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ویرایش: نویسندگان: Ankur Saxena, Nicolas Brault, Shazia Rashid سری: Big Data for Industry 4.0: Challenges and Applications ISBN (شابک) : 2020056220, 9781003093770 ناشر: CRC Press سال نشر: 2021 تعداد صفحات: [287] زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 33 Mb
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Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface Editors Contributors Part I Conceptual Chapter 1 Introduction to Big Data 1.1 Big Data: Introduction 1.2 Big Data: 5 Vs 1.2.1 Volume 1.2.2 Velocity 1.2.3 Variety 1.2.4 Veracity 1.2.5 Value 1.3 Big Data: Types of Data 1.3.1 Structured Data 1.3.2 Semi structured Data 1.3.3 Unstructured Data 1.4 Big Data Analysis: Tools and Their Installations 1.5 Big Data: Commands 1.6 Big Data: Applications 1.7 Big Data: Challenges References Chapter 2 Introduction to Machine Learning 2.1 Introduction to Machine Learning 2.2 Artificial Intelligence 2.3 Python Libraries Used in Machine Learning 2.4 Classification of Machine Learning Based on Signals and Feedback 2.4.1 Supervised Learning 2.4.2 Unsupervised Learning 2.4.3 Reinforcement Learning 2.4.4 Semisupervised Learning 2.5 Data Preprocessing Using Python in Machine Learning 2.6 Types of Machine Learning on the Basis of Output to Be Predicted 2.6.1 Regression 2.6.2 Classification 2.6.3 Clustering 2.7 Natural Language Processing for Big Data 2.8 Big Data with Deep Learning 2.9 How Machine Learning Can Be Applied to Big Data 2.10 Machine Learning in Healthcare References Part II Application Chapter 3 Machine Learning in Clinical Trials: A New Era 3.1 Introduction 3.2 ML-Based Algorithms and Methods 3.2.1 Support Vector Machine 3.2.2 Decision Trees 3.3 ML-Based Devices in Clinical Trials 3.3.1 ML-Based Classifiers and Sensor Data to Diagnose Neurological Issues in Stroke Patients 3.3.2 Detection of Severe Wounds and the Risk of Infection 3.3.3 Bone Age Analysis 3.3.4 Smart Watch and Shoulder Physiotherapy 3.4 Machine Learning in the Healthcare Sector 3.5 Machine Learning in Clinical Trials 3.5.1 Alzheimer’s Disease (AD) 3.5.2 Parkinson’s Disease (PD) 3.5.3 Attention-Deficit Hyperactivity Disorder (ADHD) 3.5.4 Cancer 3.5.5 Heart and the Circulatory System Disorders 3.6 Challenges and Future Scope in Clinical Trials with Machine Learning 3.6.1 Future Scope 3.7 Conclusion References Chapter 4 Deep Learning and Its Biological and Biomedical Applications 4.1 Introduction 4.1.1 (I) Application of Deep Learning in Biological Data Analysis 4.2 Read Quality Control Analysis 4.2.1 MiniScrub: De Novo Nanopore Read Quality Improvement Using Deep Learning 4.2.2 Deepbinner 4.3 Genome Assembly 4.3.1 CONNET: Accurate Genome Consensus in Assembling Nanopore Sequencing Data via Deep Learning 4.4 Application in Metagenomics 4.4.1 DeepARG: A Deep Learning Approach for Predicting Antibiotic Resistance Genes (ARGs) from Metagenomic Data 4.4.2 NanoARG: A Web Service for Detecting and Contextualizing Antimicrobial Resistance Genes from Nanopore-Derived Metagenomes 4.4.3 DeepBGC: A Deep Learning Genome-Mining Strategy for Biosynthetic Gene Cluster (BGC) Prediction 4.4.4 MetaPheno: A Critical Evaluation of Deep Learning and Machine Learning in Metagenome-Based Disease Prediction 4.4.5 MetagenomicDC 4.4.6 DEEPre: Sequence-Based Enzyme Commission (EC) Number Prediction by Deep Learning 4.4.7 DeepMAsED: Evaluating the Quality of Metagenomic Assemblies 4.4.8 DeepMicrobes: Taxonomic Classification for Metagenomics with Deep Learning 4.4.9 Meta-MFDL: Gene Prediction in Metagenomic Fragments with Deep Learning 4.4.10 IDMIL 4.5 Variant Calling from NGS Data 4.5.1 GARFIELD-NGS: Genomic vARiants FIltering by dEep Learning moDels in NGS 4.5.2 DeepSVR: A Deep Learning Approach to Automate Refinement of Somatic Variant Calling from Cancer Sequencing Data 4.5.3 DeepVariant: A Universal SNP and Small-Indel Variant Caller Using Deep Neural Networks 