Machine Intelligence: Computer Vision and Natural Language Processing

Cover
Half Title
Title Page
Copyright Page
Table of Contents
List of Figures
List of Tables
Preface
Contributors
Abbreviations
Chapter 1 A New Frontier in Machine Intelligence: Creativity
1.1 Introduction
1.2 A Short History of Computer Creativity
1.3 Artificial Intelligence and Creativity
1.4 Artificial Creativity: The Debate
1.5 Artificial Creativity and Copyright
1.6 Conclusion
1.7 Postscript
References
Chapter 2 Overview of Human-Computer Interaction
2.1 Introduction
2.2 The Aim of HCI
2.3 Factors in HCI
2.4 HCI Design Issues
2.4.1 Human-Machine Symbiosis
2.4.2 Human-Environment Interactions
2.4.3 Ethics, Privacy, and Security
2.4.4 Well-Being, Health, and Eudaimonia
2.4.5 Accessibility and Universal Access
2.4.6 Learning and Creativity
2.4.7 Social Organization and Democracy
2.5 HCI Implementation Issues
2.6 Important Aspects of HCI
2.7 Components of HCI
2.8 The Characteristics of HCI
2.9 HCI Principles and Best Practices
2.10 Design for HCI
2.10.1 HCI Design Approaches
2.10.2 Interface Testing Techniques
2.11 HCI Devices
2.12 HCI Tools and Technologies
2.13 HCI’s Eye-Tracking Technology
2.13.1 Eye-Tracking With Head Stabilization
2.13.2 Remote Eye-Tracking
2.13.3 Mobile Eye-Tracking
2.13.4 Embedded Or Integrated Systems
2.14 HCI’s Speech Recognition Technology
2.15 The Internet of Things (IoT) Technology
2.16 HCI’s Cloud Computing Technology
2.17 HCI Applications
2.18 Advantages of HCI
2.19 Disadvantages of HCI
2.20 Conclusion
References
Chapter 3 Edge/Fog Computing: An Overview and Insight Into Research Directions
3.1 Introduction
3.2 Edge/Fog Computing Basics
3.2.1 Edge Computing
3.2.2 Fog Computing
3.2.3 Understanding the Differences Between Edge and Fog Computing
3.2.4 Edge/Fog Computing Characteristics
3.2.4.1 Low Latency
3.2.4.2 Less Bandwidth Consumption
3.2.4.3 Mobility Support
3.2.4.4 Heterogeneity
3.2.4.5 Location Awareness
3.2.5 Benefits of Edge/Fog Computing
3.2.5.1 Cost
3.2.5.2 Speed
3.2.5.3 Scalability
3.2.5.4 Performance
3.2.5.5 Security
3.2.5.6 Reliability
3.2.5.7 Agility
3.3 Architecture of the Edge/Fog Computing
3.3.1 Basic Components of the Three-Tier Architecture
3.3.1.1 End Device Layer/Physical Layer
3.3.1.2 Fog/Edge Layer
3.3.1.3 Cloud Layer
3.4 Applications of Edge/Fog Computing
3.4.1 Healthcare Industry
3.4.2 IoT Applications
3.4.3 Augmented Reality (AR)
3.4.4 Banking and Finance Industry
3.4.5 Manufacturing Industries
3.4.6 Automobile Industry
3.4.7 Summary
3.5 Challenges and Research Directions
3.5.1 Increased Complexity
3.5.2 Privacy and Security
3.5.3 Task Scheduling/Offloading
3.5.4 Power Consumption
3.5.5 Data Management
3.5.6 Quality of Service
3.5.7 Multi-Characteristics Fog Design System
3.6 Conclusion
References
Chapter 4 Reduce Overfitting and Improve Deep Learning Models’ Performance in Medical Image Classification
4.1 Introduction
4.2 Deep Convolutional Neural Networks (DCNNs)
4.3 Medical Image Classification Using Transfer Learning
4.3.1 Challenges When Using Transfer Learning in Medical Image Classifications
4.3.2 Applications of Transfer Learning in Medical Image Classifications
4.4 Overfitting Prevention Techniques
