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The Transparency Revolution in Geohazard Science:A Systematic Review and Research Roadmap for Explainable Artificial Intelligence 认领 引用
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作者 Moein Tosan Vahid Nourani +5 位作者 Ozgur Kisi Yongqiang Zhang Sameh A.Kantoush Mekonnen Gebremichael Ruhollah Taghizadeh-Mehrjardi Jinhui Jeanne Huang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期77-117,共41页
The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unatt... The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics. 展开更多
关键词 Explainable artificial intelligence(XAI) geohazard assessment machine learning SHAP trustworthy AI model interpretability
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Explainable artificial intelligence‑based parametric analysis of cotton fiber and yarn properties 认领 引用
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作者 SARKER Baneswar CHAKRABORTY Shankar 《Journal of Cotton Research》 CAS CSCD 2026年第2期220-239,共20页
Background Effective modeling and parametric studies strengthen analyses of the influence of cotton fiber properties on the yarn properties,which are important for developing a higher-quality product.Machine learning(... Background Effective modeling and parametric studies strengthen analyses of the influence of cotton fiber properties on the yarn properties,which are important for developing a higher-quality product.Machine learning(ML)-based predictive algorithms have been adopted for accurate modeling of fiber-yarn relationships.However,the complex black-box models lack interpretability and transparency,thereby limiting parametric studies.Therefore,an integrated ML-based black-box modeling and explainable artificial intelligence(XAI)-based analysis can facilitate accurate yet interpretable parametric analysis of yarn quality.Results In this study,three ML algorithm-based models,i.e.,random forest,support vector regression,and K-nearest neighbors,were developed based on an experimental cotton fiber dataset.Five cotton fiber properties were considered input variables for predicting yarn tenacity and unevenness.Based on their performances,the most appropriate model for each yarn property was selected.Shapley additive explanations(SHAP),an XAI technique,is applied to provide interpretability to the models’predictions and study the contributions of fiber properties.Short fiber content,with a mean absolute SHAP value of 0.359,was the most significant property,followed by fiber strength(0.242).Unevenness was influenced maximally by short fiber content,with a mean absolute SHAP value of 0.735.Other fiber properties exhibited comparatively weaker and nonlinear influences.Conclusions The study conducts an analysis of fiber-yarn relationships,aided by SHAP-based analysis.The parametric interpretations of the SHAP technique were validated using linear regression and parametric sweeping,strengthening the robustness of the deduced nature of relationships.The proposed framework provides a practical and interpretable decision-support tool for the analysis of the quality of yarn and the comprehension of its production through fiber processing. 展开更多
关键词 Cotton fiber Yarn property Explainable artificial intelligence Prediction Shapley additive explanations
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Alternative Lens to Understand the Relationships Between Neighborhood Environment and Well-being with Capability Approach and Explainable Artificial Intelligence 认领 引用 被引量：2
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作者 JIAO Linshen ZHANG Min +4 位作者 ZHEN Feng QIN Xiao CHEN Peipei ZHANG Shanqi HU Yuchen 《Chinese Geographical Science》 SCIE CSCD 2025年第3期472-491,共20页
The relationship between the neighborhood environment and well-being is attracting increasingly attention from researchers and policymakers,as the goal of development has shift from economy to well-being.However,exist... The relationship between the neighborhood environment and well-being is attracting increasingly attention from researchers and policymakers,as the goal of development has shift from economy to well-being.However,existing literature predominantly adopts the utilitarian approach,understanding well-being as people’s feelings about their lives and viewing the neighborhood environment as resources that benefit well-being.The Capability Approach,a novel approach that conceptualize well-being as the freedoms to do or to be and regard environment as conversion factors that influence well-being,can offer new lens by incorporating human development in-to these topics.This paper proposes an alternative theoretical framework:well-being is conceptualized and measured by capability;neighborhood environment affects well-being by providing spatial services,functioning as environmental conversion factors,and serving as social conversion factors.We conducted a case study of Changshu City located in eastern China,utilizing multiple resource data,applying explainable artificial intelligence(XAI),namely eXtreme Gradient Boosting(XGBoost)and SHapley Additive exPlana-tions(SHAP).Our findings highlight the significance of viewing the neighborhood environment as a set of conversion factors,as it provides more explanatory power than providing spatial services.Compared to conventional research based on linear relationship as-sumption,our results demonstrate that the effects of neighborhood environment on well-being are non-linear,characterized by threshold effects and interaction effects.These insights are crucial for informing urban planning and public policy.This research enriches our un-derstanding of well-being,neighborhood environment,and their relationship as well as provides empirical evidence for the core concept of conversion factors in the capability approach. 展开更多
