Automated Machine Learning and Industrial Applications

E. Gangadevi, M. Lawanya Shri, Balamurugan Balusamy, Rajesh Kumar Dhanaraj

• Publisher: Wiley-Scrivener
• Year: 2025
• Print ISBN: 9781394272396
• eBook ISBN: 9781394272419
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

The book provides a comprehensive understanding of Automated Machine Learning's transformative potential across various industries, empowering users to seamlessly implement advanced machine learning solutions without needing extensive expertise.

Automated Machine Learning (AutoML) is a process to automate the responsibilities of machine learning concepts for real-world problems. The AutoML process is comprised of all steps, beginning with a raw dataset and concluding with the construction of a machine learning model for deployment. The purpose of AutoML is to allow non-experts to work with machine learning models and techniques without requiring much knowledge in machine learning. This advancement enables data scientists to produce the easiest solutions and most accurate results within a short timeframe, allowing them to outperform normal machine learning models. Meta-learning, neural network architecture, and hyperparameter optimization, are applied based on AutoML.

Automated Machine Learning and Industrial Applications offers an overview of the basic architecture, evolution, and applications of AutoML. Potential applications in healthcare, banking, agriculture, aerospace, and security are discussed in terms of their frameworks, implementation, and evaluation. This book also explores the AutoML ecosystem, its integration with blockchain, and various open-source tools available on the AutoML platform. It serves as a practical guide for engineers and data scientists, offering valuable insights for decision-makers looking to integrate machine learning into their workflows.

Readers will find the book:

• Aims to explore current trends such as augmented reality, virtual reality, blockchain, open-source platforms, and Industry 4.0;
• Serves as an effective guide for professionals, researchers, industrialists, data scientists, and application developers;
• Explores technologies such as IoT, blockchain, artificial intelligence, and robotics, serving as a core guide for undergraduate and postgraduate students.

Audience

Data and computer scientists, research scholars, professionals, and industrialists interested in technology for Industry 4.0 applications.

Table of contents

• Cover
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• Chapter 1 Design and Architecture of AutoML for Data Science in Next-Generation Industries
• Chapter 2 Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture
• Chapter 3 Automated Machine Learning in the Biological and Medical Healthcare Industries: Analysis Interpretation and Evaluation
• Chapter 4 Advancements in AI and AutoML for Plant Leaf Disease Identification in Sustainable Agriculture
• Chapter 5 Predictive Maintenance in Industrial Settings: Video Analytics at the Edge with AutoML
• Chapter 6 AutoCRM—An Automated Customer Relationship Management Learning System with Random Search Hyper-Parameter Optimization
• Chapter 7 The Competence of Customer Support Team for Sentiment Analysis in Chatbots Using AutoML
• Chapter 8 Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning
• Chapter 9 AutoML Ecosystem and Open-Source Platforms: Challenges and Limitations
• Chapter 10 Plant Disease Identification Using Extended-EfficientNet Deep Learning Model in Smart Farming
• Chapter 11 AutoML-Driven Deep Learning for Fake Currency Recognition
• Chapter 12 Blockchain and Automated Machine Learning-Based Advancements for Banking and Financial Sectors
• Chapter 13 Advances in Automated Machine Learning for Precision Healthcare and Biomedical Discoveries
• Chapter 14 Democratizing Machine Learning: The Rise of Automated Machine Learning (AutoML)
• Chapter 15 Open-Source Tools in Automated Machine Learning
• Index
• EULA