Computational Analysis and Deep Learning for Medical Care

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Computational Analysis and Deep Learning for Medical Care, Wiley
Principles, Methods, and Applications
Von Amit Kumar Tyagi, im heise Shop in digitaler Fassung erhältlich
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The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems.

We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications.

AUDIENCE

Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.

AMIT KUMAR TYAGI is an assistant professor and senior researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He received his PhD in 2018 from Pondicherry Central University, India. He has published more than 8 patents in the area of deep learning, Internet of Things, cyber physical systems and computer vision.

Preface xix

PART I: DEEP LEARNING AND ITS MODELS 1

1 CNN: A REVIEW OF MODELS, APPLICATION OF IVD SEGMENTATION 3

Leena Silvoster M. and R. Mathusoothana S. Kumar

1.1 Introduction 4

1.2 Various CNN Models 4

1.2.1 LeNet-5 4

1.2.2 AlexNet 7

1.2.3 ZFNet 8

1.2.4 VGGNet 10

1.2.5 GoogLeNet 12

1.2.6 ResNet 16

1.2.7 ResNeXt 21

1.2.8 SE-ResNet 24

1.2.9 DenseNet 24

1.2.10 MobileNets 25

1.3 Application of CNN to IVD Detection 26

1.4 Comparison With State-of-the-Art Segmentation Approaches for Spine T2W Images 28

1.5 Conclusion 28

References 33

2 LOCATION-AWARE KEYWORD QUERY SUGGESTION TECHNIQUES WITH ARTIFICIAL INTELLIGENCE PERSPECTIVE 35

R. Ravinder Reddy, C. Vaishnavi, Ch. Mamatha and S. Ananthakumaran

2.1 Introduction 36

2.2 Related Work 39

2.3 Artificial Intelligence Perspective 41

2.3.1 Keyword Query Suggestion 42

2.3.1.1 Random Walk–Based Approaches 42

2.3.1.2 Cluster-Based Approaches 42

2.3.1.3 Learning to Rank Approaches 43

2.3.2 User Preference From Log 43

2.3.3 Location-Aware Keyword Query Suggestion 44

2.3.4 Enhancement With AI Perspective 44

2.3.4.1 Case Study 45

2.4 Architecture 46

2.4.1 Distance Measures 47

2.5 Conclusion 49

References 49

3 IDENTIFICATION OF A SUITABLE TRANSFER LEARNING ARCHITECTURE FOR CLASSIFICATION: A CASE STUDY WITH LIVER TUMORS 53

B. Lakshmi Priya, K. Jayanthi, Biju Pottakkat and G. Ramkumar

3.1 Introduction 54

3.2 Related Works 56

3.3 Convolutional Neural Networks 58

3.3.1 Feature Learning in CNNs 59

3.3.2 Classification in CNNs 60

3.4 Transfer Learning 61

3.4.1 AlexNet 61

3.4.2 GoogLeNet 62

3.4.3 Residual Networks 63

3.4.3.1 ResNet-18 65

3.4.3.2 ResNet-50 65

3.5 System Model 66

3.6 Results and Discussions 67

3.6.1 Dataset 67

3.6.2 Assessment of Transfer Learning Architectures 67

3.7 Conclusion 73

References 74

4 OPTIMIZATION AND DEEP LEARNING-BASED CONTENT RETRIEVAL, INDEXING, AND METRIC LEARNING APPROACH FOR MEDICAL IMAGES 79

Suresh Kumar K., Sundaresan S., Nishanth R. and Ananth Kumar T.

