Results 241 to 250 of about 25,341,143 (317)

Uncovering Key Sources of Regional Ozone Simulation Biases Using Machine Learning and SHAP Analysis.

Environmental Pollution
Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and efficiently identifying key sources of simulation biases is crucial for model improvement.
Xin Yuan, Xinlong Hong, Zhijiong Huang, Li Sheng, Jinlong Zhang, Duohong Chen, Z. Zhong, Boguang Wang, Junyu Zheng   +8 more
semanticscholar   +1 more source

Security Enhancement in AAV Swarms: A Case Study Using Federated Learning and SHAP Analysis

IEEE Open Journal of Intelligent Transportation Systems
As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication,
Sushmitha Halli Sudhakara, Lida Haghnegahdar   +1 more
semanticscholar   +1 more source

Critical role of vegetation and human activity indicators in the prediction of shallow groundwater quality distribution in Jianghan Plain with LightGBM algorithm and SHAP analysis.

Chemosphere
Groundwater serves as an indispensable resource for freshwater, but its quality has experienced a notable decline over recent decades. Spatial prediction of groundwater quality (GWQ) can effectively assist managers in groundwater remediation, management,
Hanxiang Xiong, Jinghan Wang, Chi Yang, Shuyi Li, Xiaobo Li, Ruihan Xiong, Yuzhou Wang, Chuanming Ma   +7 more
semanticscholar   +1 more source

SHAP analysis and comparative performance of the HEART, HET, and SVEAT scores in 30-day MACE prediction.

American Journal of Emergency Medicine
BACKGROUND This study aimed to compare the predictive performance of the HEART, HET, and SVEAT scores for 30-day major adverse cardiovascular events (MACE) in patients presenting with acute chest pain in the emergency department (ED). METHODS The HEART,
Ali Sarıdaş, Ö. F. Aydın
semanticscholar   +1 more source

Landslide Susceptibility Zoning: Integrating Multiple Intelligent Models with SHAP Analysis

Journal of Science and Transport Technology
In this study, we aim to delineate landslide susceptibility zones within Dien Bien province, Vietnam, leveraging the capabilities of various machine learning models including Light Gradient Boosting Machine (LGBM), K-Nearest Neighbors (KNN), and Gradient Boosting (GB).
null Indra Prakash, null Dam Duc Nguyen, null Nguyen Thanh Tuan, null Tran Van Phong, null Le Van Hiep   +4 more
openaire   +1 more source

SDGs India Index Analysis using SHAP

2022 International Electronics Symposium (IES), 2022
Takako Hashimoto, Yukari Shirota, Basabi Chakraborty   +2 more
openaire   +1 more source

Advancing Cervical Cancer Risk Prediction through Multi-Target Classification, SHAP Analysis, and Feature Reduction

2025 7th International Conference on Signal Processing, Computing and Control (ISPCC)
This study presents a multi-target machine learning approach using Random Forest classifiers to predict cervical cancer risk factors based on the UCI Cervical Cancer Risk Factors dataset.
M.A.Archana, R.Poorvadevi, A.V.Sriharsha, S. Umamaheswari, S. S. Saravana Kumar, T. Kumar   +5 more
semanticscholar   +1 more source

Prediction of maize crude fat content based on improved conditional mutual information maximization and SHAP analysis.

Food Chemistry
Traditional conditional mutual information maximization (CMIM) algorithms struggled to capture nonlinear dependencies in continuous near-infrared (NIR) spectral analysis.
Haichao Zhou, Xiaodan Ma, Haiou Guan, Jiao Yang, Bingxue Wei, Yifei Zhang, Yuxin Lu   +6 more
semanticscholar   +1 more source

Occupational accident prediction modeling and analysis using SHAP

Journal of Digital Contents Society, 2021
Hyung-Rok Oh, Ae-Lin Son, ZoonKy Lee
openaire   +1 more source

Preoperative Prediction of STAS Risk in Primary Lung Adenocarcinoma Using Machine Learning: An Interpretable Model with SHAP Analysis.

Academic Radiology
BACKGROUND Accurate preoperative prediction of spread through air spaces (STAS) in primary lung adenocarcinoma (LUAD) is critical for optimizing surgical strategies and improving patient outcomes.
Ping Wang, Jianing Cui, Haoyuan Du, Zhanhua Qian, Huili Zhan, Heng Zhang, Wei Ye, Wei Meng, Rongjie Bai   +8 more
semanticscholar   +1 more source