受强制性开放获取政策约束的文章 - Mbega Ramadhani Ngata了解详情
可在其他位置公开访问的文章：4 篇
Application of machine learning in the prediction of compressive, and shear bond strengths from the experimental data in oil well cement at 80 C. Ensemble trees boosting approach
EE Nyakilla, G Jun, NA Kasimu, EF Robert, N Innocent, T Mohamedy, ...
Construction and Building Materials 317, 125778, 2022
强制性开放获取政策： 国家自然科学基金委员会
Application of Group Method of Data Handling via a Modified Levenberg-Marquardt Algorithm in the Prediction of Compressive Strength of Oilwell Cement with Reinforced Fly Ash …
EE Nyakilla, G Jun, G Charles, EX Ricky, W Hussain, SM Iqbal, ...
SPE Drilling & Completion, 1-17, 2023
强制性开放获取政策： 国家自然科学基金委员会
Application of Novel Machine Learning in the Prediction of Compressive Strength of Oil Well Cement with Reinforced Nanomaterial
EE Nyakilla, J Gu, G Charles, W Hussain, SM Iqbal, DC Kalibwami, ...
强制性开放获取政策： 国家自然科学基金委员会
Identification of the correlation between land subsidence and groundwater level in Cangzhou, North China Plain, based on time-series PS-InSAR and machine-learning approaches
MB Nafouanti, J Li, H Li, MR Ngata, D Sun, Y Huang, C Zhou, L Wang, ...
Hydrogeology Journal, 1-16, 2024
强制性开放获取政策： 国家自然科学基金委员会
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