Machine Learning and Optimization for Engineering Design (Engineering Optimization: Methods and Applications)

✍ Scribed by Apoorva S. Shastri (editor), Kailash Shaw (editor), Mangal Singh (editor)

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 175
• Category: Library

✦ Synopsis

This book aims to provide a collection of state-of-the-art scientific and technical research papers related to machine learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering applications such as smart homes, ICT-based irrigation systems, academic success prediction, future agro-industry for crop production, disease classification in plants, dental problems and solutions, loan eligibility processing, etc., and their implementation with several case studies and literature reviews are included as self-contained chapters. Additionally, the book intends to highlight the importance of study and effectiveness in addressing the time and space complexity of problems and enhancing accuracy, analysis, and validations for different practical applications by acknowledging the state-of-the-art literature survey. The book targets a larger audience by exploring multidisciplinary research directions such as computer vision, machine learning, artificial intelligence, modified/newly developed machine learning algorithms, etc., to enhance engineering design applications for society. State-of-the-art research work with illustrations and exercises along with pseudo-code has been provided here.

✦ Table of Contents

Preface
Contents
About the Editors
A Short Review of Machine Learning Techniques for Thermal, Energy and Electrical Engineering Applications
1 Introduction
2 Intuition About Machine Learning and Optimization
2.1 Types of Data in Machine Learning Model
2.2 Data Pre-Processing
2.3 Types of Machine Learning Algorithm
3 Application of Machine Learning Engineering Application
3.1 ML in Thermal Engineering
3.2 ML Application in Energy Sources
3.3 Application of ML in Electrical Appliances
4 Conclusion
References
Design of Intelligent ICT Irrigation System Using Crop Growth Big Data Analysis
1 Introduction
2 ICT Agriculture Technology Trends
3 Irrigation System Design
4 Conclusion
References
OpenCV and MQTT Based Intelligent Management System
1 Introduction
2 Methodology
3 Results
4 Conclusion
References
A Machine Learning Model for Student’s Academic Success Prediction
1 Introduction
2 Literature Review
3 Research Methodology
3.1 Data Collection and Preprocessing
3.2 Exploratory Data Analysis
3.3 Model Building and Training
4 Results and Discussion
4.1 EDA Result
4.2 Classification and Regression Analysis
5 Discussion
6 Conclusion
References
Intelligent Agro-Industry for Crop Production Considering Soil Properties and Climatic Variables to Boost Its Efficiency
1 Introduction
2 Literature Study
2.1 Role of ML Model in Transportation
2.2 Role of ML Model in Industry
2.3 Role of ML in Global Pandemic COVID-19
3 Working Model
4 Simulation Result
5 Conclusion
References
Disease Classification in Cassava Plant by Artificial Neural Network
1 Introduction
2 Methodology
3 Results and Discussions
4 Conclusion
References
Exploring the Synergies: A Comprehensive Survey of Blockchain Integration with Artificial Intelligence, Machine Learning, and IoT for Diverse Applications
1 Introduction
2 The Future of Data Storage—Blockchain Technology
2.1 Anatomy of Blockchain: Key Components and Processes
2.2 The Mathematical Roots
2.3 How Does This Technology Work?
2.4 Real-World Application Domains
3 Artificial Intelligence (AI)
3.1 AI and Blockchain Integration: Uniting Intelligence and Immutable Ledgers
4 Machine Learning (ML)
4.1 Synergizing ML and Blockchain
5 Internet of Things
5.1 IoT and Blockchain Synergy: Building a Trustworthy Connected World
6 Integrating Blockchain, AI, and ML: A Paradigm Shift in Technology
6.1 Blockchain Technology and Its Role in Enabling AI and ML
6.2 AI and ML Empowered Blockchains: Advancements in Security and Decision-Making
6.3 Leveraging Blockchain for Trust and Transparency in AI and ML
6.4 Privacy-Preserving Techniques in Blockchain-Enabled AI and ML
6.5 Key Challenges Associated with Scalability and Performance When Combining Blockchain, AI, and ML
6.6 Blockchain in Practice: Industry Applications and Use Cases
7 Future Directions
7.1 Interoperability and Standardization
7.2 Federated Learning
7.3 Explainability and Trustworthiness
7.4 Scalability and Performance Optimization
7.5 Data Privacy and Security
8 Conclusion
References
Loan Eligibility Verification by Using Ensemble ML Techniques
1 Introduction
2 Literature Survey
3 Methodology
3.1 Algorithms Used
4 Solution Design
4.1 Dataset Description
4.2 System Flow
5 Dataset Analysis
6 Results and Discussion
7 Conclusion
References
An Intelligent System for the Classification of Dental Problems Based on Bayesian Regularization Neural Network
1 Introduction
1.1 A Subsection Sample
2 Structure of Proposed System and Methodology
2.1 Dental Problems Under Consideration
3 Image Processing and Training of Neural Network
3.1 Image Processing
3.2 Learning Algorithms for the Training
4 Experimental Results
5 Conclusion
References
Development of Smart Home System Based on EEG
1 Introduction
2 Materials and Methods
2.1 Mobile Application
2.2 EEG Data Acquisition
2.3 Deep Learning Model
2.4 Smart Home System
3 Results and Discussion
4 Conclusion
References