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1. Strategic management of the enterprise digital potential: towards Industry 5.0: (Content №2 2026); ... M., & Demkah, M. (2019). Digital transformation from leveraging blockchain technology, artificial intelligence, machine learning and deep learning. Information Systems Design and Intelligent Applications: ...; Created on 25 April 2026
2. An integrated BIM–AI model for event-driven construction management: (Content №2 2026); ... P., Teodosio, B., Shringi, A., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, ...; Created on 25 April 2026
3. Methodology for assessing the condition of power plant units using digital twin models: (Content №2 2026); ... 103402. https://doi.org/10.1016/j.net.2024.103402 5. Daniell, J., Kobayashi, K., Alajo, A., & Alam, S. B. (2025). Digital twin-centered hybrid data-driven multi-stage deep learning framework for ...; Created on 25 April 2026
4. GIS-based assessment of fire impact on the landscapes of the Kherson Region: (Content №1 2026); ... X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep Learning in Remote Sensing Applications: A Meta‑analysis and Review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166-177. https://doi.org/10.1016/j.isprsjprs.2019.04.015 ...; Created on 27 February 2026
5. Substantiation of self-organization approaches in information networks to strengthen cyber resilience: (Content №1 2026); ... Security with AI: SVM-Based Deep Learning for Intrusion Detection. Sensors, 23(21), 8959. https://doi.org/10.3390/s23218959 29. Monteiro, V. (2024). The importance of entropy sources in cryptography: ...; Created on 27 February 2026
6. Neural network method for invariant recognition of vehicles in aerospace images: (Content №1 2026); ... UAV images using deep learning networks. Remote Sensing, 13, 4027. https://doi.org/10.3390/rs13204027 2. Liao, W., Chen, X., Yang, J. F., Roth, S., Goesele, M., Yang, M. Y., & Rosenhahn, B. (2020). ...; Created on 27 February 2026
7. Assessment of the impact of natural and anthropogenic factors on the air quality of urbanised areas: (Content №1 2026); ... X., Zhang, S., Chen, Y., He, L., Ren, Y., Zhang, Z., ..., & Zhang, S. (2024). Air quality forecasting using a spatiotemporal hybrid deep learning model based on VMD–GAT–BiLSTM. Scientific Reports, ...; Created on 27 February 2026
8. Mathematical modeling and analysis of heat transfer in structures with foreign elements: (Content №1 2026); ... material properties. Applied Mathematical Modelling, 92, 244-260. https://doi.org/10.1016/j.apm.2020.10.036 5. Peng, X., Li, X., Gong, Z., Zhao, X., & Yao, W. (2022). A deep learning method based ...; Created on 27 February 2026
9. Comprehensive analysis of aviation maintenance text reports using natural language processing methods: (Content №6 2025); ... Safety: Proposing a Deep Learning Approach. Journal of Advanced Transportation, 2021, 1-15. https://doi.org/10.1155/2021/5540046 22. Nanyonga, A., Joiner, K., Turhan, U., & Wild, G. (2025). ...; Created on 25 December 2025
10. Model-based designof a cone crusher adaptive control system: (Content №6 2025); ... 21. Sun, G., Huang, D., Cheng, L., Jia, J., Xiong, C., & Zhang, Y. (2022). Efficient and lightweight framework for real-time ore image segmentation based on deep learning. Minerals, 12(5), 5. ...; Created on 25 December 2025
11. Determination of rational technological conditions for the use of pump dredger suction heads: (Content №6 2025); ... prediction using deep learning for cutter suction dredger. Applied Ocean Research, 134, 103515. https://doi.org/10.1016/j.apor.2023.103515 24. Yang, J. B., Ni, F. S., & Wei, C. Y. (2015). A ...; Created on 25 December 2025
12. Empirical comparison of five deep learning architectures for GNSS time-series data forecasting: (Content №5 2025); ... Abstract: Purpose. This study aims to propose an effective deep learning model for analyzing time series of GNSS data. Methodology. Five deep learning models were investigated using the Adam optimization ...; Created on 25 October 2025
13. Quantum machine learning for fusion of multichannel optical satellite images: (Content №5 2025); ... 4. Kashtan, V., & Hnatushenko, V. (2023). Deep Learning Technology for Automatic Burned Area Extraction Using Satellite High Spatial Resolution Images. Lecture Notes in Computational Intelligence ...; Created on 25 October 2025
14. The array of GNSS for structure deformation monitoring: (Content №4 2025); ... Z., Li, Y., Liu, C., & Gao, J. (2019). On-line part deformation prediction based on deep learning. Journal of Intelligent Manufacturing, 31(3), 561-574. https://doi.org/10.1007/s10845-019-01465-0 18. Bui, ...; Created on 26 August 2025
15. Methodology of the acoustic experiment for studying the aerodynamic noise of wind turbine blade segments: (Content №4 2025); ... 14. Yang, H., Yuan, W., Zhu, W., Sun, Z., Zhang, Y., & Zhou, Y. (2024). Wind turbine airfoil noise prediction using dedicated airfoil database and deep learning technology, Applied Energy, 364, ...; Created on 26 August 2025
16. Intelligent technology for land cover monitoring due to amber mining on optical satellite images: (Content №3 2025); ... features, EfficientNet-Edge, deep learning, convolutional neural networks, optical satellite images References. 1. Clare, M., Piggott, M., & Cotter, C. (2021). Assessing erosion and flood risk ...; Created on 25 June 2025
17. Evaluation of vehicle stability parameters during wheel-obstacle impact scenarios: (Content №3 2025); ... 49. https://doi.org/10.3390/infrastructures8030049 7. Li, X., & Ren, M. (2025). Road adhesion coefficient Estimation: Physics-informed deep learning method with vehicle dynamics model. Expert Systems ...; Created on 25 June 2025
18. Modeling obstacle avoidance strategies in UAV groups: (Content №2 2025); ... 7. Koshekov, К. Т., Seidakhmetov, B. K., Savostin, А. А., Anayatova, R. K., & Fedorov, I. O. (2021). Aviation Profiling Method Based on Deep Learning Technology for Emotion Recognition by Speech ...; Created on 26 April 2025
19. Investigation of the stress-strain state of mine shaft support under long-term operation: (Content №2 2025); ... 17. OʼBrien, D., Osborne, J. A., Perez-Duenas, E., Cunningham, R., & Li, Z. (2023). Automated crack classification for the CERN underground tunnel infrastructure using deep learning. Tunnelling ...; Created on 26 April 2025
20. FoSDeT: a new hybrid machine learning model for accurate and fast detection of IoT botnet: (Content №1 2025); ... Y. (2023). IoT device identification based on network communication analysis using deep learning. Journal of Ambient Intelligence and Humanized Computing, 14(7), 9113-9129. https://doi.org/10.1007/s12652-022-04415-6. ...; Created on 25 February 2025

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