Big Data Management in Sensing – Applications in AI and IoT
- Length: 250 pages
- Edition: 1
- Language: English
- Publisher: River Publishers
- Publication Date: 2021-11-30
- ISBN-10: 8770224153
- ISBN-13: 9788770224154
- Sales Rank: #0 (See Top 100 Books)
The book is centrally focused on human computer interaction and how sensors withinsmall 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, it explores bio-nano-robotics, which is a field in nano-robotics, that deals with automatic intelligence handling, self-assembly and replication, information processing and programmability.
FRONT COVER Big Data Management in Sensing: Applications in AI and IoT Contents Preface List of Figures List of Tables List of Contributors List of Abbreviations 1 Classification of Histopathological Variants of Oral Squamous Cell Carcinoma Using Convolutional Neural Networks 1.1 Introduction 1.2 Convolutional Neural Networks 1.2.1 Convolutional Layer 1.2.2 Pooling Layer 1.2.3 Fully Connected Layers 1.2.4 Receptive Field 1.2.5 Weights 1.2.6 ReLU Layer 1.2.7 Softmax Layer 1.2.8 Dropout 1.2.9 Steps Involved in Convolutional Neural Network 1.3 Proposed Convolutional Neural Network 1.3.1 Performance Evaluation for CNN Models 1.3.2 Comparative Result Analysis 1.4 Conclusion References 2 Voice Recognition Using Natural Language Processing 2.1 Introduction 2.2 Proposed System 2.2.1 Automatic Speech Recognition 2.2.2 Auto-detect Language 2.2.3 Syntactic Analysis 2.2.4 Semantic Analysis 2.2.5 Pragmatic Analysis 2.3 Experimental Results 2.4 Conclusion References 3 Detection of Tuberculosis Using Computer-Aided Diagnosis System 3.1 Introduction 3.2 Pre-Processing 3.3 Segmentation 3.3.1 Rule-Based Algorithm 3.3.2 Pixel Classification 3.3.3 Deformable Models 3.3.4 Hybrid Methods 3.4 Feature Extraction 3.4.1 Histogram Features 3.4.2 Shape Descriptor Histogram 3.4.3 Curvature Descriptor 3.4.4 Local Binary Pattern (LBP) 3.4.5 Histogram of Gradients 3.4.6 Gabor Features 3.5 Classification 3.6 Discussion 3.7 Conclusion References 4 Forecasting Time Series Data Using ARIMA and Facebook Prophet Models 4.1 Introduction 4.2 Arima Model 4.2.1 Data Analysis Using ARIMA Model 4.3 Data Analysis Using Facebook Prophet Model 4.4 Conculsion References 5 A Novel Technique for User Decision Prediction and Assistance Using Machine Learning and NLP: A Model to Transform the E-commerce System 5.1 Introduction 5.2 Related Work 5.3 Research Methodology 5.4 Experimental Results 5.5 Conclusion and Future Scope References 6 Machine Learning-Based Intelligent Video Analytics Design Using Depth Intra Coding 6.1 Introduction 6.1.1 Object Detection 6.1.2 Deep Learning 6.1.3 Geometric Depth Modeling 6.1.3.1 Plane fitting 6.1.4 Depth Coding Based on Geometric Primitives 6.2 Video Analytics Design Using Depth Intra Coding 6.3 Results 6.4 Conclusion References 7 A Novel Approach for Automatic Brain Tumor Detection Using Machine Learning Algorithms 7.1 Introduction 7.1.1 Medical Imaging 7.2 Image Processing Approach-Detection of Brain Tumor From MRI Images 7.3 Machine Learning Approach-Detection of Brain Tumor From MRI Images 7.4 Nano-Robotic Approach-Detection of Brain Tumor From MRI Images References 8 A Swarm-Based Feature Extraction and Weight Optimization in Neural Network for Classification on Speaker Recognition 8.1 Introduction 8.1.1 Swarm-based Feature Extraction Merits 8.1.2 Objectives of Our Chapter 8.2 State of Art 8.2.1 Mel Frequency Cepstral Coefficients (MFCC) 8.2.2 Swarm Intelligence (SI) 8.2.3 Text-independent Speaker Identification 8.2.4 Voice Activity Detection (VAD) 8.3 Differential Evolution Technique (DE) 8.4 Survey on Swarm Intelligence 8.5 Our Framework and Metrics 8.6 Results and Discussion References 9 Fault Tolerance-Based Attack Detection Using Ensemble Classifier Machine Learning with IOT Security 9.1 Introduction 9.2 Background 9.2.1 IoT Security Attacks 9.2.1.1 Perception Layer Attacks 9.2.1.2 Network Layer Attacks 9.2.1.3 Routing Attacks 9.3 Deep Learning and IoT Security 9.4 Deep Learning and Big Data Technologies for IoT Security 9.5 Cloud Framework for Profound Learning, Enormous Information Advances, and IoT Security 9.5.1 Related Works 9.6 Motivation of the Proposed Methodology 9.7 Research Methodology 9.7.1 Dimensionality