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EHKP-Res:an explainable dynamic security solution for medical healthcare 认领 引用
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作者 Xiaoyan Chen Jiahong Cai +3 位作者 Weidong Xiao Yingzi Huo Jin Wang Wei Liang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期462-471,共10页
An increasing number of medical institutions and patients are adopting the practice of storing their data on medical cloud servers,which greatly facilitates the sharing of medical data.However,when many nodes are conn... An increasing number of medical institutions and patients are adopting the practice of storing their data on medical cloud servers,which greatly facilitates the sharing of medical data.However,when many nodes are connected to the medical big data architecture,the overall system’s security can be compromised.To address this,an explainable dynamic security protection scheme for medical healthcare,named EHKP-Res,has been proposed.This scheme aggregates data relevance features and integrates them with a deep residual network to predict the behavior of medical staff,addressing the issue of sudden changes in their behavioral credibility.To tackle the black-box problem,a model post-interpretation scheme using Bayesian networks to generate perturbed datasets has been proposed.It calculates sample weights and performs nonlinear fitting,deriving model interpretation through the eigenvalues of the instances.Experimental results show that the proposed Hidden KP-ABE method reduces time overhead by 14.95% compared to other access control methods and achieves a prediction accuracy of 98.34% for doctor trust metrics,effectively preventing malicious behavior by doctors. 展开更多
关键词 Attribute-based encryption Behavior prediction Deep Residual Network(ResNet) Explainable artificial intelligence Local Interpretable Model-agnostic Explanations(LIME)
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Towards Generalisable and Explainable Traffic Signal Control via Deep Reinforcement Learning and Large Language Models 认领 引用
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作者 Hao Huang Wenjie He +6 位作者 Qilie Liu Qian Liu Chao Huang Anwar PPAbdul Majeed Xiangguang Dai Gang Fang Xiaohua Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期483-497,共15页
As a government-regulated public service,traffic signal control(TSC)requires reliable and transparent decision-making.However,existing deep reinforcement learning(DRL)methods,despite improvements in control accuracy,s... As a government-regulated public service,traffic signal control(TSC)requires reliable and transparent decision-making.However,existing deep reinforcement learning(DRL)methods,despite improvements in control accuracy,still lack explainability and generalisation,severely limiting their applicability in real-world environments.To address the challenges above,this paper proposes GenEx-TSC,a generalisable and explainable TSC method that integrates deep reinforcement learning with large language models(LLMs).First,starting from vehicle-level states,we train a DRL agent incorporating intersection physical heterogeneity and neighbourhood information,which lays the evaluation foundation for constructing a high-quality LLM dataset.Subsequently,the LLM agent is optimised through a two-stage training mechanism.In the distillation stage,a lightweight LLM agent is trained using the reasoning trajectories of a larger-scale LLM agent,inheriting its semantic understanding and decision-generation capabilities and in the alignment stage,the DRL evaluation network is employed to calibrate the outputs of the distilled LLM agent,ensuring that the generated cycle-level signal timing strategies are both efficient and interpretable.We synthesise 10 intersection networks with different physical attributes in SUMO and set traffic flows of varying scales.Experimental results across diverse traffic environments demonstrate that the proposed GenEx-TSC exhibits clear advantages over traditional methods,mainstream DRL methods and LLM baselines in terms of control accuracy,generalisation and explainability. 展开更多
关键词 deep reinforcement learning explainable intelligence large language model traffic signal control
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Explainable Segmentation-Guided Mamba-Transformer Framework for Automated Cardiovascular Disease Detection 认领 引用
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作者 Ghada Atteia Abdulaziz Altamimi +4 位作者 Nihal Abuzinadah Khaled Alnowaiser Muhammad Umer Yunyoung Nam Yongwon Cho 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1263-1289,共27页
Cardiovascular diseases(CVD)remain the leading cause of global mortality,making early and accurate diagnosis essential for improving patient outcomes.However,most existing deep learning approaches address cardiac imag... Cardiovascular diseases(CVD)remain the leading cause of global mortality,making early and accurate diagnosis essential for improving patient outcomes.However,most existing deep learning approaches address cardiac image segmentation or disease classification independently,limiting their effectiveness in complex clinical decisionmaking scenarios.In this study,we propose an explainable spatio-temporal deep learning framework that integrates segmentation-guided representation learning with efficient temporal modeling for automated CVD detection.The proposed architecture incorporates the Segment Anything Model for Medical Imaging in 2D(SAM-Med2D)to achieve accurate cardiac structure segmentation,followed by Mamba-based temporal feature extraction and