INTERPRETABILITY

作品数：94被引量：136H指数：6
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相关领域：自动化与计算机技术更多>>
相关作者：邢宗义贾利民胡维礼更多>>
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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Advanced Machine Learning and Gene Expression Programming Techniques for Predicting CO_(2)-Induced Alterations in Coal Strength
《Computer Modeling in Engineering & Sciences》2025年第4期153-183,共31页Zijian Liu Yong Shi ChuanqiLi Xiliang Zhang Jian Zhou Manoj Khandelwal 
partially supported by the National Natural Science Foundation of China(42177164,52474121);the Outstanding Youth Project of Hunan Provincial Department of Education(23B0008).
Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its im...
关键词：CO_(2)-induced coal strength meta-heuristic optimization algorithms XGBoost gene expression programming model interpretability 
Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading
《Informatics and Health》2025年第1期1-8,共8页Teuku Rizky Noviandy Ghalieb Mutig Idroes Irsan Hardi 
Background:Glioma grading plays a pivotal role in neuro-oncology,directly influencing treatment strategies and patient prognoses.Despite its importance,traditional histopathological analysis has drawbacks,spurring int...
关键词：LightGBM SHAP XAI Model Interpretability Machine Learning Classification 
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 
Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography
《Light(Science & Applications)》2025年第2期490-508,共19页Shiyi Cheng Shuaibin Chang Yunzhe Li Anna Novoseltseva Sunni Lin Yicun Wu Jiahui Zhu Ann C.McKee Douglas L.Rosene Hui Wang Irving J.Bigio David A.Bas Lei Tan 
funding support from:National Institutes of Health grant R01 EY032163(S.C.,L.T);National Institutes of Health grant U54 NS115266(A.C.M.);National Institutes of Health grant P30 AG072978(A.C.M.);National Institutes of Health grant U19 AG068753(A.C.M.);National Institutes of Health grant R01 NS125307(D.L.R.);National Institutes of Health grant RO1 AG075727(D.L.R.and IJ.B);National Institutes of Health grant RF1 AG062831(D.L.R.);Boston University Kilachand Fund Award(I.J.B.,D.L.R.,D.A.B.,and L.T.);National Institutes of Health grant R00 EB023993(HW);National Institutes of Health grant R01 NS128843(H.W.);National Institutes of Health grant U01 MH117023(S.C.,A.N.,D.A.B.)。
A major challenge in neuroscience is visualizing the structure of the human brain at different scales.Traditional histology reveals micro-and meso-scale brain features but suffers from staining variability,tissue dama...
关键词：NEUROSCIENCE machine learning optical coherence tomography digital staining d imaging histological interpretability visualizing structure d reconstructionsthe 
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 
Long-term urban traffic flow forecasting based on feature fusion and S-T transformer
《The Journal of China Universities of Posts and Telecommunications》2025年第1期61-73,共13页Zhang Xijun Cui Yong Zhang Hong Xia Ziyao 
supported by the National Natural Science Foundation of China (62162040);the Gansu Province Higher Education Innovation Fund-Funded Project (2021A-028);the Gansu Province Science and Technology Program Funding Project (21ZD4GA028);the Gansu Provincial Science and Technology Plan Funding Key Project of Natural Science Foundation of China (22JR5RA226)。
As a fundamental component of intelligent transportation systems, existing urban traffic flow forecasting models tend to overlook the spatio-temporal and long-term time-dependent patterns that characterize transportat...
关键词：traffic flow forecasting multi-feature fusion TRANSFORMER INTERPRETABILITY 
Interpreting machine learning models based on SHAP values in predicting suspended sediment concentration
《International Journal of Sediment Research》2025年第1期91-107,共17页Houda Lamane Latifa Mouhir Rachid Moussadek Bouamar Baghdad Ozgur Kisi Ali El Bilali 
Machine learning(ML)has become a powerful tool for predicting suspended sediment concentration(SSC).Nonetheless,the ability to interpret the physical process is considered the main issue in applying most of ML approac...
关键词：INTERPRETABILITY Machine learning(ML) Shapley values Suspended sediment concentration(SSC) Soil erosion Bouregreg watershed(BW) 
Knowledge Driven Machine Learning Towards Interpretable Intelligent Prognostics and Health Management:Review and Case Study
《Chinese Journal of Mechanical Engineering》2025年第1期31-61,共31页Ruqiang Yan Zheng Zhou Zuogang Shang Zhiying Wang Chenye Hu Yasong Li Yuangui Yang Xuefeng Chen Robert X.Gao 
Supported in part by Science Center for Gas Turbine Project(Project No.P2022-DC-I-003-001);National Natural Science Foundation of China(Grant No.52275130).
Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpret...
关键词：PHM Knowledge driven machine learning Signal processing Physics informed INTERPRETABILITY 
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 
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