Рыстыгулова, Ә.Е. Көксеген (Автор)
ADAPTIVE REAL-TIME NETWORK INTRUSION DETECTION SYSTEM
The advent of IoT devices and fast-speed 5G networks makes the traditional static Network Intrusion Detection System (NIDS) obsolete. The static model, which is trained on a static dataset, is unable to cope with concept drift, or the changing statistical patterns of the network and polymorphic attacks. In this paper, we propose and test an adaptive real-time NIDS based on online deep learning approaches to counter model degradation when working in dynamic settings. We utilize a Hoeffding Adaptive Tree (HAT) integrated with an Adaptive Windowing (ADWIN) concept drift detector. In the prequential simulation of the continuous UNSW-NB15 data stream with 250,000 sequential network packets, we demonstrate that while the static Hoeffding Tree baseline degrades to 96.50% accuracy during critical concept drift events, the adaptive model successfully adapts and maintains 100% accuracy. Furthermore, empirical resource profiling proves the model's suitability for Edge AI deployment, requiring only 198.08 MB of peak RAM with an average inference latency of 0.37 milliseconds per packet. By combining incremental learning techniques with stringent statistical drift analysis, a highly efficient, edge-native security solution is achieved.
Copyright (c) 2026 Д. Касымов, Т.К. Жукабаева (Автор)
DATA PRIVACY MANAGEMENT MECHANISMS IN ELECTRONIC PUBLIC SERVICES OF THE REPUBLIC OF KAZAKHSTAN
The research paper is devoted to the analysis of data protection mechanisms in the provision of digital services in the Republic of Kazakhstan. The dynamic development of the digitalization process makes the issue of increasing the availability of public services, protecting the security and confidentiality of personal data relevant. The study examines the legal, organizational and technological mechanisms used in the e-government system, and analyzes their effectiveness.The study provides an overview of the regulatory frameworks governing qualitative analysis, comparative methods, and personal data protection. Special attention is paid to identifying the main risks and vulnerabilities related to data processing, storage and interagency information exchange. In addition, the main risks and problems in ensuring data confidentiality will be identified and compared with international experience. The results of the study will make it possible to form practical recommendations aimed at improving the level of information security of Kazakhstan in digital government systems.
Digital Public Services, Information Systems, e-Government, personal data, information security, data privacy.
Copyright (c) 2025 А.Ж. Алимгазиева, Д.С. Байгожанова (Автор)
MODERNIZATION OF TOLL COLLECTION SYSTEMS USING COMPUTER VISION FOR AUTOMATIC VEHICLE CLASSIFICATION
Research paper explores the use of computer vision technologies to automatically classify vehicles in the new toll collection systems. Specifically, the analysis focuses on integrating deep learning algorithms, especially convolutional neural networks (CNN), into modern toll plaza operations. Research shows that vision-systems technology improves the speed of processing and classification accuracy rates to levels that are previously unattainable. The research aids in understanding how these systems can be implemented, the challenges posed, and the prospects for practical widespread usage. The results achieved in this study suggest the strong need for the investment in improving the operational cost effectiveness, system cost, and overall satisfaction of the users of the toll collection systems.
Computer Vision, Toll Collection, Vehicle Classification, Deep Learning, Intelligent Transportation Systems
Copyright (c) 2025 Б. Аманбаев, А. Мырзакеримова, Э. Айтмуханбетова (Автор)
PREDICTING THE PROBABILITY OF PRODUCT RETURNS ON MARKETPLACES USING MACHINE LEARNING METHODS
The article examines order cancellations in the construction tools segment (Power Tools and Hand Tools) across major marketplaces during the period from October 1, 2024 to March 31, 2025. The study is based on real data from an online store, including daily orders with several transactions recorded for each marketplace.The main focus is on cancellations that happen before the customer receives the product or at the pickup point. These cases are seen as early signs of issues in logistics and service quality. To analyze the factors that influence cancellations, machine learning methods were used, such as logistic regression, Random Forest, and gradient boosting. The models were compared using classification accuracy and ROC-AUC metrics. The results show that cancellation rates differ between marketplaces. They also confirm that it is possible to build a model that can predict cancellations in advance. This can help reduce operational costs and improve sales management. Overall, the practical value of the study is in developing a tool for predicting cancellations and increasing efficiency in managing sales within the construction tools segment.
marketplaces; e-commerce; construction tools; order cancellations; behavioral factors; logistics processes; forecasting; machine learning; classification; gradient boosting; Random Forest; predictive analytics
Copyright (c) 2026 А.Т. Рахимжанов, Д.С. Байгожанова, С.Б. Кашкинбаев (Автор)
Development of an information system for personalized product recommendations based on review and rating analysis
The study presents the development of an intelligent information system designed to generate personalized product recommendations through the combined analysis of user reviews and numerical ratings. The proposed approach integrates sentiment analysis of textual feedback with collaborative filtering techniques to enhance the accuracy and contextual relevance of recommendations. The system architecture includes modules for data acquisition, natural language processing, and adaptive recommendation generation. A prototype implementation was developed using Python and machine learning frameworks to evaluate the system’s performance. Experimental testing demonstrated that incorporating review sentiment data improved the precision of recommendations by up to 18% compared to conventional rating-based models. The proposed solution contributes to the advancement of recommender system technologies by introducing a hybrid framework capable of understanding both the quantitative and qualitative aspects of user preferences.
recommender systems, sentiment analysis, collaborative filtering, machine learning, natural language processing, e-commerce personalization.
