Enterprise Digital Transformation: Technology, Tools, and Use Cases
- Length: 464 pages
- Edition: 1
- Language: English
- Publisher: Auerbach Publications
- Publication Date: 2022-02-16
- ISBN-10: 0367635895
- ISBN-13: 9780367635893
- Sales Rank: #0 (See Top 100 Books)
Digital transformation (DT) has become a buzzword. Every industry segment across the globe is consciously jumping toward digital innovation and disruption to get 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 globe are intrinsically capable of achieving bigger and better things for their constituents. Their consumers, clients, and customers will realize immense 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 a lot of digitization and digitalization technologies available 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 toward 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 vision that is realized through the IIoT and allied technologies.
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
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