Transformerdriven spatial representation learning to capture both dynamic motion patterns and anatomical dependencies in cardiac imaging sequences.To enhance transparency and clinical trust,Gradient-weighted Class Activation Mapping(GradCAM)and SHapley Additive exPlanations(SHAP)are employed to provide interpretable diagnostic insights.The framework is evaluated on three benchmark cardiovascular datasets,including EchoNet-Dynamic,CAMUS echocardiography,and UK Biobank cine cardiac magnetic resonance imaging(CMR).Experimental results demonstrate strong performance,achieving a Dice score of 91.20%for segmentation,an AUC of 95.50%,classification accuracy of 92.10%,and an MCC of 0.84,consistently outperforming multiple baseline methods.The proposed framework consistently outperforms baseline and existing methods,achieving approximately 3%-6%improvement in segmentation performance and 3%-4%improvement in classification accuracy across key evaluation metrics.The proposed approach offers a robust and explainable solution for automated cardiovascular disease detection,with significant potential to support reliable clinical deployment and improve diagnostic workflows in medical imaging practice. 展开更多
关键词 Medical imaging explainable artificial intelligence transformer segmentation cardiovascular disease detection
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An explainable deep learning approach to enhance the prediction of shield tunnel deviation 认领 引用
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作者 Jiajie Zhen Fengwen Lai +4 位作者 Ming Huang Junjie Zheng Jim S.Shiau Ping Wang Jinhuo Zheng 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期566-579,共14页
Although machine learning models have achieved high enough accuracy in predicting shield position deviations,their“black box”nature makes the prediction mechanisms and decision-making processes opaque,leading to wea... Although machine learning models have achieved high enough accuracy in predicting shield position deviations,their“black box”nature makes the prediction mechanisms and decision-making processes opaque,leading to weaker explanations and practicability.This study introduces a novel explainable deep learning framework comprising the Informer model with enhanced attention mechanisms(EAMInfor)and deep learning important features(DeepLIFT),aimed at improving the prediction accuracy of shield position deviations and providing interpretability for predictive results.The EAMInfor model attempts to integrate channel attention,spatial attention,and simple attention modules to improve the Informer model's performance.The framework is tested with the four different geological conditions datasets generated from the Xiamen metro line 3,China.Results show that the EAMInfor model outperforms the traditional Informer and comparison models.The analysis with the DeepLIFT method indicates that the push thrust of push cylinder and the earth chamber pressure are the most significant features,while the stroke length of the push cylinder demonstrated lower importance.Furthermore,the variation trends in the significance of data points within input sequences exhibit substantial differences between single and composite strata.This framework not only improves predictive accuracy but also strengthens the credibility and reliability of the results. 展开更多
关键词 Shield tunnel position deviation Machine learning Explainable AI Deep learning important features
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Explainable Hybrid AI Model for DDoS Detection in SDN-Enabled Internet of Vehicle 认领 引用
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作者 Oumaima Saidani Nazia Azim +5 位作者 Ateeq Ur Rehman Akbayan Bekarystankyzy Hala Abdel Hameed Mostafa Mohamed R.Abonazel Ehab Ebrahim Mohamed Ebrahim Sarah Abu Ghazalah 《Computers, Materials & Continua》 SCIE EI 2026年第5期499-526,共28页
The convergence of Software Defined Networking(SDN)in Internet of Vehicles(IoV)enables a flexible,programmable,and globally visible network control architecture across Road Side Units(RSUs),cloud servers,and automobil... The convergence of Software Defined Networking(SDN)in Internet of Vehicles(IoV)enables a flexible,programmable,and globally visible network control architecture across Road Side Units(RSUs),cloud servers,and automobiles.While this integration enhances scalability and safety,it also raises sophisticated cyberthreats,particularly Distributed Denial of Service(DDoS)attacks.Traditional rule-based anomaly detection methods often struggle to detectmodern low-and-slowDDoS patterns,thereby leading to higher false positives.To this end,this study proposes an explainable hybrid framework to detect DDoS attacks in SDN-enabled IoV(SDN-IoV).The hybrid framework utilizes a Residual Network(ResNet)to capture spatial correlations and a Bi-Long Short-Term Memory(BiLSTM)to capture both forward and backward temporal dependencies in high-dimensional input patterns.To ensure transparency and trustworthiness,themodel integrates the Explainable AI(XAI)technique,i.e.,SHapley Additive exPlanations(SHAP).SHAP highlights the contribution of each feature during the decision-making process,facilitating security analysts to understand the rationale behind the attack classification decision.The SDN-IoV environment is created in Mininet-WiFi and SUMO,and the hybrid model is trained on the CICDDoS2019 security dataset.The simulation results reveal the efficacy of the proposed model in terms of standard performance metrics compared to similar baseline methods. 展开更多
关键词 Explainable AI software defined networking Internet of vehicles DDoS attack ResNet BiLSTM