4.4.1 Weight Regularization
4.4.2 Activity Regularization
4.4.3 Adding Dropout Layers
4.4.4 Noise Regularization
4.4.5 Stop Training With the Early Stopping Method
4.5 Conclusion
References
Chapter 5 Motion Images Object Detection Over Voice Using Deep Learning Algorithms
5.1 Introduction
5.1.1 Self-Driving Vehicles
5.1.2 Levels of Autonomy
5.1.3 Digital Image Processing
5.1.3.1 Image Pre-Processing
5.1.4 Deep Learning
5.1.5 Convolution Neural Networks
5.1.6 Computer Vision
5.1.6.1 Applications of Computer Vision
5.1.7 Object Detection
5.1.8 YOLO: You Only Look Once
5.2 Literature Review
5.3 Proposed Methodology
5.3.1 Proposed Architecture
5.3.2 Labelled Input Image
5.3.3 Stages of the Proposed Method
5.3.4 YOLOv4 Architecture
5.3.4.1 Intersection Over Union (IoU)
5.3.4.2 MAP
5.3.4.3 Precision
5.3.4.4 Recall
5.3.4.5 Non-Maximum Suppression (NMS)
5.3.5 Google Text-To-Speech
5.3.6 YOLOv5 Models’ Approach to Checking Performance
5.3.6.1 Design of the Proposed Method
5.4 Experimental Results
5.4.1 Detection of MAP Performance
5.4.2 Experimental Outcome Results
5.4.3 Predictions
5.5 Conclusion
References
Chapter 6 Diabetic Retinopathy Detection Using Various Machine Learning Algorithms
6.1 Introduction
6.2 Background
6.2.1 Proliferative Diabetic Retinopathy
6.3 Applicability
6.4 Diabetic Retinopathy Detection
6.4.1 Vertical and Horizontal Flip in Image Augmentation
6.4.2 Random Rotation Augmentation
6.4.2.1 Transition Layer
6.4.3 DenseNet for Semantic Segmentation
6.4.4 Dataset Description
6.5 Conclusion
Note
References
Chapter 7 IIoT Applications and Services
7.1 Introduction
7.1.1 The Need for IoT
7.1.2 Integration of IoT and Technologies
7.2 Components of IoT
7.3 Programming Software for IoT
7.3.1 Python for IoT
7.3.2 Python for Backend Development
7.3.3 IDEs for Internet of Things Development (IoT)
7.4 IoT Hardware
7.5 Industrial IoT (IIoT)
7.5.1 Applications of IIoT
7.5.2 IIoT Sensors
7.5.3 IoT Sensors for Industrial Automation Solutions
7.6 Artificial Intelligence and IoT
7.7 IIoT Start-Ups in India
7.8 Challenges in Securing IoT in India
7.9 Policies and Regulations for Promoting IoT in India
7.9.1 Recommendations for IoT Devices
7.10 Applications of IoT
7.11 Use-Cases in Industrial IoT
7.11.1 Predictive Maintenance
7.11.2 Smart Metering
7.11.3 Location Tracking
7.11.4 Location Services
7.11.5 Remote Quality Monitoring
7.11.6 Supply Chain Management and Optimization
7.12 Future of IIoT
7.13 Conclusion
References
Chapter 8 Design of Machine Learning Model for Health Care Index During COVID-19
8.1 Introduction
8.2 Literature Review
8.3 Time Series Data
8.4 Development of the ARIMA Model
8.5 Conclusion
References
Chapter 9 Ubiquitous Computing and Augmented Reality in HCI
9.1 Introduction
9.2 Ubiquitous Computing (UC)
9.2.1 UC’s History
9.2.2 Characteristics of UC
9.2.3 UC’s Layers
9.2.4 UC Types
9.3 UC Devices
9.3.1 Smartwatches
9.3.2 Smart Rings
9.3.3 Advanced Medical Wearables
9.3.4 Smart Earphones
9.3.5 Smart Clothing
9.4 UC’s Applications
9.4.1 Healthcare Industry
9.4.2 Accessibility
9.4.3 Learning
9.4.4 Logistics
9.4.5 Commerce
9.4.6 Games
9.5 Advantages of UC
9.6 Disadvantages of UC
9.7 Augmented Reality (AR)
9.7.1 AR’s History in a Nutshell
