| Description |
1 online resource (288 pages) |
| Series |
River publishers series in biomedical engineering |
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River Publishers series in biomedical engineering
|
| Contents |
Preface xv List of Figures xvii List of Tables xxi List of Contributors xxiii List of Abbreviations xxvii 1 Classification of Histopathological Variants of Oral Squamous Cell Carcinoma Using Convolutional Neural Networks 1 1.1 Introduction 2 1.2 Convolutional Neural Networks 4 1.2.1 Convolutional Layer 5 1.2.2 Pooling Layer 5 1.2.3 Fully Connected Layers 5 1.2.4 Receptive Field 5 1.2.5 Weights 6 1.2.6 ReLU Layer 6 1.2.7 Softmax Layer 6 1.2.8 Dropout 6 1.2.9 Steps Involved in Convolutional Neural Network 7 1.3 Proposed Convolutional Neural Network 7 1.3.1 Performance Evaluation for CNN Models 8 1.3.2 Comparative Result Analysis 10 1.4 Conclusion 12 References 12 2 Voice Recognition Using Natural Language Processing 15 2.1 Introduction 15 2.2 Proposed System 17 2.2.1 Automatic Speech Recognition 17 2.2.2 Auto-detect Language 18 2.2.3 Syntactic Analysis 18 2.2.4 Semantic Analysis 18 2.2.5 Pragmatic Analysis 19 2.3 Experimental Results 19 2.4 Conclusion 22 References 22 3 Detection of Tuberculosis Using Computer-Aided Diagnosis System 25 3.1 Introduction 26 3.2 Pre-Processing 28 3.3 Segmentation 28 3.3.1 Rule-Based Algorithm 28 3.3.2 Pixel Classification 29 3.3.3 Deformable Models 29 3.3.4 Hybrid Methods 30 3.4 Feature Extraction 30 3.4.1 Histogram Features 30 3.4.2 Shape Descriptor Histogram 31 3.4.3 Curvature Descriptor 31 3.4.4 Local Binary Pattern (LBP) 31 3.4.5 Histogram of Gradients 32 3.4.6 Gabor Features 32 3.5 Classification 33 3.6 Discussion 34 3.7 Conclusion 37 References 38 4 Forecasting Time Series Data Using ARIMA and Facebook Prophet Models 47 4.1 Introduction 48 4.2 Arima Model 50 4.2.1 Data Analysis Using ARIMA Model 51 4.3 Data Analysis Using Facebook Prophet Model 55 4.4 Conculsion 57 References 57 5 A Novel Technique for User Decision Prediction and Assistance Using Machine Learning and NLP: A Model to Transform the E-commerce System 61 5.1 Introduction 62 5.2 Related Work 64 5.3 Research Methodology 68 5.4 Experimental Results 72 5.5 Conclusion and Future Scope 74 References 75 6 Machine Learning-Based Intelligent Video Analytics Design Using Depth Intra Coding 77 6.1 Introduction 78 6.1.1 Object Detection 80 6.1.2 Deep Learning 80 6.1.3 Geometric Depth Modeling 80 6.1.3.1 Plane fitting 80 6.1.4 Depth Coding Based on Geometric Primitives 81 6.2 Video Analytics Design Using Depth Intra Coding 82 6.3 Results 83 6.4 Conclusion 85 References 85 7 A Novel Approach for Automatic Brain Tumor Detection Using Machine Learning Algorithms 87 7.1 Introduction 88 7.1.1 Medical Imaging 89 7.2 Image Processing Approach-Detection of Brain Tumor From Mri Images 90 7.3 Machine Learning Approach-Detection of Brain Tumor From MRI Images 94 7.4 Nano-Robotic Approach-Detection of Brain Tumor From Mri Images 98 References 99 8 A Swarm-Based Feature Extraction and Weight Optimization in Neural Network for Classification on Speaker Recognition 103 8.1 Introduction 104 8.1.1 Swarm-based Feature Extraction Merits 104 8.1.2 Objectives of Our Chapter 105 8.2 State of Art 105 8.2.1 Mel Frequency Cepstral Coefficients (MFCC) 106 8.2.2 Swarm Intelligence (SI) 106 8.2.3 Text-independent Speaker Identification 106 8.2.4 Voice Activity Detection (VAD) 107 8.3 Differential