Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,y...Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,yet the key conditioning factors(e.g.,topography,hydrology)remain insufficiently understood.This study proposes a novel artificial intelligence(AI)framework that integrates four machine learning(ML)models with Shapley Additive Explanations(SHAP)method,offering a hierarchical perspective from global to local on the dominant factors controlling gully distribution in wildfireaffected areas.In a case study of Xiangjiao catchment burned on March 28,2020,in Muli County in Sichuan Province of Southwest China,we derived 21 geoenvironmental factors to assess the susceptibility of post-fire gully erosion using logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models.SHAP-based model interpretation revealed eight key conditioning factors:topographic position index(TPI),topographic wetness index(TWI),distance to stream,mean annual precipitation,differenced normalized burn ratio(d NBR),land use/cover,soil type,and distance to road.Comparative model evaluation demonstrated that reduced-variable models incorporating these dominant factors achieved accuracy comparable to that of the initial-variable models,with AUC values exceeding 0.868 across all ML algorithms.These findings provide critical insights into gully erosion behavior in wildfire-affected areas,supporting the decision-making process behind environmental management and hazard mitigation.展开更多
Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead t...Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.展开更多
In the field of precision healthcare,where accurate decision-making is paramount,this study underscores the indispensability of eXplainable Artificial Intelligence(XAI)in the context of epilepsy management within the ...In the field of precision healthcare,where accurate decision-making is paramount,this study underscores the indispensability of eXplainable Artificial Intelligence(XAI)in the context of epilepsy management within the Internet of Medical Things(IoMT).The methodology entails meticulous preprocessing,involving the application of a band-pass filter and epoch segmentation to optimize the quality of Electroencephalograph(EEG)data.The subsequent extraction of statistical features facilitates the differentiation between seizure and non-seizure patterns.The classification phase integrates Support Vector Machine(SVM),K-Nearest Neighbor(KNN),and Random Forest classifiers.Notably,SVM attains an accuracy of 97.26%,excelling in the precision,recall,specificity,and F1 score for identifying seizures and non-seizure instances.Conversely,KNN achieves an accuracy of 72.69%,accompanied by certain trade-offs.The Random Forest classifierstands out with a remarkable accuracy of 99.89%,coupled with an exceptional precision(99.73%),recall(100%),specificity(99.80%),and F1 score(99.86%),surpassing both SVM and KNN performances.XAI techniques,namely Local Interpretable ModelAgnostic Explanations(LIME)and SHapley Additive exPlanation(SHAP),enhance the system’s transparency.This combination of machine learning and XAI not only improves the reliability and accuracy of the seizure detection system but also enhances trust and interpretability.Healthcare professionals can leverage the identified important features and their dependencies to gain deeper insights into the decision-making process,aiding in informed diagnosis and treatment decisions for patients with epilepsy.展开更多
Problem:The integration of Artificial Intelligence(AI)into cybersecurity,while enhancing threat detection,is hampered by the“black box”nature of complex models,eroding trust,accountability,and regulatory compliance....Problem:The integration of Artificial Intelligence(AI)into cybersecurity,while enhancing threat detection,is hampered by the“black box”nature of complex models,eroding trust,accountability,and regulatory compliance.Explainable AI(XAI)aims to resolve this opacity but introduces a critical newvulnerability:the adversarial exploitation of model explanations themselves.Gap:Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector.There is a pressing need to systematically analyze the trade-offs between interpretability and security,evaluate defense mechanisms,and outline a path for developing robust,next-generation XAI frameworks.Solution:This review provides a systematic examination of XAI techniques(e.g.,SHAP,LIME,Grad-CAM)and their applications in intrusion detection,malware analysis,and fraud prevention.It critically evaluates the security risks posed by XAI,including model inversion and explanation-guided evasion attacks,and assesses corresponding defense strategies such as adversarially robust training,differential privacy,and secure-XAI deployment patterns.Contribution:Theprimary contributions of this work are:(1)a comparative analysis of XAI methods tailored for cybersecurity contexts;(2)an identification of the critical trade-off betweenmodel interpretability and security robustness;(3)a synthesis of defense mechanisms to mitigate XAI-specific vulnerabilities;and(4)a forward-looking perspective proposing future research directions,including quantum-safe XAI,hybrid neuro-symbolic models,and the integration of XAI into Zero Trust Architectures.This review serves as a foundational resource for developing transparent,trustworthy,and resilient AI-driven cybersecurity systems.展开更多
Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predispositio...Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predisposition,environmental exposure,unhealthy lifestyle habits,and existing medical conditions.Although existing machine learning-based methods for predicting stroke patients’health status have made significant progress,limitations remain in terms of prediction accuracy,model explainability,and system optimization.This paper proposes a multi-task learning approach based on Explainable Artificial Intelligence(XAI)for predicting the health status of stroke patients.First,we design a comprehensive multi-task learning framework that utilizes the task correlation of predicting various health status indicators in patients,enabling the parallel prediction of multiple health indicators.Second,we develop a multi-task Area Under Curve(AUC)optimization algorithm based on adaptive low-rank representation,which removes irrelevant information from the model structure to enhance the performance of multi-task AUC optimization.Additionally,the model’s explainability is analyzed through the stability analysis of SHAP values.Experimental results demonstrate that our approach outperforms comparison algorithms in key prognostic metrics F1 score and Efficiency.展开更多
