Laboratory of “Analysis and Modeling of Information Processes” named after Professor Aydarkhanov M.B.

Staff:

№	Full Name, Education, Degree, Academic Title	Position	Hirsch Index, ResearcherID, ORCID, Scopus Author ID (if available)
1	Rustam Musabayev, Candidate of Technical Sciences, Associate Professor (Docent)	Head of Laboratory, Leading Researcher	H-index: Google Scholar = 11; Scopus = 8; Web of Science = 6; ResearcherID = AAQ-9781-2020 ORCID = 0000-0001-7283-5144 Scopus Author ID = 57189003609 https://www.scopus.com/authid/detail.uri?authorId=57189003609 https://iict.kz/rustam-mussabayev/ https://iict.kz/en/information-processes-analysis-and-modeling-laboratory/ https://scholar.google.ru/citations?user=qGFbjqsAAAAJ&hl=en
2	Alexander Krasovitsky, PhD	Leading Researcher	H-index: Google Scholar = 7; Scopus = 4; WoS = 2; ResearcherID = AAR-1016-2020 ORCID = 0000-0003-2948-374X Scopus Author ID = 25652148400 https://www.scopus.com/authid/detail.uri?authorId=25652148400 https://scholar.google.com/citations?user=05Pkox4AAAAJ&hl=en
3	Alymzhan Toleu, Master in Software Engineering, Researcher	Researcher	Индекс Хирша в Google Scholar = 7 Индекс Хирша в Scopus = 4 Индекс Хирша в Web of Science = 2 ResearcherID = ABF-3510-2021 ORCID = 0000-0001-9246-319X Scopus Author ID = 57200275502 https://www.scopus.com/authid/detail.uri?authorId=57200275502 https://scholar.google.com/citations?user=hEvSAf0AAAAJ&hl=ru
4	Gulmira Tolegen, Master in Software Engineering, Researcher	Researcher	H-index: Google Scholar = 7; Scopus = 4; WoS = 3; Scopus Author ID = 57200276217 https://www.scopus.com/authid/detail.uri?authorId=57200276217 https://scholar.google.com/citations?user=eTFrD8MAAAAJ&hl=en
5	Ravil Musabayev, PhD	Senior Researcher	H-index: Google Scholar = 4; Scopus = 3; WoS = 2; ResearcherID = IXN-3806-2023 ORCID = 0000-0003-1105-5990 Scopus Author ID = 58781352900 https://www.scopus.com/authid/detail.uri?authorId=58781352900 https://rmusab.github.io/ https://official.satbayev.university/en/teachers/mussabayev-ravil-rafikovich https://github.com/rmusab https://scholar.google.com/citations?user=-sdDKWwAAAAJ
6	Olzhas Kozbagarov, researcher	Engineer	H-index: Scopus = 2; ResearcherID = CYV-4579-2022 ORCID = 0000-0002-3691-6331 Scopus Author ID = 57224011635 https://www.scopus.com/authid/detail.uri?authorId=57224011635 https://www.researchgate.net/profile/Olzhas-Kozbagarov
7	Sanzhar Murzakhmetov, Master	Engineer	H-index: Google Scholar = 6; Scopus = 4; WoS = 3; ResearcherID = AAD-8637-2021 ORCID = 0000-0001-6494-8982 Scopus Author ID = 57214069263 https://www.scopus.com/authid/detail.uri?authorId=57214069263 https://scholar.google.com/citations?user=F3B5iK8AAAAJ&hl=ru
8	Kirill Yakunin, PhD	Senior Researcher	H-index: Google Scholar = 15; Scopus = 11; WoS = 9; ResearcherID = AAD-3147-2020 ORCID = 0000-0002-7378-9212 Scopus Author ID = 55605185600 https://www.scopus.com/authid/detail.uri?authorId=55605185600 https://scholar.google.com/citations?user=y0EBPyEAAAAJ&hl=en

Current Status and Achievements:

The laboratory has successfully implemented a number of significant national-level projects, including grant-funded and program-targeted research financed by the Ministry of Science and Higher Education of the Republic of Kazakhstan. Among the most large-scale scientific studies are the development of information technologies and systems to promote sustainable personal development as one of the foundations of Digital Kazakhstan, the creation of automatic multimodal analysis and synthesis of emotional components in speech and audiovisual signals, as well as the development of a next-generation semantic text search system tailored to the Kazakh language.

