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explainable ai Enabled Infant Mortality Prediction Based on Neonatal Sepsis

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Computer Systems Science & Engineering 2023年第1期44卷 311-325页

作者： Priti Shaw Kaustubh Pachpor Suresh Sankaranarayanan Barclays Bank Bund Garden RoadPune411001India University of Illinois 60607IllinoisUSA SRM Institute of Science and Technology Chennai603203India

Neonatal sepsis is the third most common cause of neonatal mortality and a serious public health problem,especially in developing *** have been researches on human sepsis,vaccine response,and ***,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)***,which is considered the most determining condition towards infant mortality,has never been considered for mortality ***,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 ***,for assessing the prediction reliability of deep neural model which is a black box,explainable ai models like Dalex and Lime have been *** would help any non-technical personnel like doctors and practitioners to understand and accordingly make decisions.

关键词： APGAR sepsis explainable ai machine learning

Machine Fault Diagnosis Using Audio Sensors Data and explainable ai Techniques-LIME and SHAP

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Computers, Materials & Continua 2024年第9期80卷 3463-3484页

作者： Aniqua Nusrat Zereen Abir Das Jia Uddin School of Data and Sciences Brac UniversityDhaka1212Bangladesh JW KIM College of Future Studies Endicott CollegeWoosong UniversityDaejeon300-718Republic of Korea ai and Big Data Department Endicott CollegeWoosong UniversityDaejeon300-718Republic of Korea

Machine fault diagnostics are essential for industrial operations,and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited *** learning models,especially those utilizing complex algorithms like deep learning,have demonstrated major potential in extracting important information fromlarge operational *** their efficiency,machine learningmodels face challenges,making explainable ai(Xai)crucial for improving their understandability and *** importance of feature contribution and selection using Xai in the diagnosis of machine faults is examined in this *** technique is applied to evaluate different machine-learning *** 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 *** Xai is used as a targeted feature selection technique to select among 29 features of the time and frequency *** Xai approach is lightweight,trained with only targeted features,and achieved similar results as the traditional *** accuracy without Xai on baseline LR is 79.57%,whereas the approach with Xai on LR is 80.28%.

关键词： explainable ai feature selection machine learning machine fault diagnosis

GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using explainable ai

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2425-2448页

作者： Md.Atiqur Rahman Mustavi Ibne Masum Khan Md Hasib M.F.Mridha Sultan Alfarhood Mejdl Safran Dunren Che Department of Computer Science and Engineering Ahsanullah University of Science and TechnologyDhaka1208Bangladesh Department of Computer Science and Software Engineering The University of Western AustraliaPerthWA 6009Australia Department of Computer Science American International University-BangladeshDhaka1229Bangladesh Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia School of Computing Southern Illinois UniversityCarbondale62901USA

Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global *** 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 *** research introduces an innovative custom convolutional neural network(CNN)model,*** stands out as a lightweight CNN model compared to its *** research utilized the BraTS 2020 dataset for its *** 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 *** 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.

关键词： Deep learning magnetic resonance imaging convolutional neural networks explainable ai boosting algorithm ablation

Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with explainable ai

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Computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** 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 *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** 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 ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based explainable ai technique.

关键词： EfficientNet mammograms breast cancer explainable ai deep-learning transfer learning

Transparent and Accurate COVID-19 Diagnosis:Integrating explainable ai with Advanced Deep Learning in CT Imaging

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3101-3123页

作者： Mohammad Mehedi Hassan Salman A.AlQahtani Mabrook S.AlRakhami Ahmed Zohier Elhendi Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Science Technology and Innovation Department King Saud UniversityRiyadh11543Saudi Arabia

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 *** 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 *** bridge this gap,our research integrates explainable ai(Xai)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning *** 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 *** approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** 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 *** 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 *** 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 *** innovative approach marks a significant advancement in creating explainable and trustworthy ai tools for medical decisionmaking in the ongoing battle against COVID-19.