关键词 well-being neighborhood environment capability approach non-linear relationship explainable artificial intelligence(XAI)
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Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification:State of the art,performance,and clinical implications 认领 引用 被引量：1
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作者 Sami Akbulut Cemil Colak 《World Journal of Hepatology》 2025年第11期11-25,共15页
Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes ev... Hepatocellular carcinoma(HCC)remains a leading cause of cancer-related mortality globally,necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy.This review synthesizes evidence on explainable ensemble learning approaches for HCC classification,emphasizing their integration with clinical workflows and multi-omics data.A systematic analysis[including datasets such as The Cancer Genome Atlas,Gene Expression Omnibus,and the Surveillance,Epidemiology,and End Results(SEER)datasets]revealed that explainable ensemble learning models achieve high diagnostic accuracy by combining clinical features,serum biomarkers such as alpha-fetoprotein,imaging features such as computed tomography and magnetic resonance imaging,and genomic data.For instance,SHapley Additive exPlanations(SHAP)-based random forests trained on NCBI GSE14520 microarray data(n=445)achieved 96.53%accuracy,while stacking ensembles applied to the SEER program data(n=1897)demonstrated an area under the receiver operating characteristic curve of 0.779 for mortality prediction.Despite promising results,challenges persist,including the computational costs of SHAP and local interpretable model-agnostic explanations analyses(e.g.,TreeSHAP requiring distributed computing for metabolomics datasets)and dataset biases(e.g.,SEER’s Western population dominance limiting generalizability).Future research must address inter-cohort heterogeneity,standardize explainability metrics,and prioritize lightweight surrogate models for resource-limited settings.This review presents the potential of explainable ensemble learning frameworks to bridge the gap between predictive accuracy and clinical interpretability,though rigorous validation in independent,multi-center cohorts is critical for real-world deployment. 展开更多
关键词 Hepatocellular carcinoma Artificial intelligence Explainable artificial intelligence Ensemble learning Explainable ensemble learning
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Extracellular vesicles as biomarkers for metabolic dysfunctionassociated steatotic liver disease staging using explainable artificial intelligence 认领 引用
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作者 Eleni Myrto Trifylli Athanasios Angelakis +9 位作者 Anastasios G Kriebardis Nikolaos Papadopoulos Sotirios P Fortis Vasiliki Pantazatou John Koskinas Hariklia Kranidioti Evangelos Koustas Panagiotis Sarantis Spilios Manolakopoulos Melanie Deutsch 《World Journal of Gastroenterology》 SCIE CAS 2025年第22期27-48,共22页
BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highli... BACKGROUND Metabolic dysfunction-associated steatotic liver disease(MASLD)is a leading cause of chronic liver disease globally.Current diagnostic methods,such as liver biopsies,are invasive and have limitations,highlighting the need for non-invasive alternatives.AIM To investigate extracellular vesicles(EVs)as potential biomarkers for diagnosing and staging steatosis in patients with MASLD using machine learning(ML)and explainable artificial intelligence(XAI).METHODS In this single-center observational study,798 patients with metabolic dysfunction were enrolled.Of these,194 met the eligibility criteria,and 76 successfully completed all study procedures.Transient elastography was used for steatosis and fibrosis staging,and circulating plasma EV characteristics were analyzed through nanoparticle tracking.Twenty ML models were developed:Six to differentiate non-steatosis(S0)from steatosis(S1-S3);and fourteen to identify severe steatosis(S3).Models utilized EV features(size and concentration),clinical(advanced fibrosis and presence of type 2 diabetes mellitus),and anthropomorphic(sex,age,height,weight,body mass index)data.Their performance was assessed using receiver operating characteristic(ROC)-area under the curve(AUC),specificity,and sensitivity,while correlation and XAI analysis were also conducted.RESULTS The CatBoost C1a model