9.7.2 Characteristics of AR
9.7.3 AR Types
9.7.3.1 Marker-Based AR
9.7.3.2 Markerless AR
9.7.3.3 Location-Based AR
9.7.3.4 Superimposition AR
9.7.3.5 Projection-Based AR
9.8 AR Devices
9.8.1 Microsoft HoloLens
9.8.2 MagicLeap One
9.8.3 Epson Moverio
9.8.4 Google Glass Enterprise Edition
9.9 AR Applications
9.9.1 In the Military
9.9.2 3D Animals
9.9.3 Fashion
9.9.4 Gaming
9.9.5 Coloring Books
9.9.6 Obstetrics
9.9.7 Architecture
9.9.8 Sports
9.10 Advantages of AR
9.11 Disadvantages of AR
9.12 Conclusion
Acknowledgments
References
Chapter 10 A Machine Learning-Based Driving Assistance System for Lane and Drowsiness Monitoring
10.1 Introduction
10.2 Literature Review
10.2.1 System Review
10.2.2 Lane Detection Techniques
10.2.3 Robust Lane Detection in Low Light
10.2.4 Requirement
10.2.5 Use Case Diagram
10.2.6 Process Flow
10.3 Framework for Performance Analysis
10.3.1 Working On Drowsiness Detections
10.4 Image Capturing
10.4.1 Edge Detection
10.4.2 Feature Extraction Frontal Face
10.4.3 Grayscale Conversion
10.4.4 Score Calculation
10.5 Proposed Model for Lane Detection System
10.5.1 Comparing the Accuracy of the Lane Detection System
10.5.2 Implementation
10.5.2.1 System Design Approach
10.6 Computational Results
10.6.1 Accuracy Achieved
10.6.2 Assumptions
10.7 Conclusion
References
Chapter 11 Prediction of Gastric Cancer From Gene Expression Dataset Using Supervised Machine Learning Models
11.1 Introduction
11.2 Methodology
11.2.1 Dataset Description
11.2.2 Classification Techniques
11.2.2.1 Logistic Regression (LR)
11.2.2.2 Decision Tree (DT)
11.2.2.3 Naïve Bayes (NB)
11.2.2.4 K-Nearest Neighbor (KNN)
11.3 Results and Discussion
11.4 Conclusion
References
Chapter 12 Sewer Pipe Defect Detection in CCTV Images Using Deep Learning Techniques
12.1 Introduction
12.2 Related Work
12.3 Proposed Methodologies
12.3.1 Faster R-CNN for Object Detection
12.3.1.1 Convolutional Layer
12.3.1.2 Feature Map
12.3.1.3 Region Proposal Network (RPN)
12.3.1.4 Region of Interest (ROI) Pooling
12.3.1.5 Max Pooling Layer
12.3.2 System Architecture
12.3.2.1 Labeled Input Image
12.3.2.2 Identify Defects Using Faster R-CNN
12.3.2.3 Training With ZF/VGG/RESNET50 Networks
12.3.2.4 RGB Image Converted to HSV Image
12.3.3 Crack Detection
12.4 Experimental Results
12.4.1 Performance Analysis
12.4.2 Measure of Effectiveness
12.4.3 Experimental Outcome Results
12.5 Conclusion
References
Chapter 13 Learning and Reasoning Using Artificial Intelligence
13.1 Introduction
13.1.1 Human Intelligence Based On the Psychological View
13.1.2 Types of Artificial Intelligence Based On Functionality
13.1.2.1 Type 1 Category
13.1.2.2 Type 2 Category
13.1.3 Types of AI Based On Technology
13.1.4 Types of Intelligence
13.1.5 Structure of Intelligent Agents
13.1.6 How Intelligent Agents Work
13.1.7 Intelligent Agent Applications
13.1.8 Artificial Intelligence in Everyday Application
13.2 Growing Use of AI in Online Applications
13.2.1 E-Commerce
13.2.2 E-Learning Tools
13.2.3 Conducting Auctions
13.2.4 Travel Reservations
13.2.5 Online Stock Trading
13.2.6 Electronic Banking
13.2.7 Advertising and Marketing
13.2.8 Customer Service
13.3 AI and Security and Surveillance