The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conv...The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conventional methods both costly and inefficient.Recently,Artificial Intelligence(AI)has gained prominence for developing automated systems that can accurately diagnose or segment brain tumors in a shorter time frame.Many researchers have examined various algorithms that provide both speed and accuracy in detecting and classifying brain tumors.This paper proposes a newmodel based on AI,called the Brain Tumor Detection(BTD)model,based on brain tumor Magnetic Resonance Images(MRIs).The proposed BTC comprises three main modules:(i)Image Processing Module(IPM),(ii)Patient Detection Module(PDM),and(iii)Explainable AI(XAI).In the first module(i.e.,IPM),the used dataset is preprocessed through two stages:feature extraction and feature selection.At first,the MRI is preprocessed,then the images are converted into a set of features using several feature extraction methods:gray level co-occurrencematrix,histogramof oriented gradient,local binary pattern,and Tamura feature.Next,the most effective features are selected fromthese features separately using ImprovedGrayWolfOptimization(IGWO).IGWOis a hybrid methodology that consists of the Filter Selection Step(FSS)using information gain ratio as an initial selection stage and Binary Gray Wolf Optimization(BGWO)to make the proposed method better at detecting tumors by further optimizing and improving the chosen features.Then,these features are fed to PDM using several classifiers,and the final decision is based on weighted majority voting.Finally,through Local Interpretable Model-agnostic Explanations(LIME)XAI,the interpretability and transparency in decision-making processes are provided.The experiments are performed on a publicly available Brain MRI dataset that consists of 98 normal cases and 154 abnormal cases.During the experiments,the dataset was divided into 70%(177 cases)for training and 30%(75 cases)for testing.The numerical findings demonstrate that the BTD model outperforms its competitors in terms of accuracy,precision,recall,and F-measure.It introduces 98.8%accuracy,97%precision,97.5%recall,and 97.2%F-measure.The results demonstrate the potential of the proposed model to revolutionize brain tumor diagnosis,contribute to better treatment strategies,and improve patient outcomes.展开更多
Machine fault diagnostics are essential for industrial operations,and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions.Machine learni...Machine fault diagnostics are essential for industrial operations,and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions.Machine learning models,especially those utilizing complex algorithms like deep learning,have demonstrated major potential in extracting important information fromlarge operational datasets.Despite their efficiency,machine learningmodels face challenges,making Explainable AI(XAI)crucial for improving their understandability and fine-tuning.The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study.The technique is applied to evaluate different machine-learning algorithms.Extreme Gradient Boosting,Support Vector Machine,Gaussian Naive Bayes,and Random Forest classifiers are used alongside Logistic Regression(LR)as a baseline model because their efficacy and simplicity are evaluated thoroughly with empirical analysis.The XAI is used as a targeted feature selection technique to select among 29 features of the time and frequency domain.The XAI approach is lightweight,trained with only targeted features,and achieved similar results as the traditional approach.The accuracy without XAI on baseline LR is 79.57%,whereas the approach with XAI on LR is 80.28%.展开更多
Neonatal sepsis is the third most common cause of neonatal mortality and a serious public health problem,especially in developing countries.There have been researches on human sepsis,vaccine response,and immunity.Also...Neonatal sepsis is the third most common cause of neonatal mortality and a serious public health problem,especially in developing countries.There have been researches on human sepsis,vaccine response,and immunity.Also,machine learning methodologies were used for predicting infant mortality based on certain features like age,birth weight,gestational weeks,and Appearance,Pulse,Grimace,Activity and Respiration(APGAR)score.Sepsis,which is considered the most determining condition towards infant mortality,has never been considered for mortality prediction.So,we have deployed a deep neural model which is the state of art and performed a comparative analysis of machine learning models to predict the mortality among infants based on the most important features including sepsis.Also,for assessing the prediction reliability of deep neural model which is a black box,Explainable AI models like Dalex and Lime have been deployed.This would help any non-technical personnel like doctors and practitioners to understand and accordingly make decisions.展开更多
Breast cancer is a type of cancer responsible for higher mortality rates among women.The cruelty of breast cancer always requires a promising approach for its earlier detection.In light of this,the proposed research l...Breast cancer is a type of cancer responsible for higher mortality rates among women.The cruelty of breast cancer always requires a promising approach for its earlier detection.In light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors.In addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram.Accordingly,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of mammograms.For this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS databases.Maximumclassification accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing models.Furthermore,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.展开更多
Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Mag...Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more aggressive.The research introduces an innovative custom convolutional neural network(CNN)model,Glioma-CNN.GliomaCNN stands out as a lightweight CNN model compared to its predecessors.The research utilized the BraTS 2020 dataset for its experiments.Integrated with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification outcomes.Despite challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.展开更多
In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.De...In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.展开更多
In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse systems.Despite the convenience and efficiency offered by Io...In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse systems.Despite the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain threats.This paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT environments.An effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time complexity.By leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world scenarios.Additionally,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack detection.The experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.展开更多
Traditional machine learning metrics(TMLMs)are quite useful for the current research work precision,recall,accuracy,MSE and RMSE.Not enough for a practitioner to be confident about the performance and dependability of...Traditional machine learning metrics(TMLMs)are quite useful for the current research work precision,recall,accuracy,MSE and RMSE.Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%.We included in the prediction process,machine learning models(MLMs)with greater than 99%accuracy with a sensitivity of 95%–98%and specifically in the database.We need to explain the model to domain specialists through the MLMs.Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model.This is achieved by creating a model-independent,locally accurate explanation set that makes it better than the primary model.As we know that human interaction with machine learning systems on this model’s interpretability is more crucial.For supporting set validations in model selection insurance premium prediction.In this study,we proposed the use of the(LIME and SHAP)approach to understand research properly and explain a model developed using random forest regression to predict insurance premiums.The SHAP algorithm’s drawback,as seen in our experiments,is its lengthy computing time—to produce the findings,it must compute every possible combination.In addition,the experiments conducted were intended to focus on the model’s interpretability and explain its ability using LIME and SHAP,not the insurance premium charge prediction.Three experiments were conducted through experiment,one was to interpret the random forest regression model using LIME techniques.In experiment 2,we used the SHAP technique to interpret the model insurance premium prediction(IPP).展开更多
Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to ...Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.展开更多
Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartph...Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartphones and increased Internet connectivity,SMS spam has emerged as a prevalent threat.Spammers have recognized the critical role SMS plays in today’s modern communication,making it a prime target for abuse.As cybersecurity threats continue to evolve,the volume of SMS spam has increased substantially in recent years.Moreover,the unstructured format of SMS data creates significant challenges for SMS spam detection,making it more difficult to successfully combat spam attacks.In this paper,we present an optimized and fine-tuned transformer-based Language Model to address the problem of SMS spam detection.We use a benchmark SMS spam dataset to analyze this spam detection model.Additionally,we utilize pre-processing techniques to obtain clean and noise-free data and address class imbalance problem by leveraging text augmentation techniques.The overall experiment showed that our optimized fine-tuned BERT(Bidirectional Encoder Representations from Transformers)variant model RoBERTa obtained high accuracy with 99.84%.To further enhance model transparency,we incorporate Explainable Artificial Intelligence(XAI)techniques that compute positive and negative coefficient scores,offering insight into the model’s decision-making process.Additionally,we evaluate the performance of traditional machine learning models as a baseline for comparison.This comprehensive analysis demonstrates the significant impact language models can have on addressing complex text-based challenges within the cybersecurity landscape.展开更多
Blood cell disorders are among the leading causes of serious diseases such as leukemia,anemia,blood clotting disorders,and immune-related conditions.The global incidence of hematological diseases is increasing,affecti...Blood cell disorders are among the leading causes of serious diseases such as leukemia,anemia,blood clotting disorders,and immune-related conditions.The global incidence of hematological diseases is increasing,affecting both children and adults.In clinical practice,blood smear analysis is still largely performed manually,relying heavily on the experience and expertise of laboratory technicians or hematologists.This manual process introduces risks of diagnostic errors,especially in cases with rare or morphologically ambiguous cells.The situation is more critical in developing countries,where there is a shortage of specialized medical personnel and limited access to modern diagnostic tools.High testing costs and delays in diagnosis hinder access to quality healthcare services.In this context,the integration of Artificial Intelligence(AI),particularly Explainable AI(XAI)based on deep learning,offers a promising solution for improving the accuracy,efficiency,and transparency of hematological diagnostics.In this study,we propose a Ghost Residual Network(GRsNet)integrated with XAI techniques such as Gradient-weighted Class Activation Mapping(Grad-CAM),Local Interpretable Model-Agnostic Explanations(LIME),and SHapley Additive exPlanations(SHAP)for automatic blood cell classification.These techniques provide visual explanations by highlighting important regions in the input images,thereby supporting clinical decision-making.The proposed model is evaluated on two public datasets:Naturalize 2K-PBC and Microscopic Blood Cell,achieving a classification accuracy of up to 95%.The results demonstrate the model’s strong potential for automated hematological diagnosis,particularly in resource-constrained settings.It not only enhances diagnostic reliability but also contributes to advancing digital transformation and equitable access to AI-driven healthcare in developing regions.展开更多