One of the laboratory’s most important practical outcomes was the development and implementation of the “Media Analytics” information-analytical system, designed for multi-criteria analysis of large volumes of textual data from open sources. This system is based on Big Data technologies and has been officially certified and introduced at the Ministry of Science and Higher Education of the Republic of Kazakhstan. In addition, the laboratory has achieved significant success in developing new clustering and optimization algorithms, including methods for parallel and distributed data processing, which have advanced the analysis of big data and intelligent information retrieval to a new level.

The laboratory’s scientific achievements are widely represented in publications. To date, its researchers have published more than 90 scientific works, including 26 articles in international peer-reviewed journals indexed in Scopus and Web of Science. Many studies have been published by leading publishers such as Springer, IEEE, Elsevier, including journals like Pattern Recognition, Information Sciences, Mathematics, Symmetry, and IEEE Access. In 2024, the laboratory team received the Best Regular Paper Award at the international conference ACIIDS 2024, which is ranked as a category B conference by CORE.

The laboratory’s work has also earned strong public recognition. In 2024, Head of the Laboratory Rustam Rafikovich Musabayev was awarded the Honorary Badge “For Contributions to the Development of Science of the Republic of Kazakhstan”, as well as the State Prize “Best Researcher of 2023” in the field of Engineering and Technology. In 2012, a group of young scientists under his leadership became laureates of the D.A. Kunaev Prize for the best work in natural sciences. Musabayev R.R. was also a recipient of the State Research Grant for Talented Young Scientists in 2011–2012. Another notable achievement was the laboratory team’s advancement to the semifinals of the NIF$50K National Innovation Business Plan Competition under his leadership.

The laboratory is also actively engaged in training the next generation of researchers, serving as a base for the preparation of master’s and doctoral students as well as young specialists. Under the supervision of Musabayev R.R., two candidate dissertations have been successfully defended. The laboratory maintains strong international scientific ties and actively participates in joint projects and publications with researchers from Serbia, France, Russia, Mexico, and Poland.

Vision for the Next 3 Years:

The vision of the Laboratory of Analysis and Modeling of Information Processes for the next three years is centered on the advancement and integration of cutting-edge artificial intelligence technologies aimed at solving pressing challenges in big data analytics, natural language processing (NLP), and speech technologies.

Special emphasis will be placed on the development of high-performance self-learning AI models adapted for Kazakh-language and multilingual data, as well as the creation of intelligent decision-support systems based on deep analysis of textual and multimodal information.

The laboratory also plans to actively expand its work in applied machine learning, neural network methods for natural language generation and understanding, including the development of Kazakh-language large language models (LLMs), as well as speech synthesis and recognition using deep learning technologies.

A key strategic priority will be the commercialization of scientific developments — with plans to create new software products, platforms, and services for the intelligent solutions market in areas such as public administration, digital economy, cybersecurity, and fintech.

Development Prospects:

The development prospects of the Laboratory of Analysis and Modeling of Information Processes are closely linked to deepening fundamental research in artificial intelligence, machine learning, and mathematical modeling, as well as expanding applied developments focused on the real challenges of society’s digital transformation.

In the coming years, the laboratory plans to actively explore new directions such as generative models (Generative AI), large language models (LLMs), explainable AI (XAI) technologies, and the development of multi-agent systems for analyzing and synthesizing complex information.

One of the key priorities will be the creation of specialized intelligent platforms for the automation of text and multimodal data analysis based on Big Data, including solutions for the Kazakh language, thereby contributing to the growth of national digital resources.

The laboratory also intends to actively participate in international research consortia, expand cooperation with foreign scientific centers and industry partners, and pursue commercialization of its developments through the launch of innovative IT products and services built on its algorithms and solutions.

Another promising area is the research and development of swarm intelligence methods for optimizing algorithms of high computational complexity. Approaches based on the interaction of multiple intelligent agents make it possible to effectively address tasks of global optimization, clustering, and intelligent search, while demonstrating high resilience and adaptability.

Within this field, the laboratory plans to develop new hybrid algorithms that combine swarm intelligence with machine learning and deep learning methods, thereby improving the efficiency of processing large and heterogeneous data. Special emphasis will be placed on applying swarm-based systems to distributed computing, multi-agent modeling, and optimization of neural network architectures.

This approach will help elevate both fundamental research in optimization methods and applied solutions for the digital economy, cybersecurity, bioinformatics, and intelligent information systems to a new level.