4.1 Introduction 80

4.2 Related Works 82

4.3 Proposed Method 85

4.3.1 Input Dataset 86

4.3.2 Pre-Processing 86

4.3.3 Combination of DCNN and CFML 86

4.3.4 Fine Tuning and Optimization 88

4.3.5 Feature Extraction 89

4.3.6 Localization of Abnormalities in MRI and CT Scanned Images 90

4.4 Results and Discussion 92

4.4.1 Metric Learning 92

4.4.2 Comparison of the Various Models for Image Retrieval 92

4.4.3 Precision vs. Recall Parameters Estimation for the CBIR 93

4.4.4 Convolutional Neural Networks–Based Landmark Localization 96

4.5 Conclusion 104

References 104

PART II: APPLICATIONS OF DEEP LEARNING 107

5 DEEP LEARNING FOR CLINICAL AND HEALTH INFORMATICS 109

Amit Kumar Tyagi and Meghna Mannoj Nair

5.1 Introduction 110

5.1.1 Deep Learning Over Machine Learning 111

5.2 Related Work 113

5.3 Motivation 115

5.4 Scope of the Work in Past, Present, and Future 115

5.5 Deep Learning Tools, Methods Available for Clinical, and Health Informatics 117

5.6 Deep Learning: Not-So-Near Future in Biomedical Imaging 119

5.6.1 Types of Medical Imaging 119

5.6.2 Use and Benefits of Medical Imaging 120

5.7 Challenges Faced Toward Deep Learning Using Biomedical Imaging 121

5.7.1 Deep Learning in Healthcare: Limitations and Challenges 122

5.8 Open Research Issues and Future Research Directions Biomedical Imaging (Healthcare Informatics) 124

5.9 Conclusion 127

References 127

6 BIOMEDICAL IMAGE SEGMENTATION BY DEEP LEARNING METHODS 131

K. Anita Davamani, C.R. Rene Robin, S. Amudha and L. Jani Anbarasi

6.1 Introduction 132

6.2 Overview of Deep Learning Algorithms 135

6.2.1 Deep Learning Classifier (DLC) 136

6.2.2 Deep Learning Architecture 137

6.3 Other Deep Learning Architecture 139

6.3.1 Restricted Boltzmann Machine (RBM) 139

6.3.2 Deep Learning Architecture Containing Autoencoders 140

6.3.3 Sparse Coding Deep Learning Architecture 141

6.3.4 Generative Adversarial Network (GAN) 141

6.3.5 Recurrent Neural Network (RNN) 141

6.4 Biomedical Image Segmentation 145

6.4.1 Clinical Images 146

6.4.2 X-Ray Imaging 146

6.4.3 Computed Tomography (CT) 147

6.4.4 Magnetic Resonance Imaging (MRI) 147

6.4.5 Ultrasound Imaging (US) 148

6.4.6 Optical Coherence Tomography (OCT) 148

6.5 Conclusion 149

References 149

7 MULTI-LINGUAL HANDWRITTEN CHARACTER RECOGNITION USING DEEP LEARNING 155

Giriraj Parihar, Ratnavel Rajalakshmi and Bhuvana J.