Copyright (c) 2025 Smart Technologies Journal
Deep learning for multimodal gesture and emotion recognition
The application of deep learning in multimodal systems has shown significant progress, especially in streamlining gesture recognition and facilitating sign language interpretation for the hearing impaired. This paper explores the integration of gesture and emotion analysis using convolutional neural networks (CNNs) for facial expression recognition and long short-term memory (LSTM) networks for temporal gesture analysis. To evaluate the effectiveness of the algorithms, multimodal systems were tested on specialized datasets such as iMiGUE, which includes emotion videos that have been accurately annotated. These datasets enabled the evaluation of the model's performance on real-life tasks along with the comparison between different models.
deep learning, multimodal systems, emotion recognition, sign language, facial expressions, deaf individuals
Machine learning system for mutation pathogenicity prediction
The article presents the design of a distributed machine learning system for automatic classification of genetic variant pathogenicity based on ClinVar clinical data. The relevance is determined by the need to accelerate the interpretation of next-generation sequencing results in clinical practice, where manual analysis of hundreds of thousands of variants takes weeks of geneticists' work. Architectural solutions for processing large volumes of genetic data using Apache Spark MLlib technology and ensemble learning methods are investigated. Methods of system analysis of biomedical databases, feature engineering for categorical genetic features, cross-validation, and comparative analysis of classification algorithms were applied. A three-stage methodology was developed: data preparation with normalization and categorization of clinical significance, feature engineering using StringIndexer and OneHotEncoder, training three models (Logistic Regression, Random Forest, Gradient Boosted Trees) with hyperparameter optimization through Grid Search. A recommendation system with five-level variant prioritization (CRITICAL/HIGH/MEDIUM/LOW/MINIMAL) based on pathogenicity probabilities was designed. Results include a scalable architecture for processing 1млн+ records and an automated clinical recommendation generation module.
machine learning, bioinformatics, mutation pathogenicity, Apache Spark, Random Forest, genetic variant classification, ClinVar, personalized medicine.
Development of a module for anomaly detection in a network graph based on neural networks
The rapid development of artificial intelligence is fundamentally transforming our lives and making them more convenient. In particular, anomaly detection in network traffic is one of the advances that has a significant impact on society. Traditionally, anomaly detection was carried out using statistical methods; however, with the emergence of machine learning—especially deep learning—the accuracy of anomaly detection has greatly improved, and its application scope has expanded. Anomaly detection algorithms are methods used to identify data that significantly deviates from normal data and its behavior. Such algorithms make it possible to detect anomalies effectively and accurately in large data volumes, reduce risks and costs, and increase user satisfaction. Purpose of the study. The purpose of this research is to analyze the capabilities of neural network models for detecting anomalies in network traffic, evaluate their effectiveness compared with classical security methods, and develop practical recommendations for integrating them into information security systems. Practical significance. The results of this study can be applied to: - Improving intrusion detection systems (IDS/IPS) through the introduction of machine learning algorithms that enhance the accuracy and speed of threat detection. -Reducing the workload on security specialists by automating traffic analysis and minimizing false positives. - Increasing resistance to new types of attacks, including previously unknown threats, due to the ability of neural networks to detect anomalies without predefined rules. - Optimizing security resources through adaptive approaches that allow systems to learn independently from new data and changing network conditions. The integration of neural network technologies into cybersecurity systems may become a new standard of protection, providing reliable defense against dynamic and sophisticated cyber threats.
neural networks, gradient method, autoencoder, encoder–decoder, autoencoders, anomaly detection, network traffic.
Development and evaluation of an algorithm for personalizing educational trajectories using artificial intelligence methods
This article presents the development of a comprehensive system for personalizing educational trajectories based on artificial intelligence and deep learning methods. A four-level microservice architecture of the system with a dedicated Python service for ML/AI components is proposed. A five-phase personalization algorithm has been developed with three specialized machine learning modules: Deep Knowledge Tracing (DKT) with Long Short-Term Memory (LSTM) networks and an attention mechanism for tracking knowledge evolution, a hybrid architecture combining DKT and Bayesian Knowledge Tracing (BKT) for interpretable predictions, a multi-task performance predictor with an early warning system, and a hybrid recommendation system combining collaborative and content filtering with context-dependent learning. Modern neural network architectures are applied: LSTM with two layers and dropout 0.3, multi-head attention with four heads, multi-task prediction heads for regression and classification. Specialized loss functions are implemented: Bayesian Personalized Ranking (BPR) for implicit feedback and a multi-task function with weighted components. The practical significance lies in the ready-to-implement open-source architecture of ML components, which provides real-time trajectory adaptation with a prediction accuracy of over 77% and a 30% reduction in the risk of dropout.
personalized learning, deep learning, recommendation systems, microservice architecture, predictive analytics.