Мухамедиев Равиль Ильгизович

Ғылыми жобалар

Бағдарламалау, Жасанды интеллект және зияткерлік жүйелер( Машиналық оқыту), компьютерлік оқыту және тестілеу, асинхронды жүйелерді Имитациялық модельдеу.

Софтверлік және ғылыми жобаларды басқару; Жасанды интеллект технологиялары мен алгоритмдерін қолдана отырып инновациялық жобалар жасау

Ағымдағы жобалар (см. https://geoml.info/) : 

- BR24992908 "Система поддержки агротехнических мероприятий в растениеводстве на базе комплекса средств мониторинга и методов искусственного интеллекта (Agroscope)", "Support system for agricultural crop production optimization via remote monitoring and artificial intelligence methods (Agroscope)" (2024-2026)

- AP23488745 “Оперативная оценка засоленности почвы с применением маловысотных беспилотных летательных платформ", "Rapid assessment of soil salinity using low-altitude unmanned aerial platforms (RASS)" (2024-2026)

–BR21881908 Complex of urban ecological support (CUES) (2023-2025), http://geoml.info/en/cues-2/

–AP14869972, Development and adaptation of computer vision and machine learning methods for solving precision agriculture problems using unmanned aerial systems, AP14869972 (2022-2024), http://geoml.info/en/iuavt2pa-2/

- AP14869972, Разработка и адаптация методов компьютерного зрения и машинного обучения для решения задач точного земледелия с применением беспилотных летательных систем (2022-2024)

- BR18574144, Разработка системы интеллектуального анализа данных для мониторинга дамб и других инженерных сооружений в условиях техногенных и природных воздействий (2022-2024)

Aяқталды:

- AP08856412, Разработка интеллектуальных моделей обработки данных и планирования полетов для решения задач точного земледелия с применением БПЛА (2020-2022), https://geoml.info/?page_id=696

- AP09259587, Разработка методов и алгоритмов интеллектуальной ГИС многокритериального анализа данных системы здравоохранения (2021-2023) https://geoml.info/?page_id=838

- BR10965172, Космический мониторинг и ГИС для количественной оценки засоленности почв и деградации сельскохозяйственных угодий Юга Казахстана (2021-2023), https://geoml.info/?page_id=811 

- ADVANCED CENTRE FOR PHD STUDENTS AND YOUNG RESEARCHERS IN INFORMATICS (ACESYRI), 610166-EPP-1-2019-1-SK-EPPKA2-CBHE-JP, Erasmus+ (2020-2023), https://satbayev.university/en/acesyri

Жарияланымдар

Mukhamediev, R.I. State-of-the-Art Results with the Fashion-MNIST Dataset. Mathematics 2024, 12, 3174. https://doi.org/10.3390/math12203174 (CiteScore — Q1, 88%, JCR — Q1, IF: 2.3).

Mukhamediev, R.I.; Terekhov, A.; Amirgaliyev, Y.; Popova, Y.; Malakhov, D.; Kuchin, Y.; Sagatdinova, G.; Symagulov, A.; Muhamedijeva, E.; Gricenko, P. Using Pseudo-Color Maps and Machine Learning Methods to Estimate Long-Term Salinity of Soils. Agronomy 2024, 14, 2103. https://doi.org/10.3390/agronomy14092103 JCR - Q1 (Plant Sciences) / CiteScore - Q1 (Agronomy and Crop Science, 84%, WoS IF=3.3) 

Mukhamediev, R.; Kuchin, Y.; Yunicheva, N.; Kalpeyeva, Z.; Muhamedijeva, E.; Gopejenko, V.; Rystygulov, P. Classification of Logging Data Using Machine Learning Algorithms. Appl. Sci. 2024, 14, 7779. https://doi.org/10.3390/app14177779 (JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering), 79%, IF: 2.5)

