Table of Contents
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FOREWORD
PREFACE
List of Contributors
Blockchain-enabled Security for Medical Image Transmission: Prescription Data Hiding and Multi-secret Sharing-based Encryption
Abstract
INTRODUCTION
Objectives of this Chapter
RELATED WORKS
SYSTEM ARCHITECTURE
IMPLEMENTATION
Least Significant Bit Algorithm
LSB Embedding Equation
LSB Extraction Equation
RESULT AND DISCUSSION
Encryption using ECC
Decryption using ECC
Accessing Medical Image Data
Accessing Medical Image Data
ENCRYPTION PERFORMANCE
DECRYPTION PERFORMANCE
IMAGE QUALITY ANALYSIS
CONCLUSION AND FUTURE ENHANCEMENT
REFERENCES
Dynamic Resource Allocation for Internet of Thing Devices in Edge Computing: An Intelligent Fuzzy Approach
Abstract
INTRODUCTION
Literature Survey
SYSTEM ARCHITECTURE
The IoT Device Manager
The Edge Computing Environment
The Fuzzy Logic Based Resource Allocation Engine
RESULT AND DISCUSSION
Performance Analysis
Conclusion and Future Enhancement
AUTHORS’ CONTRIBUTIONS
REFERENCES
Improved GarbGenius: An AR Approach to Outfit Recommendation Systems
Abstract
INTRODUCTION
Overview of Fashion Retail Challenges
Motivations for GarbGenius Development
LITERATURE SURVEY
EXISTING TECHNOLOGY
Virtual Fitting Room Technologies
Augmented Reality (AR) Shopping Systems
Recommendation Systems
PROPOSED SYSTEM
Overview of GarbGenius Solution
Key Features and Functionality
Technical Implementation
Performance Evaluation
Evaluating Models
MPV3D Dataset
Fashion Product Images – Recommendation System
Model Comparison
Algorithmic Foundations
Virtual Try-on
Recommendation System
ARCHITECTURE
DEMO SCREENSHOTS
CONCLUSION
REFERENCES
Machine Learning in Multidisciplinary Predictions – A Contemporary Study on Tool Wear Prediction for Milling Process
Abstract
INTRODUCTION
Robotics and Autonomous Systems
Predictive Maintenance
Smart Manufacturing
Adaptive Control Systems
Human-machine Interaction
LITERATURE REVIEW
STATE-OF-THE-ART MACHINE LEARNING TECHNIQUES TOOL WEAR PREDICTION
Supervised Learning Models
Hybrid Models
TOOL WEAR DATA SOURCES
Sensor Data
Machine Tool Data and Maintenance Records
Investigational Data
Industry Collaboration and Benchmarks
MILLING OPERATIONS
Tool Wear Prediction
RESEARCH GAPS IN TOOL WEAR PREDICTION USING MACHINE LEARNING
SEGMENTS TO IDENTIFY TOOL WEAR USING SVM
RESULTS AND DISCUSSION
Decision Boundaries and Data Points
Interpretation of the Boundaries
Observations
Performance of an SVM model
CHALLENGES AND FUTURE STRATEGIES
CONCLUSION
REFERENCES
Laser Cladding on Magnesium Alloys: A Review of Surface Modification Technique
Abstract
INTRODUCTION
IMPORTANCE OF MAGNESIUM ALLOYS IN SEVERAL INDUSTRIES
LIMITATIONS OF MAGNESIUM ALLOYS
Low Hardness
Poor Wear Resistance
Corrosion Susceptibility
High Chemical Reactivity
Limited Temperature Resistance
Limited Formability
OVERVIEW OF LASER CLADDING - SURFACE MODIFICATION METHOD
Material Selection
Preparation of Surface
Laser Beam Generation
Cladding Material Deposition
Clad Layer Formation
Cooling and Solidification
ADVANTAGES OF LASER CLADDING PROCESS
Precise Control
Minimal Heat-affected Zone
Improved Properties
High Efficiency
LASER CLADDING PROCESS
Material Selection
Surface Preparation
Material Deposition
Fusion and Solidification
TYPES OF LASER SYSTEMS
CO2 Lasers
Nd:YAG Lasers
Fiber Lasers
Diode Lasers
PROCESS PARAMETERS
Laser Power
Scan Speed
Powder Feed Rate
Substrate Preheating
BENEFITS OF LASER CLADDING ON MAGNESIUM ALLOYS
Enhanced Wear Resistance
Corrosion Protection
Heat Resistance
Repair and Restoration
CHALLENGES AND CONSIDERATIONS
Material Compatibility
Thermal Stress and Distortion
Surface Preparation
Process Optimization
Quality Control and Testing
Cost and Production Considerations
RECENT ADVANCES AND FUTURE DIRECTIONS
Advanced Cladding Materials
Process Optimization and Control
