Handbook of Mathematical and Digital Engineering Foundations for Artificial Intelligence

Artificial intelligence (AI) and digital engineering have become prevalent in business, industry, government, and academia. However, the workforce still has a lot to learn on how to leverage them. This handbook presents the preparatory and operational foundations for the efficacy, applicability, risk, and how to take advantage of these tools and techniques. Handbook of Mathematical and Digital Engineering Foundations for Artificial Intelligence: A Systems Methodology provides a guide for using digital engineering platforms for advancing AI applications. The book discusses an interface of education and research in the pursuit of AI developments and highlights the facilitation of advanced education through AI and digital engineering systems. It presents an integration of soft and hard skills in developing and using AI and offers a rigorous systems approach to understanding and using AI. This handbook will be the go-to resource for practitioners and students on applying systems methodology to the body of knowledge of understanding, embracing, and using digital engineering tools and techniques. The recent developments and emergence of Chatbots (AI tools) all have mathematical foundations for their efficacy. Such AI tools include ChatGPT, GPT-4, Bard, Tidio Support Bot, Kuki AI Companion, Meena, BlenderBot, Rose AI Chatbot, Replika: AI Friend, Eviebot, and Tay. This handbook highlights the importance of mathematical and digital foundations for AI developments. The handbook will enhance the understanding and appreciation of readers about the prevailing wave of artificial intelligence products, and, thereby, fitting the current market needs.

Adedeji Badiru is a Professor of Systems Engineering at the Air Force Institute of Technology (AFIT). He is a registered professional engineer and a fellow of the Institute of Industrial Engineers as well as a Fellow of the Nigerian Academy of Engineering. He has a BS degree in Industrial Engineering, MS in Mathematics, and MS in Industrial Engineering from Tennessee University, and Ph.D. in Industrial Engineering from the University of Central Florida. He is the author of several books and technical journal articles and has received several awards and recognitions for his accomplishments. His special skills, experience, and interests center on research mentoring of faculty and graduate students. Olumuyiwa Asaolu is Professor of Systems Engineering in the Faculty of Engineering at the University of Lagos, Nigeria. He obtained a B.Sc in Civil Engineering from the University of Lagos and M.Sc and Ph.D. in Engineering Analysis from the University of Lagos. He has successfully supervised and mentored several doctoral candidates. His research interests and publications cover Artificial Intelligence, Systems Modeling and Analysis. He has conducted and published original work in robotics path planning, solving simultaneous non-linear equations, word-processing and translation of African languages, etc. He is a recipient of several scholarly awards including: Nigerian Best Researcher Software Developer (2001), the World Academy of Sciences & Inter Academy Panel Young Scientist for 2009. His research team has obtained a Patent for laughter as a biometric. Several outlets acclaimed the news of the published research including US Homeland Security Newswire, US Department of Defense on Twitter, Russian TechNews, UK Sciencespot, etc. Dr. Asaolu is Founder/Chairman of Lainos international Limited, a co-founder and Non-Executive Director of Performance Evaluation And Selection Limited (PEAS Ltd), as well as a minority Investor in Alajo Technologies Limited . He is a registered Engineer and member/Fellow of several professional bodies.

1. Artificial Intelligence within Industrial and Systems Engineering Framework. 2. Mathematics of Cantor Set for AI Searches. 3. Set-theoretic Systems for AI Applications. 4. AI Mathematical Modeling for Product Design. 5. Mathematical Formulation of the Pursuit Problem for AI Gaming. 6. AI Framework for the Financial Sector. 7. AI Neuro-Fuzzy Model for Healthcare Prediction. 8. Stochasticity in AI Mathematical Modeling. 9. Mathematical Utility Modeling for AI Application. 10. Artificial Intelligence and Human Factors Integration in Additive Manufacturing. 11. AI Systems Optimization Techniques. 12. Mathematical Modeling and Control of Resource Constraints. Appendix A: Mathematical Expressions and Collections (Series, Patterns, and Formulae). Appendix B: Cantor Set Sectioning.