Enterprise Digital Transformation: Technology, Tools, and Use Cases

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Editors
Contributors
Chapter 1: Get Technology to Contribute to Business Strategy
	Transformation Is a Strategic Initiative
	To Transform an Enterprise, You Need More Than Tech
		Tech-Only Is a Risk
		Tech Chosen without a “Choosing” Process Is a Risk
		Tech Strategy Is a Risk
		Operational and Outcome Risks
			Broken Process
	Strategy-Driven Discovery-and-Design Process
		Corporate Strategy
			Step 1: Discover Domain
			Step 2: Transform Domain
			Step 3: Design Assets
		Predicted Outcomes
	How to Discover the Right Tech
		Discover Tech in the Business Context
		Discover While Exploring Four Things
	How to Design It Right
		Design Tech in the Business Context
		Design Approach
		Designing the Encapsulated Processes
		Designing the User Interface
	Getting Your Team to Make a Strategic Contribution
		Individual Contribution Is Important
		Potentially Chaotic Team
		How to Ensure Collaboration
	Managing Transformation Outcomes
	References
Chapter 2: Introduction to Computer Vision
	Introduction
	Image Processing
	Segmentation
	Discontinuity-Based Approach
		Operation of Masks
		Point Detection
		Line-Detection Algorithm
		Include Masks for Line Detection
		Edge-Detection Algorithm
		Roberts Edge Detection
		Sobel Edge Detection
		Prewitt Edge Detection
		Kirsch Edge Detection
		Robinson Edge Detection
		Marr Hildreth Edge Detection
		LoG Edge Detection
		Canny Edge Detection
	Similarity-Based Approach
		Thresholding
		Region Growing
		Region Splitting and Merging
		Segmentation Based on Clustering
			K Means Clustering
			Deep Learning
			Neural Networks
		Deep Learning Algorithms
		Convolutional Neural Networks
		Recurrent Neural Network
		Long Short-Term Memory Network
		Deep Belief Networks
	Restricted Boltzmann Machines
	Conclusion
	References
Chapter 3: Essentials of the Internet of Things (IoT)
	Introduction
	Origin and Influences of IoT
	Basics and Terminology
	Characteristics of IoT
	IoT Deployment Levels
	IoT Terminology
	Goals and Benefits
	Risks in IoT
	Challenges in IoT
	Challenges in Designing IoT
	Challenges in Managing Data
	Challenges in Security
	Fundamental Concept and Methodology
	IoT Design Methodology
	IoT Technology and Communication Protocols
	Characteristics and Architecture
	IoT Architecture
	Services and Security Mechanisms
	IoT Security
	Case Study: Using the Meshlium Scanner for Smartphone Detection
	Case Study: Seedbed Based on IoT
		Environmental Factors and Seed Breeding
		Monitored Seedbed Construction Automation and Development
	References
Chapter 4: The Internet of Things Architectures and Use Cases
	Introduction
	Traditional Network Versus the Internet of Things
	Challenges in IoT
		IoT Challenges Based on Security Constraints
		Hardware-Based Security Constraint
		Software-Based Security Constraints
		Network-Based Security Constraints
	IoT Challenges Based on Security Requirements
		Access-Level Security Requirements
		Functional-Level Security Requirements
		IoT Features and Issues
	Components of IoT
	IoT Architecture and Protocol Stack
		Three-Layered Architecture
		Four-Layered Architecture
		Five-Layered Architecture
		Seven-Layered Architecture
			Protocol Stack
			Applications and Use Cases
	Conclusion
	References
Chapter 5: Challenges of Introducing Artificial Intelligence (AI) in Industrial Settings
	Introduction
	Strategy and Organization
		Strategy
		Organization
	Technology
		Data
		Testing and Validation
		Technology Risks
	People and Process
		People
		Process
			Decision-Making
			Type of Problem
			Make/Buy
	Advice for Implementation
	Summary
	Acknowledgments
	Abbreviations
	References
Chapter 6: Blockchain-based Circular-Secure Encryption