Additive Manufacturing (AM) Integration
Surface Engineering for Multifunctionality
In-situ Alloying and Microstructure Control
Sustainability and Eco-friendly Approaches
NOVEL CLADDING MATERIALS FOR IMPROVED PERFORMANCE
Ceramic Reinforcements
Metal Matrix Composites (MMCs)
Intermetallic Compounds
Corrosion-resistant Alloys
Hybrid Materials
Nanostructured Materials
ADVANCED PROCESS MONITORING AND CONTROL TECHNIQUES
Thermal Imaging
Spectroscopic Analysis
In-situ Process Monitoring
Closed-loop Control Systems
Adaptive Control Algorithms
Non-destructive Testing (NDT) Techniques
INTEGRATION OF LASER CLADDING WITH OTHER SURFACE MODIFICATION TECHNIQUES
Laser Cladding with Surface Pre-treatment
Laser Cladding with Physical Vapor Deposition (PVD)
Laser Cladding with Thermal Spraying
Laser Cladding with Nitriding or Carbonitriding
Laser Cladding with Surface Texturing
Laser Cladding with Surface Alloying
CONCLUSION
REFERENCES
CNN-based Classification for Leaf Disease Identification
Abstract
SYSTEM DESIGN BLOCK DIAGRAM
CNN Based System
CNN Model Layers
CNN Algorithm
Convolution Layer Output
Pooling Layer
Calculating the Maximum Value in Each Window
Output after Passing through the Pooling Layer
Vector Formation
Classification
Comparison Example
Result
Convolution Layer
ReLU Layer
Pooling Layer
CNN Recognition Process
APPLICATIONS
CONCLUSION
References
Fast Terminal Sliding Mode Controllers (FTSMC) Based on MPPT for Photovoltaic Modules
Abstract
INTRODUCTION
PROPOSED SYSTEM MODELING
MODIFIED INTERLEAVED BOOST CONVERTER (IBC)
PROBLEM FORMULATION
PV EQUIVALENT CIRCUIT
MPPT-based PV System
Proposed System
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Identification of Complex Problems in Social Networks using Neural Network Models with Representation Learning
Abstract
INTRODUCTION
NOTATIONS AND PROBLEM DEFINITIONS
NEURAL NETWORK-BASED MODELS
Framework Overview From the Encoder-decoder Perspective
Models with Embedding Look-up Tables
Skip-gram-based Models
Attributed Network Embedding Models
Heterogeneous Network Embedding Models
Dynamic Embedding Models
Autoencoder Techniques
Deep Neural Graph Representation (DNGR)
Structural Deep Network Embedding (SDNE)
Autoencoder-based Attributed Network Embedding
Graph Convolutional Approaches
Graph Convolutional Networks (GCN)
Inductive Training with GCN
Graph Attention Mechanisms
SUBGRAPH EMBEDDING
Flat Aggregation
Hierarchical Aggregation
APPLICATIONS
Node Classification
Link Prediction
Anomaly Detection
Node Clustering
DIFFERENT TYPES OF NETWORKS
Dynamic Networks
Hierarchical Network Structure
Heterogeneous Networks
Scalability
Interpretability
CONCLUSION AND FUTURE DIRECTIONS
REFERENCES
Virtual Reality in Education: Enhancing Student Engagement and Learning
Abstract
INTRODUCTION
OVERVIEW OF CHALLENGES IN EDUCATION USING VR
MOTIVATIONS FOR VIRTUAL REALITY IN EDUCATION
LITERATURE SURVEY
EXISTING TECHNOLOGY
Head-mounted Display (HMDS)
Tracking Systems
Content Creation Tools
PROPOSED SYSTEM
OVERVIEW OF VIRTUAL REALITY EDUCATION PLATFORM (VREP)
KEY FEATURES AND FUNCTIONALITY
TECHNICAL IMPLEMENTATION
Frontend
Web Interface
VR Client
VR Content Creation
3D Modelling
VR Content Authoring
Infrastructure
Security
Analytics and Reporting
PERFORMANCE EVALUATION
System Performance
VR Content Performance
User Experience
Scalability
Security
ALGORITHMIC FOUNDATIONS
3D Modeling and Rendering
Computer Vision
Machine Learning
Natural Language Processing
Data Analytics
RECOMMENDATION SYSTEM
User Profiling
Content Analysis
Collaborative Filtering
Content-based Filtering
Hybrid Approach
Recommendation Generation
Evaluation and Feedback
ARCHITECTURE
Understanding the Core Components
CONCLUSION
REFERENCES
TechTrends: Navigating the Frontier of Emerging Technologies
Edited by
V. Padmavathi
Department of Information Technology
A.V.C. College of Engineering
Mannampandal 609305, Mayiladuthurai
