INTERPRETABILITY

作品数：88被引量：135H指数：6
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相关机构：北京交通大学南京理工大学华中师范大学更多>>
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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction
《Computers, Materials & Continua》2025年第3期5057-5078,共22页Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb 
funded by Researchers Supporting Project Number(RSPD2025R947),King Saud University,Riyadh,Saudi Arabia.
Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular puls...
关键词：Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) INTERPRETABILITY LIME machine learning(ML) SHAP 
TG-Net:A Physically Interpretable Deep Learning Forecasting Model for Thunderstorm Gusts
《Journal of Meteorological Research》2025年第1期59-78,共20页Yunqing LIU Lu YANG Mingxuan CHEN Jianwei SI Maoyu WANG Wenyuan LI Jingfeng XU 
Supported by the National Key Research and Development Program of China(2022YFC3004103);Beijing Natural Science Foundation(8222051);China Meteorological Administration Key Innovation Team(CMA2022ZD04 and CMA2022ZD07);Nanjing Joint Institute for Atmospheric Sciences Beijige Open Research Fund(BJG202407).
Thunderstorm gusts are a common and hazardous type of severe convective weather,characterized by a small spatial scale,short duration,and significant destructive power.They often lead to severe disasters,highlighting ...
关键词：thunderstorm gusts deep learning INTERPRETABILITY multisource data weather forecasting 
Investigating Black-Box Model for Wind Power Forecasting Using Local Interpretable Model-Agnostic Explanations Algorithm
《CSEE Journal of Power and Energy Systems》2025年第1期227-242,共16页Mao Yang Chuanyu Xu Yuying Bai Miaomiao Ma Xin Su 
supported by the National Key R&D Program of China(Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption)under Grant(2018YFB0904200).
Wind power forecasting(WPF)is important for safe,stable,and reliable integration of new energy technologies into power systems.Machine learning(ML)algorithms have recently attracted increasing attention in the field o...
关键词：Black-box model correlation analysis feature trust index local interpretability local interpretable modelagnostic explanations(LIME) wind power forecasting 
MMGCF: Generating Counterfactual Explanations for Molecular Property Prediction via Motif Rebuild
《Journal of Computer and Communications》2025年第1期152-168,共17页Xiuping Zhang Qun Liu Rui Han 
Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural ...
关键词：INTERPRETABILITY Causal Relationship Counterfactual Explanation Molecular Graph Generation 
Uncertainties in landslide susceptibility prediction modeling:A review on the incompleteness of landslide inventory and its influence rules被引量：1
《Geoscience Frontiers》2024年第6期80-104,共25页Faming Huang Daxiong Mao Shui-Hua Jiang Chuangbing Zhou Xuanmei Fan Ziqiang Zeng Filippo Catani Changshi Yu Zhilu Chang Jinsong Huang Bingchen Jiang Yijing Li 
the National Natural Science Foundation of China(Nos.42377164,41972280 and 42272326);National Natural Science Outstanding Youth Foundation of China(No.52222905);Natural Science Foundation of Jiangxi Province,China(No.20232BAB204091);Natural Science Foundation of Jiangxi Province,China(No.20232BAB204077).
Landslide inventory is an indispensable output variable of landslide susceptibility prediction(LSP)modelling.However,the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting e...
关键词：Landslide susceptibility prediction Landslide inventory Machine learning interpretability SHapley additive explanations Partial dependence plot 
Uncertainty Quantiffcation and Interpretability for Clinical Trial Approval Prediction
《Health Data Science》2024年第1期312-325,共14页Yingzhou Lu Tianyi Chen Nan Hao Capucine Van Rechem Jintai Chen Tianfan Fu 
supported by the Sontag Foundation(to C.V.R.),the American Cancer Society(to C.V.R.),and the Department of Defense Breast Cancer Research Program(to C.V.R.).
Background:Clinical trial is a crucial step in the development of a new therapy(e.g.,medication)and is remarkably expensive and time-consuming.Forecasting the approval of clinical trials accurately would enable us to ...
关键词：APPROVAL UNCERTAINTY thereby 
A deep learning-based global tropical cyclogenesis prediction model and its interpretability analysis
《Science China Earth Sciences》2024年第12期3671-3695,共25页Bin MU Xin WANG Shijin YUAN Yuxuan CHEN Guansong WANG Bo QIN Guanbo ZHOU 
supported by the National Natural Science Foundation of China(Grant Nos.U2142211,42075141&42341202);the National Key Research and Development Program of China(Grant No.2020YFA0608000);the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100);the Fundamental Research Funds for the Central Universities。
Tropical cloud clusters(TCCs)can potentially develop into tropical cyclones(TCs),leading to significant casualties and economic losses.Accurate prediction of tropical cyclogenesis(TCG)is crucial for early warnings.Mos...
关键词：Tropical cyclogenesis prediction Deep learning Feature fusion INTERPRETABILITY Causal inference 
A physics-informed machine learning solution for landslide susceptibility mapping based on three-dimensional slope stability evaluation
《Journal of Central South University》2024年第11期3838-3853,共16页WANG Yun-hao WANG Lu-qi ZHANG Wen-gang LIU Song-lin SUN Wei-xin HONG Li ZHU Zheng-wei 
Project(G2022165004L)supported by the High-end Foreign Expert Introduction Program,China;Project(2021XM3008)supported by the Special Foundation of Postdoctoral Support Program,Chongqing,China;Project(2018-ZL-01)supported by the Sichuan Transportation Science and Technology Project,China;Project(HZ2021001)supported by the Chongqing Municipal Education Commission,China。
Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection...
关键词：machine learning physics-informed model negative samples selection INTERPRETABILITY landslide susceptibility mapping 
Deep radio signal clustering with interpretability analysis based on saliency map
《Digital Communications and Networks》2024年第5期1448-1458,共11页Huaji Zhou Jing Bai Yiran Wang Junjie Ren Xiaoniu Yang Licheng Jiao 
supported in part by the National Natural Science Foundation of China(No.62276206);the Key Research and Development Program of Shaanxi under Grant S2022-YF-YBGY-0921;the State Key Program of National Natural Science of China(No.62231027);supported by the Science and Technology on Communication Information Security Control Laboratory;。
With the development of information technology,radio communication technology has made rapid progress.Many radio signals that have appeared in space are difficult to classify without manually labeling.Unsupervised rad...
关键词：Unsupervised radio signal clustering Autoencoder Clustering features visualization Deep learning interpretability 
Interpretability and spatial efficacy of a deep-learning-based on-site early warning framework using explainable artificial intelligence and geographically weighted random forests
《Geoscience Frontiers》2024年第5期182-196,共15页Jawad Fayaz Carmine Galasso 
Earthquakes pose significant risks globally,necessitating effective seismic risk mitigation strategies like earthquake early warning(EEW)systems.However,developing and optimizing such systems requires thoroughly under...
关键词：Earthquake early warning systems Spatial regression Neural networks Japanese subduction Explainable artificial intelligence 
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