achieved an ROC-AUC of 0.71/0.86(trainest)on average across ten random five-fold cross-validations,using EV features alone to distinguish S0 from S1-S3.The CatBoost C2h-21 model achieved an ROC-AUC of 0.81/1.00(trainest)on average across ten random three-fold cross-validations,using engineered features including EVs,clinical features like diabetes and advanced fibrosis,and anthropomorphic data like body mass index and weight for identifying severe steatosis(S3).Key predictors included EV mean size and concentration.Correlation,XAI,and SHapley Additive exPlanations analysis revealed non-linear feature relationships with steatosis stages.CONCLUSION The EV-based ML models demonstrated that the mean size and concentration of circulating plasma EVs constituted key predictors for distinguishing the absence of significant steatosis(S0)in patients with metabolic dysfunction,while the combination of EV,clinical,and anthropomorphic features improved the diagnostic accuracy for the identification of severe steatosis.The algorithmic approach using ML and XAI captured non-linear patterns between disease features and provided interpretable MASLD staging insights.However,further large multicenter studies,comparisons,and validation with histopathology and advanced imaging methods are needed. 展开更多
关键词 Metabolic dysfunction-associated steatotic liver disease Extracellular vesicles Non-invasive biomarkers Machine learning Explainable artificial intelligence Transient elastography Metabolic dysfunction Hepatic steatosis
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Visual explainable artificial intelligence for graph‑based visual question answering and scene graph curation 认领 引用
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作者 Sebastian Künzel Tanja Munz‑Körner +4 位作者 Pascal Tilli Noel Schäfer Sandeep Vidyapu Ngoc Thang Vu Daniel Weiskopf 《Visual Computing for Industry,Biomedicine,and Art》 EI 2025年第1期133-161,共29页
This study presents a novel visualization approach to explainable artificial intelligence for graph-based visual question answering(VQA)systems.The method focuses on identifying false answer predictions by the model a... This study presents a novel visualization approach to explainable artificial intelligence for graph-based visual question answering(VQA)systems.The method focuses on identifying false answer predictions by the model and offers users the opportunity to directly correct mistakes in the input space,thus facilitating dataset curation.The decisionmaking process of the model is demonstrated by highlighting certain internal states of a graph neural network(GNN).The proposed system is built on top of a GraphVQA framework that implements various GNN-based models for VQA trained on the GQA dataset.The authors evaluated their tool through the demonstration of identified use cases,quantitative measures,and a user study conducted with experts from machine learning,visualization,and natural language processing domains.The authors’findings highlight the prominence of their implemented features in supporting the users with incorrect prediction identification and identifying the underlying issues.Additionally,their approach is easily extendable to similar models aiming at graph-based question answering. 展开更多
关键词 Visual question answering Explainable artificial intelligence Visual analytics Scene graphs
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Interpretability and spatial efficacy of a deep-learning-based on-site early warning framework using explainable artificial intelligence and geographically weighted random forests 认领 引用 被引量：2
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作者 Jawad Fayaz Carmine Galasso 《Geoscience Frontiers》 SCIE CAS CSCD 2024年第5期182-196,共15页
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... 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 understanding their internal procedures and coverage limitations.This study examines a deep-learning-based on-site EEW framework known as ROSERS(Real-time On-Site Estimation of Response Spectra)proposed by the authors,which constructs response spectra from early recorded ground motion waveforms at a target site.This study has three primary goals:(1)evaluating the effectiveness and applicability of ROSERS to subduction seismic sources;(2)providing a detailed interpretation of the trained deep neural network(DNN)and surrogate latent variables(LVs)implemented in ROSERS;and(3)analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW stations.ROSERS is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground motions.Goodness-of-fit testing shows that the ROSERS framework achieves good performance on this database,especially given the peculiarities of the subduction seismic environment.The trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect relationships.Finally,the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of similarity.The results indicate that on-site predictions can be considered reliable within a 2–9 km radius,varying based on the magnitude and distance from the earthquake source.This information can assist end-users in strategically placing sensors,minimizing blind spots,and reducing errors from regional extrapolation. 展开更多
