Enterprise Digital Transformation: Technology, Tools, and Use Cases

✍ Scribed by Sathyan Munirathinam (editor), Peter Augustine (editor), Pethuru Raj (editor)

• Publisher: Auerbach Publications
• Year: 2022
• Tongue: English
• Leaves: 447
• Edition: 1
• Category: Library

✦ Synopsis

Digital transformation (DT) has become a buzzword. Every industry segment across the globe is consciously jumping into of digital innovation and disruption in order to be ahead of their competitors. In other words, every aspect of running a business is being digitally empowered to reap all the benefits of the digital paradigm. All kinds of digitally enabled businesses across the continents and countries are intrinsically capable of achieving bigger and better things for their constituents. Their consumers, clients, and customers will realize immensely benefits with real digital transformation initiatives and implementations. The much-awaited business transformation can be easily and elegantly accomplished with a workable and winnable digital transformation strategy, plan, and execution.

There are several enablers and accelerators for realizing the much-discussed digital transformation. There are digitization and digitalization technologies in plenty in order to streamline and speed up the process of the required transformation. Industrial Internet of Things (IIoT) technologies in close association with decisive advancements in the artificial intelligence (AI) space can bring forth the desired transitions. The other prominent and dominant technologies towards forming digital organizations include cloud IT, edge/fog computing, real-time data analytics platforms, blockchain technology, digital twin paradigm, virtual and augmented reality (VR/AR) techniques, enterprise mobility, and 5G communication. These technological innovations are intrinsically competent and versatile enough to fulfill the varying requirements for establishing and sustaining digital enterprises.

Enterprise Digital Transformation: Technology, Tools, and Use Cases features chapters on the evolving aspects of digital transformation and intelligence. It covers the unique competencies of digitally transformed enterprises, IIoT use cases and applications. It explains promising technological solutions widely associated with digital innovation and disruption. The book focuses on setting up and sustaining smart factories that are fulfilling the Industry 4.0 vison, that is realized through IIoT and allied technologies.

✦ Table of Contents

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