Key Publications of the Laboratory Staff:

Rustam Mussabayev, Nenad Mladenovic, Bassem Jarboui, Ravil Mussabayev. How to Use K-means for Big Data Clustering? // Pattern Recognition, Volume 137, May 2023, 109269; https://doi.org/10.1016/j.patcog.2022.109269; (Q1 in Web of Science; Scopus Percentile = 95)

Mussabayev R, Mussabayev R. BiModalClust: Fused Data and Neighborhood Variation for Advanced K-Means Big Data Clustering. Applied Sciences. 2025; 15(3):1032. https://doi.org/10.3390/app15031032

Mussabayev, R., Mussabayev, R. (2024). Superior Parallel Big Data Clustering Through Competitive Stochastic Sample Size Optimization in Big-Means. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2024. Lecture Notes in Computer Science, vol 14796. Springer, Singapore. https://doi.org/10.1007/978-981-97-4985-0_18 (Scopus Percentile = 48)

Best Regular Paper Award. The ACIIDS conference is ranked as a category B conference in the prestigious CORE ranking.

Shestov A., Levichev R., Mussabayev R., Maslov E., Zadorozhny P., Cheshkov A., Mussabayev R., Toleu A., Tolegen G., Krassovitskiy A. Finetuning Large Language Models for Vulnerability Detection // IEEE Access. – 2025. – Vol. 13 – P. 38889-38900. – DOI: 10.1109/ACCESS.2025.3546700. https://ieeexplore.ieee.org/document/10908394

Mussabayev, R., Mussabayev, R. (2025). Boosting K-means for Big Data by Fusing Data Streaming with Global Optimization. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2025. Communications in Computer and Information Science, vol 2493. Springer, Singapore. https://doi.org/10.1007/978-981-96-5881-7_3

Mussabayev R. Optimizing Euclidean Distance Computation. Mathematics. 2024; 12(23):3787. https://doi.org/10.3390/math12233787

Kozbagarov O., Mussabayev R. (2024). Distributed random swap: An efficient algorithm for minimum sum-of-squares clustering. Information Sciences, vol. 681, 2024, pp. 121204. https://doi.org/10.1016/j.ins.2024.121204 (Scopus Percentile = 95)

Mussabayev, R., Mussabayev, R. (2024). Optimizing Parallelization Strategies for the Big-Means Clustering Algorithm. In: Olenev, N., Evtushenko, Y., Jaćimović, M., Khachay, M., Malkova, V. (eds) Advances in Optimization and Applications. OPTIMA 2023. Communications in Computer and Information Science, vol 1913. Springer, Cham. https://doi.org/10.1007/978-3-031-48751-4_2

Mussabayev R., Mussabayev R., Toleu A., Ibraimova A. Efficient big data clustering via VNS-accelerated optimization // Book of Abstracts: 11th International Conference on Variable Neighborhood Search (ICVNS 2025), May 12–14, 2025, Montreal, Canada / Eds. D. Aloise, S. Cavero, E.G. Pardo, A. Sifaleras. – Montreal: ICVNS, 2025. – P. 17. – ISBN 978-84-09-73575-4.

Mussabayev R., Mussabayev R. High-Performance Hybrid Algorithm for Minimum Sum-of-Squares Clustering of Infinitely Tall Data // Mathematics. 2024. Vol. 12. No. 13. Article No. 1930. ISSN 2227-7390. DOI: 10.3390/math12131930. (Q1 WoS, Scopus Percentile = 90)

Toleu, A., Tolegen, G., Krassovitskiy, A., Mussabayev, R., Zhumazhanov, B. (2025). Speaker Change Detection with Pre-trained Large Audio Model. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2025. Communications in Computer and Information Science, vol 2495. Springer, Singapore. https://doi.org/10.1007/978-981-96-5887-9_19

Tolegen, G., Toleu, A., Mussabayev, R., Krassovitskiy, A., Zhuldyzbayuly, N. (2025). Automatic Creation of Multilingual Knowledge Graph with Large Language Models. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2025. Lecture Notes in Computer Science(), vol 15683. Springer, Singapore. https://doi.org/10.1007/978-981-96-6008-7_20

Aubakirov Sh., Akhmetov I., Gelbukh A., & Mussabayev R. Dynamic optimization of min-df in the GreedSum algorithm for enhanced extractive summarization // Artificial Intelligence Review. – 2025. – Vol. 58, no. 9. – P. 270. – DOI: 10.1007/s10462-025-11276-w.