Mukhamediev, R.I.; Terekhov, A.; Sagatdinova, G.; Amirgaliyev, Y.; Gopejenko, V.; Abayev, N.; Kuchin, Y.; Popova, Y.; Symagulov, A. Estimation of the Water Level in the Ili River from Sentinel-2 Optical Data Using Ensemble Machine Learning. Remote Sens. 2023, 15, 5544. https://doi.org/10.3390/rs15235544 (Scopus Quartile: Q1, 90%, JCR Category Quartile: Q1, WoS IF=5.0) 

Mukhamediev, R.I.; Kuchin, Y.; Popova, Y.; Yunicheva, N.; Muhamedijeva, E.; Symagulov, A.; Abramov, K.; Gopejenko, V.; Levashenko, V.; Zaitseva, E.; et al. Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods. Mathematics 2023, 11, 4687. https://doi.org/10.3390/math11224687 (CiteScore — Q1, 88%, JCR — Q1, IF: 2.46).  

Kuchin, Y.; Mukhamediev, R.; Yunicheva, N.; Symagulov, A.; Abramov, K.; Mukhamedieva, E.; Zaitseva, E.; Levashenko, V. Application of Machine Learning Methods to Assess Filtration Properties of Host Rocks of Uranium Deposits in Kazakhstan. Appl. Sci. 2023, 13, 10958. https://www.mdpi.com/2076-3417/13/19/10958; https://doi.org/10.3390/app131910958 (JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering), 79%, IF: 2.5).  

Mukhamediev, Ravil I., Timur Merembayev, Yan Kuchin, Dmitry Malakhov, Elena Zaitseva, Vitaly Levashenko, Yelena Popova, Adilkhan Symagulov, Gulshat Sagatdinova, and Yedilkhan Amirgaliyev. Soil Salinity Estimation for South Kazakhstan Based on SAR Sentinel-1 and Landsat-8, 9 OLI Data with Machine Learning Models //Remote Sensing. – 2023. – Т. 15. – №. 17. – С. 4269. https://www.mdpi.com/2072-4292/15/17/4269 ; https://doi.org/10.3390/rs15174269 (Scopus Quartile: Q1, 90%, JCR Category Quartile: Q1, WoS IF=5.0)  

Zaitseva, E., Levashenko, V., Mukhamediev, R., Brinzei, N., Kovalenko, A., & Symagulov, A. (2023). Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis. Mathematics, 11(11), 2551. https://www.mdpi.com/2227-7390/11/11/2551 (CiteScore — Q1, 88%, JCR — Q1, IF: 2.46).   

Mukhamediev, R.; Amirgaliyev, Y.; Kuchin, Y.; Aubakirov, M.; Terekhov, A.; Merembayev, T.; Yelis, M.; Zaitceva, E.; Levashenko, V.; Popova, Y.; Symagulov, A.; Tabynbayeva, L. Operational Mapping of Salinization Areas in Agricultural Fields Using Machine Learning Models Based on Low-Altitude Multispectral Images. Drones 2023, 7, 357. https://doi.org/10.3390/drones7060357 (JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering), 79%, IF: 2.5). 

Zaitseva, E., Levashenko, V., Brinzei, N., Kovalenko, A., Yelis, M., Gopejenko, V., & Mukhamediev, R. Reliability Assessment of UAV Fleets //Emerging Networking in the Digital Transformation Age: Approaches, Protocols, Platforms, Best Practices, and Energy Efficiency. – Cham : Springer Nature Switzerland, 2023. – С. 335-357. https://link.springer.com/chapter/10.1007/978-3-031-24963-1_19   

Mukhamediev R. I. Yakunin, K., Aubakirov, M., Assanov, I., Kuchin, Y., Symagulov, A., Levashenko V., Zatceva E., Sokolov D., Amirgaliyev, Y. . Coverage path planning optimization of heterogeneous UAVs group for precision agriculture //IEEE Access. – 2023. – Т. 11. – №. 15. – С. 5789-5803, doi: 10.1109/ACCESS.2023.3235207, https://ieeexplore.ieee.org/abstract/document/10011226, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10011226  (CiteScore — Q1, 92%, JCR — Q1, IF: 4.1).  