Tamil Nadu, India
R. Kanimozhi
Department of Information Technology
A.V.C. College of Engineering, Mannampandal 609305
Mayiladuthurai, Tamil Nadu, India
Lakshmana Kumar Ramasamy
Department of Computer Information Science
Higher Colleges of Technology, (Government Institution)
Abu Dhabi, UAE
R. Saminathan
Department of Computer Science and Engineering
Annamalai University, Annamalainagar 608002
Tamil Nadu, India
&
Mirra Subramanian
Quorum Software
Houston, Texas, USA
BENTHAM SCIENCE PUBLISHERS LTD.
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FOREWORD
Today’s society is unrecognizable when compared with the one of ten years ago. It is a world where innovation brings changes that affect not only industries but the very foundations of people’s lives. This book, "TechTrends: Navigating the Frontier of Emerging Technologies", is a collection of ideas about these transformational technologies that should be useful to anyone interested in learning more about these technologies in order to embrace the future.
Such an environment brings the problem of growing difficulty in gaining relevant information as technology advances exponentially. However, it is critical for practitioners, legislators, scientists, and learners to understand the ways these innovations distort, enhance, or evolve their professions and global society. This book directly addresses that task by offering a brief overview of major technologies, including Artificial Intelligence, Blockchain, Internet of Things, and others.
However, this is not what is evident in TechTrends, which is therefore free from these problems due to its clarity. All in all, the authors have well addressed the requirements for details and, at the same time, avoided excess depth which would have made numbers complex. By the end of this book, the reader is going to get theoretical as well as pragmatic perceptions and examples of how these advancements can be put to use in sectors of healthcare, education, and energy.
That being said, the exposure of expertise in this book is almost unbelievable. Several authors of the articles are from the business fields and academic circles and most of them have provided their valuable insights and ideas to the readers. These are the reasons it is safe to say that TechTrends is an invaluable resource when it comes to understanding today’s world, which is filled with emerging technologies and numerous opportunities.
M. Senthilmurugan
A.V.C. College of Engineering
Mannampandal 609305, Mayiladuthurai
Tamil Nadu, India
&S. Selvamuthukumaran
A.V.C. College of Engineering
Mannampandal 609305, Mayiladuthurai
PREFACE
In the rapidly evolving landscape of today’s digital world, staying ahead of technological advancements is no longer an option but a necessity. The emergence of groundbreaking innovations is reshaping industries, redefining possibilities, and fundamentally altering the way we live and work. This book, TechTrends: Navigating the Frontier of Emerging Technologies, is designed to be your compass through this transformative journey.
From Artificial Intelligence to Blockchain and from Quantum Computing to renewable energy, this comprehensive guide examines the key technologies that are driving the future. Each chapter delves deep into the trends that matter most, with a focus on providing clear explanations and practical insights. By blending theoretical understanding with real-world applications, we aim to demystify complex concepts and make them accessible to a broad spectrum of readers.
This book is the result of the combined efforts of industry experts, researchers, and thought leaders who have generously contributed their knowledge and perspectives. Their diverse expertise ensures that the content remains relevant, timely, and rich with practical advice so that readers can immediately apply it to their own contexts.