Perfilyeva A., Bespalova K., Kuzovleva Ye., Begmanova M., Amirgaliyeva A., Vishnyakova O., Nazarenko I., Bespalov S., Zhaxylykova A., Yerzhan A., Yergali K., Perfilyeva Yu., Mussabayev R., Zhaniyazov Zh. Kazakh Tobet dogs in the genomic landscape: refining the history of livestock guardian breeds // BMC Biology. – 2025. – Vol. 23, No. 1. – P. 240. – DOI: https://doi.org/10.1186/s12915-025-02344-2.

Perfilyeva, A., Bespalova, K., Kuzovleva, Y., Mussabayev R. et al. Genetic diversity and origin of Kazakh Tobet Dogs. Scentific Reports 14, 23137 (2024). https://doi.org/10.1038/s41598-024-74061-9 (Q1 WoS; Scopus Percentile = 92)

Ravil I. Mukhamediev, Marina Yelis, Kirill Yakunin, Yelena Popova, Yan Kuchin, Adilkhan Symagulov, Nadiya Yunicheva, Elena Zaitseva, Vitaly Levashenko, Elena Muhamedijeva, Viktors Gopejenko & Rustam Mussabayev (2024) Exploring the health care system’s representation in the media through hierarchical topic modeling, Cogent Engineering, 11:1, DOI: 10.1080/23311916.2024.2324614 (Q2 WoS, Scopus Percentile = 70)

Baktibayev D., Baigozha B., Akhmetov I., Mussabayev R., Krassovitskiy A., Toleu A. Literature review on aftershock and earthquake prediction models aided by NLP summarization and ontology extraction techniques // Procedia Computer Science. 2024. Vol. 238. P. 579-586. ISSN 1877-0509. DOI: 10.1016/j.procs.2024.06.064. (Scopus Percentile = 69)

Toleu, A., Tolegen, G., Mussabayev, R. (2024). Topic Modeling with Variable Neighborhood Search. In: Nguyen, NT., et al. Advances in Computational Collective Intelligence. ICCCI 2024. Communications in Computer and Information Science, vol 2166. Springer, Cham. https://doi.org/10.1007/978-3-031-70259-4_18 (Scopus Percentile = 39)

Kozbagarov, O., Mussabayev, R., Krassovitskiy, A., Kuldeyev, N. (2024). Interpretable Dense Embedding for Large-Scale Textual Data via Fast Fuzzy Clustering. In: Nguyen, NT., et al. Advances in Computational Collective Intelligence. ICCCI 2024. Communications in Computer and Information Science, vol 2165. Springer, Cham. https://doi.org/10.1007/978-3-031-70248-8_16 (Scopus Percentile = 39)

Tolegen, G., Toleu, A., Mussabayev, R. (2024). Enhancing Low-Resource NER via Knowledge Transfer from LLM. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science, vol 14810. Springer, Cham. https://doi.org/10.1007/978-3-031-70816-9_19 (Scopus Percentile = 48)

Barakhnin V.B., Karpov M.V., Machikina E.P., Musasbayev R.R. Optimization of Database Operations in the Application for Text Corpus Analysis // In Proceedings of the 20th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS). – Novosibirsk, Russian Federation. – IEEE. – 2024. – P. 1–6

Toleu A., Tolegen G., Mussabayev R., Zhumazhanov B., Krassovitskiy A. Comparative Analysis of Distance Measures for Unsupervised Speaker Change Detection // In Proceedings of the 20th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS). – Novosibirsk, Russian Federation. – IEEE. – 2024. – P. 28–32

Tolegen, G.; Toleu, A.; Mussabayev, R. Contrastive Learning for Morphological Disambiguation Using Large Language Models in Low-Resource Settings. Appl. Sci. 2024, 14, 9992. https://doi.org/10.3390/app14219992

Akhmetov, S. Aubakirov, T. Saparov, R. Mussabayev, A. Toleu and A. Krassovitskiy, “Machine Learning Methods for Kazakh Morphology: A Comprehensive Overview,” 2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE), Novosibirsk, Russian Federation, 2024, pp. 1880-1884, doi: 10.1109/PIERE62470.2024.10804966. https://ieeexplore.ieee.org/document/10804966

Gulmira Tolegen, Alymzhan Toleu, Orken Mamyrbayev, Rustam Mussabayev. Neural Named Entity Recognition for Kazakh // Lecture Notes in Computer Science (LNCS), vol. 13452, February 2023, pp 3–15; https://doi.org/10.1007/978-3-031-24340-0_1

Gulmira Tolegen, Alymzhan Toleu, Rustam Mussabayev, Bagashar Zhumazhanov, Gulzat Ziyatbekova. Generative Pre-Trained Transformer for Kazakh Text Generation Tasks // Proc. of 19th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS 2023), 14-22 August 2023, IEEE, Moscow, pp. 1-10.

