Artificial Intelligence, Machine Learning, and Data Science Technologies: Future Impact and Well-Being for Society 5.0 (Demystifying Technologies for Computational Excellence)

✍ Scribed by Neeraj Mohan (editor), Ruchi Singla (editor), Priyanka Kaushal (editor), Seifedine Kadry (editor)

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 311
• Edition: 1
• Category: Library

✦ Synopsis

This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact.

The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other.

The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.

✦ Table of Contents

Cover
Half Title
Series Information
Title Page
Copyright Page
Table of Contents
Preface
Editor Biographies
1 Breast Cancer Diagnosis Using Machine Learning and Fractal Analysis of Malignancy-Associated Changes in Buccal Epithelium
1.1 Introduction
1.2 Malignant-Associated Changes in Buccal Epithelium
1.3 Materials and Methods of Morphometric Research and Image Analysis
1.4 Fractal Analysis of Chromatin
1.4.1 The Overall Algorithm for the Screening of Breast Cancer
1.5 Results and Discussion
1.6 Conclusion and Future Scope
References
2 Artificial Intelligence for Sustainable Health Care Advancements
2.1 Introduction
2.2 Health Care Data
2.2.1 Electronic Health Records
2.2.2 Disease Registries
2.2.3 Health Surveys
2.3 Major AI Technologies Relevant to Health Care
2.3.1 Machine Learning
2.3.2 Deep Learning
2.3.3 Natural Language Processing
2.3.4 Robotics
2.4 AI for Diagnosis and Treatment
2.4.1 Recent Applications of AI in Medical Diagnostics
2.4.2 Chatbots
2.4.3 Oncology
2.4.4 Pathology
2.4.5 Radiology
2.5 AI for Reducing Diagnostic Errors
2.5.1 Buoy Health
2.5.2 PathAI
2.5.3 Enlitic
2.6 AI for Drug Discovery
2.6.1 BioXcel Therapeutics
2.6.2 BERG Health
2.7 AI for Medical Transcription
2.8 AI for COVID-19 Diagnosis
2.9 The Role of Corporations in AI in Health Care
2.10 Benefits and Challenges of AI Services in Health Care
2.11 Looking Ahead—Future of AI in Health Care
References
3 Identification of Lung Cancer Malignancy Using Artificial Intelligence
3.1 Introduction
3.2 Background and Related Work
3.3 Proposed Model
3.3.1 Dataset (LIDC-IDRI)
3.3.2 Image Acquisition
3.3.3 Image Enhancement
3.4 Image Segmentation
3.4.1 Watershed Segmentation
3.4.2 Otsu’s Thresholding
3.5 Feature Extraction
3.5.1 Distances
3.5.2 Area
3.5.3 Convex Area
3.5.4 Perimeter
3.5.5 Convex Perimeter
3.5.6 Axis Length
3.5.6.1 Major Axis Length
3.5.6.2 Minor Axis Length
3.5.7 Compactness
3.5.8 Eccentricity
3.5.9 Standard Deviation
3.5.10 Skewness
3.5.11 Kurtosis
3.5.12 Entropy
3.5.13 Circularity Or Roundness
3.5.14 Convexity
3.5.15 Solidity
3.5.16 Bounding Box
3.5.17 Mean Intensity
3.6 Machine and Deep Learning Algorithms
3.7 Metrics of Performance Evaluation
3.7.1 Precision / Positive Predictive Value (PPV)
3.7.2 Negative Predictive Value (NPV)
3.7.3 Recall / Sensitivity / Probability of Detection / Hit Rate / True Positive Rate (TPR)
3.7.4 Specificity / Selectivity / True Negative Rate (TNR)
3.7.5 Fall Out / False Positive Rate (FPR)
3.7.6 Mis Rate / False Negative Rate (FNR)
3.7.7 False Discovery Rate (FDR)
3.7.8 False Omission Rate (FOR)
3.7.9 F1-Score / F-Measure/ Sørensen–Dice Coefficient / Dice Similarity Coefficient (DSC)
3.7.10 Accuracy
3.7.11 Confusion Matrix