7.1 Introduction 156

7.2 Related Works 157

7.3 Materials and Methods 160

7.4 Experiments and Results 161

7.4.1 Dataset Description 162

7.4.1.1 Handwritten Math Symbols 162

7.4.1.2 Bangla Handwritten Character Dataset 162

7.4.1.3 Devanagari Handwritten Character Dataset 162

7.4.2 Experimental Setup 162

7.4.3 Hype-Parameters 164

7.4.3.1 English Model 164

7.4.3.2 Hindi Model 165

7.4.3.3 Bangla Model 165

7.4.3.4 Math Symbol Model 165

7.4.3.5 Combined Model 166

7.4.4 Results and Discussion 167

7.4.4.1 Performance of Uni-Language Models 167

7.4.4.2 Uni-Language Model on English Dataset 168

7.4.4.3 Uni-Language Model on Hindi Dataset 168

7.4.4.4 Uni-Language Model on Bangla Dataset 169

7.4.4.5 Uni-Language Model on Math Symbol Dataset 169

7.4.4.6 Performance of Multi-Lingual Model on Combined Dataset 171

7.5 Conclusion 177

References 178

8 DISEASE DETECTION PLATFORM USING IMAGE PROCESSING THROUGH OPENCV 181

Neetu Faujdar and Aparna Sinha

8.1 Introduction 182

8.1.1 Image Processing 183

8.2 Problem Statement 183

8.2.1 Cataract 183

8.2.1.1 Causes 184

8.2.1.2 Types of Cataracts 184

8.2.1.3 Cataract Detection 185

8.2.1.4 Treatment 186

8.2.1.5 Prevention 186

8.2.1.6 Methodology 186

8.2.2 Eye Cancer 192

8.2.2.1 Symptoms 194

8.2.2.2 Causes of Retinoblastoma 194

8.2.2.3 Phases 195

8.2.2.4 Spreading of Cancer 196

8.2.2.5 Diagnosis 196

8.2.2.6 Treatment 197

8.2.2.7 Methodology 199

8.2.3 Skin Cancer (Melanoma) 202

8.2.3.1 Signs and Symptoms 203

8.2.3.2 Stages 203

8.2.3.3 Causes of Melanoma 204

8.2.3.4 Diagnosis 204

8.2.3.5 Treatment 205

8.2.3.6 Methodology 206

8.2.3.7 Asymmetry 207

8.2.3.8 Border 208

8.2.3.9 Color 208

8.2.3.10 Diameter Detection 209

8.2.3.11 Calculating TDS (Total Dermoscopy Score) 210

8.3 Conclusion 210

8.4 Summary 212

References 212

9 COMPUTER-AIDED DIAGNOSIS OF LIVER FIBROSIS IN HEPATITIS PATIENTS USING CONVOLUTIONAL NEURAL NETWORK 217

Aswathy S. U., Ajesh F., Shermin Shamsudheen and Jarin T.