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Addressing Background Bias in Explainable Orange Fruit Disease Classification Using Deep Learning 认领 引用
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作者 Naeem Ullah Javed Ali Khan +3 位作者 Michelina Ruocco Antonio Della Cioppa Ivanoe De Falco Giovanna Sannino 《Computers, Materials & Continua》 SCIE EI 2026年第8期1998-2030,共33页
Fruit diseases significantly impact agricultural productivity,yet automated detection systems often fail to provide interpretable predictions and are sensitive to background variations in images,particularly in orange... Fruit diseases significantly impact agricultural productivity,yet automated detection systems often fail to provide interpretable predictions and are sensitive to background variations in images,particularly in orange fruit disease datasets.Current deep learning approaches are prone to background bias,which reduces explainability and generalization.To address this,we propose a deep learning framework that explicitly reduces background noise and bias in orange fruit disease image classification while providing interpretable,pixel-level predictions.The framework integrates existing architectural components,including grouped convolutions with channel shuffling,Leaky ReLU and clipped ReLU activations,and attention-based feature extraction,within a bias-aware design motivated by explainability analysis.The contribution lies in the problem-driven integration of these components and a background standardization preprocessing step to improve explanation reliability.A Grid Search algorithm is used to optimize the hyperparameters.Data augmentation is applied to enhance generalization.We used perceptual hashing to ensure no duplicate images existed between training and testing sets,thereby preventing data leakage and maintaining dataset integrity.For interpretability,we employ Local Interpretable Model-agnostic Explanations(LIME);however,initial explanations highlighted irrelevant background regions.To address this,we introduce a novel preprocessing step using the GrabCut algorithm and morphological operations to standardize image backgrounds,ensuring explanations focus solely on diseased regions.Unlike existing methods,our background standardization technique,based on GrabCut and white background standardization,improves the relevance of LIME explanations by reducing background-focused attributions from 62.2%to 7.7%of cases,while yielding modest,consistent improvements in classification accuracy(0.15%–0.24%).We further evaluate DeepOrangeNet’s feature extraction by classifying its learned representations using six classifiers,including linear discriminant analysis,fine decision tree,Gaussian Naive Bayes,fine k-nearest neighbors,linear support vector machine,and logistic regression,demonstrating its superior adaptability.DeepOrangeNet has been compared with the state-of-the-art methods,proving not only its accuracy but also its explainable and lightweight architecture for real-world agricultural implementation. 展开更多
关键词 Background bias orange fruit disease classification agricultural productivity explainable artificial intelligence deep learning
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A Modified Gorilla Troops Optimizer-Based Explainable Machine Learning for Early Cardiovascular Disease Prediction 认领 引用
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作者 Israt Jahan Afsana Begum +5 位作者 Bibhas Roy Chowdhury Piyas Fahmid Al Farid Fatama Jannat Tisha Shahrin Islam Abu Saleh Musa Miah Hezerul Abdul Karim 《Computers, Materials & Continua》 SCIE EI 2026年第9期665-690,共26页
Transforming underlying cardiovascular risk into actionable clinical decisions remains a major challenge in contemporary healthcare.Despite advances in cardiology,early-stage cardiovascular disease often remains undet... Transforming underlying cardiovascular risk into actionable clinical decisions remains a major challenge in contemporary healthcare.Despite advances in cardiology,early-stage cardiovascular disease often remains undetected,which hinders timely intervention and leads to preventable deaths.To overcome this problem,this study presents an explainable machine learning framework for the early diagnosis of cardiovascular disease(CVD).Initially,this study examined several data-balancing strategies,for example,SMOTE(Synthetic Minority Oversampling Technique),SMOTETomek(Synthetic Minority Over-sampling Technique+Tomek Links),Tomek Links,ADASYN(Adaptive Synthetic Sampling),and SMOTE-ENN(Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors)within the data-preprocessing pipeline.We proposed a novel Adaptive Inertia Weight Gorilla Troops Optimizer(AIW-GTO)to overcome classical GTO’s(Gorilla Troops Optimizer)unstable convergence by adaptively controlling step sizes.It uses large exploratory steps early for wide search and smaller steps later for finetuned local optimization,which ensures stable convergence and enhanced optimization accuracy.Several machine learning techniques,namely XGBoost,Random Forest,SVM(Support Vector Machine),LightGBM(Light Gradient Boosting Machine),and MLP(Multilayer Perceptron)classifier,were evaluated on the multi-regional UCI heart disease dataset.The experimental findings revealed that,by integrating AIW-GTO Optimization and class imbalance mitigation,LightGBM and XGBoost individually achieved a benchmark accuracy of 93.48%and 91.85%,respectively.Moreover,a weighted ensemble of them further improved the accuracy to 94.02%.Sensitivity analysis further evaluated the model’s ability to perform under incomplete clinical test data.To enhance ethical considerations and clinical trust,SHAP(SHapley Additive exPlanations)and LIME(Local Interpretable Model-agnostic Explanations)were utilized to provide model explainability and identify the most influential features affecting prediction outcomes.Analysis indicated that ECG-related(Electrocardiogram)features,including ST_Slope(exercise-induced ST change)and Oldpeak(ST depression magnitude),emerged as key predictors of CVD risk.Overall,the proposed framework provides a clinically reliable and interpretable approach for early cardiovascular risk assessment to enable proactive patient management. 展开更多