Yakunin, K.; Mukhamediev, R.I.; Yelis, M.; Kuchin, Y.; Symagulov, A.; Levashenko, V.; Zaitseva, E.; Aubakirov, M.; Yunicheva, N.; Muhamedijeva, E.; Gopejenko, V.; Popova, Y. Analysis of the Correlation between Mass-Media Publication Activity and COVID-19 Epidemiological Situation in Early 2022 //Information. – 2022. – Т. 13. – №. 9. – С. 434.  https://doi.org/10.3390/info13090434 (CiteScore — Q2, 72%, JCR — Q2, IF: 3.1).

Mukhamediev, R. I., Popova, Y., Kuchin, Y., Zaitseva, E., Kalimoldayev, A., Levashenko V., Symagulov, A., Abdoldina F., Gopejenko V., Yakunin K., Muhamedijeva E., Yelis, M. Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges //Mathematics. – 2022. – Т. 10. – №. 15. – С. 2552. https://doi.org/10.3390/math10152552 (CiteScore — Q1, 88%, JCR — Q1, IF: 2.46).

Mukhamediev R. I. et al. Estimation of Filtration Properties of Host Rocks in Sandstone-type Uranium Deposits Using Machine Learning Methods //IEEE Access. – 2022. – T.10. –  C.18855-18872. DOI 10.1109/ACCESS.2022.3149625; https://ieeexplore.ieee.org/abstract/document/9706226 (CiteScore — Q1, 90%, JCR — Q2, IF: 4.1).

Kirill Yakunin, Ravil I. Mukhamediev, Elena Zaitseva , Vitaly Levashenko, Marina Yelis, Adilkhan Symagulov, Yan Kuchin, Elena Muhamedijeva, Margulan Aubakirov and Viktors Gopejenko. Mass media as a mirror of the COVID-19 pandemic // Computation 2021, 9(12), 140; https://doi.org/10.3390/computation9120140 (CiteScore — Q2, 71%, JCR — Q3, IF: 2.2).  

Mukhamediev R. I. et al. Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country //Applied Sciences. – 2021. – Т. 11. – №. 21. – С. 10171. https://doi.org/10.3390/app112110171 (CiteScore — Q1, 75%, JCR — Q2, IF: 2.7).  

Л. Л. Садовская, А. Е. Гуськов, Д.В. Косяков, Р. И. Мухамедиев. Обработка текстов на естественном языке: обзор публикаций//Иcкусственный интеллект и принятие решений.- 3/2021  c. 95-115, DOI 10.14357/20718594210306

Mukhamediev R. I. et al. From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review //Applied Sciences. – 2021. – Т. 11. – №. 12. – С. 5541. https://doi.org/10.3390/app11125541  (CiteScore — Q1, 75%, JCR — Q2, IF: 2.7).

Yakunin K. et al. KazNewsDataset: Single Country Overall Digital Mass Media Publication Corpus //Data. – 2021. – Т. 6. – №. 3. – С. 31. https://doi.org/10.3390/data6030031  (CiteScore — Q2, 70%, JCR — , IF: 2.6).

Mukhamediev R. I. et al. Classification of Negative Information on Socially Significant Topics in Mass Media //Symmetry. – 2020. – Т. 12. – №. 12. – С. 1945. https://doi.org/10.3390/sym12121945  (CiteScore — Q1, 93%, JCR — Q2, IF: 2.7).

R. Muhamedyev, K. Yakunin, YA. Kuchin, A. Symagulov, S. T. Buldybayev ,S. Murzahmetov and A. Abdurazakov. The use of machine learning "black boxes" explanation systems to improve the quality of school education //Cogent Engineering. – 2020. – Т. 7. – №. 3. – С. 1518571  https://doi.org/10.1080/23311916.2020.1769349  (CiteScore — Q2, 64%, JCR — , IF: 0.385).

Kuchin Y. I., Mukhamediev R. I., Yakunin K. O. One method of generating synthetic data to assess the upper limit of machine learning algorithms performance //Cogent Engineering. – 2020. – Т. 7. – №. 1. – С. 1718821.  https://doi.org/10.1080/23311916.2020.1718821 (CiteScore — Q2, 64%, JCR — , IF: 0.385).