4.5.4 Clairvoyante: A Multi-Task Convolutional Deep Neural Network for Variant Calling in Single-Molecule Sequencing 4.6 SNP Effect Prediction 4.6.1 DeepSEA: Predicting Effects of Non-Coding Variants with Deep-Learning-Based Sequence Model 4.6.2 DANN: A Deep Learning Approach for Annotating the Pathogenicity of Genetic Variants 4.6.3 DeepMAsED 4.6.4 DeFine 4.7 Gene Expression Analysis (Bulk RNASEq, Single-Cell RNAseq) 4.7.1 Decode 4.7.2 DESC 4.7.3 scAnCluster: Integrating Deep-Supervised, Self-Supervised, and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation 4.7.4 Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data 4.8 Transcription Factor/Enhancer (ChipSeq) 4.8.1 Enhancer Recognition and Prediction during Spermatogenesis Based on Deep Convolutional Neural Networks 4.8.2 DeepEnhancer: Predicting enhancers with deep convolutional neural networks 4.8.3 DeepHistone: A Deep Learning Approach to Predict Histone Modifications 4.8.4 An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites (TFBSs) Using Deep Learning 4.9 RNA Protein Interaction Prediction 4.9.1 Recent Methodology Progress of Deep Learning for RNA-Protein Interaction Prediction 4.9.2 iDEEP: RNA-Protein Binding Motifs Mining with a New Hybrid Deep-Learning-Based Cross-Domain Knowledge Integration Approach 4.9.3 iDEEPS: Prediction of RNA-Protein Sequence and Structure Binding Preferences Using Deep Convolutional and Recurrent Neural Networks 4.9.4 Applications of Deep Learning in Biomedical Research 4.10 Deep Learning in Disease Diagnosis 4.10.1 Breast Cancer Screening Commercially Available Solutions 4.10.2 Early Melanoma Detection Commercially Available Solutions 4.10.3 Lung Cancer Screening Commercially Available Solutions 4.10.4 Diabetic Retinopathy Screening Commercially Available Solutions 4.10.5 Cardiac Risk Assessment from Electrocardiograms (ECGs) Commercially Available Solution 4.10.6 Early Stroke Diagnosis from Head CT Scans Commercially Available Solutions 4.11 Deep Learning in Diagnosis of Rare Diseases 4.12 Conclusions Bibliography Chapter 5 Applications of Machine Learning Algorithms to Cancer Data 5.1 Introduction 5.2 Overview of Feature Selection Approaches 5.2.1 Main Steps in Feature Selection 5.2.1.1 Preprocessing Step 5.2.1.2 Determining the Direction of Selection 5.2.1.3 Determining the Stopping Criteria 5.2.1.4 Evaluating the Selection 5.2.1.5 Validation Methods 5.2.2 Challenges in Feature Selection 5.3 Overview of Classification Methods 5.3.1 Overview of Popular ML Algorithms 5.4 Recent Applications of ML in Cancer Diagnosis/Classification 5.5 Web-Based Tools 5.6 Conclusion References Chapter 6 Pancreatic Cancer Detection by an Integrated Level Set-Based Deep Learning Model 6.1 Introduction 6.2 Related Work 6.3 Integrated Level Set-Based Deep Learning 6.4 Laplacian-Based Preprocessing 6.5 Integrated Level Set-Based Segmentation 6.6 N-Ternary Patterns 6.7 CNN-Based Deep Learning 6.8 Performance Analysis 6.9 Conclusion References Chapter 7 Early and Precision-Oriented Detection of Cervical Cancer: A Deep-Learning-Based Framework 7.1 Introduction 7.2 Deep Learning Networks and Cervical Cancer 7.3 Deep Learning Models 7.4 Deep-Learning-Based Classification of Cervical Cancer 7.4.1 Image Preprocessing and Data Augmentation 7.4.2 Region of Interest Extraction 7.4.3 Feature Extraction and Mapping 7.4.4 Cervical Cancer Classification 7.5 Results and Observations 7.5.1 Cervical Cancer Dataset 7.5.2 Model Validation 7.5.3 Classification Results 7.6 Limitations of Deep Learning for Cancer Prediction and Future Possibilities 7.7 Conclusion Acknowledgments References Chapter 8 Transformation of mHealth in Society 8.1 What Is mHealth? 