Applied Artificial Intelligence and Machine Learning Techniques for Engineering Applications

Edited by Ravichander Janapati,Shubham Tayal,Usha Desai,Rakesh Sengupta,Steven L Fernandes

ISBN13: 9781032753249

Imprint: CRC Press

Publisher: Taylor & Francis Ltd

Format: Hardback

Published: 15/08/2025

Availability: Not yet available

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 field of biomedical signaling and/ or human machine interface.
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
  • Algorithms & data structures
  • Artificial intelligence
  • Professional & Vocational
  • Tertiary Education (US: College)
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