Economics, Econometrics and Finance, Management of Technology and Innovation, Organizational Behavior and Human Resource Management, Strategy and Management
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Scopus Publications
Scopus Publications
A Hybrid BERT-CNN with Multihead Self-Attention for Automated Cyberbullying Detection Meruert Yerekesheva, Oxana Akhmetova, Assel Kaziyeva, Daniyar Sultan, Aigerim Toktarova, Rustam Abdrakhmanov, Tolep Abdimukhan Engineering Technology and Applied Science Research, 2026 This paper presents a novel hybrid deep learning architecture, the CustomBERTCNNAttentionModel, designed for the automated detection of cyberbullying in social media text. The proposed model integrates the contextual language understanding capabilities of Bidirectional Encoder Representations from Transformers (BERT) with the local feature extraction strengths of Convolutional Neural Networks (CNNs) and the dynamic relevance weighting of multihead self-attention mechanisms. Evaluated on the Kaggle Cyberbullying Dataset, which includes both binary and multiclass labels, the model demonstrates superior performance compared to traditional classifiers and ensemble methods. The architecture effectively handles imbalanced and noisy text data, achieving an accuracy of 0.9853 in binary classification tasks. A comprehensive evaluation using standard metrics and visual analysis through confusion matrices confirms the model's robustness and its capacity to generalize across diverse types of cyberbullying. These results highlight the effectiveness of combining transformer-based embeddings with attention-enhanced convolutional structures for detecting harmful online behavior and contribute to the advancement of intelligent moderation systems.
Hierarchical Swin Transformer Encoder-Decoder Architecture for Robust Cerebrovascular Abnormality Segmentation in Multimodal MRI Nazbek Katayev, Zhanel Bakirova, Assel Kaziyeva, Aigerim Altayeva, Karakat Zhanabaykyzy, Daniyar Sultan International Journal of Advanced Computer Science and Applications, 2025 This study presents a hierarchical Swin Transformer–based framework for automated segmentation of cerebrovascular structures using multimodal magnetic resonance imaging. The proposed architecture integrates patch partitioning, linear embedding, hierarchical windowed self-attention, and a multilevel encoder–decoder design to address the inherent challenges of vascular segmentation, including irregular morphology, small-caliber vessel visibility, and intensity variability across MRI modalities. A multimodal fusion module enhances the ability to capture complementary anatomical and vascular information, while skip-connected decoding ensures the preservation of fine-grained spatial features essential for accurate vessel reconstruction. The model was evaluated using a combination of open-access datasets and demonstrated superior performance across multiple quantitative metrics, achieving higher Dice similarity, precision, sensitivity, and specificity compared to existing state-of-the-art methods. Qualitative analysis further revealed accurate recovery of major arterial pathways, distal branches, and complex vascular topologies, confirming the model’s robustness in both global and localized segmentation tasks. The results highlight the discriminative strength of hierarchical attention mechanisms and emphasize their role in improving cerebrovascular characterization. Overall, the proposed framework offers a reliable and anatomically coherent approach for vascular segmentation, with strong potential for integration into clinical neuroimaging workflows and advanced cerebrovascular research applications.
Use of artificial intelligence and human chipping in forensic medicine: a review Mukhtar B. Sadykov, Yernar N. Begaliyev, Dmitry V. Bakhteev, Assel N. Kaziyeva, Oleg B. Khussainov Russian Journal of Forensic Medicine, 2024 This article outlines some aspects of the use of artificial intelligence and human chipping in forensic medicine.An overview of the use of artificial intelligence and human chipping in forensic medical examination is provided, as well as a definition of the ethical and legal issues of integrating artificial intelligence and human chipping into forensic medical examination.This article discusses the work of scientists of Kazakhstan and foreign countries aimed at studying the use of artificial intelligence and human chipping in forensic medical examination. Notably, the greatest application of artificial intelligence systems at this stage of development of science and technology has been found in forensic psychiatry. We noted the lack of research on the use of human microchipping for forensic purposes. In general, studies addressing the synergy between artificial intelligence and human chipping for use in forensic medicine and the legal and ethical aspects of their use in this area are limited.Considering the generalization of scientific works and analysis of domestic and foreign experience on the issue, we present the ethical and legal aspects of the integration of artificial intelligence and human chipping in forensic medicine. Owing to the lack of sufficient sources in modern scientific literature devoted to the integrated use of artificial intelligence and human chipping in forensic examination as a whole, a SWOT analysis was conducted, which showed that these technologies have some advantages and opportunities regarding efficiency and accuracy.Based on a study of the literature and practice of using artificial intelligence and human chipping in forensic medicine, legal aspects related to the synergy of artificial intelligence and human chipping have been identified, which can be divided into several key areas: confidentiality and data security, informed consent, ownership, and control.Furthermore, based on the literature and the practice of using artificial intelligence and human chipping in forensic medicine, legal aspects related to the synergy of artificial intelligence and human chipping were identified, which can be divided into several key areas: respect for autonomy, confidentiality and ethics of data processing, lack of harm, and informed consent.
The green economy determinants to ensure sustainable modernisation of Kazakhstan Assel Kaziyeva, Lazzat Zhazylbek, Kairatbek Kh. Shadiyev, Yerkin Nessipbekov, Raushan Azbergenova Rivista Di Studi Sulla Sostenibilita, 2023 The country is landlocked and located on the vast inland space of Eurasia, where climatic conditions impede agriculture and living. In this study, the authors attempt to structure and identify possible factors contributing to the development of a green economy in Kazakhstan. The processes of the green economy are considered in cooperation with the state, society, and business. The selected factors are grouped. The state group includes factors of international cooperation and the implementation of state projects of international significance, the society group includes environmental culture promotion, the volunteer movement, and environmental education, the busi- ness factor includes the use of green technologies and eco-tourism. This approach makes it possible to take a systematic look at the key provisions of environmental policy.