This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for...This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for feature extraction from the PC.The effects of different neighborhood sizes(k=5,10,20,50,and 100)have been evaluated to assess their impact on classification performance.After that,17 geometric and spatial features were extracted from the PC.Next,ensemble methods,AdaBoost.M2,random forest,and decision tree,have been compared with Artificial Neural Networks to classify the main discontinuity sets.The McNemar test indicates that the classifiers are statistically significant.The random forest classifier consistently achieves the highest performance with an accuracy exceeding 95%when using a neighborhood size of k=100,while recall,F-score,and Cohen's Kappa also demonstrate high success.SHapley Additive exPlanations(SHAP),an Explainable AI technique,has been used to evaluate feature importance and improve the explainability of black-box machine learning models in the context of rock discontinuity classification.The analysis reveals that features such as normal vectors,verticality,and Z-values have the greatest influence on identifying main discontinuity sets,while linearity,planarity,and eigenvalues contribute less,making the model more transparent and easier to understand.After classification,individual discontinuity sets were detected using a revised DBSCAN from the main discontinuity sets.Finally,the orientation parameters of the plane fitted to each discontinuity were derived from the plane parameters obtained using the Random Sample Consensus(RANSAC).Two real-world datasets(obtained from SfM and LiDAR)and one synthetic dataset were used to validate the proposed method,which successfully identified rock discontinuities and their orientation parameters(dip angle/direction).展开更多
In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and ...In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and machine learning(ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics.Furthermore,newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable.Hence,effective detection depends on identifying the most critical features.Traditional feature selection(FS)methods often struggle to enhance ML model performance and instead decrease it.To combat these issues,we propose an innovative method using explainable AI(XAI)to enhance FS in ML models and improve the identification of phishing websites.Specifically,we employ SHapley Additive exPlanations(SHAP)for global perspective and aggregated local interpretable model-agnostic explanations(LIME)to deter-mine specific localized patterns.The proposed SHAP and LIME-aggregated FS(SLA-FS)framework pinpoints the most informative features,enabling more precise,swift,and adaptable phishing detection.Applying this approach to an up-to-date web phishing dataset,we evaluate the performance of three ML models before and after FS to assess their effectiveness.Our findings reveal that random forest(RF),with an accuracy of 97.41%and XGBoost(XGB)at 97.21%significantly benefit from the SLA-FS framework,while k-nearest neighbors lags.Our framework increases the accuracy of RF and XGB by 0.65%and 0.41%,respectively,outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset,showcasing its potential.展开更多
Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)...Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)to clarify the contribution of each input feature in USS prediction.Three ML models,artificial neural network(ANN),extreme gradient boosting(XGBoost),and random forest(RF),were employed,with accuracy evaluated using mean squared error,mean absolute error,and coefficient of determination(R^(2)).The RF achieved the highest performance with an R^(2) of 0.82.SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction.SHAP dependence plots reveal that the ANN captures smoother,linear feature-output relationships,while the RF handles complex,non-linear interactions more effectively.This suggests a non-linear relationship between USS and input features,with RF outperforming ANN.These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.展开更多
Although digital changes in power systems have added more ways to monitor and control them,these changes have also led to new cyber-attack risks,mainly from False Data Injection(FDI)attacks.If this happens,the sensors...Although digital changes in power systems have added more ways to monitor and control them,these changes have also led to new cyber-attack risks,mainly from False Data Injection(FDI)attacks.If this happens,the sensors and operations are compromised,which can lead to big problems,disruptions,failures and blackouts.In response to this challenge,this paper presents a reliable and innovative detection framework that leverages Bidirectional Long Short-Term Memory(Bi-LSTM)networks and employs explanatory methods from Artificial Intelligence(AI).Not only does the suggested architecture detect potential fraud with high accuracy,but it also makes its decisions transparent,enabling operators to take appropriate action.Themethod developed here utilizesmodel-free,interpretable tools to identify essential input elements,thereby making predictions more understandable and usable.Enhancing detection performance is made possible by correcting class imbalance using Synthetic Minority Over-sampling Technique(SMOTE)-based data balancing.Benchmark power system data confirms that the model functions correctly through detailed experiments.Experimental results showed that Bi-LSTM+Explainable AI(XAI)achieved an average accuracy of 94%,surpassing XGBoost(89%)and Bagging(84%),while ensuring explainability and a high level of robustness across various operating scenarios.By conducting an ablation study,we find that bidirectional recursive modeling and ReLU activation help improve generalization and model predictability.Additionally,examining model decisions through LIME enables us to identify which features are crucial for making smart grid operational decisions in real time.The research offers a practical and flexible approach for detecting FDI attacks,improving the security of cyber-physical systems,and facilitating the deployment of AI in energy infrastructure.展开更多