	Introduction
	Password Vulnerability
	Password-Cracking Attacks
	Common Causes of Knowledge Breaches
	Preventive Steps for Violations of Data
	Blockchain Structure
	Hash Functions in Blockchain
	Hashing in Password Security
	Blockchain-Based Circular Fused Encryption
	Wedges Algorithm for Adding Salt
	Conclusion
	References
Chapter 7: Security Challenges and Attacks in MANET-IoT Systems
	Introduction
	Classification of Routing Protocols in MANET-IoT Systems
		Table-Driven Approach
		On-Demand Approach
	Existing Routing Approaches in MANET-IoT Systems
		Centralized Routing
		Distributed Routing
	Classification of Attacks in MANET-IoT Systems
		Basic Classification
			Active Attacks
			Passive Attacks
		Layer-Based Classification
			Application Layer
			Transport Layer
			Network Layer (Routing Attacks)
			Data Link Layer
			Physical Layer
	Routing Attacks and Existing Defense Mechanisms
		Routing Attacks on Data Packets
		Routing Attacks on Control Packets
	Classification of Existing Defense Mechanisms
	Discussion
		Analysis of Existing Defense Mechanisms
		Open-Research Challenges
			Identification of Strategically Different Packet-Drop Attacks
			Cooperative Node Attacks
			Identity-Based Attacks
	Conclusion and Future Works
	References
Chapter 8: Machine and Deep Learning (ML/DL) Algorithms for Next-Generation Healthcare Applications
	Introduction
	The Significance of Deep Learning Using Natural Language
		The Promise of Deep Learning
		Deep Learning Algorithms
	Restricted Boltzmann Machine (RBM)
	Autoencoders
	Deep Belief Networks (DBNs)
	Convolutional Neural Network (CNN)
	Natural Language Processing
	Challenges of Natural Language
		From Linguistics to Natural Language Processing
		Medical Imaging Analytics and Diagnostics
	Define a CAD
	Machine Learning
		Applications of ML in Treatment
		Applications of ML in Medical Workflows
		Secure, Private, and Robust ML for Healthcare Challenges
	ML for Healthcare Challenges
	Conclusions
	References
Chapter 9: A Review of Neuromorphic Computing:: A Promising Approach for the IoT-Based Smart Manufacturing
	Introduction
	The Paradigm Shift in Computing Technology
	Motivation
	Choice of Models
		Neuron Models
			Bio-Plausible Model
			Biologically Inspired Model
			Neuron Model with Additional Bio-inspired Mechanism
			Integrate and Fire
			Digital Spiking Neuron
			McCullock and Pitts Model
		Synapse Models
			Biologically Inspired Synapse Implementation
			ANN Synapse Implementation
		Network Models
			Feed-Forward Network Model
			Recurrent Neural Network (RNN) Model
			Stochastic Neural Network Models
			Unsupervised Learning Models
			Vision-Inspired Models
			Spiking Neural Network (SNN) Model
	Learning Algorithms
		Supervised Learning Algorithms
		Unsupervised Learning Algorithms
	Devices for Neuromorphic Computing
		Memristors
		Conductive-Bridging RAM (CBRAM)
		Phase Change Memory
		Floating Gate Transistors
		Optical Components
	Hardware Implementation Technologies
	Applications
	Conclusion
	Notes
	References
Chapter 10: Text Summarization for Automatic Grading of Descriptive Assignments:: A Hybrid Approach
	Introduction
	Literature Survey
	Adaptation of a New Technique for Autograding Descriptive Assignments
		Text Preprocessing Module
		Assignment Correction Module Using Hybrid RAKE-ROUGE Algorithm
			Hybrid RAKE-ROUGE Algorithm
			Keyword Extraction Using RAKE Algorithm
			Keyword Comparison Using ROUGE Metric
		Plagiarism-Detection Module
			Cosine Similarity
			Jaccard Similarity
			Pearson Correlation Coefficient
		Peer-Review Module
	Results and Discussion
	Conclusion
	References
Chapter 11: Building Autonomous IIoT Networks Using Energy Harvesters
	Introduction
	Concept of Energy Harvesting Explained
	Energy Requirements of IIoT Sensors and Extent of Autonomy
