Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (xvi, 252 pages).

Edition: First edition.

ISBN: 9781003257721 , 1003257720 , 9781000983609 , 1000983609 , 9781000983654 , 100098365X

Series Statement: Explainable AI (XAI) for Engineering Applications

Content: The text presents concepts of explainable artificial intelligence (XAI) in solving real world biomedical and healthcare problems. It will serve as an ideal reference text for graduate students and academic researchers in diverse fields of engineering including electrical, electronics and communication, computer, and biomedical Presents explainable artificial intelligence (XAI) based machine analytics and deep learning in medical science Discusses explainable artificial intelligence (XA)I with the Internet of Medical Things (IoMT) for healthcare applications Covers algorithms, tools, and frameworks for explainable artificial intelligence on medical data Explores the concepts of natural language processing and explainable artificial intelligence (XAI) on medical data processing Discusses machine learning and deep learning scalability models in healthcare systems This text focuses on data driven analysis and processing of advanced methods and techniques with the help of explainable artificial intelligence (XAI) algorithms. It covers machine learning, Internet of Things (IoT), and deep learning algorithms based on XAI techniques for medical data analysis and processing. The text will present different dimensions of XAI based computational intelligence applications. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and biomedical engineering.

Note: Chapter 1 Explainable AI (XAI): Concepts and TheoryTanvir Habib Sardar, Sunanda Das, Bishwajeet Kumar PandeyAbstractIntroductionFormal Definitions of Explainable Artificial IntelligenceThe Working Mechanism of Explainable Artificial Intelligence: How Explainable Artificial Intelligence Generates ExplanationsHow Humans Reason (with Errors)How Explainable Artificial Intelligence Support Reason and Solve Human Error IssueApplications and Impact Areas of Explainable Artificial IntelligenceThreat DetectionObject DetectionAdversarial ML PreventionOpen Source Intelligence (OSI)Automated Medical DiagnosisAutonomous VehiclesBenefits of Explainable Artificial IntelligenceResearch Challenges of Explainable Artificial IntelligenceUse Cases of Explainable Artificial IntelligenceLimitations of Explainable Artificial IntelligenceConclusionReferencesChapter 2: Utilizing Explainable Artificial Intelligence to Address Deep Learning in Biomedical DomainPriyanka SharmaAbstract2.1 Introduction: Background and Driving Forces2.2 XAI Taxonomy2.3 Review of State of Art 2.3.1 Methods focused on features2.3.2 Global methods2.3.3 Concept Methods2.3.4 Surrogate Methods2.3.5 Local, Pixel-based Techniques2.3.6 Human Centered Methods2.4 Deep Learning -Reshaping Healthcare 2.4.1 Deep Learning Methods2.4.1.1 Multi-layer Perceptron or Deep Feed Forward Neural Network2.4.1.2 Restricted Boltzmann Machine2.4.1.3 Deep Belief Network2.4.1.4 Autoencoder2.4.1.5 Convolutional Neural Network2.4.1.6 Recurrent Neural Network2.4.1.7 Long Short- Term Memory (LSTM) and Gated Recurrent Unit (GRU)2.4.2 Deep Learning Applications in Healthcare2.5 Results2.6 Benefits and Drawbacks of XAI Methods2.7 ConclusionChapter 3 Explainable Fuzzy Decision Tree for Medical Data ClassificationAuthors: Swathi Jamjala Narayanan, Boominathan Perumal, Sangeetha SamanAbstract3.1. Introduction3.2. Literature survey3.3. Fuzzy classification problem3.4. Induction of fuzzy decision tree3.4.1 Fuzzy c-means clustering (FCM)3.4.2 Cluster validity indices and Optimality Condition3.4.2.1 Separation and Compactness (SC)3.4.2.2 Compact Overlap (CO)3.4.2.3 Fukuyama and Sugeno (FS)3.4.2.4 Xie and Beni (XB)3.4.2.5 Partition entropy3.4.2.6 Fuzzy hyper volume (FHV)3.4.2.7 PBMF3.4.2.8 Partition coefficient3.4.3 Basics of developing Fuzzy ID33.5. Case Study: Explainable FDT for HCV Medical Data3.6. Conclusion and Future workChapter 4 Statistical Algorithm for Change Point Detection in Multivariate Time Series of Medicine Data Based on Principles of Explainable Artificial IntelligenceD. Klyushin, A. UrazovskyiAbstract4.1 Introduction4.2 Detection of change points in multivariate time series4.3 Petuninʼs ellipses and ellipsoids4.4 Numerical experiments4.4.1 Almost non-overlapped uniform distributions with different locations4.4.2 Uniform distributions with different locations that initially are strongly overlapped, then slightly overlapped, and finally are not overlapped4.4.3 Almost non-overlapped normal distributions with different locations4.4.4 Normal distributions with the same location and scales that are gradually begin to differ4.4.5Normal distributions with the same locations and strongly different scales4.4.6. Exponential distributions with different parameters4.4.7 Gamma-distributions with the same location and different scales4.4.8Gamma-distributions with different locations and the same scale4.4.9Gumbel distributions with different locations and the same scale4.4.10Gumbel distributions with the same location and different scales4.4.11 Rayleighdistributions with different scales4.4.12Laplacedistributions with different means and the same variance4.4.13 Laplacedistributions with the same location and different scales4.4.14 Logistic distributions with different locations and the same scale4.4.15 Logistic distributions with the same location and different scales4.4.16 Conclusion on numerical experiments4.5 Quasi-real experiments4.5.1 Simulation of tachycardia4.5.2 Simulation of coronavirus pneumonia4.5.3 Simulation of cancer lung4.5.4 Simulation of physical activity4.5.5 Simulation of stress/panic attack4.5.6 Conclusion on quasi-real experiments4.6 Conclusion4.7 ReferencesChapter 5 XAI and Machine learning for Cyber security: A Systematic Review Gousia Habib*, Shaima Qureshi5.1. Introduction to Explainable AI (XAI). 