基金the National Natural Science Foundation of China(42377170,42407212)the National Funded Postdoctoral Researcher Program(GZB20230606)+3 种基金the Postdoctoral Research Foundation of China(2024M752679)the Sichuan Natural Science Foundation(2025ZNSFSC1205)the National Key R&D Program of China(2022YFC3005704)the Sichuan Province Science and Technology Support Program(2024NSFSC0100)。
文摘Wildfires significantly disrupt the physical and hydrologic conditions of the environment,leading to vegetation loss and altered surface geo-material properties.These complex dynamics promote post-fire gully erosion,yet the key conditioning factors(e.g.,topography,hydrology)remain insufficiently understood.This study proposes a novel artificial intelligence(AI)framework that integrates four machine learning(ML)models with Shapley Additive Explanations(SHAP)method,offering a hierarchical perspective from global to local on the dominant factors controlling gully distribution in wildfireaffected areas.In a case study of Xiangjiao catchment burned on March 28,2020,in Muli County in Sichuan Province of Southwest China,we derived 21 geoenvironmental factors to assess the susceptibility of post-fire gully erosion using logistic regression(LR),support vector machine(SVM),random forest(RF),and convolutional neural network(CNN)models.SHAP-based model interpretation revealed eight key conditioning factors:topographic position index(TPI),topographic wetness index(TWI),distance to stream,mean annual precipitation,differenced normalized burn ratio(d NBR),land use/cover,soil type,and distance to road.Comparative model evaluation demonstrated that reduced-variable models incorporating these dominant factors achieved accuracy comparable to that of the initial-variable models,with AUC values exceeding 0.868 across all ML algorithms.These findings provide critical insights into gully erosion behavior in wildfire-affected areas,supporting the decision-making process behind environmental management and hazard mitigation.
文摘Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.
文摘In the field of precision healthcare,where accurate decision-making is paramount,this study underscores the indispensability of eXplainable Artificial Intelligence(XAI)in the context of epilepsy management within the Internet of Medical Things(IoMT).The methodology entails meticulous preprocessing,involving the application of a band-pass filter and epoch segmentation to optimize the quality of Electroencephalograph(EEG)data.The subsequent extraction of statistical features facilitates the differentiation between seizure and non-seizure patterns.The classification phase integrates Support Vector Machine(SVM),K-Nearest Neighbor(KNN),and Random Forest classifiers.Notably,SVM attains an accuracy of 97.26%,excelling in the precision,recall,specificity,and F1 score for identifying seizures and non-seizure instances.Conversely,KNN achieves an accuracy of 72.69%,accompanied by certain trade-offs.The Random Forest classifierstands out with a remarkable accuracy of 99.89%,coupled with an exceptional precision(99.73%),recall(100%),specificity(99.80%),and F1 score(99.86%),surpassing both SVM and KNN performances.XAI techniques,namely Local Interpretable ModelAgnostic Explanations(LIME)and SHapley Additive exPlanation(SHAP),enhance the system’s transparency.This combination of machine learning and XAI not only improves the reliability and accuracy of the seizure detection system but also enhances trust and interpretability.Healthcare professionals can leverage the identified important features and their dependencies to gain deeper insights into the decision-making process,aiding in informed diagnosis and treatment decisions for patients with epilepsy.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01395).
文摘Problem:The integration of Artificial Intelligence(AI)into cybersecurity,while enhancing threat detection,is hampered by the“black box”nature of complex models,eroding trust,accountability,and regulatory compliance.Explainable AI(XAI)aims to resolve this opacity but introduces a critical newvulnerability:the adversarial exploitation of model explanations themselves.Gap:Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector.There is a pressing need to systematically analyze the trade-offs between interpretability and security,evaluate defense mechanisms,and outline a path for developing robust,next-generation XAI frameworks.Solution:This review provides a systematic examination of XAI techniques(e.g.,SHAP,LIME,Grad-CAM)and their applications in intrusion detection,malware analysis,and fraud prevention.It critically evaluates the security risks posed by XAI,including model inversion and explanation-guided evasion attacks,and assesses corresponding defense strategies such as adversarially robust training,differential privacy,and secure-XAI deployment patterns.Contribution:Theprimary contributions of this work are:(1)a comparative analysis of XAI methods tailored for cybersecurity contexts;(2)an identification of the critical trade-off betweenmodel interpretability and security robustness;(3)a synthesis of defense mechanisms to mitigate XAI-specific vulnerabilities;and(4)a forward-looking perspective proposing future research directions,including quantum-safe XAI,hybrid neuro-symbolic models,and the integration of XAI into Zero Trust Architectures.This review serves as a foundational resource for developing transparent,trustworthy,and resilient AI-driven cybersecurity systems.
基金funded by the Excellent Talent Training Funding Project in Dongcheng District,Beijing,with project number 2024-dchrcpyzz-9.