关键词 Earthquake early warning systems Spatial regression Neural networks Japanese subduction Explainable artificial intelligence
Modeling and Predictive Analytics of Breast Cancer Using Ensemble Learning Techniques:An Explainable Artificial Intelligence Approach 认领 引用 被引量：1
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作者 Avi Deb Raha Fatema Jannat Dihan +8 位作者 Mrityunjoy Gain Saydul Akbar Murad Apurba Adhikary Md.Bipul Hossain Md.Mehedi Hassan Taher Al-Shehari Nasser A.Alsadhan Mohammed Kadrie Anupam Kumar Bairagi 《Computers, Materials & Continua》 SCIE EI 2024年第12期4033-4048,共16页
Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer type.This disease arises when cells in the breast undergo unregulate... Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer type.This disease arises when cells in the breast undergo unregulated proliferation,resulting in the formation of a tumor that has the capacity to invade surrounding tissues.It is not confined to a specific gender;both men and women can be diagnosed with breast cancer,although it is more frequently observed in women.Early detection is pivotal in mitigating its mortality rate.The key to curbing its mortality lies in early detection.However,it is crucial to explain the black-box machine learning algorithms in this field to gain the trust of medical professionals and patients.In this study,we experimented with various machine learning models to predict breast cancer using the Wisconsin Breast Cancer Dataset(WBCD)dataset.We applied Random Forest,XGBoost,Support Vector Machine(SVM),Multi-Layer Perceptron(MLP),and Gradient Boost classifiers,with the Random Forest model outperforming the others.A comparison analysis between the two methods was done after performing hyperparameter tuning on each method.The analysis showed that the random forest performs better and yields the highest result with 99.46%accuracy.After performance evaluation,two Explainable Artificial Intelligence(XAI)methods,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-Agnostic Explanations(LIME),have been utilized to explain the random forest machine learning model. 展开更多
关键词 Breast cancer prediction machine learning models explainable artificial intelligence random forest hyperparameter tuning
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Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis 认领 引用
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作者 Anwer Mustafa Hilal Imène ISSAOUI +5 位作者 Marwa Obayya Fahd N.Al-Wesabi Nadhem NEMRI Manar Ahmed Hamza Mesfer Al Duhayyim Abu Sarwar Zamani 《Computers, Materials & Continua》 SCIE EI 2022年第5期3853-3867,共15页
The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence(XAI),a process that explains how p... The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence(XAI),a process that explains how prediction is done in AI models.Biomedical mental disorder,i.e.,Autism Spectral Disorder(ASD)needs to be identified and classified at early stage itself in order to reduce health crisis.With this background,the current paper presents XAI-based ASD diagnosis(XAI-ASD)model to detect and classify ASD precisely.The proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization(BFO)-based Feature Selection(FS)technique.In addition,Whale Optimization Algorithm(WOA)with Deep Belief Network(DBN)model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of WOA.In order to ensure a better ASD diagnostic outcome,a series of simulation process was conducted on ASD dataset. 展开更多
关键词 Explainable artificial intelligence autism spectral disorder feature selection data classification machine learning metaheuristics
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Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification 认领 引用 被引量：3
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作者 Amit Singhal Krishna Kant Agrawal +3 位作者 Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期401-441,共41页
The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and ... The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment. 展开更多
关键词 Explainable artificial intelligence artificial intelligence XAI healthcare cancer image classification
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Explainable Artificial Intelligence Solution for Online Retail 认领 引用
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作者 Kumail Javaid Ayesha Siddiqa +5 位作者 Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan 《Computers, Materials & Continua》 SCIE EI 2022年第6期4425-4442,共18页
Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for them.In the case of the online shopping business,it’s very important to find ... Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for them.In the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business.In this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one year.The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the future.The permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the future.The performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC curve.RF model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree classifier.Hence,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying behaviour.Our research also reveals the importance of page value as a key feature for capturing online purchasing trends.This may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business. 展开更多
