Machine Learning Approaches for Prediction of the Compressive Strength of Alkali Activated Termite Mound Soil
Publication Type
Journal Article
Publication Date (Issue Year)
2021
Journal Name
Applied Sciences
Abstract
Abstract
Keywords
Keywords
Rsif Scholar Name
Linda Bih Numfor
Thematic Area
Minerals, Mining and Materials Engineering
Africa Host University (AHU)
Nelson Mandela African Institution of Science and Technology (NM-AIST), Tanzania
Recommended Citation
Numfor, L. B. (2021). Machine Learning Approaches for Prediction of the Compressive Strength of Alkali Activated Termite Mound Soil. Applied Sciences, 11 (11), 4754. https://doi.org/10.3390/APP11114754
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