文摘Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predisposition,environmental exposure,unhealthy lifestyle habits,and existing medical conditions.Although existing machine learning-based methods for predicting stroke patients’health status have made significant progress,limitations remain in terms of prediction accuracy,model explainability,and system optimization.This paper proposes a multi-task learning approach based on Explainable Artificial Intelligence(XAI)for predicting the health status of stroke patients.First,we design a comprehensive multi-task learning framework that utilizes the task correlation of predicting various health status indicators in patients,enabling the parallel prediction of multiple health indicators.Second,we develop a multi-task Area Under Curve(AUC)optimization algorithm based on adaptive low-rank representation,which removes irrelevant information from the model structure to enhance the performance of multi-task AUC optimization.Additionally,the model’s explainability is analyzed through the stability analysis of SHAP values.Experimental results demonstrate that our approach outperforms comparison algorithms in key prognostic metrics F1 score and Efficiency.
文摘The diagnosis of brain tumors is an extended process that significantly depends on the expertise and skills of radiologists.The rise in patient numbers has substantially elevated the data processing volume,making conventional methods both costly and inefficient.Recently,Artificial Intelligence(AI)has gained prominence for developing automated systems that can accurately diagnose or segment brain tumors in a shorter time frame.Many researchers have examined various algorithms that provide both speed and accuracy in detecting and classifying brain tumors.This paper proposes a newmodel based on AI,called the Brain Tumor Detection(BTD)model,based on brain tumor Magnetic Resonance Images(MRIs).The proposed BTC comprises three main modules:(i)Image Processing Module(IPM),(ii)Patient Detection Module(PDM),and(iii)Explainable AI(XAI).In the first module(i.e.,IPM),the used dataset is preprocessed through two stages:feature extraction and feature selection.At first,the MRI is preprocessed,then the images are converted into a set of features using several feature extraction methods:gray level co-occurrencematrix,histogramof oriented gradient,local binary pattern,and Tamura feature.Next,the most effective features are selected fromthese features separately using ImprovedGrayWolfOptimization(IGWO).IGWOis a hybrid methodology that consists of the Filter Selection Step(FSS)using information gain ratio as an initial selection stage and Binary Gray Wolf Optimization(BGWO)to make the proposed method better at detecting tumors by further optimizing and improving the chosen features.Then,these features are fed to PDM using several classifiers,and the final decision is based on weighted majority voting.Finally,through Local Interpretable Model-agnostic Explanations(LIME)XAI,the interpretability and transparency in decision-making processes are provided.The experiments are performed on a publicly available Brain MRI dataset that consists of 98 normal cases and 154 abnormal cases.During the experiments,the dataset was divided into 70%(177 cases)for training and 30%(75 cases)for testing.The numerical findings demonstrate that the BTD model outperforms its competitors in terms of accuracy,precision,recall,and F-measure.It introduces 98.8%accuracy,97%precision,97.5%recall,and 97.2%F-measure.The results demonstrate the potential of the proposed model to revolutionize brain tumor diagnosis,contribute to better treatment strategies,and improve patient outcomes.
基金funded by Woosong University Academic Research 2024.
文摘Machine fault diagnostics are essential for industrial operations,and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions.Machine learning models,especially those utilizing complex algorithms like deep learning,have demonstrated major potential in extracting important information fromlarge operational datasets.Despite their efficiency,machine learningmodels face challenges,making Explainable AI(XAI)crucial for improving their understandability and fine-tuning.The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study.The technique is applied to evaluate different machine-learning algorithms.Extreme Gradient Boosting,Support Vector Machine,Gaussian Naive Bayes,and Random Forest classifiers are used alongside Logistic Regression(LR)as a baseline model because their efficacy and simplicity are evaluated thoroughly with empirical analysis.The XAI is used as a targeted feature selection technique to select among 29 features of the time and frequency domain.The XAI approach is lightweight,trained with only targeted features,and achieved similar results as the traditional approach.The accuracy without XAI on baseline LR is 79.57%,whereas the approach with XAI on LR is 80.28%.
文摘Neonatal sepsis is the third most common cause of neonatal mortality and a serious public health problem,especially in developing countries.There have been researches on human sepsis,vaccine response,and immunity.Also,machine learning methodologies were used for predicting infant mortality based on certain features like age,birth weight,gestational weeks,and Appearance,Pulse,Grimace,Activity and Respiration(APGAR)score.Sepsis,which is considered the most determining condition towards infant mortality,has never been considered for mortality prediction.So,we have deployed a deep neural model which is the state of art and performed a comparative analysis of machine learning models to predict the mortality among infants based on the most important features including sepsis.Also,for assessing the prediction reliability of deep neural model which is a black box,Explainable AI models like Dalex and Lime have been deployed.This would help any non-technical personnel like doctors and practitioners to understand and accordingly make decisions.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R432),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Breast cancer is a type of cancer responsible for higher mortality rates among women.The cruelty of breast cancer always requires a promising approach for its earlier detection.In light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors.In addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram.Accordingly,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of mammograms.For this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS databases.Maximumclassification accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing models.Furthermore,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.
基金This research is funded by the Researchers Supporting Project Number(RSPD2024R1027),King Saud University,Riyadh,Saudi Arabia.