关键词 Explainable artificial intelligence online retail neural network random forest regression
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Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System 认领 引用
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作者 Reda Salama Mahmoud Ragab 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期2917-2932,共16页
In the Internet of Things(IoT)based system,the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems(... In the Internet of Things(IoT)based system,the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems(UCS).The UCS necessitates heterogeneity,management level,and data transmission for distributed users.Simultaneously,security remains a major issue in the IoT-driven UCS.Besides,energy-limited IoT devices need an effective clustering strategy for optimal energy utilization.The recent developments of explainable artificial intelligence(XAI)concepts can be employed to effectively design intrusion detection systems(IDS)for accomplishing security in UCS.In this view,this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System(BXAI-IDCUCS)model.The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment.The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization(EADSO)algorithm to accomplish this.Besides,deep neural network(DNN)is employed for detecting and classifying intrusions in the IoT network.Lastly,blockchain technology is exploited for secure inter-cluster data transmission processes.To ensure the productive performance of the BXAI-IDCUCS model,a comprehensive experimentation study is applied,and the outcomes are assessed under different aspects.The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%,a packet loss rate of 0.71%,a throughput of 92.95 Mbps,energy consumption of 0.0891 mJ,a lifetime of 3529 rounds,and accuracy of 99.38%. 展开更多
关键词 Blockchain internet of things ubiquitous computing explainable artificial intelligence clustering deep learning
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Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection 认领 引用
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作者 Abdullah M.Basahel Mohammad Yamin 《Computer Systems Science & Engineering》 SCIE EI 2023年第7期209-224,共16页
Recent advancements in the Internet of Things(Io),5G networks,and cloud computing(CC)have led to the development of Human-centric IoT(HIoT)applications that transform human physical monitoring based on machine monitor... Recent advancements in the Internet of Things(Io),5G networks,and cloud computing(CC)have led to the development of Human-centric IoT(HIoT)applications that transform human physical monitoring based on machine monitoring.The HIoT systems find use in several applications such as smart cities,healthcare,transportation,etc.Besides,the HIoT system and explainable artificial intelligence(XAI)tools can be deployed in the healthcare sector for effective decision-making.The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage.This article presents a new quantum-inspired differential evolution with explainable artificial intelligence based COVID-19 Detection and Classification(QIDEXAI-CDC)model for HIoT systems.The QIDEXAI-CDC model aims to identify the occurrence of COVID-19 using the XAI tools on HIoT systems.The QIDEXAI-CDC model primarily uses bilateral filtering(BF)as a preprocessing tool to eradicate the noise.In addition,RetinaNet is applied for the generation of useful feature vectors from radiological images.For COVID-19 detection and classification,quantum-inspired differential evolution(QIDE)with kernel extreme learning machine(KELM)model is utilized.The utilization of the QIDE algorithm helps to appropriately choose the weight and bias values of the KELM model.In order to report the enhanced COVID-19 detection outcomes of the QIDEXAI-CDC model,a wide range of simulations was carried out.Extensive comparative studies reported the supremacy of the QIDEXAI-CDC model over the recent approaches. 展开更多
关键词 Human-centric IoT quantum computing explainable artificial intelligence healthcare COVID-19 diagnosis
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X-SHIELD:Regularization for eXplainable Artificial Intelligence 认领 引用
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作者 Iván Sevillano-García Julián Luengo Francisco Herrera 《Machine Intelligence Research》 EI CSCD 2026年第3期528-544,共17页