3.7.12 ROC-AUC Score
3.8 Classification
3.8.1 Bidirectional Long Short-Term Memory Networks (BLSTM)
3.9 Experiments and Results
3.10 Future Enhancement
3.11 Conclusion
References
4 Deep Learning in Content-Based Medical Image Retrieval
4.1 Introduction
4.2 Related Work
4.2.1 Image Retrieval Based On Content (CBIR)
4.2.2 Medical Image Recovery Based On Content (CBMIR)
4.2.3 Deep Learning
4.3 Methodology
4.3.1 Phase 1
4.3.1.1 The DCNN Model Architecture
4.3.1.2 Details of Training
4.3.2 Phase 2
4.3.3 Phase 3: Optimization
4.3.3.1 Spotted Hyena Optimizer (SHO)
4.4 Dataset
4.5 Experimental Results
4.6 Performance Metrics
4.7 Conclusion
References
5 Implication of Image Pre-Processing in Object Detection Using Machine Learning Techniques
5.1 Introduction
5.2 Literature Review
5.3 Preprocessing Methods
5.3.1 Contrast Adjustment
5.3.2 Intensity Adjustment
5.3.3 Histogram Equalization
5.3.4 Morphological Operation
5.4 Image Enhancement Techniques
5.4.1 Intensity Enhancement (IE)
5.4.2 Histogram Equalization (HE)
5.4.3 Adaptive Histogram Equalization (AHE)
5.4.4 Contrast Limited Adaptive Histogram Equalization (CLAHE)
5.4.5 Brightness Preserving Bi-Histogram Equalization (BBHE)
5.4.6 Dualistic Sub-Image Histogram Equalization (DSHIE)
5.4.7 Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE)
5.4.8 Background Brightness Preserving Histogram Equalization (BBPHE)
5.5 Performance Metrics for Image Enhancement Techniques
5.5.1 Mean Squared Error (MSE)
5.5.2 Peak Signal to Noise Ratio (PSNR)
5.5.3 Absolute Mean Brightness Error (AMBE)
5.5.4 Contrast
5.5.5 Entropy
5.5.6 Normalized Absolute Error (NAE)
5.5.7 Measure of Enhancement (EME)
5.5.8 Measure of Enhancement By Entropy (EMEE)
5.5.9 Contrast Improvement Index (CII)
5.5.10 Tenegrad Measurement (TM)
5.6 Image Segmentation Techniques
5.6.1 Thresholding
5.6.1.1 Global Thresholding
5.6.1.2 Variable Thresholding
5.6.1.3 Multiple Thresholding
5.6.2 Edge Detection Based Techniques
5.6.3 Region Based Techniques
5.6.3.1 Region Growing Methods
5.6.3.2 Region Splitting and Merging Methods
5.6.4 Clustering Based Techniques
5.6.5 Watershed Based Techniques
5.6.6 Partial Differential Equation Based Techniques
5.6.7 Artificial Neural Network Based Techniques
5.6.8 Fuzzy Logic Based Techniques
5.6.9 Genetic Algorithm Based Techniques
5.6.10 Saliency Based Technique
5.7 Experimental Results
5.8 Conclusion
References
6 Forecasting Time Series Data Using Artificial Neural Network: A Review
6.1 Introduction
6.2 Historical Developments in the Field of ANN
6.3 Methodology
6.4 Classification of ANN
6.4.1 Feed-Forward Neural Networks
6.4.2 Recurrent Neural Networks
6.4.3 Elman Back Propagation Neural Networks
6.4.4 Time-Delay Feed-Forward Neural Network
6.5 Structural Design of ANN
6.6 Review of Applications, Results and Discussions
6.6.1 River Flow Forecasting
6.6.2 Environmental Pollution Forecasting
6.6.3 Stock Market Forecasting
6.6.4 Agriculture Related Forecasting
6.7 Conclusions
References
7 Internet of Things: An Emerging Paradigm for Social Safety and Security
7.1 Introduction
7.2 The IoT System: Tools and Technologies
7.2.1 Sensors
7.2.1.1 Temperature Sensor
7.2.1.2 Thermistor
7.2.1.3 Thermocouples
7.2.1.4 Hair Tension Moisture Sensor
7.2.1.5 Light Sensors
7.2.1.6 Smart Acoustic Sensors