关键词 Cardiovascular disease(CVD) machine learning preventive cardiology class imbalance handling model optimization gorilla troops optimizer UCI heart disease dataset explainable AI(XAI) SHAP LIME ensemble technique automated diagnosis clinical decision support system(CDSS) risk stratification
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Explainable AI for predicting the strength of bio-cemented sands 认领 引用
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作者 Waleed El-Sekelly Muhammad Nouman Amjad Raja Tarek Abdoun 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1552-1569,共18页
The biological stabilization of soil using microbially induced carbonate precipitation(MICP)employs ureolytic bacteria to precipitate calcium carbonate(CaCO3),which binds soil particles,enhancing strength,stiffness,an... The biological stabilization of soil using microbially induced carbonate precipitation(MICP)employs ureolytic bacteria to precipitate calcium carbonate(CaCO3),which binds soil particles,enhancing strength,stiffness,and erosion resistance.The unconfinedcompressive strength(UCS),a key measure of soil strength,is critical in geotechnical engineering as it directly reflectsthe mechanical stability of treated soils.This study integrates explainable artificialintelligence(XAI)with geotechnical insights to model the UCS of MICP-treated sands.Using 517 experimental data points and a combination of various input variables—including median grain size(D50),coefficientof uniformity(Cu),void ratio(e),urea concentration(Mu),calcium concentration(Mc),optical density(OD)of bacterial solution,pH,and total injection volume(Vt)—fivemachine learning(ML)models,including eXtreme gradient boosting(XGBoost),Light gradient boosting machine(LightGBM),random forest(RF),gene expression programming(GEP),and multivariate adaptive regression splines(MARS),were developed and optimized.The ensemble models(XGBoost,LightGBM,and RF)were optimized using the Chernobyl disaster optimizer(CDO),a recently developed metaheuristic algorithm.Of these,LightGBM-CDO achieved the highest accuracy for UCS prediction.XAI techniques like feature importance analysis(FIA),SHapley additive exPlanations(SHAP),and partial dependence plots(PDPs)were also used to investigate the complex non-linear relationships between the input and output variables.The results obtained have demonstrated that the XAI-driven models can enhance the predictive accuracy and interpretability of MICP processes,offering a sustainable pathway for optimizing geotechnical applications. 展开更多
关键词 Microbially induced carbonate precipitation(MICP) Bio-cementation Unconfined compressive strength(UCS) Explainable artificialintelligence(XAI) Optimization
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Explainable Anomaly Detection for System Logs in Distributed Environments 认领 引用
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作者 Zhaojun Gu Wenlong Yue Chunbo Liu 《Computers, Materials & Continua》 SCIE EI 2026年第6期2272-2296,共25页
Anomaly detection in system logs is a critical technical means for identifying potential faults and security risks.In distributed environments,traditional deep learning-based log anomaly detection methods often suffer... Anomaly detection in system logs is a critical technical means for identifying potential faults and security risks.In distributed environments,traditional deep learning-based log anomaly detection methods often suffer from shortcomings in transparency,computational overhead,and data privacy protection.To address these issues,this paper proposes a federated learning-driven lightweight and explainable log anomaly detection framework named FedXLog.The framework adapts to heterogeneous logs through hierarchical feature extraction,introduces the Federated Gradient Trajectory Aggregation algorithm(FedGradTrace)to enhance the explainability of the parameter aggregation process,constructs lightweight models using knowledge distillation,and achieves globally consistent explanatory capabilities by integrating hash feature alignment.Experimental results demonstrate that FedXLog possesses the dual advantages of high detection accuracy and lightweight deployment for heterogeneous logs in distributed scenarios.It can effectively identify key decision-making features and locate typical root causes of anomalies.Notably,the framework has been specifically optimized for the unique characteristics of distributed logs.Distinguished from general federated explainable methods,it can directly support abnormal root cause localization in Operations and Maintenance scenarios.This further verifies the application value of scenario-specific adaptation of federated learning in the field of log analysis,thereby expanding the scope of application of explainable log anomaly detection. 展开更多
关键词 Anomaly detection log parsing federated learning explainable artificial intelligence
