1st Edition
Engineering Mathematics and Artificial Intelligence Foundations, Methods, and Applications
The fields of Artificial Intelligence (AI) and Machine Learning (ML) have grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between mathematics and AI/ML and provides an overview of the current research streams.
Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind ML and shows how mathematics can be used in AI. The book illustrates how to improve existing algorithms by using advanced mathematics and offers cutting-edge AI technologies. The book goes on to discuss how ML can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between ML and complex mathematical techniques is also highlighted within the book.
This book is written for researchers, practitioners, engineers, and AI consultants.
Chapter 1 Multiobjective Optimization: An Overview
Matteo Rocca
Chapter 2 Inverse Problems
Didier Auroux
Chapter 3 Decision Tree for Classification and Forecasting
Mariangela Zenga and Cinzia Colapinto
Chapter 4 A Review of Choice Topics in Quantum Computing and Some Connections with Machine Learning
Faisal Shah Khan
Chapter 5 Sparse Models for Machine Learning
Jianyi Lin
Chapter 6 Interpretability in Machine Learning
Marco Repetto
Chapter 7 Big Data: Concepts, Techniques, and Considerations
Kate Mobley, Namazbai Ishmakhametov, Jitendra Sai Kota,
and Sherrill Hayes
Chapter 8 A Machine of Many Faces: On the Issue of Interface in Artificial
Intelligence and Tools from User Experience
Stefano Triberti, Maurizio Mauri, and Andrea Gaggioli
Chapter 9 Artificial Intelligence Technologies and Platforms
Muhammad Usman, Abdullah Abonamah, and Marc Poulin
Chapter 10 Artificial Neural Networks
Bryson Boreland, Herb Kunze, and Kimberly M. Levere
Chapter 11 Multicriteria Optimization in Deep Learning
Marco Repetto and Davide La Torre
Chapter 12 Natural Language Processing:
Current Methods and Challenges
Ali Emami
Chapter 13 AI and Imaging in Remote Sensing
Nour Aburaed and Mina Al-Saad
Chapter 14 AI in Agriculture
Marie Kirpach and Adam Riccoboni
Chapter 15 AI and Cancer Imaging
Lars Johannes Isaksson, Stefania Volpe, and Barbara Alicja Jereczek-Fossa
Chapter 16 AI in Ecommerce: From Amazon and TikTok, GPT-3
and LaMDA, to the Metaverse and Beyond
Adam Riccoboni
Chapter 17 The Difficulties of Clinical NLP
Vanessa Klotzman
Chapter 18 Inclusive Green Growth in OECD Countries: Insight
from the Lasso Regularization and Inferential Techniques
Andrea Vezzulli, Isaac K. Ofori, Pamela E. Ofori,
and Emmanuel Y. Gbolonyo
Chapter 19 Quality Assessment of Medical Images
Ilona Anna Urbaniak and Ruben Nandan Pinto
Chapter 20 Securing Machine Learning Models: Notions and Open Issues
Lara Mauri and Ernesto Damiani
Biography
Herb Kunze is a Professor of Mathematics at the University of Guelph, in Guelph, Ontario, Canada. He received his Ph.D. in Applied Mathematics from the University of Waterloo in 1997. He has held research funding from the Natural Sciences and Engineering Research Council (NSERC) throughout his career. Among his research interests are fractal-based methods in analysis, including a wide array of both direct and inverse problems; neural networks and artificial intelligence; mathematical imaging; and qualitative properties of differential equations. His work combines rigorous theoretical elements with application-driven considerations. He has over 100 research publications, generally in high-impact, refereed journals.
Davide La Torre is an Applied Mathematician, Researcher, and University Professor. Currently he holds the position of Full Professor and Director of the SKEMA Institute for Artificial Intelligence. He is also the Head of the (Programme Grande Ecole) Finance and Quants track. His research and teaching interests include Artificial Intelligence and Machine Learning for Business, Business and Industrial Analytics, Economic Dynamics, Mathematical and Statistical Modeling, Operations Management, Operations Research, and Portfolio Management. He holds a master’s in Applied and Industrial Mathematics (1997, magna cum laude) and a Ph.D. in Computational Mathematics and Operations Research (2002) both from the University of Milan, Milan, Italy, and an HDR in Applied Mathematics from the Université Côte d'Azur (2021). He also holds professional certificates in Big Data and Analytics (2017), Machine Learning, and Quantum Computing (2021) from the Massachusetts Institute of Technology, Cambridge, USA. In the past, he held permanent and visiting university professor positions in Europe, Canada, the Middle East, Central Asia, and Australia. He also served as Departmental Chair and Program Head at several universities. He has more than 150 publications in Scopus, most of them published journals ranging from Engineering to Business.
Adam Riccoboni is an AI entrepreneur, an author, and the CEO of Critical Future, a technology and strategy consultancy, trusted by some of the world’s biggest brands, with a strong record in pioneering AI development. He is an Award-winning entrepreneur, the founder of high-growth businesses, as featured in the Financial Times, ESPN, BBC, USA Today. He is also a Guest Lecturer on Artificial Intelligence at ESCP, UK Business School.
Manuel Ruiz Galán received his Ph. D, from the University of Granada, Spain in 1999. He is a Full Professor in the Mathematics Department at the University of Granada, Spain with more than 50 research papers and book chapters to his credit. Dr. Galán has been a member and principal investigator in several projects with national funds (Spanish Government), particularly on topics focusing on convex and numerical analysis and their applications. He has acted as a guest editor for some special issues of the journal Optimization and Engineering, Mathematical Problems in Engineering, and the Journal of Function Spaces and Applications. In addition, he is a member of the editorial board of the publication Minimax Inequalities and its Applications.