9.1 Introduction 218

9.2 Overview of System 219

9.3 Methodology 219

9.3.1 Dataset 220

9.3.2 Pre-Processing 221

9.3.3 Feature Extraction 221

9.3.4 Feature Selection and Normalization 223

9.3.5 Classification Model 225

9.4 Performance and Analysis 227

9.5 Experimental Results 232

9.6 Conclusion and Future Scope 232

References 233

PART III: FUTURE DEEP LEARNING MODELS 237

10 LUNG CANCER PREDICTION IN DEEP LEARNING PERSPECTIVE 239

Nikita Banerjee and Subhalaxmi Das

10.1 Introduction 239

10.2 Machine Learning and Its Application 240

10.2.1 Machine Learning 240

10.2.2 Different Machine Learning Techniques 241

10.2.2.1 Decision Tree 242

10.2.2.2 Support Vector Machine 242

10.2.2.3 Random Forest 242

10.2.2.4 K-Means Clustering 242

10.3 Related Work 243

10.4 Why Deep Learning on Top of Machine Learning? 245

10.4.1 Deep Neural Network 246

10.4.2 Deep Belief Network 247

10.4.3 Convolutional Neural Network 247

10.5 How is Deep Learning Used for Prediction of Lungs Cancer? 248

10.5.1 Proposed Architecture 248

10.5.1.1 Pre-Processing Block 250

10.5.1.2 Segmentation 250

10.5.1.3 Classification 252

10.6 Conclusion 253

References 253

11 LESION DETECTION AND CLASSIFICATION FOR BREAST CANCER DIAGNOSIS BASED ON DEEP CNNS FROM DIGITAL MAMMOGRAPHIC DATA 257

Diksha Rajpal, Sumita Mishra and Anil Kumar

11.1 Introduction 257

11.2 Background 258

11.2.1 Methods of Diagnosis of Breast Cancer 258

11.2.2 Types of Breast Cancer 260

11.2.3 Breast Cancer Treatment Options 261

11.2.4 Limitations and Risks of Diagnosis and Treatment Options 262

11.2.4.1 Limitation of Diagnosis Methods 262

11.2.4.2 Limitations of Treatment Plans 263

11.2.5 Deep Learning Methods for Medical Image Analysis: Tumor Classification 263

11.3 Methods 265

11.3.1 Digital Repositories 265

11.3.1.1 DDSM Database 265

11.3.1.2 AMDI Database 265

11.3.1.3 IRMA Database 265

11.3.1.4 BreakHis Database 265

11.3.1.5 MIAS Database 266

11.3.2 Data Pre-Processing 266

11.3.2.1 Advantages of Pre-Processing Images 267

11.3.3 Convolutional Neural Networks (CNNs) 268

11.3.3.1 Architecture of CNN 269

11.3.4 Hyper-Parameters 272

11.3.4.1 Number of Hidden Layers 273

11.3.4.2 Dropout Rate 273

11.3.4.3 Activation Function 273

11.3.4.4 Learning Rate 274

11.3.4.5 Number of Epochs 274

11.3.4.6 Batch Size 274

11.3.5 Techniques to Improve CNN Performance 274

11.3.5.1 Hyper-Parameter Tuning 274

11.3.5.2 Augmenting Images 274

11.3.5.3 Managing Over-Fitting and Under-Fitting 275

11.4 Application of Deep CNN for Mammography 275

11.4.1 Lesion Detection and Localization 275

11.4.2 Lesion Classification 279

11.5 System Model and Results 280

11.5.1 System Model 280

11.5.2 System Flowchart 281

11.5.2.1 MIAS Database 281

11.5.2.2 Unannotated Images 281

11.5.3 Results 282

11.5.3.1 Distribution and Processing of Dataset 282

11.5.3.2 Training of the Model 283

11.5.3.3 Prediction of Unannotated Images 286

11.6 Research Challenges and Discussion on Future Directions 286

11.7 Conclusion 288

References 289

12 HEALTH PREDICTION ANALYTICS USING DEEP LEARNING METHODS AND APPLICATIONS 293

Sapna Jain, M. Afshar Alam, Nevine Makrim Labib and Eiad Yafi

12.1 Introduction 294

12.2 Background 298

12.3 Predictive Analytics 299

12.4 Deep Learning Predictive Analysis Applications 305

12.4.1 Deep Learning Application Model to Predict COVID-19 Infection 305

12.4.2 Deep Transfer Learning for Mitigating the COVID-19 Pandemic 308

12.4.3 Health Status Prediction for the Elderly Based on Machine Learning 309

12.4.4 Deep Learning in Machine Health Monitoring 311

12.5 Discussion 319

12.6 Conclusion 320

References 321

13 AMBIENT-ASSISTED LIVING OF DISABLED ELDERLY IN AN INTELLIGENT HOME USING BEHAVIOR PREDICTION—A RELIABLE DEEP LEARNING PREDICTION SYSTEM 329

Sophia S., Sridevi U.K., Boselin Prabhu S.R. and P. Thamaraiselvi

13.1 Introduction 330

13.2 Activities of Daily Living and Behavior Analysis 331

13.3 Intelligent Home Architecture 333

13.4 Methodology 335

13.4.1 Record the Behaviors Using Sensor Data 335

13.4.2 Classify Discrete Events and Relate the Events Using Data Analysis Algorithms 335

13.4.3 Construct Behavior Dictionaries for Flexible Event Intervals Using Deep Learning Concepts 335

13.4.4 Use the Dictionary in Modeling the Behavior Patterns Through Prediction Techniques 336

13.4.5 Detection of Deviations From Expected Behaviors Aiding the Automated Elderly Monitoring Based on Decision Support Algorithm Systems 336

