Applied Machine Learning Scientific and Methodological Laboratory

The Applied Machine Learning Scientific and Methodological Laboratory was established in February 2022 within the framework of the ERASMUS+ project Advanced Centre for PhD Students and Young Researchers in Informatics (ACeSYRI), reg.no. 610166-EPP-1-2019-1-SK-EPPKA2-CBHE-JP.

Head of Laboratory: Professor R. I. Mukhamediev
 

Laboratory Mission

1. Development of machine learning methods for solving applied problems in various fields.

2. Collection, analysis, and processing of information, including the use of UAVs and Earth remote sensing systems.

3. Creation of methodological materials for educational activities and support of young researchers’ scientific work.

Material and Technical Infrastructure

The laboratory is equipped with modern high-performance hardware:

• HP Z2 Workstation TWR / Z2 TWR G4,
  i9-9900k 3.6 GHz / 16 GB RAM (monitor, keyboard, mouse) — 2 units

• Multispectral Camera Micasense RedEdge-MX — 1 unit

• Multispectral Camera Micasense Altum — 1 unit

• Jetson Nano microcomputers — 2 units

• Server units (RAM 128 GB, VRAM 24/48 GB, HDD 10 TB) — 2 units

Major Research Projects

The laboratory participates in several key national and international projects:

• AP08856412 – Development of intelligent models for data processing and flight planning in precision agriculture using UAVs (2020–2022)

• AP14869972 – Computer vision and machine learning methods for precision agriculture (2022–2024)

• BR21881908 – Environmental support system for an urban agglomeration (2023–2025)

Additional research activities include:

• soil salinity mapping in South Kazakhstan;

• evaluation of healthcare institutions;

• assessment of hydraulic structures;

• analysis of water flow and water quality;

• and other studies within projects BR18574144, AP14869110, BR10965172.

Scientific Output (Publications)

1. 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. – Vol. 15. – P. 5544. – DOI: 10.3390/rs15235544.

2. 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. – Vol. 11. – P. 4687. – DOI: 10.3390/math11224687.

3.  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. – Vol. 13. – P. 10958. – DOI: 10.3390/app131910958.

4.  Mukhamediev R. I., Merembayev T., Kuchin Y., Malakhov D., Zaitseva E., Levashenko V., Popova Y., Symagulov A., Sagatdinova G., Amirgaliyev Y. Soil Salinity Estimation for South Kazakhstan Based on SAR Sentinel-1 and Landsat-8, 9 OLI Data with Machine Learning Models // Remote Sensing. – 2023. – Vol. 15. – P. 4269. – DOI: 10.3390/rs15174269.

5. Zaitseva E., Levashenko V., Mukhamediev R., Brinzei N., Kovalenko A., Symagulov A. Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis // Mathematics. – 2023. – Vol. 11. – P. 2551. – DOI: 10.3390/math11112551.

6.  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. – Vol. 7. – P. 357. – DOI: 10.3390/drones7060357.

7.  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. – P. 335–357. – DOI: 10.1007/978-3-031-24963-1_19.

8.  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. – Vol. 11. – P. 5789–5803. – DOI: 10.1109/ACCESS.2023.3235207.

9.  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. – Vol. 13. – P. 434. – DOI: 10.3390/info13090434.

10.  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. – Vol. 10. – P. 2552. – DOI: 10.3390/math10152552.

11.  Mukhamediev R. I., Kuchin Y. et al. Estimation of Filtration Properties of Host Rocks in Sandstone-type Uranium Deposits Using Machine Learning Methods // IEEE Access. – 2022. – Vol. 10. – P. 18855–18872. – DOI: 10.1109/ACCESS.2022.3149625.

Contacts

Head of Laboratory, Professor
R. I. Mukhamediev
E-mail: ravil.muhamedyev@gmail.com

Lead Software Engineer
Adilkhan Symagulov
E-mail: asmogulove00@gmail.com