	State of the Art and Possible Autonomous IIoT in Major Industries
	Future Scope of Expansion of Autonomous IIoT Deployment
	References
Chapter 12: An Interactive TUDIG Application for Tumor Detection in MRI Brain Images Using Cascaded CNN with LBP Features
	Introduction
	Related Works
	Materials and Methods
		Database and Workstation
		Feature Extraction Using LBP
		Convolutional Neural Network (CNN)
		Classification Using Cascaded CNN
		Fully Connected (FC) Layer
		Softmax Classification
		Loss Function
		Training
		Evaluation
		TUDIG Application
	Experimental Results and Discussion
		Effectiveness of LBP
		Effectiveness of Cascaded CNN
			Tumor Detection Performance of Proposed Network Using BRATS-2018 Dataset
			Performance Comparison of Proposed Network with Existing Methods Using BRATS-2018 Dataset
			Performance of TUDIG Application
	Conclusion
	References
Chapter 13: Virtual Reality in Medical Training, Patient Rehabilitation and Psychotherapy: Applications and Future Trends
	Introduction
		VR in Medical Training
			Surgical Training
			Anatomy Teaching
		Virtual Reality in Patient Rehabilitation
			Motor Skills Impairment Rehabilitation
			Autism Spectrum Disorder (ASD)
			Stroke Rehabilitation
			Pediatric Motor Rehabilitation
			VR in Lower-Limb Rehabilitation
		VR in Psychotherapy
	References
Chapter 14: Complexity Measures of Machine Learning Algorithms for Anticipating the Success Rate of IVF Process
	Introduction
	Risk Factors and Tests for Predicting Infertility in Men
	Masculine Infertility Treatments
	Advantages of IVF
	Literature Survey
	Study of Machine Learning Classification Algorithms
	Dataset
	Data Pre-Processing
	Machine Learning Classifiers
	Training and Validation
	Performance Analysis of the Classification Algorithms
	Results and Discussions
	Build the Proposed Model
	Prediction Using the Proposed Model
	Conclusion
	References
Chapter 15: Commuter Traffic Congestion Control Evasion in IoT-Based VANET Environment
	Introduction
	State-of-the-Art Reviews
	Preliminary Study for the Proposed Model
	Performance Metrics
	Initial Evaluation of the Model
	Implementation of the Proposed Model for Congestion Avoidance
	Algorithms
		Identifying the Vehicle Speed
	Calculating the Distance between the Vehicles
	Wireless Access in Vehicular Environment (WAVE)
	Physical and MAC Layer Parameters
	Observed Results and Discussion
		Packet Delivery Ratio
		Dropped Packets
		Delay
		Routing Overhead
		Throughput
		Improved CAV-AODV
	Conclusion
	References
Chapter 16: Dyad Deep Learning-Based Geometry and Color Attribute Codecs for 3D Airborne LiDAR Point Clouds
	Introduction
		Point Cloud Image
		Preprocessing Methods
		Deep Learning (DL) Model
		Dyad Deep Learning Model
		Point Cloud Compression and Decompression
	Related Work
		Preprocessing Methods
		Point Cloud Compression
		Deep Learning on Point Clouds
	Proposed Methodology
		Alternate Signal Sampling (ASiS)
		Min-Max Signal Transformation (MiST)
		Dyad Deep Learning Codec (DDLC)
	Performance Metrics
		Chamfer Pseudo-Distance (CPD)
		Hausdorff Distance (HD)
		Point-to-Point Metrics (p2p)
	Experimental Results
		Datasets
		Implementation of the Proposed DDLCPCD Algorithm
		Performance Analysis
		Subjective Analysis
		Objective Analysis
	Conclusion
	References
Chapter 17: Digital Enterprise Software Productivity Metrics and Enhancing Their Business Impacts Using Machine Learning
	Introduction
	The Need for Business-Oriented Software Metrics
	Traditional Software Productivity Metrics
	Productivity Metrics in Software Engineering
	Data Mining in Software Productivity Measurement
		Data Collection
		Data Understanding
		Exploratory Data Analysis (EDA)
		Feature Scaling
		Model Selection
	Conclusions
	References
Index