This book explores the synergy between VLSI and Machine Learning and its applications across various domains. It will investigate how Machine Learning techniques can enhance the design and testing of VLSI circuits, improve power efficiency, optimize layouts, and enable novel architectures.

Chapter 1. Optimizing Circuit Synthesis: Integrating Neural Networks and Evolutionary Algorithms for Increased Design Efficiency
Chapter 2. Study of Physical Processes Analysis and Phenomena of Insights of Trapping in the Performance Degradation in AlGaN/GaN HEMTs
Chapter 3. Framework for Design and Performance Evaluation of Memory using Memristor
Chapter 4. Innovative Design and Optimization of High-Power Amplifiers: A Comparative Study with GaN HEMT and CMOS Technologies
Chapter 5. Exploring FPGA Architecture Designs for Matrix Multiplication in Machine Learning
Chapter 6. Silicon Chip Design and Testing
Chapter 7. A Novel Deep Learning Approach for Early Brain Tumour Detection
Chapter 8. TCAD Augmented Machine Learning for the Prediction of Device Behavior and Failure Analysis
Chapter 9. Opportunities and Challenges for ML-Based FPGA Backend Flow
Chapter 10. Role of Machine Learning Applications in VLSI Design
Chapter 11. Application of Artificial Intelligence/Machine Learning in VLSI Design
Chapter 12. FinFET-Based 9T SRAM for Enhanced Performance in AI/ML Applications
Chapter 13. Power Consumption and SNM Analysis of 6T and 7T SRAM using 90nm Technology
Chapter 14. Transforming Electronics: An Extensive Analysis of Hyper-FET Technological Developments and Utilisation
Chapter 15. VLSI Realization of Smart Systems using Blockchain and Fog Computing

Engineering Management

The book explores the transformative impact of AI on material science and construction practices in the Industry 4.0 landscape. It enquires into AI history and applications, examining material optimization, smart materials, and AI in construction.

Preface. Is AI the Architect of Tomorrow’s Materials in the Age of Industry 4.0? History of AI. Artificial Intelligence in Construction Materials. Industry 4.0 and Construction. Automation in Construction Industry. Robotics in Construction. AI-assisted Building Design. AI in Fabrication and Construction. Smart Infrastructure and AI. Ethical and Societal Implications of AI in Construction. Future Perspectives and Trends. The Conclusive Role of AI in Industry 4.0 Construction Materials. Index.

AI in MRI-based Brain Disease Prediction presents a comprehensive exploration of artificial intelligence technologies in the analysis of magnetic resonance imaging (MRI) for brain disease prediction. 

Preface. INTRODUCTION OF BRAIN AND BRAIN MRI. Brain and Magnetic Resonance Brain Imaging. Technical Foundations. AI-Empowered Fast Magnetic Resonance Imaging. MRI-BASED BRAIN DISEASE PREDICTION. Unveiling the Interdisciplinary Landscape of Brain MRI in Ophthalmology. Brain Disease Diagnosis Through AI-MRI Integration. Advancements in Intelligent Auxiliary Diagnosis for Glioma using Multimodal MRI Images. Graph-based Deep Learning for MRI-based Brain Network Analysis. AI in Stroke Segmentation Study. Multi-Scale Feature Fusion-based Sweet Spots Localization from Microelectrode Recordings in STN-DBS Surgery. Intelligent Diagnosis and Classification of Intracerebral Hemorrhage. Prediction and Diagnosis for Autism Spectrum Disorder. Multi-Structure Segmentation for STN -DBS Surgery via Contrastive Learning. Alzheimer’s Disease Diagnosis Methods Based on Biomedical Data. Applications of Hypergraph Learning for Brain Disorder Diagnosis with Neuroimaging: A Survey. Index.

Applications of Blockchain and Computational Intelligence in Environmental Sustainability

Machine Learning - Design

The book explores the complex correlation between blockchain technology and sustainability, demonstrating the potential of cutting-edge computational intelligence methods to address critical environmental and societal challenges.

1. Introduction to Blockchain and Sustainability. 2. Blockchain in Environmental Conservation. 3. Smart Contracts and Sustainable Business Models. 4. Blockchain Integration in Renewable Energy. 5. Sustainable Agriculture and Food Supply Chains. 6. Blockchain Applications in Sustainable Agriculture. 7. Blockchain and Circular Economy. 8. Sustainability Reporting and Transparency. 9. Blockchain for Carbon Credits and Emissions Reduction.

Provides a comprehensive analysis of the tools and techniques used for the development of efficient and robust decentralized infrastructures for various real-life scenarios, ranging from financial investment through blockchain protocols, shared transportation systems and communication networks, to bioinformatics and military applications.

Preface. Recent Developments in the Theory and Applicability of Swarm Search. Modeling and Prediction of Ride Sharing Utilization Dynamics. The Dynamics of Autonomous Drone Swarms in Surveillance: Exploring Efficiency, Feasibility, and Complexity. Defending Large-Scale Critical Infrastructures using a Swarm of Drones. Socioeconomic Patterns of Twitter Activity. From Microbes to Methane: AI-Based Predictive Modeling of Feed Additive Efficacy in Dairy Cows.

Computer Science (General)

The text presents an artificial intelligence-based framework, algorithms, and applications from the perspective of society 5.0.

