1st Edition

Engineering Mathematics and Artificial Intelligence Foundations, Methods, and Applications

    529 Pages 146 B/W Illustrations
    by CRC Press

    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


    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.