Kuchin Y. et al. Assessing the Impact of Expert Labelling of Training Data on the Quality of Automatic Classification of Lithological Groups Using Artificial Neural Networks //Applied Computer Systems. – 2020. – Т. 25. – №. 2. – С. 145-152. https://sciendo.com/pdf/10.2478/acss-2020-0016

Р. И. Мухамедиев, Я. И. Кучин, К. О. Якунин, Е. Л. Мухамедиева, С. В. Костарев Предварительные результаты оценки литологических классификаторов для урановых месторождений пластово-инфильтрационного типа //Cloud of Science. – 2020. – Т. 7. – №. 2. – С. 258-272.

R. I. Mukhamediev et al. Multi-Criteria Spatial Decision Making Supportsystem for Renewable Energy Development in Kazakhstan // IEEE Access.-2019.- T. 7.- c. 122275-122288. 10.1109/ACCESS.2019.2937627 (CiteScore — Q1, 91%, JCR — Q1, IF: 4.6).

Mukhamedyev R. et al. Assessment of the dynamics of publication activity in the field of natural language processing and deep learning //International Conference on Digital Transformation and Global Society. – Springer, Cham, 2019. – С. 130-135. 

Кітаптар

Терехов А.Г., Амиргалиев Е.Н., Мухамедиев Р.И. Интеллектуальные методы анализа рек, водохранилищ и гидротехнических сооружений. Монография. - Алматы: ИИВТ МНВО РК, 2024. 219 с.              ISBN 978-601-7042-51-6

Bolgov, R., Mukhamediev, R., Pereira, R., & Mityagin, S. Digital Geography: Proceedings of the International Conference on Internet and Modern Society (IMS 2022). – Springer Nature, 2024.https://link.springer.com/book/10.1007/978-3-031-50609-3

Мухамедиев Р.И., Амиргалиев Е.Н. Введение в машинное обучение: Учебник. – Алматы, 2023. – 471 с. https://www.litres.ru/book/edilhan-nesiphanovich-amirgaliev/vvedenie-v-mashinnoe-obuchenie-70255501/

Мухамедиев Р. И., Киселева С.В., Мустакаев Р.Р., Якунин К.О., Мухамедиева Е.Л., Абдолдина Ф.Н. Интеллектуальная геоинформационная система оценки  ресурсов возобновляемых источников энергии Казахстана: монография. – Алматы, 2017. – 211 с. ISBN 978-601-332-004-5

Мухамедиев Р.И.Методы машинного обучения в задачах геофизических исследований. – Рига. – 2016. – 200 с. ISBN 978-9934-14-876-7

Muhamedyev R. Introduction to data base management systems.Training book. Almaty, Kazakhstan / Мухамедиев Р.И. Введение в Системы управления базами данных. Практикум. Алматы, МУИТ, 2010.-127 с., ISBN 978-602-7257-12-5

Докторанттардың әлеуетті зерттеу жұмыстары

Overall: Machine Learning, Natural Language processing, Artificial Intelligence, Computer Vision, Scientometrics (bibliometrics)

***Machine Learning & Computer Vision - applied research related to data processing and machine learning and remote sensing of the earth's surface using satellites and UAVs. Current areas of research: 1) Hydrological system, hydraulic structures, forecasting of river flows in Kazakhstan, water pollution. 2) Soil salinity 3) Recognition, identification and classification tasks using images obtained from a UAV 4) Applications of Explainable Machine Learning 5) Processing of logging data.

***Artificial Intelligence – applied research related to optimization problems using genetic programming, swarm algorithms, etc. Current areas of research 1) Control of a group of UAVs and ground vehicles in various applied tasks.

***Scientometrics (bibliometrics) – research into the development of scientific fields and forecasting. Current areas of research 1) Aspects of the influence of artificial intelligence in various applied areas and forecasting publication activity in AI sphere.

Машинное обучение, обработка естественного языка, искусственный интеллект, машинное зрение, наукометрия