13.4 AI and Medical Image Processing
13.4.1 Cardiovascular Abnormalities
13.4.2 Musculoskeletal Injury
13.4.3 Neurological Diseases
13.4.4 Thoracic Condition and Complications
13.5 Advantages of AI
13.5.1 Automation
13.5.2 Smart Decisions By AI
13.5.3 Increased Customer Experience
13.5.4 Medical Support System
13.5.5 Data Analysis
13.5.6 Stability of Business
13.5.7 Managing Repetitive Tasks
13.5.8 Minimizing Errors
13.6 Training AI
13.6.1 Success of AI Training
13.6.2 Train, Test and Maintain AI and Machine Learning Models
13.7 Conclusion
References
Chapter 14 A Novel Auto Encoder-Network-Based Ensemble Technique for Sentiment Analysis Using Tweets On COVID-19 Data
14.1 Introduction
14.2 Background and Related Work
14.2.1 Classification of Sentiments
14.2.2 Subjectivity Classification
14.2.3 Opinion Spam Identification
14.2.4 Detection of Language Implicitly
14.2.5 Extraction of Aspects
14.2.6 Datasets Associated With Sentiment Analysis
14.2.7 Approaches to Sentiment Analysis
14.2.8 Levels of Sentiment Analysis
14.3 Research Methodology
14.3.1 Data Extraction
14.3.2 Data Pre-Processing
14.3.3 Polarity Classification
14.3.4 Tweet Classification
14.3.5 Auto-Encoder
14.3.6 Adam Optimization
14.3.7 Ensemble Techniques for Sentiment Analysis
14.3.8 Decision Tree Classifier
14.3.9 Gradient Boosting Classifier
14.3.10 Logistic Regression
14.3.11 Genetic Algorithm
14.3.12 Support Vector Machine
14.3.13 Dataset Visualization
14.3.14 Word Cloud
14.4 Packages/Libraries of Python
14.4.1 Mlrose: Machine Learning, Randomized Optimization and Search
14.4.2 TfidfVectorizer
14.4.3 Train_test_split
14.4.4 Logistic Regression
14.5 Conclusion
Acknowledgments
References
Chapter 15 Economic Sustainability, Mindfulness, and Diversity in the Age of Artificial Intelligence and Machine Learning
15.1 Use of AI and ML in Traditional Industry
15.2 Use of AI to Create an Agricultural Database
15.3 Use of AI to Be Sensitive to Users’ Emotions
15.4 Livelihood Training and a Caution for CSR Funders
15.5 Organic Farming and Sustainable Livelihoods
15.5.1 Agarbatti Smoke and Tobacco Smoke
15.6 The Role of Banks and Financial Institutions
15.7 Diversity
15.8 Mindfulness
15.9 Conclusion
References
Chapter 16 Adopting Streaming Analytics for Healthcare and Retail Domains
16.1 Introduction
16.2 Healthcare Data Sources and Basic Analytics
16.2.1 Patient Data
16.2.2 Medical Imaging Records
16.2.3 Sensing Device Data
16.2.4 Mining Clinical Notes
16.2.5 Community Data Analysis
16.3 Retail Domain
16.3.1 Retail Industry
16.3.2 Customer Satisfaction in Real-Time Analysis
16.3.3 Technological Applications in the Retail Domain
16.3.3.1 Inventory Tracking
16.3.3.2 Customer Service
16.3.3.3 Data Warehousing
16.3.4 Retail Data Sources and Analysis
16.4 Different Ways of Streaming Analytics
16.5 Real-Time Streaming Analytics in Healthcare Use Cases
16.5.1 Data Stream Computing in Healthcare
16.5.1.1 Stream Computing Technology: Apache
16.5.1.2 Healthcare Analytics With Big Data: A Generic Architecture
16.6 Medical Signal Analysis
16.7 Big Data Analytical Signal Processing
16.8 Big Data Analytics in Genomics
16.8.1 Securing Genomic Data
16.8.2 Privacy
16.8.3 Data Ownership
16.9 Conclusion
References
Index