7.2.1.7 Hydrophone
7.2.1.8 Geophone
7.2.1.9 Hydro-Pressure Sensor
7.2.1.10 Optical Sensor
7.2.1.11 Motion Sensor
7.2.1.12 Passive Infrared Sensor (PIR)
7.2.1.13 Gyroscope Sensors
7.2.1.14 Electrochemical Breathalyzer
7.2.1.15 Electronic Nose
7.2.1.16 Image Sensors
7.2.2 Actuators
7.2.2.1 Linear Actuators
7.2.2.2 Motors
7.2.2.3 Relays
7.2.2.4 Solenoids
7.3 Connectivity
7.3.1 LPWANs
7.3.2 Cellular (3G/4G/5G)
7.3.3 Zigbee
7.3.4 Bluetooth
7.3.5 Wi-Fi
7.3.6 RFID
7.4 IoT Applications
7.4.1 Smart Homes
7.4.2 Smart Cities
7.4.3 Wearables
7.4.4 Agriculture
7.4.5 Smart Supply Chain
7.4.6 Smart Retail
7.4.7 Transportation
7.4.8 Smart Grid
7.4.9 Healthcare
7.4.10 Industry
7.5 IOT Applications for Social Safety and Security
7.5.1 Urban Data Privacy
7.5.2 Personal Safety
7.5.3 Crime Detection and Indicators
7.5.4 Emergency Services Coordination
7.5.5 Firefighting
7.5.6 Crowdsourcing and Crowdsensing
7.5.7 Smart Grid Security
7.5.8 Smart Street Lighting
7.5.9 Counter-Terrorism
7.5.10 Traffic Safety and Incident Management
7.6 Case Study (Intrusion Detection and Tackling System for Agricultural Fields)
7.6.1 Animal Intrusion and Handling
7.6.2 Development of IDTS
7.6.3 Deployment and Discussion
7.7 Privacy and Security Issues With IoT
7.7.1 Insufficient Testing and Updating
7.7.2 Default Passwords and Credentials
7.7.3 Malware and Ransomware
7.7.4 Data Security and Privacy Concerns (Mobile, Web, Cloud)
7.7.5 Small IoT Incursions That Bypass Detection
7.7.6 Data Handling and Security
7.7.7 Home Seizures
7.7.8 Remote Vehicle Access
7.7.9 Unreliable Communications
7.8 Conclusion
References
8 Artificial Intelligence: Encouraging Students in Higher Education to Seize Its Potential
8.1 Introduction
8.2 Ethos for Higher Education
8.3 Some Bio-Inspired and Human Fuzzy Techniques in AI
8.3.1 Fuzzy Logic Types 1 and 2
8.3.2 Genetic Algorithm
8.3.3 Artificial Neural Network
8.3.4 Particle Swarm Optimization
8.3.5 Invasive Weed Optimization
8.3.6 Gray Wolf Optimization
8.3.7 Lion Optimization Algorithm
8.3.8 Mine Blast Optimization
8.3.9 Whale Optimization Algorithm
8.4 Application of Artificial Intelligence in Real World Problems
8.4.1 E-Commerce Marketing
8.4.2 Control Engineering
8.4.3 Surgery and Operations in Medical Science
8.4.4 AI in Agriculture
8.4.5 In Aviation and Space Organizations
8.5 Conclusion
References
9 Thermal Performance of Natural Insulation Materials for Energy Efficient Buildings
9.1 Introduction
9.2 Insulation for Thermal Comfort, Thermal Performance of Insulation and Sustainable Insulation Materials
9.2.1 Insulation for Thermal Comfort
9.2.2 Thermal Performance
9.2.3 Sustainable Insulation Materials
9.3 Natural Insulation Materials, Their Thermal Properties, Environmental Impact and Sustainable Aspects
9.3.1 Natural Insulation Materials
9.3.2 Sustainability of Natural Insulation Materials
9.3.3 Environmental Impact of Natural Insulation Materials
9.3.4 Thermal Performance of Natural Insulation Materials
9.3.5 Reduction in Cooling Load
9.3.6 Reduction in Energy Consumption
9.4 Conclusion
References
10 Maximum Power Point Tracking Techniques for PV Framework Under Partial Shaded Conditions: Towards Green Energy ...
10.1 Introduction
10.2 Perturb & Observe MPPT Technique
10.3 Incremental Conductance MPPT Techniques