1. Introduction and Role of Society 5.0 in Human Centric Development. 2. AI Architectures and Technologies for Raising Society 5.0. 3. Disruptive Technologies and Sustainable Development Goals for Society 5.0. 4. Use of Big Data Architecture in Society 5.0. 5. Ethics and Regulations in the field of Artificial Intelligence for Sustainable Development in Society 5.0.  6. Opportunities and Challenges in AI Society 5.0. 7. Emergence of AI in Education: A way forward for Societal Development. 8. Applications and Use of AI in E-Commerce - Opportunities and Challenges in Society 5.0. 9. AI based Machine Learning and Deep Learning Smart Electricity Grids for Society 5.0. 10. Securing Smart Grids using Machine Learning Algorithms. 11. Energy Management in IoT enabled Smart Grid: A Review. 12. E-Healthcare and Society 5.0. 13. Role of Disruptive Technologies in the Smart Healthcare Sector of Society 5.0. 14. Agriculture in Society 5.0. 15. Personalized navigation system in Society 5.0. 16. Role of Geospatial Technology in the Development of Society 5.0. 17. Application of Machine Learning for IoT security: A step towards society 5.0. 18. Emerging Trends in Cyber Security Challenges with Reference to Pen Testing Tools in Society 5.0. 19. Role of Delay Sensitive Smart Health Framework Using Nature Inspired Load Balancer in society 5.0. 20. Performance Comparison of AODV, DSDV, and DSR Routing Protocols in Wireless Sensor Networks. 21. Enhancing Rating and Learning through Clustering in Artificial Intelligence. 22. Aquatic Weed mining using Artificial Intelligence

Challapalli, Adithya | Li, Guoqiang

Artificial Intelligence Assisted Structural Optimization explores the use of machine learning and correlation analysis within the forward design and inverse design frameworks to design and optimize lightweight load bearing structures as well as mechanical metamaterials.

1. Introduction to Structures with Complex Geometrical Configurations. 2. Structural Optimization. 3. Introduction to Machine Learning-Assisted Structural Optimization. 4. Structural Optimization of Biomimetic Rods Using Machine Learning Regression. 5. Structural Optimization of Lattice Structures. 6. Inverse Machine Learning Using Generative Adversarial Networks. 7. Design and Optimization of Mechanical Metamaterials Using Correlation Analysis. 8. Summary and Future Perspectives.

Material science is one of the sciences that utilizes Artificial Intelligence techniques, including machine learning and nature-inspired optimization algorithms, most extensively. This book reviews the latest methods and applications of AI in material science.

Preface. Artificial Intelligence Computational Intelligence and Opportunities. Urban Heat Island (UHI) Effect and its Mitigation Schemes in Built-up Areas with Artificial Intelligence Techniques. The Role of Artificial Intelligence in Advancing the Digitalization of Machining Processes. Evaluating Process Variability through the Implementation of Fuzzy Triangular Distribution in Dynamic Value Stream Mapping. Innovative Electrode Tool Manufacturing Methods for Electrode Discharge Machining. Implementation of AI in Manufacturing Industries: A Case Study. Constructional and Technological Approach to Improve the Mechanical Performance of Diesel Engines. Artificial Intelligence and Product Innovation: Expectations and Concerns. Artificial Intelligence and Game Theory in the Intensive Care Units. Application of AI in Material Science to Accelerate Material Innovation. Contributions of Machine Learning to Material Science: A Short Overview. The Use of Metaheuristics in Material Science: A Short Overview. Advances in Artificial Intelligence—Will Artificial Intelligence Support Transfusion Medicine Knowledge and Technology? Index.

Computing & IT Security

This book discusses various recent techniques and solutions related to IoT deployment, especially security, and privacy. It addresses a variety of subjects, including a comprehensive overview of the IoT, and covers in detail the security challenges at each layer.

1. Google trend analysis of airport passenger throughputs: case study of Murtala Muhammed International Airpor 2. Blockchain Technology Overview: Architecture, proposed and Future Trends 3. Innovative Approach for Optimized IoT Security based on Spatial Network 4. The combination of blockchain and Internet of Things (IoT) Applications, Opportunities and Challenges for Industry 5. Security Issues in Internet of Medical Things 6. Intrusion detection Framework using AdaBoost algorithm and Chi-squared technique 7. A Collaborative Intrusion Detection Approach Based on Deep Learning and Blockchain 8. GVGB-IDS: An Intrusion Detection System using Graphic Visualization and Gradient Boosting for cloud Monitoring 9. Design and Implementation of Intrusion Detection Model with Machine Learning Techniques for IoT Security

This reference text presents an overview of the blockchain-based internet of things systems, along with the opportunities, challenges, and solutions in diverse fields such as business, education, agriculture, and healthcare.

1. List of contributors. Preface. 1. Blockchain: Concept and Emergence 2. Blockchains in loT: Introduction, Features, and Vulnerabilities 3. Blockchain-Based Internet of Things (B-IoT): Challenges, Solutions, Opportunities, Open Research Questions, and Future Trends 4. Revolutionizing IoT with Blockchain: A State-of-the-Art Review 5. A Framework for Smart and resilient supply chains based on Blockchain and the Internet of Things 6. Pharma-Blocks: Blockchain-IoT Platform for Pharmaceutical Sector 7. Edge Intelligence Decentralized Blockchain-based Internet of Things (B-IoT) for Sustainable Healthcare 8. Analysis of AI Embedded Block Chain Security Model for Healthcare and Financial Transactions 9. Implementation of a Blockchain-based Secure Cloud Computing Mechanism for Transactions 10. Geo-location Based Smart Land Registry Process with Privacy Preservation Using Blockchain Technology and IPFS 11. Safeguarding Digital Environments: Harnessing the Power of Blockchain for Enhanced Malware Detection and IoT Security 12. Synergizing Information Diffusion: Exploring IoT and Blockchain Integration in Online Social Networks

Computer Engineering

This comprehensive text/ book presents a broad review of both traditional (i.e., conventional) and the deep learning aspects of object detection in various adversarial conditions of real-world in a clear, insightful, and highly comprehensive style.

1. Fundamentals of Object Detection 2. Background of Degradation 3. Imaging Modalities for Object Detection 4. Real Time Benchmark Datasets for Object Detection 5. Artifacts Impact on Different Object Visualization 6. Visibility Enhancement of Images in Degraded Vision 7. Object Detection in Degraded Vision 8. Hands-on Practical for Object Detection Approaches in Degraded Vision

Alruwaili, Ahmed | Islam, Sardar M. N. | Gondal, Iqbal

Robotics & Cybernetics

Cybersecurity in Robotic Autonomous Vehicles introduces a novel Intrusion Detection System (IDS) specifically designed for AVs, which leverages data prioritization in CAN IDs to enhance threat detection and mitigation. It offers a pioneering intrusion detection model for AVs that uses machine and deep learning algorithms.