Evolution Technique (DE) 107 8.4 Survey on Swarm Intelligence 107 8.5 Our Framework and Metrics 108 8.6 Results and Discussion 110 References 112 9 Fault Tolerance-Based Attack Detection Using Ensemble Classifier Machine Learning with IOT Security 115 9.1 Introduction 116 9.2 Background 118 9.2.1 IoT Security Attacks 118 9.2.1.1 Perception Layer Attacks 118 9.2.1.2 Network Layer Attacks 119 9.2.1.3 Routing Attacks 119 9.3 Deep Learning and IoT Security 120 9.4 Deep Learning and Big Data Technologies for IoT Security 123 9.5 Cloud Framework for Profound Learning, Enormous Information Advances, and IoT Security 124 9.5.1 Related Works 124 9.6 Motivation of the Proposed Methodology 126 9.7 Research Methodology 126 9.7.1 Dimensionality Reduction 128 9.7.2 Independent Component Analysis 129 9.7.3 Principal Component Analysis 130 9.7.4 Cloud Architecture 131 9.7.5 Encryption Decryption Using OTP 131 9.7.6 OTP Algorithm 135 9.7.7 Ensemble Classifier SVM, Random Forest Classification 136 9.7.8 Random Forest 139 9.8 Performance Metrics 140 9.9 Dataset Description 141 9.10 Conclusion 145 References 146 10 Design a Novel IoT-Based Agriculture Automation Using Machine Learning 149 10.1 Introduction 150 10.2 Literature Survey 151 10.3 Novel IoT-Based Agriculture Automation Using Machine Learning 153 10.4 Conclusion 156 References 156 11 Building a Smart Healthcare System Using Internet of Things and Machine Learning 159 11.1 Smart Healthcare--An Introduction 160 11.2 Background Study 161 11.3 Motivation of This Work 162 11.4 Internet of Things-Enabled Safe Smart Hospital Cabin Door Knocker 162 11.5 Smart Healthcare System Communication Protocol 164 11.6 IoT-Cloud Based Smart Healthcare Data Collection System 165 11.7 Use of Machine Learning in Different Fields of Medical Science 166 11.8 Illness Identification/Diagnosis 167 11.8.1 Discovery of Drug & Manufacturing 167 11.8.2 Diagnosis of Medical Imaging 168 11.8.3 Clinical Trial 168 11.8.4 Epidemic Outbreak Prediction 168 11.8.5 Robotic Surgery 168 11.8.6 Smart Health Record 169 11.9 Challenge'S Faced Towards 5G With Iot and Machine Learning Technique 169 11.9.1 5G and IoT Empower More Assault Vectors 169 11.9.2 Smarter Bots Can Likewise Misuse These Assault Vectors 170 11.10 Future Possibility of Smart Healthcare With Internet of Things 171 11.11 Conclusion and Future Scope 173 References 174 12 Research Issues and Future Research Directions Toward Smart Healthcare Using Internet of Things and Machine Learning 179 12.1 Introduction 180 12.2 Background Work 180 12.3 Healthcare and Internet of Things 185 12.4 Internet of Things-Based Healthcare Solutions 185 12.4.1 Clinical Care 186 12.4.2 Distant Checking 186 12.5 Machine Learning-Based Healthcare 186 12.5.1 Future Model of Healthcare-based IoT and Machine Learning 187 12.6 Wearable System for Smart Healthcare 189 12.7 Communication Standards 190 12.8 Challenges in Healthcare Adoption with IoT and Machine Learning 191 12.9 Improving Adoption of Healthcare System with IoT and Machine Learning 192 12.9.1 Proof-based Consideration 192 12.9.2 Self-learning and Personal Growth 193 12.9.3 Normalization 194 12.9.4 Protection and Security 194 12.9.5 Intelligent Announcing and Representation 195 12.10 Proposed Solution Based on IOT and Machine Learning for Smart Healthcare Systems 195 12.11 Conclusion 198 References 199 13 A Novel Adaptive Authentication Scheme for Securing Medical Information