13.5 Senior Analytics Care Model 337

13.6 Results and Discussions 338

13.7 Conclusion 341

Nomenclature 341

References 342

14 EARLY DIAGNOSIS TOOL FOR ALZHEIMER’S DISEASE USING 3D SLICER 343

V. Krishna Kumar, M.S. Geetha Devasena and G. Gopu

14.1 Introduction 344

14.2 Related Work 345

14.3 Existing System 347

14.4 Proposed System 347

14.4.1 Usage of 3D Slicer 350

14.5 Results and Discussion 353

14.6 Conclusion 356

References 356

PART IV: DEEP LEARNING – IMPORTANCE AND CHALLENGES FOR OTHER SECTORS 361

15 DEEP LEARNING FOR MEDICAL HEALTHCARE: ISSUES, CHALLENGES, AND OPPORTUNITIES 363

Meenu Gupta, Akash Gupta and Gaganjot Kaur

15.1 Introduction 364

15.2 Related Work 365

15.3 Development of Personalized Medicine Using Deep Learning: A New Revolution in Healthcare Industry 367

15.3.1 Deep Feedforward Neural Network (DFF) 367

15.3.2 Convolutional Neural Network 367

15.3.3 Recurrent Neural Network (RNN) 369

15.3.4 Long/Short-Term Memory (LSTM) 369

15.3.5 Deep Belief Network (DBN) 370

15.3.6 Autoencoder (AE) 370

15.4 Deep Learning Applications in Precision Medicine 370

15.4.1 Discovery of Biomarker and Classification of Patient 370

15.4.2 Medical Imaging 371

15.5 Deep Learning for Medical Imaging 372

15.5.1 Medical Image Detection 372

15.5.1.1 Pathology Detection 372

15.5.1.2 Detection of Image Plane 373

15.5.1.3 Anatomical Landmark Localization 373

15.5.2 Medical Image Segmentation 373

15.5.2.1 Supervised Algorithms 374

15.5.2.2 Semi-Supervised Algorithms 374

15.5.3 Medical Image Enhancement 375

15.5.3.1 Two-Dimensional Super-Resolution Techniques 375

15.5.3.2 Three-Dimensional Super-Resolution Techniques 375

15.6 Drug Discovery and Development: A Promise Fulfilled by Deep Learning Technology 375

15.6.1 Prediction of Drug Properties 376

15.6.2 Prediction of Drug-Target Interaction 377

15.7 Application Areas of Deep Learning in Healthcare 377

15.7.1 Medical Chatbots 377

15.7.2 Smart Health Records 377

15.7.3 Cancer Diagnosis 378

15.8 Privacy Issues Arising With the Usage of Deep Learning in Healthcare 379

15.8.1 Private Data 379

15.8.2 Privacy Attacks 380

15.8.2.1 Evasion Attack 380

15.8.2.2 White-Box Attack 380

15.8.2.3 Black-Box Attack 380

15.8.2.4 Poisoning Attack 381

15.8.3 Privacy-Preserving Techniques 381

15.8.3.1 Differential Privacy With Deep Learning 381

15.8.3.2 Homomorphic Encryption (HE) on Deep Learning 382

15.8.3.3 Secure Multiparty Computation on Deep Learning 383

15.9 Challenges and Opportunities in Healthcare Using Deep Learning 383

15.10 Conclusion and Future Scope 386

References 387

16 A PERSPECTIVE ANALYSIS OF REGULARIZATION AND OPTIMIZATION TECHNIQUES IN MACHINE LEARNING 393

Ajeet K. Jain, PVRD Prasad Rao and K. Venkatesh Sharma

16.1 Introduction 394

16.1.1 Data Formats 395

16.1.1.1 Structured Data 395

16.1.1.2 Unstructured Data 396

16.1.1.3 Semi-Structured Data 396

16.1.2 Beginning With Learning Machines 397

16.1.2.1 Perception 397

16.1.2.2 Artificial Neural Network 398

16.1.2.3 Deep Networks and Learning 399

16.1.2.4 Model Selection, Over-Fitting, and Under-Fitting 400

16.2 Regularization in Machine Learning 402

16.2.1 Hamadard Conditions 403

16.2.2 Tikhonov Generalized Regularization 404

16.2.3 Ridge Regression 406

16.2.4 Lasso—L1 Regularization 406

16.2.5 Dropout as Regularization Feature 407

16.2.6 Augmenting Dataset 408

16.2.7 Early Stopping Criteria 408

16.3 Convexity Principles 409

16.3.1 Convex Sets 410

16.3.1.1 Affine Set and Convex Functions 411

16.3.1.2 Properties of Convex Functions 411

16.3.2 Optimization and Role of Optimizer in ML 413

16.3.2.1 Gradients-Descent Optimization Methods 414

16.3.2.2 Non-Convexity of Cost Functions 416

16.3.2.3 Basic Maths of SGD 418

16.3.2.4 Saddle Points 418

