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All issues Volume 69 (2024) ITM Web Conf., 69 (2024) 01008 References
Open Access
Issue
ITM Web Conf.
Volume 69, 2024
International Conference on Mobility, Artificial Intelligence and Health (MAIH2024)
Article Number 01008
Number of page(s) 7
Section Artificial Intelligence
DOI https://doi.org/10.1051/itmconf/20246901008
Published online 13 December 2024
  1. Essarioui, A., Ben-Amar, H., Khoulassa, S., Meziani, R., Amamou, A., & Mokrini, F., 2018, “Gestion du Bayoud du palmier dattier dans les oasis marocaines,” Rev. Mar. Sci. Agron. Vét., 6, no. 4, pp. 537–543. [Google Scholar]
  2. El Hilali Alaoui Y., Bouda S., Amar H.B., Bouhlali E.D.T., Khoulassa S., Boutagayout A., et al. Enhancing Date Palm (Phoenix dactylifera L.) resilience: Strategies against Bayoud spread in Moroccan oases. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing; 2024. p. 012001. [CrossRef] [Google Scholar]
  3. Sedra, M. H., 2000, “Biological and genetic characteristics of bayoud resistant Moroccan date palm cultivars and strains,” Agric Water, 20, pp. 5566. [Google Scholar]
  4. El Bouhssini, M., & Faleiro, J. R., 2018, Date Palm Pests and Diseases: Integrated Management Guide, Beirut, Lebanon: International Center for Agricultural Research in the Dry Areas (ICARDA). [Google Scholar]
  5. Essarioui, A., Meziani, R., & Mokrini, F., 2020, “Resource competitive interactions as mechanism of date palm Bayoud disease suppression,” Moroccan Journal of Agricultural Sciences, 1, no. 2. (Numéro de page manquant). [Google Scholar]
  6. LeBlanc, N., Essarioui, A., Kinkel, L., & Kistler, H. C., 2017, “Phylogeny, plant species, and plant diversity influence carbon use phenotypes among Fusarium populations in the rhizosphere microbiome,” Phytobiomes, 1, no. 3, pp. 150–157. https://doi.org/10.1094/PBIOMES-06-17-0028-R. [CrossRef] [Google Scholar]
  7. Muhammad, Ali P. J., 2022, “Investigating the Impact of Min-Max Data Normalization on the Regression Performance of K-Nearest Neighbor with Different Similarity Measurements,” ARO-The Scientific Journal of Koya University, 10, no. 1, pp. 85–91. https://doi.org/10.14500/aro.10955. [CrossRef] [Google Scholar]
  8. Kumar, S., 2024, “Logistic Regression,” Python for Accounting and Finance, Cham: Palgrave Macmillan, pp. 99–115. https://doi.org/10.1007/978-3-031-54680-8_19. [Google Scholar]
  9. Acito, F., 2023, “K Nearest Neighbors,” Predictive Analytics with KNIME, Cham: Springer, pp. 189–204. https://doi.org/10.1007/978-3-031-45630-5_10. [Google Scholar]
  10. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J., 2017, Classification and Regression Trees, Taylor & Francis. (Ville de publication manquante). [CrossRef] [Google Scholar]
  11. Jauk, S., Kramer, D., Sumerauer, S., Veeranki, S. P. K., Schrempf, M., & Puchwein, P., 2024, “Machine learning-based delirium prediction in surgical inpatients: a prospective validation study,” JAMIA Open, 7, no. 3, 00ae091. https://doi.org/10.1093/jamiaopen/ooae091. [CrossRef] [Google Scholar]
  12. Wang, P., Chen, S., & Yang, S., 2022, “Recent Advances on Penalized Regression Models for Biological Data,” Mathematics, 10, no. 19, p. 3695. https://doi.org/10.3390/math10193695. [CrossRef] [Google Scholar]
  13. Uddin, S., Haque, I., Lu, H., & Moni, M. A., 2022, “Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction,” Scientific Reports, 12, no. 1, pp. 1–12. (DOI manquant). [CrossRef] [PubMed] [Google Scholar]
  14. Guidotti, R., Monreale, A., Setzu, M., & Volpi, G., 2024, “Generative Model for Decision Trees,” Proceedings of the AAAI Conference on Artificial Intelligence, 38, no. 19, pp. 21116–21124. (DOI manquant). [CrossRef] [MathSciNet] [Google Scholar]
  15. Han, J., Kamber, M., & Pei, J., 2011, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, ISBN: 978-0-12-381479-1. (Ville de publication manquante). [Google Scholar]
  16. Elbouazaoui, A., Douira, A., Ilyass, M., & Ahmed, S., 2022, “Integrating Sowing Date with Chickpea Genotypes in Managing Fusarium Wilt in Morocco,” Agriculture, 12, no. 6, p. 773. https://doi.org/10.3390/agriculture12060773. [CrossRef] [Google Scholar]
  17. Segura-Mena, R. A., Stoorvogel, J. J., García-Bastidas, F., et al., 2021, “Evaluating the Potential of Soil Management to Reduce the Effect of Fusarium oxysporum f. sp. cubense in Banana (Musa AAA),” European Journal of Plant Pathology, 160, no. 3, pp. 441–455. https://doi.org/10.1007/s10658-021-02255-2. [CrossRef] [Google Scholar]
  18. Lal, D., Dev, D., & Kumari, S., 2024, “Fusarium wilt pandemic: current understanding and molecular perspectives,” Funct Integr Genomics, 24, p. 41. https://doi.org/10.1007/s10142-024-01319-w. [CrossRef] [Google Scholar]

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