Stored in Clouds 201 13.1 Introduction 202 13.2 Adaptive Authentication Scheme 204 13.3 Information Storage/Update 205 13.4 Integrity Check 208 13.5 Performance Analysis 209 13.5.1 Process Delay 209 13.5.2 Integrity Check Bytes 210 13.5.3 Overhead 210 13.6 Conclusion 212 References 212 14 E-Tree MSI Query Learning Analytics on Secured Big Data Streams 215 14.1 Introduction 216 14.2 Literature Review 217 14.3 Proposed Framework-Secured Framework for Balancing Load Factor Using Ensemble Tree Classification 218 14.3.1 Fast Predictive Look-ahead Scheduling Approach 220 14.3.2 Parallel Ensemble Tree Classification (PETC) 221 14.3.3 Bilinear Quadrilateral Mapping 222 14.4 Conclusion 222 References 223 15 Lethal Vulnerability of Robotics in Industrial Sectors 227 15.1 Introduction 228 15.1.1 Robotics' Impact on Manufacturing Industries 228 15.2 Robotics and Innovation 228 15.2.1 Data Collection 229 15.2.2 Walking Robots 229 15.2.3 Various Robot Names and Dimensions 230 15.3 Robot Service in Hotels 231 15.3.1 Study 1A 233 15.3.2 Study 1B 233 15.4 Cyber Security Attacks on Robotic Platforms 234 15.5 Conclusion 235 References 236 16 Smart IoT Assistant for Government Schemes and Policies Using Natural Language Processing 239 16.1 Introduction 240 16.2 Literature Survey 240 16.3 Proposed Smart System 243 16.3.1 Data Extraction 244 16.3.2 Data Processing 244 16.3.3 Sending SMS 245 16.3.4 Language Translation 245 16.3.5 Text-To-Speech 245 16.3.5.1 Input text 246 16.3.5.2 Text analysis 246 16.3.5.3 Phonetic analysis 246 16.3.5.4 Speech database 246 16.3.5.5 Concatenation & Waveform generation 247 16.3.5.6 Synthesized speech 247 16.4 Methodology 247 16.4.1 Input Text Data 247 16.4.2 URL Data Extraction 248 16.4.3 Image to Text Conversion 248 16.4.4 Extract Text from PDF 248 16.4.5 SMS Update 249 16.4.6 GSM 249 16.4.7 Language Selection 249 16.4.8 Text-To-Speech 249 16.4.9 GUI 250 16.5 Experimental Results 250 16.6 Conclusion 252 References 252 Index 255 About the Editors 257 |
| Summary |
The book is centrally focused on human computer Interaction and how sensors within small and wide groups of Nano-robots employ Deep Learning for applications in industry. It covers a wide array of topics that are useful for researchers and students to gain knowledge about AI and sensors in nanobots. Furthermore, the book explores Deep Learning approaches to enhance the accuracy of AI systems applied in medical robotics for surgical techniques. Secondly, we plan to explore bio-nano-robotics, which is a field in nano-robotics, that deals with automatic intelligence handling, self-assembly and replication, information processing and programmability |
| Notes |
Print version record |
| Subject |
Human-computer interaction -- Data processing
|
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Biosensors -- Data processing
|
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Machine learning -- Industrial applications
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Big data -- Industrial applications
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Machine learning -- Industrial applications
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| Form |
Electronic book
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| Author |
Fernandez, Renny, editor
|
|
Fernandez, Terrance Frederick, editor
|
| ISBN |
9788770224147 |
|
8770224145 |
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