文摘Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more aggressive.The research introduces an innovative custom convolutional neural network(CNN)model,Glioma-CNN.GliomaCNN stands out as a lightweight CNN model compared to its predecessors.The research utilized the BraTS 2020 dataset for its experiments.Integrated with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification outcomes.Despite challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.
基金the Deanship for Research Innovation,Ministry of Education in Saudi Arabia,for funding this research work through project number IFKSUDR-H122.
文摘In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.
文摘In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse systems.Despite the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain threats.This paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT environments.An effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time complexity.By leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world scenarios.Additionally,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack detection.The experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.
文摘Traditional machine learning metrics(TMLMs)are quite useful for the current research work precision,recall,accuracy,MSE and RMSE.Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%.We included in the prediction process,machine learning models(MLMs)with greater than 99%accuracy with a sensitivity of 95%–98%and specifically in the database.We need to explain the model to domain specialists through the MLMs.Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model.This is achieved by creating a model-independent,locally accurate explanation set that makes it better than the primary model.As we know that human interaction with machine learning systems on this model’s interpretability is more crucial.For supporting set validations in model selection insurance premium prediction.In this study,we proposed the use of the(LIME and SHAP)approach to understand research properly and explain a model developed using random forest regression to predict insurance premiums.The SHAP algorithm’s drawback,as seen in our experiments,is its lengthy computing time—to produce the findings,it must compute every possible combination.In addition,the experiments conducted were intended to focus on the model’s interpretability and explain its ability using LIME and SHAP,not the insurance premium charge prediction.Three experiments were conducted through experiment,one was to interpret the random forest regression model using LIME techniques.In experiment 2,we used the SHAP technique to interpret the model insurance premium prediction(IPP).
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)support program(IITP-2025-RS-2023-00259497)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Republic of Korea government(MSIT)(No.IITP-2025-RS-2023-00254129+1 种基金Graduate School of Metaverse Convergence(Sungkyunkwan University))was supported by the Basic Science Research Program of the National Research Foundation(NRF)funded by the Republic of Korean government(MSIT)(No.RS-2024-00346737).
文摘Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.
文摘Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartphones and increased Internet connectivity,SMS spam has emerged as a prevalent threat.Spammers have recognized the critical role SMS plays in today’s modern communication,making it a prime target for abuse.As cybersecurity threats continue to evolve,the volume of SMS spam has increased substantially in recent years.Moreover,the unstructured format of SMS data creates significant challenges for SMS spam detection,making it more difficult to successfully combat spam attacks.In this paper,we present an optimized and fine-tuned transformer-based Language Model to address the problem of SMS spam detection.We use a benchmark SMS spam dataset to analyze this spam detection model.Additionally,we utilize pre-processing techniques to obtain clean and noise-free data and address class imbalance problem by leveraging text augmentation techniques.The overall experiment showed that our optimized fine-tuned BERT(Bidirectional Encoder Representations from Transformers)variant model RoBERTa obtained high accuracy with 99.84%.To further enhance model transparency,we incorporate Explainable Artificial Intelligence(XAI)techniques that compute positive and negative coefficient scores,offering insight into the model’s decision-making process.Additionally,we evaluate the performance of traditional machine learning models as a baseline for comparison.This comprehensive analysis demonstrates the significant impact language models can have on addressing complex text-based challenges within the cybersecurity landscape.
文摘Blood cell disorders are among the leading causes of serious diseases such as leukemia,anemia,blood clotting disorders,and immune-related conditions.The global incidence of hematological diseases is increasing,affecting both children and adults.In clinical practice,blood smear analysis is still largely performed manually,relying heavily on the experience and expertise of laboratory technicians or hematologists.This manual process introduces risks of diagnostic errors,especially in cases with rare or morphologically ambiguous cells.The situation is more critical in developing countries,where there is a shortage of specialized medical personnel and limited access to modern diagnostic tools.High testing costs and delays in diagnosis hinder access to quality healthcare services.In this context,the integration of Artificial Intelligence(AI),particularly Explainable AI(XAI)based on deep learning,offers a promising solution for improving the accuracy,efficiency,and transparency of hematological diagnostics.In this study,we propose a Ghost Residual Network(GRsNet)integrated with XAI techniques such as Gradient-weighted Class Activation Mapping(Grad-CAM),Local Interpretable Model-Agnostic Explanations(LIME),and SHapley Additive exPlanations(SHAP)for automatic blood cell classification.These techniques provide visual explanations by highlighting important regions in the input images,thereby supporting clinical decision-making.The proposed model is evaluated on two public datasets:Naturalize 2K-PBC and Microscopic Blood Cell,achieving a classification accuracy of up to 95%.The results demonstrate the model’s strong potential for automated hematological diagnosis,particularly in resource-constrained settings.It not only enhances diagnostic reliability but also contributes to advancing digital transformation and equitable access to AI-driven healthcare in developing regions.