Рустам Мусабаев, Равиль Мусабаев, Нурсултан Кульдеев. Использование конкурентной оптимизации и стохастической выборки в Big-means для эффективной параллельной кластеризации K-means // Текст доклада на 21-ой международной конференции «Математические методы распознавания образов» ММРО-23. Россия, г. Москва, 12–15 декабря 2023 г., PP. 1-4

Toleu A., Tolegen G., Mussabayev R., Krassovitskiy A., Ualiyeva I.  Data-Driven Approach for Spellchecking and Autocorrection // Symmetry. -Vol. 14 (11), 2261. -2022. P. 1-16.  https://doi.org/10.3390/sym14112261 (Scopus Percentile = 78, Web of Science = Q2, Impact factor: 2.94)

Jangabylova A., Krassovitskiy A., Mussabayev R., Ualiyeva I. Greedy Texts Similarity Mapping. Computation 2022, 10(11), 200. PP. 1-16; https://doi.org/10.3390/computation10110200; (Scopus Percentile = 68; Web of Science = Q2)

Akhmetov I, Mussabayev R, Gelbukh A. 2022. Reaching for upper bound ROUGE score of extractive summarization methods. PeerJ Computer Science 8:e1103; https://doi.org/10.7717/peerj-cs.1103; (Scopus Percentile = 53, Web of Science = Q2)

Akhmetov I., Gelbukh A., Mussabayev R. Topic-Aware Sentiment Analysis of News Articles // Computación y Sistemas, Vol. 26, No. 1, 2022, P.423–439;  https://doi.org/10.13053/CyS-26-1-4179; (Scopus Percentile = 28)

Toleu A., Tolegen G., Mussabayev R. Language-Independent Approach for Morphological Disambiguation. – Proceedings of the 29th International Conference on Computational Linguistics (COLING 2022). -October 12–17, 2022.  P.5288–5297

Tolegen G., Toleu A., Mussabayev R., Krassovitskiy A.. A Clustering-based Approach for Topic Modeling via Word Network Analysis. 7th International Conference on Computer Science and Engineering (UBMK 2022). -IEEE. P.1-6; https://doi.org/10.1109/UBMK55850.2022.9919530

Tolegen G.; Toleu A.; Mussabayev R. A Finite State Transducer Based Morphological Analyzer for Kazakh Language. 7th International Conference on Computer Science and Engineering (UBMK 2022). -IEEE. P.1-6; https://doi.org/10.1109/UBMK55850.2022.9919445

Krassovitsky A., Mussabayev R. Speech Signal Processing for Low Resource Languages // Proceedings of the 20th Intern. Scient. Conf. Information Technologies and Management 2022. -ISMA University of Applied Science. -Riga. -Latvia. -2022. -P. 30-31

Krassovitsky A., Mussabayev R. New Aggregative Algorithms for Robust Speech Signal Segmentation // Proceedings of the 20th Intern. Scient. Conf. Information Technologies and Management 2022. -ISMA University of Applied Science. -Riga. -Latvia. -2022. -P. 33-35

Toleu A., Tolegen G., Mussabayev R., Krassovitskiy A. Comparison of various topic modeling approaches. The 20th International Scientific Conference Information Technologies and Management, April 21-22, 2022, ISMA University of Applied Science, Riga, Latvia. P.1-3

Козбагаров О.Б., Мусабаев Р.Р. Sentence-Based Topic Modelling: New Way to Interpretative Topic Modelling and Automatic Topic Labelling. Труды VII Междунар. науч.-практ. конф. “Информатика и прикладная математика”.20-21 октября 2022 г., Алматы, C.260-270

Akhmetov I.,  Gelbukh A., Mussabayev R. Greedy Optimization Method for Extractive Summarization of Scientific Articles, IEEE Access, 9, 2021, PP. 168141-168153; https://doi.org/10.1109/ACCESS.2021.3136302; (Scopus Percentile = 90; Web of Science Q2)