10.4 Simulation Model of Proposed System
10.5 Results and Discussion
10.6 Conclusions
References
11 Forecasting Energy Consumption Using Deep Echo State Networks Optimized With Genetic Algorithm
11.1 Introduction
11.2 Related Work
11.3 Deep Echo State Network Optimized With Genetic Algorithm
11.4 Description of Datasets and Preprocessing
11.5 Experimental Analysis of Data Center Power Consumption Dataset
11.6 Experimental Analysis of Individual Household Power Consumption Dataset
11.7 Performance Evaluation
11.8 Conclusion
Acknowledgements
References
12 State-Of-The-Art Natural Language Processing Techniques
12.1 Introduction
12.1.1 Syntax
12.1.2 Semantics
12.2 Related Work
12.2.1 NLP – Language Modelling
12.2.2 NLP – Word, Sentence and Document Embedding
12.2.3 NLP-Machine Reading
12.2.4 NLP-Machine Translation
12.2.5 NLP – Dialogue System (Open-Domain, Generative)
12.2.6 NLP – Dialogue System
12.2.7 NLP – Dialogue System (Task-Oriented)
12.2.8 NLP – Text Generation
12.2.9 NLP – Text Summarization
12.2.10 NLP – Text Classification
12.2.11 NLP – Sentiment Analysis
12.2.12 NLP – Lexicon and Datasets
12.3 Applications of NLP
12.4 NLP Datasets
12.4.1 NLP Datasets for Sentiment Analysis
12.4.2 Text Datasets
12.4.3 Audio Speech Datasets for NLP
12.5 Conclusion
References
13 Modern Approaches in HR Analytics Towards Predictive Decision-Making for Competitive Advantage
13.1 Introduction
13.2 Review of Literature
13.2.1 HR Analytics: A Modern HR Decision-Making Tool
13.2.1.1 Concept of HR Analytics
13.2.1.2 Analytical Methods for HR
13.2.1.3 IT Technology and Central Storage of Data
13.2.1.4 Make Insightful Judgment
13.2.1.5 Problems of Data Governance in HR Analytics
13.2.1.6 Strategic Study
13.3 Measuring the Business Advantage Through HR Predictive Analytics
13.3.1 Employee Acquisition Opportunities of Predictive Analytics
13.3.2 Effects of Workforce Performance Predictive Analytics
13.3.3 Effects of Employee Retention Predictive Analytics
13.4 HR Analytics and Predictive Decision-Making Model
13.5 Findings and Conclusion
13.6 Limitation and Scope
References
14 Digital Transformation: Utilization of Analytics and Machine Learning in Smart Manufacturing
14.1 Introduction
14.2 Digital Transformation, Analytics, and Machine Learning
14.3 Digital Transformation in Smart Manufacturing
14.4 Analytics and Smart Manufacturing
14.5 Machine Learning and Smart Manufacturing
14.6 Conclusion
References
15 A Robust Cyber Security: Challenges and Opportunities
15.1 Introduction
15.2 Literature Survey
15.3 Getting Familiar With Hacking
15.3.1 How Do Hackers Think?
15.3.2 Types of Hackers
15.3.3 What Path Does a Hacker/Attacker Follow?
15.4 Development of Cyber Security Management Model
15.4.1 Mistakes in Cyber Security
15.5 Challenges in Cyber Security
15.5.1 Large Number of IoT Devices
15.5.2 NFV-SDN Integrated Edge Cloud Platform
15.5.3 Data Privacy and Security
15.5.4 Offloading and Interaction Between Edge and IoT Devices
15.6 Opportunities in Cyber Security
15.6.1 Blockchain and Zero-Trust Security
15.6.2 Artificial Intelligence (AI) and Machine Learning (ML)
15.6.3 Weak/Lightweight IOT Security
15.6.4 Cyber Defense Based in Deception
15.6.5 Isolated Identity of the Devices Under the Internet of Things
15.7 Conclusion
References
Index