5.2 Principles followed by XAI Algorithm.5.3 Types of Explainability.5.4 Some Critical Applications of Explainability 5.5 Related Work. 5.6 Historical Origins of the Need for Explainable AI.5.7 Taxonomy of MAP of explainability approaches. 5.8 Challenges posed by XAI.5.8.1 A Black Box Attack on XAI in Cybersecurity.5.8.2 Manipulation of Adversarial Models to Deceive Neural Network Interpretations. 5.8.3 Geometry is responsible for the manipulation of explanations. 5.8.4 Saliency Method's Unreliability.5.8.5 Misleading Black Box Explanations are used to manipulate user trust. 5.9 Various Suggested Solutions for XAI security Challenges 5.9.1 Addressing Manipulation of User Trust through Misleading Black Box Explanations. 5.9.2 Improved Interpretability of Deep Learning.5.9.3 Heat-map explanations Defense against adversarial cyber-attacks.5.9.4 Curvature minimization.5.9.5 Weight decay.5.9.6 Smoothing activation functions.5.10 Conclusion ReferencesChapter 6 Classification and regression tree (CART) modelling approach to predict the number of lymph node dissection among endometrial cancer patientsPrafulla Kumar Swain, Manas Ranjan Tripathy, Pravat Kumar Sarangi, Smruti Sudha PattnaikAbstract6.1 Introduction6.2 Data source6.3 Methods used6.3.1 Regression Tree6.3.2 Optimal threshold value (cut off point)6.3.3 Regression tree algorithm6.3.4 Optimal threshold value (cut off point)6.3.5 Validation of models6.4 Applications to EC Data6.5 Discussion6.6 ConclusionReferencesChapter 7: Automated Brain Tumor Analysis using Deep Learning based FrameworkAmiya Halder, Rudrajit Choudhuri, Apurba Sarkar 7.1 Introduction 7.2 Related Works7.3 Background7.3.1 Autoencoder7.3.2 Convolutional Autoencoder7.3.3 Pre-trained Deep Classification Architectures7.4 Proposed Methodology7.4.1 Image Denoising7.4.2 Tumor Detection and Tumor Grade Classification7.4.2.1 Data Acquisition7.4.2.2 Experimental Setup: Fine Tuning the Architectures7.4.3 Model Training7.5 Result Analysis7.5.1 Evaluation Metrics7.5.2 Performance Evaluation7.6 ConclusionChapter 8 A Robust Framework for Prediction of Diabetes Mellitus using Machine LearningSarthak Singh, Rohan Singh, Arkaprovo Ghosal, Tanmaya MahapatraAbstract8.1 introduction8.2 Background8.3 Related Work 8.4 Conceptual Approach8.5 Evaluation8.6 Discussion8.7 ConclusionReferencesChapter 9 Effective Feature Extraction for Early Recognition and Classification of Triple Modality Breast Cancer Images Using Logistic Regression AlgorithmManjula Devarakonda Venkata , Sumalatha LingamguntaAbstract9.1 Introduction9.2 Symptoms of Breast Cancer9.3 Need for Early detection9.4 Datasets used9.5 Classification of Medical features from three modalities using Logistic Regression Algorithm9.5.1 Pre processing9.6 Results9.6.1 Pre-processed US images9.6.2 Preprocessed Mammogram Image9.6.3 Pre processed MRI images9.7 ConclusionReferencesChapter 10: Machine Learning and Deep Learning Models Used to Detect Diabetic Retinopathy and Its StagesS. Karthika, M. DurgadeviAbstractIntroductionConventional ML & DL AlgorithmsML - Support Vector Machine (ML- SVM) ML - K_Nearest Neighbors (ML- KNN) ML - Random Forest (ML- RF) ML - Neural Networks (ML- NN) Deep Learning (DL) DL - Classic Neural NetworksDL - Convolutional Neural Networks (DL- CNN)DL - LSTMNs (Long Short-Term Memory Networks) DL - Recurrent Neural Networks (DL- RNN)DL - Generative Adversarial Networks (DL - GAN)DL -Reinforcement LearningRetinal Image Datasets used in DR detectionDR Process DetectionNon-Proliferative Diabetic RetinopathyProliferative diabetic retinopathyTechniques for Detecting Microaneurysms (MA) Techniques for Detecting Hemorrhage (HEM)Techniques for Detecting Exudate (E , Siva Sathya11.1 Introduction11.2 Components of NLP11.2.1 Natural Language Understanding (NLU)11.2.2 Natural Language Generation (NLG)11.3 Stages of NLP11.3.1 Phonological Analysis11.3.2 Morphological and Lexical Analysis11.3.3 Syntactic Analysis11.3.4 Semantic Analysis11.3.5 Discourse Integration 11.3.6 Pragmatic Analysis11.4 Applications & Techniques11.4.1 Optical Character Recognition (OCR) 11.4.2 Named Entity Recognition (NER) 11.4.3 Question Answering 11.4.4 Chatbots11.4.5 Machine Translation11.4.6 Sentiment Analysis11.4.7 Topic Modelling11.4.8 Automatic Text Summarization (ATS)11.4.9 Co-reference Resolution11.4.10 Disease Prediction11.4.11 Text Classification11.4.12 Cognitive Assistant (CA)11.4.13 Automatic Speech Recognition (ASR)11.5 NLP Systems in Health Care 11.6 ConclusionReferences

Language: English

DOI: 10.1201/9781003257721

URL: https://www.taylorfrancis.com/books/9781003257721