文摘This study presents a framework for the semi-automatic detection of rock discontinuities using a threedimensional(3D)point cloud(PC).The process begins by selecting an appropriate neighborhood size,a critical step for feature extraction from the PC.The effects of different neighborhood sizes(k=5,10,20,50,and 100)have been evaluated to assess their impact on classification performance.After that,17 geometric and spatial features were extracted from the PC.Next,ensemble methods,AdaBoost.M2,random forest,and decision tree,have been compared with Artificial Neural Networks to classify the main discontinuity sets.The McNemar test indicates that the classifiers are statistically significant.The random forest classifier consistently achieves the highest performance with an accuracy exceeding 95%when using a neighborhood size of k=100,while recall,F-score,and Cohen's Kappa also demonstrate high success.SHapley Additive exPlanations(SHAP),an Explainable AI technique,has been used to evaluate feature importance and improve the explainability of black-box machine learning models in the context of rock discontinuity classification.The analysis reveals that features such as normal vectors,verticality,and Z-values have the greatest influence on identifying main discontinuity sets,while linearity,planarity,and eigenvalues contribute less,making the model more transparent and easier to understand.After classification,individual discontinuity sets were detected using a revised DBSCAN from the main discontinuity sets.Finally,the orientation parameters of the plane fitted to each discontinuity were derived from the plane parameters obtained using the Random Sample Consensus(RANSAC).Two real-world datasets(obtained from SfM and LiDAR)and one synthetic dataset were used to validate the proposed method,which successfully identified rock discontinuities and their orientation parameters(dip angle/direction).
文摘In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and machine learning(ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics.Furthermore,newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable.Hence,effective detection depends on identifying the most critical features.Traditional feature selection(FS)methods often struggle to enhance ML model performance and instead decrease it.To combat these issues,we propose an innovative method using explainable AI(XAI)to enhance FS in ML models and improve the identification of phishing websites.Specifically,we employ SHapley Additive exPlanations(SHAP)for global perspective and aggregated local interpretable model-agnostic explanations(LIME)to deter-mine specific localized patterns.The proposed SHAP and LIME-aggregated FS(SLA-FS)framework pinpoints the most informative features,enabling more precise,swift,and adaptable phishing detection.Applying this approach to an up-to-date web phishing dataset,we evaluate the performance of three ML models before and after FS to assess their effectiveness.Our findings reveal that random forest(RF),with an accuracy of 97.41%and XGBoost(XGB)at 97.21%significantly benefit from the SLA-FS framework,while k-nearest neighbors lags.Our framework increases the accuracy of RF and XGB by 0.65%and 0.41%,respectively,outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset,showcasing its potential.
基金Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study
文摘Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)to clarify the contribution of each input feature in USS prediction.Three ML models,artificial neural network(ANN),extreme gradient boosting(XGBoost),and random forest(RF),were employed,with accuracy evaluated using mean squared error,mean absolute error,and coefficient of determination(R^(2)).The RF achieved the highest performance with an R^(2) of 0.82.SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction.SHAP dependence plots reveal that the ANN captures smoother,linear feature-output relationships,while the RF handles complex,non-linear interactions more effectively.This suggests a non-linear relationship between USS and input features,with RF outperforming ANN.These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.
基金the Deanship of Scientific Research and Libraries in Princess Nourah bint Abdulrahman University for funding this research work through the Research Group project,Grant No.(RG-1445-0064).
文摘Although digital changes in power systems have added more ways to monitor and control them,these changes have also led to new cyber-attack risks,mainly from False Data Injection(FDI)attacks.If this happens,the sensors and operations are compromised,which can lead to big problems,disruptions,failures and blackouts.In response to this challenge,this paper presents a reliable and innovative detection framework that leverages Bidirectional Long Short-Term Memory(Bi-LSTM)networks and employs explanatory methods from Artificial Intelligence(AI).Not only does the suggested architecture detect potential fraud with high accuracy,but it also makes its decisions transparent,enabling operators to take appropriate action.Themethod developed here utilizesmodel-free,interpretable tools to identify essential input elements,thereby making predictions more understandable and usable.Enhancing detection performance is made possible by correcting class imbalance using Synthetic Minority Over-sampling Technique(SMOTE)-based data balancing.Benchmark power system data confirms that the model functions correctly through detailed experiments.Experimental results showed that Bi-LSTM+Explainable AI(XAI)achieved an average accuracy of 94%,surpassing XGBoost(89%)and Bagging(84%),while ensuring explainability and a high level of robustness across various operating scenarios.By conducting an ablation study,we find that bidirectional recursive modeling and ReLU activation help improve generalization and model predictability.Additionally,examining model decisions through LIME enables us to identify which features are crucial for making smart grid operational decisions in real time.The research offers a practical and flexible approach for detecting FDI attacks,improving the security of cyber-physical systems,and facilitating the deployment of AI in energy infrastructure.