As artificial intelligence systems become integral across domains,the demand for explainability,called eXplainable artificial intelligence(XAI),grows.Existing efforts have focused primarily on generating and evaluatin... As artificial intelligence systems become integral across domains,the demand for explainability,called eXplainable artificial intelligence(XAI),grows.Existing efforts have focused primarily on generating and evaluating explanations for black-box models,while a critical gap in directly enhancing models remains through these evaluations.It is important to consider the potential of this explanation process to improve model quality with feedback on training as well.XAI may be used to improve model performance while increasing model explainability.Under this view,this paper introduces Transformation-Selective hidden input evaluation for learning dynamics(T-SHIELD),a regularization family designed to improve model quality by hiding features of input,forcing the model to generalize without those features.Within this family,we propose XAI-SHIELD(X-SHIELD),a regularization for explainable artificial intelligence that uses explanations to select specific features to hide.In contrast to conventional approaches,XSHIELD regularization seamlessly integrates into the objective function,enhancing model explainability while also improving performance.Experimental validation on benchmark datasets underscores X-SHIELD′s effectiveness in improving performance and overall explainability.The improvement is validated through experiments comparing models with and without X-SHIELD regularization,with further analysis exploring the rationale behind its design choices.This establishes X-SHIELD regularization as a promising pathway for developing reliable artificial intelligence regularization. 展开更多
关键词 EXplainable artificial intelligence(XAI) deep learning XAI metrics explanation-based regularization RED-XAI
Explainable Artificial Intelligence(XAI)for Material Design and Engineering Applications:A Quantitative Computational Framework 认领 引用 被引量：3
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作者 Bokai Liu Pengju Liu +1 位作者 Weizhuo Lu Thomas Olofsson 《International Journal of Mechanical System Dynamics》 EI CSCD 2025年第2期236-265,共30页
The advancement of artificial intelligence(AI)in material design and engineering has led to significant improvements in predictive modeling of material properties.However,the lack of interpretability in machine learni... The advancement of artificial intelligence(AI)in material design and engineering has led to significant improvements in predictive modeling of material properties.However,the lack of interpretability in machine learning(ML)-based material informatics presents a major barrier to its practical adoption.This study proposes a novel quantitative computational framework that integrates ML models with explainable artificial intelligence(XAI)techniques to enhance both predictive accuracy and interpretability in material property prediction.The framework systematically incorporates a structured pipeline,including data processing,feature selection,model training,performance evaluation,explainability analysis,and real-world deployment.It is validated through a representative case study on the prediction of high-performance concrete(HPC)compressive strength,utilizing a comparative analysis of ML models such as Random Forest,XGBoost,Support Vector Regression(SVR),and Deep Neural Networks(DNNs).The results demonstrate that XGBoost achieves the highest predictive performance(R2=0.918),while SHAP(Shapley Additive Explanations)and LIME(Local Interpretable Model-Agnostic Explanations)provide detailed insights into feature importance and material interactions.Additionally,the deployment of the trained model as a cloud-based Flask-Gunicorn API enables real-time inference,ensuring its scalability and accessibility for industrial and research applications.The proposed framework addresses key limitations of existing ML approaches by integrating advanced explainability techniques,systematically handling nonlinear feature interactions,and providing a scalable deployment strategy.This study contributes to the development of interpretable and deployable AI-driven material informatics,bridging the gap between data-driven predictions and fundamental material science principles. 展开更多
关键词 explainable artificial intelligence(XAI) high-performance concrete material informatics predictive modeling science cloud deployment
Developing Explainable Artificial Intelligence Models for Space Science Applications 认领 引用 被引量：1
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作者 Ambuj Kumar Agarwal Rajat Bhardwaj +3 位作者 Gyanendra Tiwary Abeer A.Aljohani Ruchi Kawatra Abhijit Das 《Space(Science & Technology)》 EI CSCD 2025年第1期492-507,共16页