Yakunin K, Kalimoldayev M, Mukhamediev RI, Mussabayev R, Barakhnin V, Kuchin Y, Murzakhmetov S, Buldybayev T, Ospanova U, Yelis M, Zhumabayev A, Gopejenko V, Meirambekkyzy Z, Abdurazakov A. KazNewsDataset: Single Country Overall Digital Mass Media Publication Corpus // Data. -2021. Vol. 6(3). -P. 1-12 (Web of Science Q2, Scopus Percentile = 73)

Akhmetov I., Mladenovic N., Mussabayev R. (2021) Using K-Means and Variable Neighborhood Search for Automatic Summarization of Scientific Articles. In: Mladenovic N., Sleptchenko A., Sifaleras A., Omar M. (eds) Variable Neighborhood Search. ICVNS 2021. Lecture Notes in Computer Science, vol 12559. Springer, Cham. https://doi.org/10.1007/978-3-030-69625-2_13 (Scopus Percentile = 50)

Mukhamediev R., Kuchin Y., Musabayev R., Buldybayev T., Murzakhmetov S. Classification of negative publication in mass media using topic modeling // Journal of Physics: Conference Series, 2021, 1727, 012019, https://doi.org/10.1088/1742-6596/1727/1/012019; (Scopus Percentile = 18)

Kozbagarov O., Mussabayev R., Mladenovic N. A New Sentence-Based Interpretative Topic Modeling and Automatic Topic Labeling. Symmetry. 2021; 13(5):837. https://doi.org/10.3390/sym13050837

Mladenovic N., Jarboui B., Elleuch S., Mussabayev R., Rusetskaya O. (2021) Variable Neighborhood Programming as a Tool of Machine Learning. In: Pardalos P.M., Rasskazova V., Vrahatis M.N. (eds) Black Box Optimization, Machine Learning, and No-Free Lunch Theorems. Springer Optimization and Its Applications, vol 170. Springer, Cham. https://doi.org/10.1007/978-3-030-66515-9_9

Yakunin K., Murzakhmetov S., Mussabayev R., Muhamedyev R. News Popularity Prediction Using Topic Modelling. 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST). DOI: 10.1109/SIST50301.2021.9465884

Мусабаев Р.Р. Анализ и распознавание информационных структур речевого сигнала // Материалы VI-ой Международной научной конференции «Информатика и прикладная математика» . – 2021. – Алматы, РК,  – С. 339-347

Мусабаев Р.Р., Красовицкий А.М., Меркебаев А. Применение метода поиска с чередующимися окрестностями VNS для решения задачи глобальной оптимизации в процессе кластерного анализа сегментов речевого сигнала // Материалы VI-ой Международной научной конференции «Информатика и прикладная математика» . – 2021. – Алматы, РК,  – С. 336-338

Nurlybayeva S., Akhmetov I., Gelbukh A., Mussabayev R. (2021) Plagiarism Detection in Students’ Answers Using FP-Growth Algorithm. In: Batyrshin I., Gelbukh A., Sidorov G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science, vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_12

Moldabek A., Almurzayev M., Lerman P., Akhmetov I., Mussabayev R., Krassovitskiy A. Analyzing and classification memes by content // VI-International Scientific Conference “Computer Science and Applied Mathematics”, 2021, Almaty, Kazakhstan. pp. 263-270

Gulmira Tolegen, Alymzhan Toleu, Rustam Mussabayev. Experiments with fuzzy clustering on topic modeling. VI-International Scientific Conference “Computer Science and Applied Mathematics”, 2021, Almaty, Kazakhstan. pp 271-276.

Мухамедиев Р. И., Мусабаев Р. Р., Булдыбаев Т., Кучин Я., Сымагулов А., Оспанова У., Якунин К., Мурзахметов С., Сагындык Б. Эксперименты по оценке средств массовой информации на основе тематической модели корпуса текстов. Cloud of Science. 2020. T. 7. No 1, С. 87-101.

Якунин К., Красовицкий А.М., Уалиева И.М., Мейрамбеккызы Ж., Мусабаев Р.Р.  Анализ новостных тематических трендов в сфере информационной безопасности // Матер. Межд. науч.-практич. конф. «Актуальные проблемы информационной безопасности в Казахстане». – 2020. – Алматы. – С. 247-254.

