This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging...This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.展开更多
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 ...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 and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets.展开更多
The methods of network attacks have become increasingly sophisticated,rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively.In recent years,artificial int...The methods of network attacks have become increasingly sophisticated,rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively.In recent years,artificial intelligence has achieved significant progress in the field of network security.However,many challenges and issues remain,particularly regarding the interpretability of deep learning and ensemble learning algorithms.To address the challenge of enhancing the interpretability of network attack prediction models,this paper proposes a method that combines Light Gradient Boosting Machine(LGBM)and SHapley Additive exPlanations(SHAP).LGBM is employed to model anomalous fluctuations in various network indicators,enabling the rapid and accurate identification and prediction of potential network attack types,thereby facilitating the implementation of timely defense measures,the model achieved an accuracy of 0.977,precision of 0.985,recall of 0.975,and an F1 score of 0.979,demonstrating better performance compared to other models in the domain of network attack prediction.SHAP is utilized to analyze the black-box decision-making process of the model,providing interpretability by quantifying the contribution of each feature to the prediction results and elucidating the relationships between features.The experimental results demonstrate that the network attack predictionmodel based on LGBM exhibits superior accuracy and outstanding predictive capabilities.Moreover,the SHAP-based interpretability analysis significantly improves the model’s transparency and interpretability.展开更多
Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges ...Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges related to trust and interpret ability in clinical applications.To address this issue,explainable artificial intelligence(XAI)techniques have been applied to medical image analysis.While showing promising potential,XAI also brings significant ethical risks in practice—most notably,the problem of spurious explanations.Such explanations may rise further concerns regarding patient privacy,data security,and the attribution of decisionmaking authority in medical contexts.This paper analyzes the application of XAI methods—particularly saliency aps—in medical image interpretation,identifies the underlying causes of spurious explanations,and proposes possible mitigation strategies.The aim is to contribute to the responsible and sustainable integration of explainable AI into clinical practice.展开更多
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00222536).
文摘This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.
文摘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 and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets.
基金supported by the National Natural Science Foundation of China Project(No.62302540)please visit their website at https://www.nsfc.gov.cn/(accessed on 18 June 2024).
文摘The methods of network attacks have become increasingly sophisticated,rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively.In recent years,artificial intelligence has achieved significant progress in the field of network security.However,many challenges and issues remain,particularly regarding the interpretability of deep learning and ensemble learning algorithms.To address the challenge of enhancing the interpretability of network attack prediction models,this paper proposes a method that combines Light Gradient Boosting Machine(LGBM)and SHapley Additive exPlanations(SHAP).LGBM is employed to model anomalous fluctuations in various network indicators,enabling the rapid and accurate identification and prediction of potential network attack types,thereby facilitating the implementation of timely defense measures,the model achieved an accuracy of 0.977,precision of 0.985,recall of 0.975,and an F1 score of 0.979,demonstrating better performance compared to other models in the domain of network attack prediction.SHAP is utilized to analyze the black-box decision-making process of the model,providing interpretability by quantifying the contribution of each feature to the prediction results and elucidating the relationships between features.The experimental results demonstrate that the network attack predictionmodel based on LGBM exhibits superior accuracy and outstanding predictive capabilities.Moreover,the SHAP-based interpretability analysis significantly improves the model’s transparency and interpretability.
文摘Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges related to trust and interpret ability in clinical applications.To address this issue,explainable artificial intelligence(XAI)techniques have been applied to medical image analysis.While showing promising potential,XAI also brings significant ethical risks in practice—most notably,the problem of spurious explanations.Such explanations may rise further concerns regarding patient privacy,data security,and the attribution of decisionmaking authority in medical contexts.This paper analyzes the application of XAI methods—particularly saliency aps—in medical image interpretation,identifies the underlying causes of spurious explanations,and proposes possible mitigation strategies.The aim is to contribute to the responsible and sustainable integration of explainable AI into clinical practice.