1. Introduction.  2. Theoretical Lens.  3. Exploring CAN Bus Security: Insights and Analysis.  4. Research Design.  5. Results and Discussion.  6. Conclusions and Future Research.  

Rawat, Seema | Ahuja, Neelu Jyothi | Katal, Avita | Kumar, Praveen | Urooj, Shabana

Data Analytics using Machine Learning Techniques on Cloud Platforms examines how machine learning and cloud computing combine to drive data-driven decision-making across industries.

Preface
Author biography
Introduction
1. Data Analytics: An Overview 2. Data Analytics: Tools and Technologies 3. Data Analytics: Statistical Approach 4. Supervised and Unsupervised Methods of Machine Learning used in Data Analytics 5. Opportunities and Challenges for Data Analytics Integrated with Machine Learning 6. Cloud Computing: A Change in the IT Infrastructure Landscape 7. Redefining Data Analytics with Machine Learning and Cloud 8. Data Analytics and Cloud together: A powerful combination for E-commerce and Supply Chain logistics 9. Data Analytics, Machine Learning, and Cloud Together: A Powerful Combination for Healthcare & Education 10. Security and Privacy issues for data analytics using machine learning in cloud computing 11. Future Trends for ML-Based Data Analytics in the Cloud

Botev, Zdravko | Kroese, Dirk P. | Taimre, Thomas

Statistical Computing

The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin rich variety of ideas and machine learning algorithms in data science.

Preface Notation 1. Importing, Summarizing, and Visualizing Data 2. Statistical Learning 3. Monte Carlo Methods 4. Unsupervised Learning 5. Regression 6. Feature Selection and Shrinkage 7. Reproducing Kernel Methods 8. Classification 9. Decision Trees and Ensemble Methods 10. Deep Learning 11. Reinforcement Learning Appendix A. Linear Algebra Appendix B. Functional Analysis Appendix C. Multivariate Differentiation and Optimization Appendix D. Probability and Statistics Appendix E. Python Primer Bibliography Index

Mittal, Mamta | Raheja, Nidhi Grover

Computer Graphics & Visualization

This book gives a holistic overview of creating appropriate charts by describing a sequence of visualizations. The book presents step by step implementation in Tableau to convey information through innovative stories with interactive dashboards for visual data analysis of a dataset. 

Chapter 1: Getting Started with Data Visualization. 1.1 Introduction to Data and Its Types. 1.2 Data Analysis Lifecycle. 1.3 Data Visualization in Data Analysis. 1.4 Popular Tools for Data Visualization. Key Notes. Test Your Skills. References. Chapter 2: Tableau for Visualization. 2.1 Introducing Tableau. 2.2 Different Tableau Products. 2.3 Tableau Server Architecture. 2.4 Tableau Download and Installation. 2.5 Tableau Data Types. 2.6 Tableau File Types. 2.7 Data Preparation Tasks. 2.8 Publishing in Tableau Public. Key Notes. Test Your Skills. References. Chapter 3: Connecting Data in Tableau. 3.1 Different Data Sources in Tableau. 3.2 Extracting Data in Tableau. 3.3 RDBMS Basics and Types of Keys. 3.4 Data Joins in Tableau. 3.5 Data Import and Blending in Tableau. 3.6 Data Sorting in Tableau. 3.7 Data Pre-Processing Using Tableau Prep. Key Notes. Test Your Skills. References. Chapter 4: Table Calculations and Level of Detail. 4.1 Introduction to Calculations. 4.2 Tableau Functions. 4.3 Tableau Operators. 4.4 Tableau Calculations. 4.5 Level of Detail (LOD) Expressions. Key Notes. Test Your Skills. References. Chapter 5: Sorting and Filters in Tableau. 5.1 Filters in Tableau. 5.2 Sorting in Tableau. 5.3 Group, Hierarchy, and Set in Tableau. Key Notes. Test Your Skills. References. Chapter 6: Charts in Tableau. 6.1 Introducing Charts in Tableau. 6.2 Color Schemes and Palettes in Tableau. 6.3 Colour Choosing Best Practices. Key Notes. Test Your Skills. References. Chapter 7: Comparison Charts in Tableau. 7.1 Introduction to Comparison Charts. 7.2 Studying Changes across Time: Trends and Forecasting. 7.3 Trend Lines and Forecasting. 7.4 Statistical Models for Trend Analysis. Key Notes. Practice Case Study. Test Your Skills. References. Chapter 8: Distribution Charts in Tableau. 8.1. Introduction to Distribution Charts. 8.2. Histogram with Its Types and Components. 8.3. Scatter Plot and Matrix. 8.4. Bubble Chart for Distribution. 8.5. Radar Chart for Multiv

Machine Learning - Design

Data-Driven Fault Diagnosis delves into the application of machine learning techniques for achieving robust and efficient fault diagnosis in industrial components.

1. Introduction, 2. Fault Diagnosis of the Pelton Turbine, 3. Fault Diagnosis of the Francis Turbine, 4. Fault Diagnosis of the Centrifugal Pump, 5. Fault Diagnosis of Bearing, 6. The Future of Machine Learning in Fault Diagnosis

Machine Learning - Design

This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising of domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas.