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Bridging AI and Cyber Defense:A Stacked Ensemble Deep Learning Model with Explainable Insights 认领 引用
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作者 Faisal Albalwy Muhannad Almohaimeed 《Computers, Materials & Continua》 SCIE EI 2026年第5期559-578,共20页
Intrusion detection in Internet of Things(IoT)environments presents challenges due to heterogeneous devices,diverse attack vectors,and highly imbalanced datasets.Existing research on the ToN-IoT dataset has largely em... Intrusion detection in Internet of Things(IoT)environments presents challenges due to heterogeneous devices,diverse attack vectors,and highly imbalanced datasets.Existing research on the ToN-IoT dataset has largely emphasized binary classification and single-model pipelines,which often showstrong performance but limited generalizability,probabilistic reliability,and operational interpretability.This study proposes a stacked ensemble deep learning framework that integrates random forest,extreme gradient boosting,and a deep neural network as base learners,with CatBoost as the meta-learner.On the ToN-IoT Linux process dataset,the model achieved near-perfect discrimination(macro area under the curve=0.998),robust calibration,and superior F1-scores compared with standalone classifiers.Interpretability was achieved through SHapley Additive exPlanations–based feature attribution,which highlights actionable drivers ofmalicious behavior,such as command-line patterns,process scheduling anomalies,and CPU usage spikes,and aligns these indicators with MITRE ATT&CK tactics and techniques.Complementary analyses,including cumulative lift and sensitivity-specificity trade-offs,revealed the framework’s suitability for deployment in security operations centers,where calibrated risk scores,transparent explanations,and resource-aware triage are essential.These contributions bridge methodological rigor in artificial intelligence/machine learning with operational priorities in cybersecurity,delivering a scalable and explainable intrusion detection system suitable for real-world deployment in IoT environments. 展开更多
关键词 Cybersecurity IoT intrusion detection stacked ensemble learning deep learning explainable AI(XAI) probability calibration SHAP interpretability ToN-IoT dataset MITRE ATT&CK
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Explainable machine learning for predicting the mechanical strength of eco-friendly geopolymer concrete 认领 引用
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作者 Veerabhadrappa ALGUR Poornima HULIPALLED +3 位作者 Shivakumar KAPPE Srishaila JAGALUR MAHALINGASHARMA Sharvani VITTA Zameer Ahamed Haneef MOHAMED 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第2期337-363,共27页
This study presents a clear machine learning framework aimed at forecasting the mechanical properties of environmentally sustainable geopolymer concrete(GPC)made from Ground Granulated Blast Furnace Slag(GGBS)and Suga... This study presents a clear machine learning framework aimed at forecasting the mechanical properties of environmentally sustainable geopolymer concrete(GPC)made from Ground Granulated Blast Furnace Slag(GGBS)and Sugarcane Bagasse Ash(SCBA).Four ensemble machine learning models:Random Forest(RF),AdaBoost,Gradient Boosting(GB)and XGBoost(XGB)were employed to estimate the Compressive Strength(CS),Split Tensile Strength(STS)and Flexural Strength(FS).Particle Swarm Optimization(PSO)and Bat Optimization Algorithm(BAT)algorithms were employed to optimize the hyperparameter of the model.The best test predictive accuracy with R2values for CS,STS and FS are 0.983(GB-BAT),0.991(RF-BAT)and 0.985(XGB-PSO)respectively with lower error metrics.To improve the model’s interpretability,we used SHapley Additive exPlanations and sensitivity analysis.The findings indicated that the anticipated results were significantly influenced by the GGBS content,curing duration and molarity.The study emphasizes a synergistic effect between GGBS replacement and curing age in enhancing strength development.Integrating explainable Artificial Intelligence(AI)with predictive modeling enhances clarity and provides a reliable way to get results without having lot of laboratory work.This framework is a useful tool for designing mixes based on data and encourages eco-friendly methods of building with cement-free concrete. 展开更多
关键词 GPC machine learning strength prediction GGBS SCBA explainable AI
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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 Context-Aware Fusion Network for Non-Small Cell Lung Cancer Analysis with Application to Smart Healthcare Systems 认领 引用
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作者 Muhammad Waqar Zeshan Aslam Khan +3 位作者 Arthur Chang Zhishan Guo Chun-Liang Lai Chuan-Yu Chang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1208-1235,共28页