The integration of explainable artificial intelligence(XAI)in space science has ushered in a new era of transparency and reliability in AI-driven applications.This paper delves into the transformative role of XAI in e... The integration of explainable artificial intelligence(XAI)in space science has ushered in a new era of transparency and reliability in AI-driven applications.This paper delves into the transformative role of XAI in enhancing various aspects of space missions,from satellite imagery analysis to planetary science and human–AI collaboration.The introduction highlights the imperative of explainability in AI,emphasizing the need for transparent and ethical decision-making in high-stakes space missions.In the background,this paper explores the evolution of AI in space science and the emergence of XAI as a critical field.The challenges posed by the complexity of space data and the stringent reliability and safety requirements are examined,underscoring the necessity of robust and interpretable AI systems.The paper discusses various XAI techniques,including model-agnostic approaches like LIME(Local Interpretable Model-agnostic Explanations)and SHAP(SHapley Additive exPlanations),intrinsic methods such as decision trees,and generalized additive models.Visualization tools for XAI,including feature importance plots and heatmaps,are also discussed,demonstrating their role in making AI decisions more interpretable and actionable.Three case studies illustrate the practical applications of XAI in space science:monitoring deforestation in Earth observation,facilitating discoveries in planetary science,and enhancing human–AI collaboration in space missions.These examples showcase how XAI improves transparency and reliability and enables more effective decision-making.Finally,the paper looks toward the future,discussing emerging technologies in XAI and their potential to revolutionize space science.Integrating XAI,human–AI collaboration,NLP advancements,and quantum computing is a key trend in space exploration. 展开更多
关键词 explainable artificial intelligence space science human ai collaborationthe satellite imagery analysis enhancing various aspects space missionsfrom artificial intelligence xai planetary science
An explainable artificial intelligence framework reveals mutations associated with drug resistance in Mycobacterium tuberculosis 认领 引用
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作者 Hui Cen Peng Zhang +2 位作者 Yunchao Ling Guoping Zhao Guoqing Zhang 《Biosafety and Health》 CSCD 2025年第6期384-394,共11页
Understanding the mechanisms of drug resistance inMycobacterium tuberculosis(MTB)is essential for the rapid detection of resistance and for guiding effective treatment,ultimately contributing to reducing the global bu... Understanding the mechanisms of drug resistance inMycobacterium tuberculosis(MTB)is essential for the rapid detection of resistance and for guiding effective treatment,ultimately contributing to reducing the global burden of tuberculosis(TB).Under anti-TB drugs pressure,MTB continues to accumulate resistance loci.The current repertoire of known resistance-associated mutations requires further refinement,necessitating efficient methods for the timely identification of potential resistance sites.Here,we introduce xAI-MTBDR,an explainable artificial intelligence framework designed to identify potential resistance-associated mutations and predict drug resistance in MTB.It outperforms state-of-the-art methods in predicting drug resistance for all first-line drugs,and scoring each mutation’s contribution to resistance.By leveraging public whole-genome sequencing data from nearly 40,000 MTB isolates,the framework identified 788 candidate resistance-related mutations and revealed 27 potential resistance markers,several of which are positioned closer to their respective drugs in protein structures than known resistance mutations,suggesting a potentially more direct role in mediating resistance.Furthermore,these scores enabled the framework to efficiently subgroup isolates with different resistance mechanisms and reflect varying levels of resistance.The framework serves as a valuable tool for accurate detection of drug-resistant MTB and offers new insights into its underlying mechanisms. 展开更多
关键词 Mycobacterium tuberculosis Antimicrobial resistance Drug resistance mechanisms Explainable artificial intelligence Machine learning
Data-driven modeling of polymer electrolyte fuel cells:Towards predictive analytics with explainable artificial intelligence 认领 引用
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作者 Ali Malek Max Dreger +4 位作者 Nima Shaigan Chaojie Song Kourosh Malek Jasna Jankovic Michael Eikerling 《Energy and AI》 EI CSCD 2025年第3期691-703,共13页
Polymer electrolyte fuel cells will be an essential technology of the emerging hydrogen economy.However,optimizing their cost and performance necessitates understanding of how different parameters affect their operati... Polymer electrolyte fuel cells will be an essential technology of the emerging hydrogen economy.However,optimizing their cost and performance necessitates understanding of how different parameters affect their operation.This optimization problem involves numerous interrelated design and operational parameters.However,developing the required understanding through experimental studies alone would be inefficient.Physical modelling is a much-needed complement to experiment but is constrained by simplifying assumptions that diminish the models’predictive capabilities.As a supplement to experiment and physical modelling,we employ a data-based assessment that leverages machine learning techniques to support and enhance decisionmaking.We first evaluate the predictive accuracy of various machine learning