1. Revolutionizing Healthcare through Federated Learning: A Secure and Collaborative Approach. 2. Revolutionizing Healthcare: Unleashing the Power of Digital Health. 3. Federated Deep Learning Systems in Healthcare. 4. Applications of Federated Deep Learning Models in Healthcare Era. 5. Machine Learning for Healthcare- Review and future Aspects. 6. Federated Multi Task Learning to Solve Various Healthcare Challenges. 7. Smart System for Development of Cognitive Skills Using Machine Learning. 8. Patient-Driven Federated Learning (PD-FL) – An Overview. 9. An Explainable and Comprehensive Federated Deep Learning in Practical Applications: Real World Benefits and Systematic Analysis Across Diverse Domains. 10. Federated deep learning system for application of health care of pandemic situation. 11. The integration of federated deep learning with Internet of Things in the healthcare sector. 12. FireEye: An IoT-Based Fire Alarm and Detection System for Enhanced Safety. 13. Safeguarding Data Privacy and Security in Federated Learning Systems. 14. Computer Vision Based Fruit Diseases Detection System using Deep Learning. 15. Tailoring Medicine Through Personalized Healthcare Solutions. 16. FedHealth in Wearable Healthcare, Orchestrated Federated Deep Learning for Smart Healthcare: Health Monitoring and Healthcare Informatics Lensing Challenges and Future Directions. 17. From Scarce to Abundant: Enhancing Learning with Federated Transfer Techniques. 18. Federated Learning-Based AI Approaches for Predicting Stroke Disease.

Tripathy, Somanath | Kasyap, Harsh | Fang, Minghong

As data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing. Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data.

1. Introduction to Machine Learning
a. Types of Learning
b. Learning Tasks
c. Cost Function
d. Optimization
e. Evaluation Metrics
f. Artificial Neural Network
g. Implementation
2. Federated Learning
a. Importance of FL
b. Types of FL
c. Applications in FL
d. Challenges in FL
e. Security and Privacy Issues
f. Defense Techniques
g. Privacy-Preserving Byzantine-Robust FL
h. Implementation
3. Poisoning Attacks in FL
a. Attacker
b. Label flipping attack
c. Gaussian attack
d. LIE attack
e. Krum attack
f. Trim attack
g. Shejwalkar attack
h. Scaling attack
i. Edge attack
j. Vulnerabilities in Cosine Similarity-based Defenses
k. Implementation
4. Inference Attacks in FL
a. Attacker goal
b. Data reconstruction attacks
c. Membership inference attacks
d. Property inference attacks
e. Implementation
5. Byzantine Robust Defenses
a. Design goals
b. Krum
c. Median and Trimmed Mean
d. Bulyan
e. FoolsGold
f. FLTrust
g. Moat
h. DeFL
i. RDFL
j. FLTC
k. Implementation
6. Privacy-Preserving FL
a. Differential Privacy
b. DPFL: A Client Level
c. Homomorphic
d. BatchCrypt: HE-based Scheme
e. Threshold Multi-key HE Scheme
f. Secure Multi-Party Computation
g. Practical Secure Aggregation
h. Summary
i. Implementation

With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios – such as in healthcare, finance, IoT, and edge computing.

1. Introduction to Federated Learning
Vaneeza Mobin 
2. Foundations of Deep Learning
Sajid Ullah 
3. Chronicles of Deep Learning
Syed Atif Ali Shah and Nasir Algeelani 
4. User Participation and Incentives in Federated Learning
Muhammad Ali Zeb and Samina Amin 
5. A Hybrid Recommender System for MOOC Integrating Collaborative and Content-based Filtering
Samina Amin and Muhammad Ali Zeb 
6. Federated Learning in Healthcare
Muhammad Hamza 
7. Scalability and Efficiency in Federated Learning
Alyan Zaib 
8. Privacy Preservation in Federated Learning
P. Keerthana, M. Kavitha, and Jayasudha Subburaj 
9.  Federated Learning: Trust, Fairness, and Accountability
Sana Daud 
10.  Federated Optimization Algorithms
S. Biruntha, S. Rajalakshmi, M. Kavitha, and Rama Ranjini

Tcharkhtchi, Abbas | Vanaei, Hamid Reza | Khelladi, Sofiane

From the Mathematics of Ancient Civilizations to Thinking Machines

This book provides an overview of AI’s historical development, while understanding the cultural and scientific foundations that make AI possible. Through easy-to-understand explanations of complex ideas, and a focus on both technological advances and ethical considerations, it provides an understanding of AI’s past, present, and future.

1  Introduction: Parallel Evolution of Civilizations and Intelligence
2  Artificial Intelligence at Its Origin
3  The Age of Enlightenment and Development of Thinking Machines
4  From Calculator to Computer
5  The Main Models of AI
6  Advanced Applications of AI & Ethical Challenges
7 The Final Words

Tcharkhtchi, Abbas | Vanaei, Hamid Reza | Khelladi, Sofiane

From the Mathematics of Ancient Civilizations to Thinking Machines

This book provides an overview of AI’s historical development, while understanding the cultural and scientific foundations that make AI possible. Through easy-to-understand explanations of complex ideas, and a focus on both technological advances and ethical considerations, it provides an understanding of AI’s past, present, and future.

1  Introduction: Parallel Evolution of Civilizations and Intelligence
2  Artificial Intelligence at Its Origin
3  The Age of Enlightenment and Development of Thinking Machines
4  From Calculator to Computer
5  The Main Models of AI
6  Advanced Applications of AI & Ethical Challenges
7 The Final Words

Utilizing Artificial Intelligence, Internet of Medical Things and Blockchain

In this book, the Role of Artificial Intelligence, Internet of Things and Blockchain in smart healthcare is explained through the detailed study of Artificial Neural Network, Fuzzy Set Theory, Intuitionistic Fuzzy Set, Machine learning and Big Data technology. 