Lung cancer(LC)is among the dangerous cancers spreading progressively,and a timely LC diagnosis becomes a dire need of the time.Various imaging-based studies have been conducted for accurate LC examination through com... Lung cancer(LC)is among the dangerous cancers spreading progressively,and a timely LC diagnosis becomes a dire need of the time.Various imaging-based studies have been conducted for accurate LC examination through computed tomography(CT),X-ray,and histopathology.Worldwide,the proportion of LC-affected patients in hospitals is growing,thereby increasing imaging data for fast processing and early examination.To facilitate histopathological imaging-based automated and timely decision making for accurate LC prediction,a Context Aware Fusion Network(CAFNet)for holistic feature learning and spatially localized feature learning is proposed in this study for the efficient extraction and processing of global as well as local features,respectively.CAFNet exploits histopathological tissues to ensure local and global attributes uniformity for extracting contextual information.The conducted research achieves histopathological image enhancement using median filtering(MF)and contrast-limited-adaptive-HistogramEqualization(CLAHE).Moreover,the classifying power of the proposed CAFNet is enhanced through superior attributes extraction strategies,such as Mobile Inverted Bottleneck Convolution(MIBConv)employed with Spatial Attention with Residual Learning(SARL)and Channel Attention with Residual Learning(CARL).An innovative,partially adaptive optimization approach is utilized to fine-tune the degree of adaptivity in the learning process of the network.The descriptive behavior of CAFNet is explored through explainable artificial intelligence(XAI)strategies like Gradient-Weighted Class Activation Mapping(GradCAM)and Local Interpretable Model-Agnostic Explanation(LIME).The proposed network achieved an improved average classification accuracy of 7.36%while reducing models’complexity by 85%to 99%as compared to the existing benchmark models.The study also addresses users’accessibility challenges by providing a web-based interface using Gradio for users’real-time interaction. 展开更多
关键词 Lung cancer histopathology context-aware network explainable AI web-based Interface
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An Explainable Centralized Deep Learning Model for Gastrointestinal Polyp Segmentation Using the Kvasir-SEG Dataset 认领 引用
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作者 Hafeez Rahman Naveed Butt +4 位作者 Naila Sammar Naz Fahad Ahmed Muhammad Saleem Adnan Khan Khan Muhammad Adnan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1076-1101,共26页
Gastrointestinal polyps are well-known precursors to colorectal cancer(CRC),making their accurate detection and segmentation during colonoscopy essential for early diagnosis and cancer prevention.Deep learning-based s... Gastrointestinal polyps are well-known precursors to colorectal cancer(CRC),making their accurate detection and segmentation during colonoscopy essential for early diagnosis and cancer prevention.Deep learning-based segmentation models trained on publicly available datasets such as Kvasir-SEG have demonstrated promising performance;however,two key challenges remain:limited robustness across diverse polyp morphologies and endoscopic imaging conditions,and the lack of interpretable decision-making mechanisms that support clinical trust and validation.Many existing centralized segmentation approaches are primarily optimized using overlap-based metrics such as the Dice coefficient and intersection over union(IoU),without adequately analyzing challenging cases such as small,flat,or low-contrast polyps or providing insight into the visual cues influencing model predictions.This study presents an explainable centralized deep learning segmentation model for gastrointestinal polyp segmentation using the Kvasir-SEG dataset.The approach integrates a ResUNet++-Lite encoder-decoder segmentation model with Grad-CAM and masked Grad-CAM visualizations to analyze the spatial regions influencing segmentation predictions.The study focuses on establishing a reproducible and interpretable experimental model that combines systematic preprocessing,data augmentation,centralized training,and explainability analysis.Experimental evaluation on an 80:20 train-test split of the Kvasir-SEG dataset,where data augmentation was applied after splitting,demonstrates stable training behavior and competitive segmentation performance,achieving a pixel accuracy of 0.964,a Dice coefficient of 0.858,and an IoU of 0.791 on the held-out test set.Qualitative explainability results further indicate that the model consistently focuses on anatomically relevant polyp regions.Overall,the study illustrates how segmentation performance and explainable AI techniques can be integrated to support the development of clinically interpretable AI-assisted colonoscopy systems. 展开更多
关键词 Gastrointestinal polyp segmentation deep learning ResUNet++-Lite Kvasir-SEG dataset explainable AI(XAI) Grad-CAM masked Grad-CAM MEDICAL image analysis colonoscopy
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Spatial Patterns and Multidimensional Drivers of Land Use Inefficiency in Quanzhou,China:Insights from Explainable Machine Learning 认领 引用
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作者 KE Zehua WEI Wei +1 位作者 HONG Mengyao XIA Junnan 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第7期1265-1282,共18页
As urban development shifts from outward expansion to stock-based regeneration,inefficient urban land has become a critical constraint on land resource allocation,urban renewal,and sustainable spatial governance.To su... As urban development shifts from outward expansion to stock-based regeneration,inefficient urban land has become a critical constraint on land resource allocation,urban renewal,and sustainable spatial governance.To support differentiated urban renewal and sustainable land governance,this study examined the spatial patterns,factor contributions,and their interaction mechanisms of inefficient land use in 2022 within the urban development boundary of Quanzhou,Fujian Province southeastern of China.Based on the official 2022 citywide survey of inefficient urban land,multisource geographic and socioeconomic data were integrated with spatial