models,including artificial neural networks,to predict the polarization behavior of polymer electrolyte fuel cells,harnessing an extensive experimental dataset.We then apply explainable artificial intelligence techniques,including Gini feature importance and Shapley additive explanations value analyses,to understand how these models incorporate data into the prediction process.Probabilistic analyses can help identify relationships between predictions and feature values.We demonstrate that insights derived from Shapley additive explanations value analysis are consistent with literature data on the thermodynamics and kinetics of relevant electrochemical reaction and transport processes.Our study highlights the potential of interpretable and explainable tools to offer a holistic analysis of the impacts of various interrelated operational and design parameters on the performance of the fuel cell.In the future,such explainable tools could help identify gaps in experimental data and pinpoint research priorities. 展开更多
关键词 Polymer electrolyte fuel cells Membrane electrode assembly Machine learning Explainable artificial intelligence Performance prediction Shapley additive explanations
Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics 认领 引用
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作者 Andrew Glaws Jackson W.Schall +5 位作者 Andrew Ballen Amy E.Louks Kristopher O.Davis Axel F.Palmstrom Juliette Ugirumurera Dana B.Kern 《Energy and AI》 EI CSCD 2025年第4期571-583,共13页
As the demand for low-cost,high-efficiency solar energy technologies grows,metal halide perovskite(MHP)solar cells have emerged as a promising candidate for next-generation photovoltaics due to their high power conver... As the demand for low-cost,high-efficiency solar energy technologies grows,metal halide perovskite(MHP)solar cells have emerged as a promising candidate for next-generation photovoltaics due to their high power conversion efficiencies.However,their poor durability and issues with manufacturing consistency remain significant barriers to commercialization.In this work,we develop deep learning models to support materials characterization and provide insight into features and processes influencing performance.The models are trained using transfer learning of a pretrained model to predict relevant current-voltage(IV)metrics based on different combinations of input electroluminescence(EL)and photoluminescence(PL)images of MHP devices.We examine which image types are most informative in accurately predicting different IV metrics.Additionally,we use explainable artificial intelligence(XAI)techniques to provide insights into specific spatial features in the devices that drive differences in performance.We find that stabilized luminescence images(e.g.those collected after biasing the devices for at least 1 min)are better for predicting metrics of open-circuit voltage(by PL)and short-circuit current(by PL with EL),but that predicting fill factor and overall power output may use the time-evolution of EL images.Based on attribution masks generated by integrated gradients for each device performance metric,we further suggest different loss mechanisms associated with categories of large and small spatial defects.Overall,this case study highlights the potential applicability of XAI methodology for streamlining MHP device analysis and accelerating detailed understanding of the relationships between spatial defects and impacts on performance. 展开更多
关键词 Perovskite Photovoltaic Explainable artificial intelligence
Unveiling fundamental principles:visualizing T cell immunity with explainable artificial intelligence 认领 引用
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作者 Liyun Tu Aoyu Xu +4 位作者 Hantao Lou Yongzhi Huang Jianqing Zheng Yu Chen Song Zhang 《Medicine Plus》 2025年第1期15-23,共9页
Machine learning(ML)and immune responses exhibit remarkable parallels in their mechanisms for sensing,processing information,and generating adaptive responses.In Artificial Neural Networks(ANNs),input signals such as ... Machine learning(ML)and immune responses exhibit remarkable parallels in their mechanisms for sensing,processing information,and generating adaptive responses.In Artificial Neural Networks(ANNs),input signals such as images,sequences,or structured data,are processed through input layers that extract key features.Similarly,immune cells rely on a diverse array of receptors,including pattern recognition receptors(PRRs),antigen receptors(TCRs and BCRs),cytokine receptors,and checkpoint receptors,to detect and respond to external or internal stimuli.Both systems translate these signals into actionable processes,ANNs use hidden layers to identify patterns and generate predictions,while im-mune cells activate signaling cascades to initiate cellular programs(Fig.1) 展开更多
关键词 explainable artificial intelligence fundamental principles extract key featuressimilarlyimmune cells artificial neural networks anns input pattern recognition receptors prrs antigen machine learning ml checkpoint receptorsto visualizing t cell immunity
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