About the Book
This book presents a collection of extended papers selected from the 22nd IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2023) and focuses on deep learning architectures and their applications in domains such as health care, security and threat detection, education, fault diagnosis, and robotic control in industrial environments. Novel ways of using convolutional neural networks, transformers, autoencoders, graph-based neural networks, large language models for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and models in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.
Key Features:
· Presents state-of-the-art research on deep learning
· Covers modern real-world applications of deep learning
· Provides value to students, academic researchers, professionals, software engineers in the industry, and innovative product developers.
Table of Contents:
Preface
Editor Bios
List of Contributors
Part I Deep Learning for Computer Vision
Chapter 01 Automated Image Segmentation Using Self-Iterative Training and Self-Supervised Learning with Uncertainty Scores
Jinyoon Kim, Tianjie Chen, and Md Faisal Kabir
Chapter 02 Energy Efficient Glaucoma Detection: Leveraging GAN-based Data Augmentation for Advanced Diagnostics
Krish Nachnani
Chapter 03 Deep JPEG Compression Artifact Removal with Harmonic Networks
Hasan H. Karaoglu, Ender M. Eksioglu
Chapter 04 Modeling Face Emotion Perception from Naturalistic Face Viewing: Insights from Fixational Events and Gaze Strategies "Meisam J. Seikavanidi
Maria J. Barrett, Paolo Burelli
Part II Deep Learning for Natural language Processing
Chapter 05 Large Language Models for Automated Short-Answer Grading and Student Misconception Detection in STEM
Indika Kahanda, Nazmul Kazi, and James Becker
Chapter 06 Word class and syntax rule representations spontaneously emerge in recurrent language models
Patrick Krauss, Kishore Surendra, Paul Stoewer, Andreas Maier, Claus Metzner, and Achim Schilling
Chapter 07 Detection of Emerging Cyberthreats through Active Learning
Joel Brynielsson, Amanda Carp, and Agnes Tegen
Chapter 08 Enhanced Health Information Retrieval with Explainable Biomedical Inconsistency Detection using Large Language Models
Prajwol Lamichhane, Indika Kahanda, Xudong Liu, Karthikeyan Umapathy, Sandeep Reddivari, and Andrea Arikawa
Chapter 09 Human-like e-Learning Mediation Agents
Chukwuka Victor Obionwu, Diptesh Mukherjee, Andreas Nurnberger, Aarathi Vijayachandran Bhagavathi, Aishwarya Suresh, Eathorne Choongo, Bhavya Baburaj Chovatta Valappil, Amit Kumar, and Gunter Saake
Part III Deep Learning for Real World Predictive Modelling
Chapter 10 Transformer Graph Neural Networks (T-GNN) for Home Valuation
Faraz Moghimi, Reid Johnson, and Andy Krause
Chapter 11 Model Error Clustering Approach for HVAC and Water Heater in Residential Subpopulations
Viswadeep Lebakula, Eve Tsybina, Jeff Munk, and Justin Hill
Chapter 12 A Hybrid Physics-Informed Neural Network - SEIRD Model for Forecasting COVID-19 Intensive Care Unit Demand in England "Michael Ajao-Olarinoye
Vasile Palade, Fei He, Petra A Wark, Zindoga Mukandavire, and Seyed Mousavi
Part IV Deep Learning Methodological Approaches in Other Applications
Chapter 13 A Novel Data Reduction Technique for Medicare Fraud Detection with Gaussian Mixture Models
John T. Hancock III, Taghi M. Khoshgoftaar
Chapter 14 Convolutional Recurrent Deep Q-Learning for Gas Source Localization with a Mobile Robot
Iliya Kulbaka, Ayan Dutta, Ladislau Bölöni, O. Patrick Kreidl, and Swapnoneel Roy
Chapter 15 Conditioned Cycles in Sparse Data Domains: Applications to the Physical Sciences Maria Barger, Randy Paffenroth, and Harsh Pathak
Chapter 16 Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning
Ardian Selmonaj, Oleg Szehr, Giacomo Del Rio, Alessandro Antonucci, Adrian Schneider, and Michael Rüegsegger
About the Author :
Dr. Uche Onyekpe is a Machine Learning Expert at Ofcom (Office of Communications, UK), where he focuses on developing assessment/audit strategies for AI algorithms used by online platforms such as Instagram, TikTok, and X. He also serves as the Director of the African Institute for Artificial Intelligence, a nonprofit organization dedicated to advancing AI across the African continent.
Dr. Onyekpe previously held academic positions at York St John University and Coventry University on Machine Learning. His professional experience spans various sectors, including health, construction, and transport, where he has led projects at the intersection of artificial intelligence and these fields. He has published numerous research papers in these areas and has several years of experience working as a consultant within the robotics and social care. He has delivered keynote talks at reputable seminars and events on machine learning and applications.
Vasile Palade is a Professor of Artificial Intelligence and Data Science in the Centre for Computational Science and Mathematical Modelling at Coventry University, UK. He previously held several academic and research positions at the University of Oxford - UK, University of Hull - UK, and the University of Galati - Romania. His research interests are in machine learning, with a focus on neural networks and deep learning, and with main application to computer vision, natural language processing, autonomous driving, smart cities, health, among others. Prof. Palade is author and co-author of more than 300 papers in journals and conference proceedings as well as several books on machine learning and applications. He is an Associate Editor for several reputed journals, such as IEEE Transactions on Neural Networks and Learning Systems, and Neural Networks. He has delivered keynote talks to reputed international conferences on machine learning and applications.
Prof. M. Arif Wani completed his M.Tech. in Computer Technology at the Indian Institute of Technology, Delhi, and his PhD in Computer Vision at Cardiff University, UK. He is a Professor at the University of Kashmir, having previously served as a Professor at California State University Bakersfield.
His research interests are in the area of machine learning, with a focus on neural networks, deep learning, computer vision, pattern recognition, and classification tasks. He has published many papers in reputed journals and conferences in these areas. Dr. Wani has co-authored the book ‘Advances in Deep Learning’ and co-edited many books on Machine Learning and Deep Learning applications.