1. Industry 5.0 in Smart Healthcare Sector. 2. Technology Foresight for Better Healthcare 3. Integration of Explainable Artificial Intelligence with IoT and Blockchain Technology in Industry 5.0. 4. Use of Artificial Intelligence in Health Services: An Overview of Medical Education 5. Predictive Analytics and Early Intervention of Diseases using Industry 5.0 6. The Impact of Industry 5.0: The Artificial Intelligence, IoT and Blockchain Revolution in Home Healthcare 7. Blockchain Technology for Healthcare: Revolutionizing the Future of Medicine 8. Internet of Things (IoT) in the Indian Healthcare Sector: A Comparative Study during Pre and Post-Pandemic Period. 9. Edge AI and Blockchain for Smart Sustainability Healthcare Systems. 10. A Comprehensive Review on the Cognitive Radio for the Implementation in 5G Wireless Communication for the Development of Healthcare. 11. Quantum Immortality. 12. TransADD: Transformer-Based Network for Alzheimer’s Disease Detection Using Brain MRI Images. 13. On-Body Sensing Solutions for Automatic Health Monitoring Systems using IOT. 14. Deep Learning and Fuzzy Logic System Implementation In Smart Health Care. 15. A Boost for Health: Blockchain-Powered IoT-BC Boosting in Wearable Health Devices. 16. MPPEDet: Medical Personal Protective Equipment Detection Using Deep Learning Algorithm. 17. Evaluation of the Computerized Accounting Information System (Cais) In Smart Healthcare Systems: Examples of Turkey. 18. Bouc-Wen Hysteresis Modelling and Tracking Control of Piezoelectric Actuator for Precision Nano-positioning Systems in Healthcare. 19. Investigating ChatGPT Usability in Promoting Smart Health Awareness. 20. Epileptic Seizure Prediction framework using Medical Internet of Things. 21. Healthcare: Synergistic Integration of AI, IOT, and Blockchain for Sustainable and Efficient Smart Healthcare Solutions. 22. Healthcare in the Era of Generative AI

Cognitive Artificial Intelligence.

This book provides an illustration of the various methods and structures that are utilized in machine learning to make use of data that is generated by IoT devices. Numerous industries utilize machine learning, specifically Machine learning-as-a-Service (MLaaS) to realize IoT to its full potential. 

1. Recent Advances in Machine Learning Strategies and its Applications 2. Understanding the Concept of IoT 3. Unlocking the Power of IoT: An In-Depth Exploration 4. Machine Learning in Internet of Things 5. Role of Machine Learning in Real Life Environment 6. Efficient Blockchain Based Edge Computing System Using Transfer Learning Model 7. Introducing a Compact and High-Speed Machine Learning Accelerator for IoT enabled health monitoring systems 8. Realization of smart city based on IoT and AI 9. Sentiment Analysis of airline tweets using Machine Learning Algorithms and Regular Expression 10. Smart Workspace Automation: Harnessing IoT and AI for Sustainable Urban Development and Improved Quality of Life 11. Application of Digital Image Watermarking in the Internet of Things and Machine Learning

Machine Learning and Python for Human Behavior, Emotion, and Health Status Analysis

It is a gateway to the dynamic intersection of Python programming, smart home technology, and advanced machine learning applications, making it an invaluable resource for those eager to explore this rapidly growing field.

Industrial Engineering & Manufacturing

This book presents the use of Machine Learning and Artificial Intelligence in advanced and new manufacturing processes including core concepts and techniques of machine learning. It covers recent developments and research breakthroughs of tribological properties of polymer, metals, and ceramic based additive manufactured components.

ContentsPreface......................................................................................................................viiEditors.................................................................................................................... viiiContributors..............................................................................................................xiAcknowledgments...................................................................................................xiv
Chapter 1 Introduction to Machine Learning in Advanced Manufacturing.........1Nishant Ranjan, Vinay Kumar, and Shivani
Chapter 2 Overview of Different Machine Learning Techniques andAlgorithms for Data Acquisition and Pre-processing inAdvanced Manufacturing................................................................... 17Amit Vajpayee, Abhineet Anand, Ankit Sharma, PalakpreetKaur, Jaspreet Singh, and Amit Verma
Chapter 3 Recent Advancement of Machine Learning in Machining/Joining/Forming Processes................................................................38Tanmay Tiwari, Aswani Kumar Singh, Chandra SekharRakurty, Rashi Tyagi, and Gopal Nadkarni
Chapter 4 Machine Learning for Product Design and Customization inAdvanced Manufacturing Practices...................................................63Vinay Kumar and Nishant Ranjan
Chapter 5 Machine Learning in Additive Manufacturing..................................77Rajnish Prakash Modanwal, Aswani Kumar Singh, R. DurgaPrasad Reddy, Bhavesh Chaudhary, Varun Sharma, RashiTyagi, Dan Sathiaraj, and Jayaprakash Murugesan
Chapter 6 Application of Machine Learning Beyond Manufacturing................94Abhishek Bhattacharjee, Ajay Kumar Badhan, Raman Kumar,Harpreet Kaur Channi, Rajender Kumar, andKulwinder Singh MannCh

Systems & Control Engineering

This book highlights applications that include machine learning methods to enhance new developments in complex and unmanned systems. The main topics covered under this title include: machine learning, artificial intelligence, cryptography, submarines, drones, security in healthcare, Internet of Things and robotics.

Section 1: Machine Learning for Complex Systems  1. Echo State Networks to Solve Classification Tasks  2. Continual Learning for Camera Localisation  3.  Classifying Ornamental Fish Using Deep Learning Algorithms and Edge Computing Devices  4. Power Amplifier Modeling Comparison for Highly and Sparse Nonlinear Behavior Based on Regression Tree,Random Forest, and CNN for Wideband Systems  5. Models and Methods for Anomaly Detection in Video Surveillance  6. Deep Learning to Classify Pulmonary Infectious Diseases  7. Memristor-based Ring Oscillators as Alternatives for Reliable Physical Unclonable Functions  Section 2: Machine Learning for Unmanned Systems  8. Past and Future Data to Train an Artificial Pilot for Autonomous Drone Racing  9. Optimization of UAV Flight Controllers for Trajectory Tracking by Metaheuristics  10. Development of a Synthetic Dataset Using Aerial Navigation to Validate a Texture Classification Model  11. Coverage Analysis in Air-Ground Communications Under Random Disturbances in an Unmanned Aerial Vehicle  12. A Review of Noise Production and Mitigation in UAVs  13. An Overview of NeRF Methods for Aerial Robotics  14. Warehouse Inspection Using Autonomous Drones and Spatial AI  15.Cognitive Dynamic Systems for Cyber-Physical Engineering  16. EEG-Based Motor and Imaginary Movement Classification: ML Approach      

Machine Learning Hybridization and Optimization for Intelligent Applications

Machine Learning - Design

This book discusses state-of-the-art reviews of the existing machine-learning techniques and algorithms including hybridizations and optimizations. It is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.

