Ravil Muhamedyev

Scientific projects

Programming, artificial intelligence and intelligent systems (machine learning), computer training and testing, simulation of asynchronous systems.

Management of software and scientific projects;  Innovative projects using artificial intelligence technologies and algorithms.

Current projects (see https://geoml.info/):

–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/

–BR18574144, Development of a data mining system for monitoring dams and other engineering structures under the conditions of man-made and natural impacts (2022-2024).

- AP08856412, Development of Intelligent Data Processing and Flight Planning Models for Precision Farming Tasks Using UAVs (2020-2022), https://geoml.info/?page_id=686&lang=en

- AP09259587, Developing methods and algorithms of intelligent GIS for multi-criteria analysis of healthcare data, (2021-2023), https://geoml.info/?page_id=710&lang=en

- BR18574144, Space monitoring and GIS for quantifying soil salinity and degradation of agricultural land in southern Kazakhstan (2021-2023), https://geoml.info/?page_id=823&lang=en

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

Publications

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, 2324614, DOI: 10.1080/23311916.2024.2324614 (Scopus Quartile: Q2, 64%,  WoS IF=1.9) 

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 (CiteScore — Q1, 75%, JCR — Q2, IF: 2.7).  

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 (CiteScore — Q1, 79%, JCR — Q2, IF: 4.8). 

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 — Q2, 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. 

Muhamedyev R. et al. New bibliometric indicators for prospectivity estimation of research fields //Annals of Library and Information Studies (ALIS). – 2018. – Т. 65. – №. 1. – С. 62-69. http://nopr.niscair.res.in/handle/123456789/44217

Mukhamediev R. I., Aligulyev R. M., Muhamedijeva J. Estimation of Relationship Between Domains of ICT Semantic Network //International Conference on Digital Transformation and Global Society. – Springer, Cham, 2017. – С. 130-135.

Muhamedyev R. et al. Visualization of the Renewable Energy Resources //International Conference on Augmented Reality, Virtual Reality and Computer Graphics. – Springer International Publishing, 2016. – С. 218-227.

Muhamedyev R. Machine learning methods: An overview //CMNT. - 19(6). – 2015. - P. 14-29.

Muhamedyev R. et al. Comparative analysis of classification algorithms //Application of Information and Communication Technologies (AICT), 2015 9th International Conference on. – IEEE, 2015. – P. 96-101.

Muhamediyev R. I., Amirgaliyev Y., Iskakov S. K. Integration of Results from Recognition Algorithms Applied to the Uranium Deposits (Selected Papers from The 6th International Conference on Soft Computing and Intelligent Systems and The 13th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2012)) //Journal of advanced computational intelligence and intelligent informatics. – 2014. – Т. 18. – №. 3. – P. 347-352.

Textbook

Mukhamediev R.I., Amirgaliev E.N. Introduction to Machine Learning: Textbook. – Almaty, 2023. – 471 p. https://www.litres.ru/book/edilhan-nesiphanovich-amirgaliev/vvedenie-v-mashinnoe-obuchenie-70255501/

Potential research studies of doctoral students

Overall: Application of 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.