Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications
Series: Materials, Devices, and Circuits;
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 11 August 2025
- ISBN 9781032753249
- Binding Hardback
- No. of pages196 pages
- Size 234x156 mm
- Language English
- Illustrations 50 Illustrations, black & white; 24 Illustrations, color; 21 Halftones, black & white; 2 Halftones, color; 29 Line drawings, black & white; 22 Line drawings, color; 15 Tables, black & white 700
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Short description:
This book presents machine learning applications in the field of engineering with a focus on deep learning-based machine learning approaches. It examines the relationship between three different multidisciplinary engineering branches: Biomedical Engineering, Signal Processing, and Computer Science.
MoreLong description:
This book presents various machine learning applications in the field of engineering with a focus on deep learning-based machine learning approaches. It examines the relationship between three different multidisciplinary engineering branches: biomedical engineering, signal processing, and computer science.
Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications explores recent advancements in the use of AI/ML in practical engineering applications by inviting top experts to share the outcomes of their most recent work. Among the topics explored are detection, measurement, and monitoring of signals (biosensors and biomedical devices) and the use of diagnostic interpretations of bioelectric data using signal-processing techniques. The authors also address several machine learning tasks, such as classification (supervised learning) and clustering (unsupervised learning), in the context of engineering. Finally, the book also describes the development of new biomaterials for use in the body.
The book will be a great help to researchers and academics working in the fields of biomedical signaling and/or human-machine interface.
MoreTable of Contents:
Chapter 1- AI in Communication: A chatbot to practice a clinical interview in Spanish (BOTES)
Chapter 2- AI-driven Diagnostic Assistance for the Gastrointestinal Tract
Chapter 3- Data-driven Techniques for Fault Diagnosis and Predictive Maintenance
Chapter 4- Harnessing Machine Learning for Peptidase Inhibitor Prediction in Therapeutic Discovery
Chapter 5- Enhancing Breast Cancer Detection with Radiomics and Machine Learning: A Comprehensive Analysis Using MRI Datasets
Chapter 6- Enhancing Deep Learning-based Colon Cancer Detection Using Attention Module
Chapter 7-Acoustic-Based Parkinson's Disease Diagnosis Using Transfer Learning: Combining VGG-16 with Light Gradient Boosting Machine (LGBM) Classifier
Chapter 8- Advances in Machine Learning for QSAR Modeling: Enhancing Drug Discovery Through Predictive Precision and Data Integration
Chapter 9-Stability Analysis of Recurrent Shunting On-Center Off-Surround Neural Networks with Nonlinear Transfer Functions: An Energy Function Approach
Chapter 10-ChatGPT: A Critical Analysis of its Performance and Virtue Exploration
Chapter 11-Fundus Image Restoration and Enhancement using Multi Resolution CNN Framework
Chapter 12-An Application of Improved Support Vector Machine Classifier for the Study of Breast Cancer Detection
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