Whether you are a technologist looking to stay ahead of the curve, a business leader seeking competitive advantages, a student aiming to expand your knowledge, or a policymaker grappling with the implications of emerging technologies, TechTrends offers something valuable. It is not just a reference book but a toolkit for navigating the complexities of tomorrow’s innovations.
As you embark on this exploration of the frontier of emerging technologies, I hope you find the insights within inspiring and empowering. It is my sincere belief that with the right knowledge and understanding, we can harness these technologies to create a better, more connected, and more equitable future for all.
Welcome to the Frontier. Let’s explore it together.
V. Padmavathi
Department of Information Technology
A.V.C. College of Engineering Mannampandal 609305
Mayiladuthurai, Tamil Nadu, IndiaR. Kanimozhi
Department of Information Technology
A.V.C. College of Engineering, Mannampandal 609305
Mayiladuthurai, Tamil Nadu, IndiaLakshmana Kumar Ramasamy
Department of Computer Information Science
Higher Colleges of Technology, (Government Institution)
Abu Dhabi, UAER. Saminathan
Department of Computer Science and Engineering
Annamalai University, Annamalainagar 608002
Tamil Nadu, India
&Mirra Subramanian
Quorum Software
List of Contributors
A. KanthimathinathanDepartment of CSE, Annamalai University, Annamalainagar 608002, Tamil Nadu, IndiaA. RagavendiranDepartment of Electrical & Electronics Engineering, A.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaB. N. KarthikDepartment of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, IndiaB. AsaithambiDepartment of Manufacturing Engineering, Annamalai University, Annamalainagar 608002, Tamil Nadu, IndiaB.S. SathishkumarA.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaD. SanthoshDepartment of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, IndiaG. RamachandranDepartment of CSE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai 600062, Tamil Nadu, IndiaG. Vishal Ponn RanganDepartment of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, IndiaG.B. SathishkumarDepartment of Manufacturing Engineering, Annamalai University, Annamalainagar 608002, Tamil Nadu, India
Department of Mechanical Engineering, Arasu Engineering College, Kumbakonam, Tamil Nadu, IndiaI. MahendravarmanDepartment of Electrical & Electronics Engineering, A.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaJ. Sharmila DeviDepartment of Instrumentation and Control Engineering, A.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaK. Suryaa NarayananDepartment of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, IndiaP. AnbalaganDepartment of Computer Science and Engineering, Annamalai University, Annamalainagar 608002, Tamil Nadu, IndiaP. BalasubramanianDepartment of Instrumentation and Control Engineering, A.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaR. ManivannanDepartment of CSE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai 600062, Tamil Nadu, IndiaR. KanimozhiDepartment of Instrumentation and Control Engineering, A.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaR. RamyaDepartment of CSE, A.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaS. SaravananDepartment of Computer Science and Engineering, Annamalai University, Annamalainagar 608002, Tamil Nadu, IndiaS. ThiyaneshwaranDepartment of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, IndiaS. SundaraselvanDepartment of Mechanical Engineering, Arasu Engineering College, Kumbakonam, Tamil Nadu, IndiaS.K. RajalakshmiA.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaS.A. ChithradeviDepartment of Electrical & Electronics Engineering, A.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaS. KannanDepartment of ECE, Kings College of Engineering, Punnalkulam, Thanjavur, IndiaS. RamapriyaDepartment of CSE, A.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaSelva Adaikala L. GermeniDepartment of Computer Science and Engineering, Arasu Engineering College, Kumbakonam, Tamil Nadu, IndiaShahul S. HameedDepartment of Computer Science and Engineering, Arasu Engineering College, Kumbakonam, Tamil Nadu, IndiaT. BalamuruganDepartment of Mechanical Engineering, Arasu Engineering College, Kumbakonam, Tamil Nadu, IndiaV. MahavaishnaviDepartment of Artificial Intelligence and Data Science, Panimalar Engineering College, Poonamalle, Chennai, IndiaV. PadmavathiDepartment of Instrumentation and Control Engineering, A.V.C. College of Engineering, Mannampandal 609305, Mayiladuthurai, Tamil Nadu, IndiaV. SrinivasanDepartment of Manufacturing Engineering, Annamalai University, Annamalainagar 608002, Tamil Nadu, India