Krassovitskiy A., Mladenovic N., Mussabayev R. Decomposition/Aggregation K-means for Big Data // In: Kochetov Y., Bykadorov I., Gruzdeva T. (eds) Communications in Computer and Information Science (CCIS) book series. -2020.  – V. 1275. -Springer, Cham. Int. Conf. on Mathematical Optimization Theory and Operations Research (MOTOR 2020). – P. 409-420. https://doi.org/10.1007/978-3-030-58657-7_32

Toleu A., Tolegen G., Mussabayev R. (2020) Deep Learning for Multilingual POS Tagging. In: Hernes M., Wojtkiewicz K., Szczerbicki E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. – P. 15-24 https://doi.org/10.1007/978-3-030-63119-2_2

Khoroshilov Al-dr A., Musabayev R.R., Kozlovskaya Ya.D., Nikitin Yu. A., Khoroshilov A. A. Automatic Detection and Classification of Information Events in Media Texts // Automatic Documentation and Mathematical Linguistics, – 2020, – Vol. 54, No. 4, – P. 202–214. (Web of Science 2020 ESCI JCI Q4)

Akhmetov I., Krassovitsky A., Ualiyeva I., Gelbukh A., Mussabayev R. An Open-Source Lemmatizer for Russian Language based on Tree Regression Models // Research in Computing Science. – № 149(3). – COMIA 2020, – Mexico. – 2020. – Р. 147–153

Mussabayev R., Kozbagarov O. Word Sense Induction: similarity measure to induce word senses// The 18th int. sc. conf. Information technologies and management. – 2020. – ISMA, Riga, Latvia. – P. 73

Tolegen G., Toleu A., Mussabayev R. Voted-Perceptron Approach for Kazakh Morphological Disambiguation // Proc. of the 1st Joint SLTU and CCURL Workshop. – SLTU-CCURL, – 2020, – P. 258–264

Yakunin K., Mukhamedyev R., Mussabayev R. etc. Classification of negative publication in mass media using topic modeling // Journal of Physics: Conf. Series. – 2020. – C. 1-12 (Scopus (17%), Cite Score =0.7) (В процессе опубликования???)

Yakunin K., Mukhamediev R., Mussabayev R., Buldybayev T., Kuchin Y., Murzakhmetov S., Yunussov R., Ospanova U. Mass Media Evaluation Using Topic Modelling. In: Alexandrov D.A., Boukhanovsky A.V., Chugunov A.V., Kabanov Y., Koltsova O., Musabirov I. (eds) Digital Transformation and Global Society. DTGS 2020. Communications in Computer and Information Science, 2020, vol 1242. Springer, Cham. https://doi.org/10.1007/978-3-030-65218-0_13

Yakunin K., Ionescu G., Murzakhmetov S., Mussabayev R., Filatova O., Mukhamediev R. Propaganda Identification Using Topic Modelling // Procedia Computer Science. -2020. -V.178. -P.205-212. https://doi.org/10.1016/j.procs.2020.11.022; (Source type = Conference Proceeding; 2020 Scopus CiteScore Percentile = 68)

Mukhamediev R., Yakunin K., Mussabayev R., Buldybayev T., Kuchin Y., Murzakhmetov S., Yelis M. Classification of Negative Information on Socially Significant Topics in Mass Media // Symmetry. -2020. – V. 12(12). -P. 1-23.  https://doi.org/10.3390/sym12121945

Mussabayev, R.R., Kalimoldayev, M.N., Amirgaliyev, Y.N., Tairova, A.T., Mussabayev, T.R. (2018). Calculation of 3D Coordinates of a Point on the Basis of a Stereoscopic System. Open Engineering. -2018. -V.8(1). -P.109-117 (Scopus SJR = 0.22,  Percentile = 38)

Krassovitskiy A., Mussabayev R. (2018) Energy-Based Centroid Identification and Cluster Propagation with Noise Detection. In: Nguyen N., Pimenidis E., Khan Z., Trawiński B. (eds) Computational Collective Intelligence // ICCCI 2018. Lecture Notes in Computer Science. – Springer, Cham, 2018. – Vol 11055. – Р. 523-533. https://doi.org/10.1007/978-3-319-98443-8_48: 01.10.2018

Mussabayev R.R., Kalimoldayev M.N., Amirgaliyev Ye.N., Mussabayev T.R. Automatic speech segmentation using throat-acoustic correlation coefficients // Open Engineering. – 2016 // https://www.degruyter.com/page/special-issue-mathematical-modelling-in-applied-sciences : 2016 (Scopus).