Mahamat, Assia Aboubakar; Boukar, Moussa Mahamat; Ibrahim, Nurudeen Mahmud; Stanislas, Tido Tiwa; Bih, Numfor Linda; Obianyo, Ifeyinwa Ijeoma; Savastano Júnior, Holmer 
Source: Applied Sciences. Unidade: FZEA
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, MATERIAIS DE CONSTRUÇÃO, SUSTENTABILIDADE
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MAHAMAT, Assia Aboubakar et al. Machine learning approaches for prediction of the compressive strength of alkali activated termite mound soil. Applied Sciences, v. 11, n. 11, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.3390/app11114754. Acesso em: 19 fev. 2026.APA
Mahamat, A. A., Boukar, M. M., Ibrahim, N. M., Stanislas, T. T., Bih, N. L., Obianyo, I. I., & Savastano Júnior, H. (2021). Machine learning approaches for prediction of the compressive strength of alkali activated termite mound soil. Applied Sciences, 11( 11), 1-13. doi:10.3390/app11114754NLM
Mahamat AA, Boukar MM, Ibrahim NM, Stanislas TT, Bih NL, Obianyo II, Savastano Júnior H. Machine learning approaches for prediction of the compressive strength of alkali activated termite mound soil [Internet]. Applied Sciences. 2021 ; 11( 11): 1-13.[citado 2026 fev. 19 ] Available from: https://doi.org/10.3390/app11114754Vancouver
Mahamat AA, Boukar MM, Ibrahim NM, Stanislas TT, Bih NL, Obianyo II, Savastano Júnior H. Machine learning approaches for prediction of the compressive strength of alkali activated termite mound soil [Internet]. Applied Sciences. 2021 ; 11( 11): 1-13.[citado 2026 fev. 19 ] Available from: https://doi.org/10.3390/app11114754