1. Big Data Computing: Transforming From Cloud Computing to Edge Scheduling Perspectives Review. 2. Decision Making in the Field of Unmanned Aerial Vehicles: State-of-the-Art. 3. A Brief Study on Understanding and Handling COVID-19: Test Bed for Forecasting with Deep Learning and Machine Learning Algorithms. 4. AgTech: Using Sensors and Machine Learning to Revolutionize Farming Practices (IoT). 5. Developing an AI-based Multi-Task Transfer Learning Framework for Automating Judicial Contracts. 6. Analysis of Deep Learning Methodologies for Handling Non-Medical Big Data and Very Limited Medical Data with Feature Extraction and Annotation Techniques. 7. Introduction to Virtualization Security and Cloud Security. 8.Security Breaches in IoT Applications: An Extensive Study. 9.An Efficient and Accurate Classifcation Algorithm for ECG Signals Using PNN and KNN. 10. Big Data Analytics: The Classification of Remote Sensing Images Using Machine Learning Techniques. 11. Segmentation of Transmission Tower Components Based on Machine Learning. 12. A Systematic Analysis of Robot Path Planning and Optimization Techniques. 13.Pneumonia Prediction Model Using Deep Learning on Docker. 14. A Sequential Deep Learning Model Approach to OCR-Based Handwritten Digit Recognition for Physically Impaired People. 15. A Deep Learning Strategy for Sign Language Classification and Recognition for Hearing-Impaired People. 16. Non-fungible Tokens (NFT): The Design and Development of the "Obstacle Assault" Game and "Turtle Sidestep" Game. 17. Design and Development of 2D Space Shooter Game and Arcade Game Using Unity. 18. An Ensemble Technique Using Genetic Algorithm and Deep Learning for the Prediction of Rice Diseases. 19. History of Machine Learning. 20. Internet of Things Start-Ups: An Overview of the Privacy and Security in IoT Start-Ups.

Nanoscience & Nanotechnology

Explores the complex relationship between data science and medical science, highlighting the significant impact of machine learning algorithms in multiple areas of healthcare. Discusses topics such as wearable devices and mental health management through the use of machine learning technology.

1. Machine Learning Algorithms in Disease Diagnosis and Management 2. Machine Learning-Based Diagnosis and Treatment of Cancer 3. Machine Learning-Based Detection and Management of Cardiovascular Diseases 4. Monitoring the Health Status of Thyroid Patients Using Machine Learning 5. Machine Learning-Based Wearable Devices for Healthcare Applications 6. Prediction of Diabetes Using Machine Learning 7. Mental Health Index Management Using Machine Learning 8. Machine Learning Approaches for Electronic Health Record Phenotyping

1. Machine Vision Analysis in Industry 5.0: Fundamentals, Applications, and Challenges 2. Fundamentals of Image Analysis and Machine Vision: Application in Sustainability Practices Adopted by Marketing Professionals in Indian MNCs as Part of Industry 5.0 3. Machine Learning for Image Analysis and Machine Vision in Industry 5.0 4. Optimization Approaches in Feature Integration in Machine Vision 5. Machine Learning- based Image Analysis  for Industrial Waste Management: Issues  and Future Scope 6. Fundamentals of Image Analysis and Machine Vision: Application in Industry 5.0 7. 3D Skeleton based action recognition using learning technique 8. An ensemble learning model for smoke classification and localization  based on fractional order optical flow 9. Comprehensive Study of Machine Vision and Image Analysis in Industry 5.0 10. Pixel to Harvest: Cultivating Agriculture  with Image Analysis and Machine Vision in  Industry5.0
INDEX
 

Mechanics of Additive & Advanced Manufacturing, Inverse Methods and Machine Learning, Volume 5

Proceedings of the 2025 Annual Conference on Experimental and Applied Mechanics

Manufacturing & Processing

Mechanics of Additive and Advanced Manufacturing, Inverse Methods and Machine Learning, Volume 5 of the Proceedings of the 2025 SEM Annual Conference & Exposition on Experimental and Applied Mechanics, the fifth volume of five from the Conference, brings together contributions to this important area of research and engineering.

1 Effect of Coatings and Additives on theWater Absorption and Mechanical Characteristics of Additive Manufactured Nylon Polymers
2 Electrically Aligned Epoxy-Based Nanocomposites: Processing and Characterization
3 Interfacial Characterization of Metal Wire Inlays for 3D Printed FDM Parts
4 Accelerated Mechanical Behavior Characterization of Structural Materials
5 Visco-plastic and Damage Characterization of Sheet Metals for Railway Crash Applications
6 Design and Optimization of Shock Absorbers Made of Grade Density Foams
7 Design, Fabrication and Characterization of Layered Jamming Bistable Composite Structures for Assistive Robotics
8 Assessing the Effects of Strain Rate, Print Orientation and Post Print Annealing in FDM PLA Specimens in an Undergraduate Materials Science Course
 

Sinha, Adwitiya | Manju, | Singh, Samayveer

Metaheuristics and Reinforcement Techniques for Smart Sensor Applications

1. Sensor Networks Overview 2. Sensor Network Applications 3. Coverage in Wireless Sensor Networks 4. Connectivity and Communication in Wireless Sensor Networks 5. Energy Efficient Clustering in Sensor Network 6. Routing Methods with Adjustable Sensing Range 7. Performance Evaluation Metrics for Energy Constrained Sensor Network 8. Index

Multi-Criteria Decision-Making and Optimum Design with Machine Learning

Operations Research

As Multi-Criteria Decision-Making (MCDM) continues to grow and evolve, Machine Learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems. This book is intended to guide researchers, practitioners, and students interested in the intersection of ML and MCDM for optimal design.