statistical analysis,extreme gradient boosting(XGBoost),and Shapley additive explanations(SHAP).The results show that 5396 inefficient land parcels were identified in 2022,covering 7762.44 ha.Industrial land accounted for the largest area share(59.23%),followed by residential land(38.67%)and commercial land(2.10%).Inefficient land displayed a clear northwest-southeast distributional orientation and was mainly distributed across core urban areas,traditional industrial agglomeration zones,and county-level urban nodes.For total inefficient land,the Global Moran’s I reached 0.97,and High-High clusters were mainly located in Licheng District,southern Jinjiang City,and the county-level urban centers of Dehua and Yongchun,indicating concentration in old urban districts,mixed industrial-residential spaces,and county-level built-up areas.The SHAP results reveal type-specific but multidimensional mechanisms.Industrial inefficiency reflected the compatibility between production-space organization,transport and service support,ecological-location conditions,and market value;residential inefficiency was shaped by the coordination among parcel organization,community services,population demand,and mobility support;and commercial inefficiency depended on the matching among development intensity,road and public transport conditions,population distribution,and ecological-location conditions.These findings suggest type-specific renewal strategies,including parcel consolidation and production-supporting function improvement for industrial land,neighborhood-scale service coordination for residential land,and functional matching among development intensity,road and public transport capacity,population distribution,and ecological-location conditions for commercial land. 展开更多
关键词 inefficient land use explainable machine learning urban spatial governance coastal development sustainable transition interaction effects Quanzhou,China
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Explainable Ensemble Learning Framework for Early Detection of Autism Spectrum Disorder:Enhancing Trust,Interpretability and Reliability in AI-Driven Healthcare 认领 引用
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作者 Menwa Alshammeri Noshina Tariq +2 位作者 NZ Jhanji Mamoona Humayun Muhammad Attique Khan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期1233-1265,共33页
Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning sy... Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning system for the early detection of Autism Spectrum Disorder(ASD)in children.Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning.For this,we combined several different models,including Random Forest,XGBoost,and Neural Networks,into a single,more powerful framework.We used two different types of datasets:(i)a standard behavioral dataset and(ii)a more complex multimodal dataset with images,audio,and physiological information.The datasets were carefully preprocessed for missing values,redundant features,and dataset imbalance to ensure fair learning.The results outperformed the state-of-the-art with a Regularized Neural Network,achieving 97.6%accuracy on behavioral data.Whereas,on the multimodal data,the accuracy is 98.2%.Other models also did well with accuracies consistently above 96%.We also used SHAP and LIME on a behavioral dataset for models’explainability. 展开更多
关键词 Autism spectrum disorder(ASD) artificial intelligence in healthcare explainable AI(XAI) ensemble learning machine learning early diagnosis model interpretability SHAP LIME predictive analytics ethical AI healthcare trustworthiness
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Explainable Ensemble Learning Approach for Ovarian Cancer Diagnosis Using Clinical Data 认领 引用
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作者 Daniyal Asif Nabil Kerdid +1 位作者 Muhammad Shoaib Arif Mairaj Bibi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期1050-1076,共27页
Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potent... Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potential to process complex datasets and support decision-making in OC diagnosis.Nevertheless,traditional ML models tend to be biased,overfitting,noisy,and less generalized.Moreover,their black-box nature reduces interpretability and limits their practical clinical applicability.In this study,we introduce an explainable ensemble learning(EL)model,TreeX-Stack,based on a stacking architecture that employs tree-based learners such as Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),and Extreme Gradient Boosting(XGBoost)as base learners,and Logistic Regression(LR)as the meta-learner to enhance ovarian cancer(OC)diagnosis.Local Interpretable ModelAgnostic Explanations(LIME)are used to explain individual predictions,making the model outputs more clinically interpretable and applicable.The model is trained on the dataset that includes demographic information,blood test,general chemistry,and tumor markers.Extensive preprocessing includes handling missing data using iterative imputation with Bayesian Ridge and addressing multicollinearity by removing features with correlation coefficients above 0.7.Relevant features are then selected using the Boruta feature selection method.To obtain robust and unbiased performance estimates during hyperparameter tuning,nested cross-validation(CV)with grid search is employed,and all experiments are repeated five times to ensure statistical reliability.TreeX-Stack demonstrates excellent diagnostic performance,achieving an accuracy of 0.9027,a precision of 0.8673,a recall of 0.9391,and an F1-score of 0.9012.Feature-importance analyses using LIME and permutation importance highlight Human Epididymis Protein 4(HE4)as the most significant biomarker for OC.The combination of high predictive performance and interpretability makes TreeX-Stack a reliable tool for clinical decision support in OC diagnosis. 展开更多