关键词： explainable ai COVID-19 CT images deep learning

An explainable ai model for power plant NOx emission control

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Energy and ai 2024年第1期15卷 171-180页

作者： Yuanye Zhou Ioanna Aslanidou Mikael Karlsson Konstantinos Kyprianidis School of Business Society and EngineeringMälardalens UniversityUniversitetsplan 172220VästeråsSweden Future Energy Centre Mälardalens UniversityUniversitetsplan 172220VästeråsSweden School of Innovation Design and EngineeringMälardalens UniversityUniversitetsplan 172220VästeråsSweden

In recent years,developing Artificial Intelligence(ai)models for complex system has become a popular research *** have been several successful ai models for predicting the Selective Non-Catalytic Reduction(SNCR)system in power plants and large ***,all these models are in essence black box models and lack of explainability,which are not able to give new *** this study,a novel explainable ai(Xai)model that combines the polynomial kernel method with Sparse Identification of Nonlinear Dynamics(SINDy)model is proposed to find the governing equation of SNCR system based on 5-year operation data from a power *** proposed model identifies the system’s governing equation in a simple polynomial format with polynomial order of 1 and only 1 independent variable among original 68 input *** addition,the explainable ai model achieves a considerable accuracy with less than 21%deviation from base-line models of partial least squares model and artificial neural network model.

关键词： explainable ai SINDy Kernel SNCR Power plant Boiler

explainable ai and Interpretable Model for Insurance Premium Prediction

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Journal on Artificial Intelligence 2023年第1期5卷 31-42页

作者： Umar Abdulkadir Isa Anil Fernando Department of Computer and Information Science University of StrathclydeGlasgowUK

Traditional machine learning metrics(TMLMs)are quite useful for the current research work precision,recall,accuracy,MSE and *** 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 *** need to explain the model to domain specialists through the ***-understandable explanations in addition to ML professionals must establish trust in the prediction of our *** is achieved by creating a model-independent,locally accurate explanation set that makes it better than the primary *** we know that human interaction with machine learning systems on this model’s interpretability is more *** supporting set validations in model selection insurance premium *** 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 *** SHAP algorithm’s drawback,as seen in our experiments,is its lengthy computing time—to produce the findings,it must compute every possible *** 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 *** experiments were conducted through experiment,one was to interpret the random forest regression model using LIME *** experiment 2,we used the SHAP technique to interpret the model insurance premium prediction(IPP).

关键词： LIME SHAP innovative explainable ai random forest machine learning insurance premium

explainable-ai-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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Science China(Information Sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-ai-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable ai model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable ai solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-ai-based two-stage solution.

关键词： explainable ai object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

CrossLinkNet: An explainable and Trustworthy ai Framework for Whole-Slide Images Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4703-4724页

作者： Peng Xiao Qi Zhong Jingxue Chen Dongyuan Wu Zhen Qin Erqiang Zhou Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China

In the intelligent medical diagnosis area,Artificial Intelligence(ai)’s trustworthiness,reliability,and interpretability are critical,especially in cancer *** neural networks,while excellent at processing natural images,often lack interpretability and adaptability when processing high-resolution digital pathological *** limitation is particularly evident in pathological diagnosis,which is the gold standard of cancer diagnosis and relies on a pathologist’s careful examination and analysis of digital pathological slides to identify the features and progression of the ***,the integration of interpretable ai into smart medical diagnosis is not only an inevitable technological trend but also a key to improving diagnostic accuracy and *** this paper,we introduce an innovative Multi-Scale Multi-Branch Feature Encoder(MSBE)and present the design of the CrossLinkNet *** MSBE enhances the network’s capability for feature extraction by allowing the adjustment of hyperparameters to configure the number of branches and *** CrossLinkNet Framework,serving as a versatile image segmentation network architecture,employs cross-layer encoder-decoder connections for multi-level feature fusion,thereby enhancing feature integration and segmentation *** quantitative and qualitative experiments on two datasets demonstrate that CrossLinkNet,equipped with the MSBE encoder,not only achieves accurate segmentation results but is also adaptable to various tumor segmentation tasks and scenarios by replacing different feature ***,CrossLinkNet emphasizes the interpretability of the ai model,a crucial aspect for medical professionals,providing an in-depth understanding of the model’s decisions and thereby enhancing trust and reliability in ai-assisted diagnostics.

关键词： explainable ai security trustworthy CrossLinkNet whole slide images