1. Innovations in Technical Methodologies - Advancing Decision-Making and Optimization. 2. Review of Fuzzy Systems for Multi-Criteria Optimization Tools: Applications in Engineering Design. 3. Optimizing Ti-6Al-4V Milling Under MQL Conditions Using SVR, NSGA-II & TOPSIS. 4. Decision of 3D Printing Parameters for Optimum Tensile Strength Using the Taguchi-based Response Surface Method. 5. An Enhanced Network Optimization using the Max product for Multi-Criteria Decision Making. 6. Optimizing surface roughness of H13 steel machined by wire EDM technique. 7. Impact toughness of PBT/PA6 composite reinforced with glass fibers. 8. The effect of chamber temperature on the flexural strength of thermoplastic polyurethane plastic via FDM technology. 9. Enhancement in Underwater Imagery Using Multi-Criteria Decision Making with Machine Learning Techniques. 10. Optimal Site Selection of Electric Vehicle Charging Station Based on AHP-VIKOR method. 11. Optimum Indices on Topological Intuitionistic Fuzzy Graph. 12. Advancements in Multi-Criteria Decision Making: Exploring Innovative Approaches. 13. Overview of Machine Learning Techniques for Multi-Criteria Decision-Making. 14. Multi-Criteria Decision-Making  Analysis on Selection of Electric Vehicle Power Station Location Using Neutrosophic TOPSIS Method. 15. MCDM Modeling using  Machine Learning via Spherical Neutrosophic Similarlity Measures. 16. A Study On Machine Learning Twig Graphs On The Hyper Wiener Index Of Complete Graph. 17. Enhancing Multi-Criteria Decision Making through Cryptographic Security Systems. 18. AI-Powered Decision-Making Applications for Sustainable Development. 19. Interface for the Empirical Analysis of Artificial Intelligent Algorithms for Better Decision Making. 20. Multi-Criterion Analysis of Fusion Sort: A Hybrid Approach to Sorting Algorithms. 21. Cruising through the choices: Unraveling destination decision-making dilemmas with social networks – A dynamic exploration via MCDM technique. 22. 

Hellgren, Jonas | Lindgren, Johannes

Machine Learning - Design

Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) that teaches agents to learn optimal behavior through interaction, feedback, and long-term goals.

About the Authors. Introduction. Preface. Acknowledgements. Cover. 1 From Rules to Learning. 2 From Markov to Bellman. 3 Reinforcement Learning Concepts. 4 Temporal Difference Learning. 5 Monte Carlo Methods. 6 n-Step Learning. 7 Safe-Action Reinforcement Learning. 8 Non-Episodic Learning. 9 Next-Level Concepts. 10 Policy Gradient Methods. 11 Actor-Critic Methods. 12 Deep Reinforcement Learning. 13 Monte Carlo Tree Search. 14 Combining Learning and Search. 15 Multi-Agent Reinforcement Learning. 16 Outlook. Appendix. Index.

Hellgren, Jonas | Lindgren, Johannes

Machine Learning - Design

Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) that teaches agents to learn optimal behavior through interaction, feedback, and long-term goals.

About the Authors. Introduction. Preface. Acknowledgements. Cover. 1 From Rules to Learning. 2 From Markov to Bellman. 3 Reinforcement Learning Concepts. 4 Temporal Difference Learning. 5 Monte Carlo Methods. 6 n-Step Learning. 7 Safe-Action Reinforcement Learning. 8 Non-Episodic Learning. 9 Next-Level Concepts. 10 Policy Gradient Methods. 11 Actor-Critic Methods. 12 Deep Reinforcement Learning. 13 Monte Carlo Tree Search. 14 Combining Learning and Search. 15 Multi-Agent Reinforcement Learning. 16 Outlook. Appendix. Index.

Systems & Computer Architecture

This book aims to provide a comprehensive overview of the various services that are available to help cities develop their smart communities. It includes a variety of topics such as artificial intelligence, blockchain, advanced computing and the Internet of Everything. 

1. Introduction to Smart Cities: An Overview of Blockchain, AI, and Advanced Computing 
Ajeet Kumar Sharma and Rakesh Kumar
2. The Role of Blockchain in Building Smart Cities: Opportunities and Challenges
Munish Kumar, Abhishek Bhola, and Poonam Jaglan
3. Advanced Computing Technologies for Efficient and Sustainable Energy Management in Smart Cities 
Kassian T.T. Amesho, Abner Kukeyinge Shopati, Sadrag P. Shihomeka, Timoteus Kadhila, Bhisham Sharma, and E.I. Edoun
4. Blockchain-Enabled Smart Contracts for Secure and Transparent Governance in Smart Cities 
Anurag Dhal, Manish Kumar, Kapil Sharma, and Pradeepta Kumar Sarangi
5. AI-Driven Traffic Management Systems: Reducing Congestion and Improving Safety in Smart Cities 
Sivaram Ponnusamy, Harshita Chourasia, Seema Babusing Rathod, and Darshan Patil
6. Advanced Computing for Smart Waste Management and Recycling in Smart Cities
Kassian T.T. Amesho, Sadrag P. Shihomeka, Timoteus Kadhila, Abner Kukeyinge Shopati, Sumarlin Shangdiar, Bhisham Sharma, and E.I. Edoun
7. AI-Driven Healthcare Services and Infrastructure in Smart Cities
Nitish Katal
8. Advanced Computing for Smart Water Management in Smart Cities
Aishwarya Kumari, Rajan Kumar Maurya, and Pooja Sharma
9. AI-Enabled Smart Homes and Buildings in Smart Cities
Kamal Deep Garg, Palakpreet Kaur, and Parul Sharma
10. Advanced Computing for Smart Public Transportation Systems in Smart Cities 
Sivaram Ponnusamy, Harshita Chourasia, Seema Babusing Rathod, and Darshan Patil
11. Empowering Smart Cities through Intelligent Prompt Engineering: Unleashing the Potential of Language Models for Seamless IoT Integration, Personalized Experiences, and Efficient Urban Govern

Dwivedi, Shri Prakash | Shankar Singh, Ravi

Cognitive Artificial Intelligence.