Reduction 9.7.2 Independent Component Analysis 9.7.3 Principal Component Analysis 9.7.4 Cloud Architecture 9.7.5 Encryption Decryption Using OTP 9.7.6 OTP Algorithm 9.7.7 Ensemble Classifier SVM, Random Forest Classification 9.7.8 Random Forest 9.8 Performance Metrics 9.9 Dataset Description 9.10 Conclusion References 10 Design a Novel IoT-Based Agriculture Automation Using Machine Learning 10.1 Introduction 10.2 Literature Survey 10.3 Novel IoT-Based Agriculture Automation Using Machine Learning 10.4 Conclusion References 11 Building a Smart Healthcare System Using Internet of Things and Machine Learning 11.1 Smart Healthcare—An Introduction 11.2 Background Study 11.3 Motivation of This Work 11.4 Internet of Things–Enabled Safe Smart Hospital Cabin Door Knocker 11.5 Smart Healthcare System Communication Protocol 11.6 IoT-Cloud Based Smart Healthcare Data Collection System 11.7 Use of Machine Learning in Different Fields of Medical Science 11.8 Illness Identification/Diagnosis 11.8.1 Discovery of Drug & Manufacturing 11.8.2 Diagnosis of Medical Imaging 11.8.3 Clinical Trial 11.8.4 Epidemic Outbreak Prediction 11.8.5 Robotic Surgery 11.8.6 Smart Health Record 11.9 Challenge’s Faced Towards 5G With Iot and Machine Learning Technique 11.9.1 5G and IoT Empower More Assault Vectors 11.9.2 Smarter Bots Can Likewise Misuse These Assault Vectors 11.10 Future Possibility of Smart Healthcare With Internet of Things 11.11 Conclusion and Future Scope References 12 Research Issues and Future Research Directions Toward Smart Healthcare Using Internet of Things and Machine Learning 12.1 Introduction 12.2 Background Work 12.3 Healthcare and Internet of Things 12.4 Internet of Things-Based Healthcare Solutions 12.4.1 Clinical Care 12.4.2 Distant Checking 12.5 Machine Learning-Based Healthcare 12.5.1 Future Model of Healthcare-based IoT and Machine Learning 12.6 Wearable System for Smart Healthcare 12.7 Communication Standards 12.8 Challenges in Healthcare Adoption with IoT and Machine Learning 12.9 Improving Adoption of Healthcare System with IoT and Machine Learning 12.9.1 Proof-based Consideration 12.9.2 Self-learning and Personal Growth 12.9.3 Normalization 12.9.4 Protection and Security 12.9.5 Intelligent Announcing and Representation 12.10 Proposed Solution Based on IOT and Machine Learning for Smart Healthcare Systems 12.11 Conclusion References 13 A Novel Adaptive Authentication Scheme for Securing Medical Information Stored in Clouds 13.1 Introduction 13.2 Adaptive Authentication Scheme 13.3 Information Storage/Update 13.4 Integrity Check 13.5 Performance Analysis 13.5.1 Process Delay 13.5.2 Integrity Check Bytes 13.5.3 Overhead 13.6 Conclusion References 14 E-Tree MSI Query Learning Analytics on Secured Big Data Streams 14.1 Introduction 14.2 Literature Review 14.3 Proposed Framework-Secured Framework for Balancing Load Factor Using Ensemble Tree Classification 14.3.1 Fast Predictive Look-ahead Scheduling Approach 14.3.2 Parallel Ensemble Tree Classification (PETC) 14.3.3 Bilinear Quadrilateral Mapping 14.4 Conclusion References 15 Lethal Vulnerability of Robotics in Industrial Sectors 15.1 Introduction 15.1.1 Robotics’ Impact on Manufacturing Industries 15.2 Robotics and Innovation 15.2.1 Data Collection 15.2.2 Walking Robots 15.2.3 Various Robot Names and Dimensions 15.3 Robot Service in Hotels 15.3.1 Study 1A 15.3.2 Study 1B 15.4 Cyber Security Attacks on Robotic Platforms 15.5 Conclusion References 16 Smart IoT Assistant for Government Schemes and Policies Using Natural Language Processing 16.1 Introduction 16.2 Literature Survey 16.3 Proposed Smart System 16.3.1 Data Extraction 16.3.2 Data Processing 16.3.3 Sending SMS 16.3.4 Language Translation 16.3.5 Text-To-Speech 16.3.5.1 Input text 16.3.5.2 Text analysis 16.3.5.3 Phonetic analysis 16.3.5.4 Speech database 16.3.5.5 Concatenation & Waveform generation 16.3.5.6 Synthesized speech 16.4 Methodology 16.4.1 Input Text Data 16.4.2 URL Data Extraction 16.4.3 Image to Text Conversion 16.4.4 Extract Text from PDF 16.4.5 SMS Update 16.4.6 GSM 16.4.7 Language Selection 16.4.8 Text-To-Speech 16.4.9 GUI 16.5 Experimental Results 16.6 Conclusion References Index About the Editors BACK COVER
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