16.3.2.5 Gradient Pointing in the Wrong Direction 420

16.3.2.6 Momentum-Based Optimization 423

16.4 Conclusion and Discussion 424

References 425

17 DEEP LEARNING-BASED PREDICTION TECHNIQUES FOR MEDICAL CARE: OPPORTUNITIES AND CHALLENGES 429

S. Subasree and N. K. Sakthivel

17.1 Introduction 430

17.2 Machine Learning and Deep Learning Framework 431

17.2.1 Supervised Learning 433

17.2.2 Unsupervised Learning 433

17.2.3 Reinforcement Learning 434

17.2.4 Deep Learning 434

17.3 Challenges and Opportunities 435

17.3.1 Literature Review 435

17.4 Clinical Databases—Electronic Health Records 436

17.5 Data Analytics Models—Classifiers and Clusters 436

17.5.1 Criteria for Classification 438

17.5.1.1 Probabilistic Classifier 439

17.5.1.2 Support Vector Machines (SVMs) 439

17.5.1.3 K-Nearest Neighbors 440

17.5.2 Criteria for Clustering 441

17.5.2.1 K-Means Clustering 442

17.5.2.2 Mean Shift Clustering 442

17.6 Deep Learning Approaches and Association Predictions 444

17.6.1 G-HR: Gene Signature–Based HRF Cluster 444

17.6.1.1 G-HR Procedure 446

17.6.2 Deep Learning Approach and Association Predictions 446

17.6.2.1 Deep Learning Approach 446

17.6.2.2 Intelligent Human Disease-Gene Association Prediction Technique (IHDGAP) 447

17.6.2.3 Convolution Neural Network 447

17.6.2.4 Disease Semantic Similarity 449

17.6.2.5 Computation of Scoring Matrix 450

17.6.3 Identified Problem 450

17.6.4 Deep Learning–Based Human Diseases Pattern Prediction Technique for High-Dimensional Human Diseases Datasets (ECNN-HDPT) 451

17.6.5 Performance Analysis 453

17.7 Conclusion 457

17.8 Applications 458

References 459

18 MACHINE LEARNING AND DEEP LEARNING: OPEN ISSUES AND FUTURE RESEARCH DIRECTIONS FOR THE NEXT 10 YEARS 463

Akshara Pramod, Harsh Sankar Naicker and Amit Kumar Tyagi

18.1 Introduction 464

18.1.1 Comparison Among Data Mining, Machine Learning, and Deep Learning 465

18.1.2 Machine Learning 465

18.1.2.1 Importance of Machine Learning in Present Business Scenario 467

18.1.2.2 Applications of Machine Learning 467

18.1.2.3 Machine Learning Methods Used in Current Era 469

18.1.3 Deep Learning 471

18.1.3.1 Applications of Deep Learning 471

18.1.3.2 Deep Learning Techniques/Methods Used in Current Era 473

18.2 Evolution of Machine Learning and Deep Learning 475

18.3 The Forefront of Machine Learning Technology 476

18.3.1 Deep Learning 476

18.3.2 Reinforcement Learning 477

18.3.3 Transfer Learning 477

18.3.4 Adversarial Learning 477

18.3.5 Dual Learning 478

18.3.6 Distributed Machine Learning 478

18.3.7 Meta Learning 478

18.4 The Challenges Facing Machine Learning and Deep Learning 478

18.4.1 Explainable Machine Learning 479

18.4.2 Correlation and Causation 479

18.4.3 Machine Understands the Known and is Aware of the Unknown 479

18.4.4 People-Centric Machine Learning Evolution 480

18.4.5 Explainability: Stems From Practical Needs and Evolves Constantly 480

18.5 Possibilities With Machine Learning and Deep Learning 481

18.5.1 Possibilities With Machine Learning 481

18.5.1.1 Lightweight Machine Learning and Edge Computing 481

18.5.1.2 Quantum Machine Learning 482

18.5.1.3 Quantum Machine Learning Algorithms Based on Linear Algebra 482

18.5.1.4 Quantum Reinforcement Learning 483

18.5.1.5 Simple and Elegant Natural Laws 483

18.5.1.6 Improvisational Learning 484

18.5.1.7 Social Machine Learning 485

18.5.2 Possibilities With Deep Learning 485

18.5.2.1 Quantum Deep Learning 485

18.6 Potential Limitations of Machine Learning and Deep Learning 486

18.6.1 Machine Learning 486

18.6.2 Deep Learning 487

18.7 Conclusion 488

Acknowledgement 489

Contribution/Disclosure 489

References 489

Index 491
Artikel-Details
Anbieter:
Wiley
Autor:
Amit Kumar Tyagi
Artikelnummer:
9781119785736
Veröffentlicht:
02.08.2021
Seitenanzahl:
528