Results 11 to 20 of about 2,430,587 (138)

An interpretable probabilistic prediction algorithm for shield movement performance

Frontiers in Earth Science
Total thrust and torque are two key indicators of shield movement performance. Most existing data-driven machine learning studies focus on developing more accurate models for predicting total thrust and torque but overlook the interpretability of the ...
Yapeng Zhang, Yapeng Zhang, Long Liu, Long Liu, Jian Wu, Jian Wu, Shaoxiang Zeng, Shaoxiang Zeng, Jianquan Hu, Yuanqin Tao, Yuanqin Tao, Yong Huang, Yong Huang, Xuetao Zhou, Xuetao Zhou, Xu Liang   +15 more
doaj   +2 more sources

Natural Gradient Boosting for Probabilistic Prediction of Soaked CBR Values Using an Explainable Artificial Intelligence Approach [PDF]

Buildings
The California bearing ratio (CBR) value of subgrade is the most used parameter for dimensioning flexible and rigid pavements. The test for determining the CBR value is typically conducted under soaked conditions and is costly, labour-intensive, and time-
Esteban Díaz, Giovanni Spagnoli
doaj   +2 more sources

Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis

Engineering Applications of Computational Fluid Mechanics
In recent decades, securing drinkable water sources has become a pressing concern for populations in various regions worldwide. Therefore, to address the growing need for potable water, contemporary water purification technologies can be employed to ...
Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, Sungwon Kim, Kaywan Othman Ahmed, Salim Heddam   +5 more
doaj   +2 more sources

Uncertainty aware and explainable construction cost prediction using a hybrid probabilistic learning model. [PDF]

Sci Rep
This paper presents a unified probabilistic framework for construction cost forecasting, NGBoost-ETR (Natural Gradient Boosting with Extra Trees base learners) that delivers predictive accuracy, calibrated uncertainty, and SHAP-based interpretability ...
Chen L, Khalid OW, Tiang JJ, Tiang SS, Shin DY, Choo YH, Sharma A, Lim WH.   +7 more
europepmc   +2 more sources

From Composition to Ionic Conductivity: Machine Learning-Guided Discovery and Experimental Validation of Argyrodite-Type Lithium-Ion Electrolytes. [PDF]

Small
A composition‐driven machine learning framework, Elements‐To‐Ionics (E2I), is developed to predict the ionic conductivity of argyrodite‐type Li6PS5I solely from elemental features. Guided by E2I, the targeted synthesis of multi‐element substitutions yielded high‐conductivity (>10−2 S cm−1) sulfide solid electrolytes after hot‐pressing.
Kong S, Yu Z, Matsui N, Kamiya M, Iwamizu Y, Tanaka Y, Kubota N, Nomoto K, Hori S, Hirayama M, Suzuki K, Kanno R.   +11 more
europepmc   +2 more sources

Prediction of rockhead using a hybrid N-XGBoost machine learning framework

Journal of Rock Mechanics and Geotechnical Engineering, 2021
The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation. Although the conventional site investigation methods (i.e.
Xing Zhu, Jian Chu, Kangda Wang, Shifan Wu, Wei Yan, Kiefer Chiam   +5 more
doaj   +1 more source

Accurate and interpretable prediction of chemical oxygen demand using explainable boosting algorithms with SHAP analysis. [PDF]

Sci Rep
Accurate prediction of Chemical Oxygen Demand (COD) is vital for effective water quality management and pollution control. This study compares six ensemble boosting models, AdaBoost, CatBoost, XGBoost, LightGBM, HistGBRT, and NGBoost, for estimating COD ...
Merabet K, Kim S, Heddam S, Di Nunno F, Granata F, Kisi O, Adnan RM, Zounemat-Kermani M, Külls C.   +8 more
europepmc   +2 more sources

Quantifying scattering characteristics of mangrove species from Optuna-based optimal machine learning classification using multi-scale feature selection and SAR image time series

International Journal of Applied Earth Observations and Geoinformation, 2023
Mangroves play a significant role in carbon sequestration and storage. Mapping mangrove species and monitoring their conditions have been a crucial issue for achieving sustainable development goals.
Bolin Fu, Yiyin Liang, Zhinan Lao, Xidong Sun, Sunzhe Li, Hongchang He, Weiwei Sun, Donglin Fan   +7 more
doaj   +1 more source

Exploring Key Genes of Glutathione Metabolism in Systemic Lupus Erythematosus Based on Mendelian Randomisation, Single-Cell RNA Sequencing and Multiple Machine Learning Approaches. [PDF]

IET Syst Biol
This study identifies key metabolites and genes associated with systemic lupus erythematosus (SLE) by integrating Mendelian randomisation, multi‐omics, machine learning, and SHAP. It reveals the critical involvement of the glutathione metabolic pathway and highlights LAP3 as a key gene in SLE development.
Wang K, Li X, Tang Y, Zhao L.
europepmc   +2 more sources

Probabilistic prediction of oceanographic velocities with multivariate gaussian natural gradient boosting [PDF]

, 2023
Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks.
Rick Lumpkin, Schuler, Alejandro, Sykulski, Adam M, Michael O’Malley, Lumpkin, Rick, Adam M. Sykulski, Alejandro Schuler, O'Malley, Michael   +7 more
core   +1 more source