8.2 P’s of mHealth 8.3 Constituents of mHealth 8.3.1 The Sensory Device or Mobile Health 8.3.2 Telehealth 8.3.3 Electronic Health Records 8.3.4 Wireless Health 8.4 Services Offered by mHealth 8.5 Penetration of mHealth into Society 8.6 Distribution of Smart Devices 8.7 Reasons behind Success and Failures of mHealth 8.8 mHealth and Sister Technologies 8.9 Limitations and Regulations 8.9.1 Privacy Policy and Terms and Conditions 8.9.2 Request (Explicit) Consent 8.9.3 Making a Choice (Multiple Purposes) 8.9.4 Access to User’s Data 8.9.5 Privacy Dashboard 8.9.6 Permission Customization 8.9.7 The Right to be Forgotten 8.9.8 Sensitive Data 8.10 Promises and Challenges of mHealth References Chapter 9 Artificial Intelligence and Deep Learning for Medical Diagnosis and Treatment 9.1 Introduction 9.2 Deep Learning 9.3 Deep Learning Architecture 9.4 Types of Deep Learning Algorithms 9.5 Deep Learning Libraries 9.6 Application to Medical Diagnosis and Treatment 9.6.1 Imaging 9.6.2 Genomics 9.7 Tutorial References Part III Ethics Chapter 10 Ethical Issues and Challenges with Artificial Intelligence in Healthcare 10.1 Medical Ethics 10.1.1 A History of Medical Ethics: From the Hippocratic Oath to the Nuremberg Code and Beyond 10.1.1.1 Definitions 10.1.1.2 The Hippocratic Oath and Its Values 10.1.1.3 From the Nuremberg Code to the Oviedo Convention 10.1.2 The Philosophical Foundations of Medical Ethics: Deontology and Teleology 10.1.2.1 Ethical Dilemmas in Medical Ethics 10.1.2.2 Deontological Ethics 10.1.2.3 Teleological ethics (or consequentialism) 10.1.3 From “Principlism” to Virtue Ethics: 10.1.3.1 The “Principlism” of Beauchamp and Childress 10.1.3.2 Virtue Ethics 10.1.3.3 Synthetic Tables of Ethical Theories (Table 10.2) 10.2 Ethics of Artificial Intelligence 10.2.1 Ethics of Technology and Ethics of AI 10.2.2 From the Myths of Artificial Intelligence to Neo-Luddism: A Plea for Regulation of AI 10.2.3 AI for Social Good: A New Principlism? 10.2.3.1 What Ethics for AI ? 10.2.3.2 Principles and Factors 10.2.3.3 Implementation 10.2.3.4 Tensions 10.2.3.5 Limitations 10.3 Principlism, Virtue Ethics, and AI in Medicine 10.3.1 A New Principlism for AI in Medicine 10.3.2 The New Principlism in a Clinical and Public Health Context 10.3.3 The New Principlism and Virtue Ethics: For a Responsible AI in Healthcare Notes References Chapter 11 Epistemological Issues and Challenges with Artificial Intelligence in Healthcare 11.1 Key Issues in Philosophy of Artificial Intelligence (AI) 11.1.1 A Short History of AI: From 17th Century’s Calculators to the Dartmouth Artificial Intelligence Conference (2005) 11.1.1.1 From Pascal to Turing: A Prehistory of AI 11.1.1.2 From Turing to Dartmouth: A First Definition of AI 11.1.1.3 Neural Networks versus Expert Systems 11.1.1.4 Emergence and Development: Neural Networks and Machine Learning (ML) 11.1.2 The Modern Concepts of Artificial Intelligence and Big Data 11.1.3 AI: From Data to Knowledge 11.1.3.1 Truth and Proof 11.1.3.2 Machine Learning: Logic, Reliability, and Knowledge 11.1.3.3 From Proof to Trustworthiness 11.1.3.4 Explicability and Dependability 11.1.3.5 Biases and Errors 11.1.4 AI and Big Data: From the Myth to a New Paradigm? 11.2 Key Issues in Philosophy of Medicine: Medicine, Health, and Disease 11.2.1 The Hippocratic Conception of Medicine, Health, and Disease 11.2.1.1 Hippocrates and the Birth of Clinical Medicine 11.2.1.2 Hippocrates and the Theory of Four Humors 11.2.1.3 Hippocrates’ Conception of Health and Disease 11.2.2 The Modern Conception of Medicine, Health, and Disease: From Anatomo-Clinical to Experimental Medicine 11.2.2.1 Bichat and the Foundations of Anatomo-Clinical Medicine 11.2.2.2 C. Bernard and the Foundations of Experimental Medicine 11.2.2.3 The Modern Conception of Health and Disease 11.2.3 The Contemporary Conception of Medicine, Health, and Disease 11.2.3.1 The Molecularization of Medicine in the 20th Century 11.2.3.2 The Quantification of Medicine45 and the Birth of Evidence-Based Medicine (EBM) 11.2.3.3 From the Medicine of Disease to the Medicine of Health 11.3 Conclusion: Personalized Medicine or the Introduction of AI and Big Data in Medicine Notes References Index