The object of this study is the process of detecting threats and vulnerabilities in hacker forums, which are a well-known source of potential dangers for Internet users. However, the problem of analyzing and classifying data from these forums is its complexity due to such features of the participants' language as specific slang, jargon, etc., which requires the use of modern tools of their processing. This paper explores the application of machine learning to devise an effective method for analyzing sentiment and trends in hacker forums to identify potential threats and vulnerabilities in cyberspace. All necessary stages of the process of detecting threats and vulnerabilities have been developed, ranging from data collection and preprocessing to the training of a model that is capable of processing “raw” unstructured data from hacker forums. The implementation of six popular machine learning algorithms, namely k Nearest Neighbors (kNN), Random Forest, Naive Bayes, Logistic Regression, Support Vector Machines (SVM), and Decision Tree algorithms have been studied with a view to determining their efficiency of threat and vulnerability detection and classification. The experiments have been conducted on real data (150,000 messengers). It has been determined that the Random Forest algorithm coped with the task the best (accuracy=0.89, recall=0.84, precision=0.91, F1-score=0.87 and ROC-AUC=0.89). The proposed tool based on machine learning not only collects data that poses a potential threat but also processes and classifies it according to the specified keywords. This allows detecting threats and vulnerabilities at a high speed. The results of the study make it possible to identify potential trends in threats and vulnerabilities. This will contribute to the improvement of cybersecurity systems and ensure more reliable protection of information resources

This paper introduces H-STGNN-ODE-DA, a novel model for voice sentiment analysis that combines multi-scale acoustic feature extraction, hierarchical graph neural networks (GNNs), Neural Ordinary Differential Equations (Neural ODEs), and domain-adversarial adaptation. Designed to enhance accuracy and robustness under real-world conditions, the model was evaluated on IEMOCAP, MELD, and EmoDB datasets, outperforming state-of-the-art approaches such as LSTM, CNN, GCN, GAT, DANN, and SPECTRA. Notably, it achieved a 4.0% improvement over GAT on MELD. Neural ODEs enabled effective modeling of continuous emotional transitions, while domain-adversarial adaptation ensured robustness to domain shifts. Ablation studies confirmed the critical role of each component in achieving high performance. The model demonstrated strong cross-domain transferability, maintaining high accuracy across diverse recording conditions. These results position H- STGNN-ODE-DA as a robust and versatile solution for real-world applications in speech processing, including virtual assistants, social media analysis, and customer service systems.

This study proposes a four-layer AI agent architecture for automating routine web security operations, integrating Large Language Model (LLM) reasoning with a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) detection engine and implementing a Reasoning-Acting (ReAct) loop for autonomous testing with human-in-the-loop validation. The proposed architecture was empirically evaluated across 50 web applications sourced from OWASP WebGoat, DVWA, and custom-developed test environments over a six-month period. The experimental results demonstrate that the AI agent achieved an overall detection accuracy of 89.2% (95% CI: 86.4-92.0%), significantly outperforming traditional automated methods (67.4% accuracy, p < 0.001). Mean Time to Remediation (MTTR) decreased from 74.3 days to 28.5 days (61.6% reduction), while false positive rates decreased from 24.3% to 4.8%. According to these findings, AI agent-driven automation can substantially enhance the efficiency and reliability of web security testing. However, human expertise remains important for assessing complex vulnerabilities and detecting zero-day threats.

Мaқaлa жaйлы өмір сүру үшін қолдaнылaтын заманауи энергия үнемдеу технологиялaрына aрнaлғaн. Қaзіргі зaмaнғы энергия үнемдеу технологиялaрын іске асыру тәсілдері сипатталған және дамытудың негізгі бағыттары көрсетілген. "Ақылды үй" жүйесін басқару туралы айтылады. Диммерлердің көмегімен ыңғайлы және үнемді жарықтандыру жайлы сипaтталған. Электр қондырғыларын қашықтан басқару және әртүрлі өндірушілердің розеткалары мен ажыратқыштары қалай ерекшеленетіні сипaтталған. Жарықтандыруды, суды тұтынуды, микроклиматты энергияны тиімді басқару принциптері көрсетілген. KNX технологиясы егжей-тегжейлі сипатталған. Мақалада қазіргі уақытта адамзат жайлылықтың артуына бет бұруының қазіргі заманғы пәтерлердің көптеген тұрмыстық техникалар мен аудио-бейне кешенінен басқа, кондиционерлеу, жылыту, жарықтандыру және қорғау жүйелерін де қамтылғаны жөнінде. Мұны «ақылды үй» автоматикасы - барлық үй құрылғылары мен механизмдерінің «электронды ми» басқаруға арналған арнайы жүйе арқылы орталықтандырылған түрде жасауға болады. Тұрғын үй прототипінің моделі InSat MasterSCADA бағдарламалық ортасында жүзеге асырылған. Соған қоса алдыңғы жұмыстарға шолу жасалынған.

The rapid deployment of 5G networks and the proliferation of Internet of Things (IoT) devices have significantly increased the complexity of urban electromagnetic radiation (EMR) environments. Conventional ground-based monitoring systems are spatially limited and unable to provide three-dimensional field characterization. This paper proposes an integrated IoT–UAV framework for high-resolution EMR monitoring, spatial reconstruction, and intelligent source classification. A four-layer architecture combining distributed sensing, edge computing, cloud analytics, and visualization is developed. A formal electromagnetic propagation model is introduced to ensure consistency between broadband exposure measurements and frequency-selective spectral analysis. A CNN–LSTM architecture is implemented for spectral–temporal source classification, achieving 95% validation accuracy across five EMR categories. Simulation-based validation demonstrates up to an eightfold improvement in spatial coverage compared to fixed ground networks while maintaining a practical anomaly detection threshold of −55 dBm in the spectrum-analysis RF chain. The proposed framework establishes a mathematically consistent and practically deployable solution for next-generation EMR monitoring systems.

Цифровизация ключевых процессов требует внедрения научно обоснованных подходов к проектированию автоматизированных систем, способных адаптироваться к изменяющимся условиям внешней среды. В данной работе предлагается гибридная модель массового обслуживания с динамическими параметрами, предназначенная для применения в автоматизированных системах, ориентированных на распределение ресурсов или заявок в условиях переменной нагрузки. Модель объединяет элементы марковских процессов с немарковскими задержками и механизмами динамического перераспределения потоков, что позволяет учитывать временные колебания, приоритеты и ограничения системы. Проведён анализ вероятностных характеристик модели, включая распределение времени ожидания и степень загруженности обслуживающих каналов. Разработаны методы адаптивной оптимизации, обеспечивающие устойчивое функционирование системы при изменяющихся параметрах входного потока. Предложенная модель может быть использована в различных прикладных задачах, включая автоматизацию процессов в образовании, логистике и производственной сфере, где требуется гибкое и эффективное управление распределением ресурсов. Представлены результаты имитационного моделирования, демонстрирующие устойчивость и адаптивность модели при различных сценариях нагрузки. Полученные данные могут служить основой для разработки автоматизированных систем управления, обеспечивающих высокую эффективность и надёжность функционирования.