1. Introduction 2. Structural Pattern Recognition 3. Graph Matching Algorithms: A Survey 4. Graph Matching using Extensions to Graph Edit Distance 5. Graph Matching using Centrality Measures 6. Geometric Graph Matching 7. Graph Kernels and Embedding 8. Graph Matching in Image Analysis 9. Graph Matching in Social Network Analysis 10. Recent Advances and Future Directions. A. Graph Matching ToolsBibliographyIndex

The book explores the use of AI and ML techniques for the design, characterization, and development of prediction analysis of sustainable polymer composites.

Preface. Artificial Intelligence in Material Science. Data Driven Artificial Intelligence Based Approach for the Determination of Structural Stress Distribution in ASTM D3039 Tensile Specimens of Carbon-Epoxy and Kevlar-Epoxy Based Composite Materials. Image Segmentation for Evaluating the Microstructure Features obtained from Magnesium Composites Processed through Squeeze Casting. Experimental Investigation of Bagasse Ash in Concrete Material. Computational Material Science for Cheminformatics Feature Descriptive Language (CFDL) with Categorical Data. Explicit Dynamic Crash Analysis of a Car using a Metal, Composite Material and an Alloy. Optimizing Friction Stir Spot Welded ABS Weld Strength using JAYA and Cohort Intelligence Algorithm. Supervised Machine Learning Based Classification of Dimensional Deviation of FDM 3D Printed Samples. Polymer Composite Flexural Strength Estimation using K-Nearest Neighbouring Classification Algorithm. Supervised Machine Learning Based Classification of Surface Roughness of Fused Deposition Modeling3D Printed Samples. Polymer Composite Impact Strength Estimation using K-Nearest Neighbouring Classification Algorithm. Index.

Technological Advancement in Internet of Medical Things and Blockchain for Personalized Healthcare

Cognitive Artificial Intelligence.

The text covers techniques, and frameworks pertaining to the internet of medical things and highlights computing algorithms to establish a pre-learned intelligent system for improving and automating the diagnosis process in personal healthcare systems as well as the protection of health records using blockchain technology.

1. Advanced Enabling Technologies of IoMT in Personalized Healthcare. 2. Deep learning interpretation of biomedical data in IoMT. 3. Machine learning for decision support systems in IoMT. 4. Secure Healthcare Systems: Recent and Future Applications. 5. Transforming Healthcare Management: Combining Blockchain, P2P Networks, and Digital Platforms. 6. Convergence of IoMT and Blockchain for Emerging Personalized Healthcare System: Challenges and use Cases. 7. Role of Access Control Mechanism for Blockchain-Enabled IoMT in Personalized Healthcare. 8. Protecting the Privacy of IoMT-Based Health Records Using Blockchain Technology. 9. Securing IoMT Devices to Protect the Future of Healthcare from rising cyber attacks. 10.  Smart hand-hygiene compliance and temperature monitoring system to tackle COVID-19 like pathogens in healthcare institutions. 11. LDS – LVAT: Lie Detection System - Layered Voice Technology.

Computer Engineering

Identifying and stopping the dissemination of fabricated news, hate speech, or deceptive information camouflaged as legitimate news poses a significant technological hurdle. This book presents emergent methodologies and technological approaches of natural language processing through machine learning for counteracting such instances.

1. Analysis of fake news detection and prevention of various fake news detection algorithms and its research challenges 2. Detection and Prevention of Fake News and Hate Speech through Artificial Intelligence Learning Techniques and Natural Language Processing 3. Detection of Fake News and Hate Crimes on Elections using Machine learning Approaches 4. Analytics of text and social media for Challenges of Hateful & Offensive Speech Detection 5. The Ripple Effect of Fake News and Hate Speech on Elections and its countermeasures using Machine Learning Methodologies: A Critical Analysis 6. Explainable Models for the Detection of Incidents of Fake News and Hate Speech 7. Gender biases and politics: The semiotics of hate speech on the internet 8. Social Media Platforms and COVID-19 Related Misinformation in India: Acceptance, Spread, and Implications 9. SIR Model for Understanding the Spread of Fake News and Hate Speech 10. Impact on Fake News in Social Media and Current Technology in Detection of Fake News in Social Media 11. The role of the connotative meaning of Emotive Expressions in Shaping Political Conflicts. An Analytical study of Political Emotive Expressions in Arab Spring Countries 12. Detecting Fake News and Hate Speech Using Machine Learning: An Overview of Frameworks 13. Hate content identification in code-mixed social media data 14. Social Media Skirmish: Dealing with fake news, misleading information and propaganda using AI and ML 15. Detection and prevention of fake news and hate speech through machine learning and natural language processing 16. Fake News and Hate Speech: Influence on society and Detection by Machine Language

“With AI Towards Sustainable Building Structures” is an insightful exploration of how state of the art AI techniques can be integrated into the building industry to support a more sustainable future. It draws on personal experience and includes interviews with leading experts in AI and the building sector. 

Preface. List of Abbreviations. AI and Building Industry Impacts in Perspective. Measuring the Sustainability of Building Structures. The Digital Transformation in Construction. Machine Learning and Big Data. Large Language Models. Multimodal Generative AI. AI Agents. Reinforcement Learning. Conclusions. References. Index.