关键词 Ovarian cancer ensemble learning machine learning stacking explainable artificial intelligence medical data analysis clinical data HE4
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From Hardening to Understanding: Adversarial Training vs. CF-Aug for Explainable Cyber-Threat Detection System 认领 引用
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作者 Malik Al-Essa Mohammad Qatawneh +2 位作者 Ahmad Sami Al-Shamayleh Orieb Abualghanam Wesam Almobaideen 《Computers, Materials & Continua》 SCIE EI 2026年第6期582-599,共18页
Machine Learning(ML)intrusion detection systems(IDS)are vulnerable to manipulations:small,protocol-valid manipulations can push samples across brittle decision boundaries.We study two complementary remedies that resha... Machine Learning(ML)intrusion detection systems(IDS)are vulnerable to manipulations:small,protocol-valid manipulations can push samples across brittle decision boundaries.We study two complementary remedies that reshape the learner in distinct ways.Adversarial Training(AT)exposes themodel to worst-case,in-threat perturbations during learning to thicken local margins;Counterfactual Augmentation(CF-Aug)adds near-boundary exemplars that are explicitly constrained to be feasible,causally consistent,and operationally meaningful for defenders.The main goal of this work is to investigate and compare how AT and CF-Aug can reshape the decision surface of the IDS.eXplainable Artificial Intelligence(XAI)is used to analyze the shifts in global feature importance stability under both AT and CF perturbation to link these shifts to the accuracy of the IDS in detecting cyber-threats.This yields a clear picturewhen boundary hardening(AT)or boundary sculpting(CF-Aug)better serves IDS.Twowell-known techniques are used to generate adversarial samples,namely the Fast Gradient Sign Method(FGSM)and the Projected Gradient Descent(PGD)techniques.We have achieved better accuracy with AT and CF-Aug compared to the baseline IDS. 展开更多
关键词 eXplainable artificial intelligence(XAI) intrusion detection systems(IDS) counterfactual explanation adversarial training deep learning
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Explainable electroencephalography-based attentiondeficit/hyperactivity disorder detection model with a combination of ternary pattern and twin wavelet transform 认领 引用
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作者 Yavuz Atas Serkan Kırık +9 位作者 Kübra Yıldırım Burak Tasci Prabal Datta Barua Ferhat Balgetir Sengul Dogan Turker Tuncer Ru-San Tan Elizabeth Palmer Aruna Devi U Rajendra Acharya 《World Journal of Psychiatry》 SCIE 2026年第3期141-158,共18页
BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental condition characterized by inattention,impulsivity,and hyperactivity.Traditional diagnosis relies on clinical evaluation,which is... BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental condition characterized by inattention,impulsivity,and hyperactivity.Traditional diagnosis relies on clinical evaluation,which is timeconsuming and subjective.Electroencephalography(EEG)signals provide an objective alternative,and machine learning methods can improve their diagnostic utility.AIM To develop an explainable EEG-based model for ADHD detection by integrating a novel combination ternary pattern(CTP)feature extractor with twin wavelet transform(TWT)for multilevel signal analysis,and to evaluate its effectiveness in providing accurate,channel-wise,and fusion-based classification results for objective and rapid ADHD diagnosis.METHODS A new EEG dataset containing more than 7000 segments from 137 ADHD patients and 150 controls was studied.A novel feature engineering framework was developed,combining a new CTP extractor with statistical features.A multilevel feature extraction structure was designed using a newly proposed TWT for signal decomposition.Extracted features were reduced to the most informative 263 using neighborhood component analysis.Channelwise classification was performed with k-nearest neighbors,followed by iterative majority voting across 20 EEG channels.RESULTS Single-channel analysis achieved up to 99.12%accuracy.By applying majority voting,overall classification accuracy increased to 99.97%,with similarly high sensitivity and specificity.CONCLUSION Our study introduces a large ADHD EEG dataset and a novel model integrating TWT and CTP.The model provides highly accurate,channel-wise,and fusion-based results,offering a promising objective tool for rapid ADHD diagnosis. 展开更多
关键词 Attention-deficit/hyperactivity disorder detection Combination ternary pattern Electroencephalography signal classification Explainable feature engineering Twin wavelet transform
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Cascading Class Activation Mapping:A Counterfactual Reasoning-Based Explainable Method for Comprehensive Feature Discovery 认领 引用
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作者 Seoyeon Choi Hayoung Kim Guebin Choi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1043-1069,共27页
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati... Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods. 展开更多
关键词 Explainable AI class activation mapping counterfactual reasoning shortcut learning feature discovery
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