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All issues Volume 367 (2026) EPJ Web Conf., 367 (2026) 03006 References
Open Access
Issue
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
Article Number 03006
Number of page(s) 12
Section Smart and Sustainable Systems
DOI https://doi.org/10.1051/epjconf/202636703006
Published online 29 April 2026
  1. Akkem, Y., Biswas, S. K., & Varanasi, A. (2024). A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Engineering Applications of Artificial Intelligence, 131, 107881. [Google Scholar]
  2. Anuradha, D., Kuchipudi, R., Ashreetha, B., Naga Ramesh, J. V., & Rami, A. (2024). Enhancing agricultural yield forecasting with deep convolutional generative adversarial networks and satellite data. International Journal of Advanced Computer Science and Applications, 15(2), 661–674. [Google Scholar]
  3. Ashok, T., & Varma, P. S. (2021). Crop prediction based on environmental factors using machine learning ensemble algorithms. In Intelligent Computing and Innovation on Data Science: Proceedings of ICTIDS 2019, (pp. 581–594). Springer Singapore. [Google Scholar]
  4. Hasan, M., Marjan, M. A., Uddin, M. P., Afjal, M. I., Kardy, S., Ma, S., & Nam, Y. (2023). Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation. Frontiers in Plant Science, 14, 1234–1240. [Google Scholar]
  5. Jain, N., Kumar, A., Garud, S., Pradhan, V., & Kulkarni, P. (2017). Crop selection method based on various environmental factors using machine learning. International Research Journal of Engineering and Technology (IRJET), 4(2), 551–558. [Google Scholar]
  6. Ujjainia, S., Gautam, P., & Veenadhari, S. (2021). A crop recommendation system to improve crop productivity using ensemble technique. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 10(4), 102–105. [Google Scholar]
  7. Venkatesh, R., Balasubramanian, C., & Kaliappan, M. (2021). Rainfall prediction using generative adversarial networks with convolution neural network. Soft Computing, 25(6), 4725–4738. [Google Scholar]
  8. Khaki, S.; Wang, L. Crop Yield Prediction Using Deep Neural Networks. Front. Plant Sci. 2019, 10, 00621, 1–10. [Google Scholar]
  9. Preeja Pradeep, Marta Caro-Martínez,Anjana Wijekoon,A practical exploration of the convergence of Case-Based Reasoning and Explainable Artificial Intelligence 2024. https://doi.org/10.1016/j.eswa.2024.124733 [Google Scholar]
  10. Baishya, M., & Dutta, L. (2025). Tiny ML based crop recommendation system for precision agriculture 5.0. Smart Agricultural Technology, 101247. [Google Scholar]
  11. Bandara, P.; Weerasooriya, T.; Ruchirawya, T.H.; Nanayakkara, W.J.M.; Dimantha, M.A.C.; Pabasara, M.G.P. Crop Recommendation System: A Study on Agricultural Decision-Making. Int. J. Comput. Appl. 2020, 175(22), 0975–8887. [Google Scholar]
  12. Jahankhani, H., Bowen, G., Herzog, N. J., & Herzog, D. J. (Eds.). (2025). Symbiotic Intelligence: Advancing Forecasting Through Human-AI Collaboration. CRC Press. [Google Scholar]
  13. Airlangga, G., Nugroho, O. I. A., & Lim, B. H. P. (2025). A Comparative Study of Ensemble Learning and Neural Networks for the Heart Disease Prediction. Sinkron: jurnal dan penelitian teknik informatika, 9(1), 493–501. [Google Scholar]
  14. Shams, M. Y., Gamel, S. A., & Talaat, F. M. (2024). Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision- making. Neural Computing and Applications, 36(11), 5695–5714. [Google Scholar]
  15. Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., Borodulin, A., & Tynchenko, [Google Scholar]
  16. Y. (2024). Predicting sustainable crop yields: Deep learning and explainable AI tools. Sustainability, 16(21), 9437 [Google Scholar]
  17. K.V.Sambasivarao, Anasuya, Devi Bhima Sesha Roopa Roopa Devi Bhima,[ 2026] “Artificial Intelligence, Computational Intelligence and Inclusive Technologies”. https://doi.org/10.1201/9781003740100 [Google Scholar]
  18. Mangena Venu Madhavan, Aditya Khamparia, Deepak Gupta, Sagar Pande, Prayag Tiwari, M. Shamim Hossain.[ 9 June 2021]. ”Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning”. https://doi.org/10.1007/s00521-021-06171-8. [Google Scholar]
  19. S. Manoharan, Alexandru Tugui, Zubair Baig [12 June 2024] “Artificial Intelligence and Smart Energy”, https://doi.org/10.1007/978-3-031-61471-2. [Google Scholar]
  20. Rashmi Gera, Anupriya Jain,” Harnessing IoT and machine learning for sustainable agriculture: Predictive crop yield modeling in smart farming”. doi: 10.32629/jai.v7i4.1108. [Google Scholar]
  21. Cumming, G.S. (2002), COMPARING CLIMATE AND VEGETATION AS LIMITING FACTORS FOR SPECIES RANGES OF AFRICAN TICKS.Ecology, 83: 255–268. https://doi.org/10.1890/0012-9658 (2002)083[0255: CCAVAL]2.0.CO;2. [Google Scholar]
  22. Yao, K., Wang, Y., Fan, S., Fu, J., Wan, J., & Cao, Y. (2023). Improved and accurate fault diagnostic model for gas turbine based on 2D-wavelet transform and generative adversarial network. Measurement Science and Technology, 34(7), 075104 [Google Scholar]
  23. Balasubramaniam S, Seifedine Kadry,” Advances in Deep Generative Models for Healthcare and Medical Applications”[9 December 2025]. https://doi.org/10.1201/9781003602088. [Google Scholar]
  24. JoãoP.F.Pimentel, RaquelM.QuiguaOrozco, Samilla Beatrizde Rezende, LucasLima, MarlonH.Cardoso.[ March 12, 2026]”Harnessing AI for Antimicrobial Peptide Innovation against Multidrug Resistance” 111.93.109.98 (UTC) JACSAu2026,6,678−690. [Google Scholar]
  25. Yaganteeswarudu, Biswas Saroj Kumar, Varanasi Aruna, [ 19 January 2024] "A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network". https://doi.org/10.1016/j.engappai.2024.107881 [Google Scholar]

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