Bøker av Dong Hwa Kim

How to Teach and Learn AI shows how an economy can be changed by AI, which will also affect existing social patterns. In order to survive and thrive in the twenty-first century, people must be aware of and recognize new paradigms. It is imperative they adjust to them. Otherwise, they will crash! For successful living, people must foresee and use this new technology. How to Teach and Learn AI will show you how to achieve this.Author Dong Hwa Kim wrote this book for the benefit of those who not only enjoy this type of topic but also for those primarily in the field of education, be they a student, professor, or other professionals such as policymakers who are involved with AI. He desires to transfer his knowledge to others, especially beginners, based on his vast knowledge of the field. Certainly, there are many books on the market regarding AI, but generally, they are focused on specified topics of AI for research and learning. In contrast, How to Teach and Learn AI focuses on AI to teach in underdeveloped countries. The author attempts to provide motivation by showing how AI is extremely important and how it can impact a nation's economy and jobs. In teaching at various universities around the globe, he has seen resistance toward the new paradigm due to old, ineffective habits. The author introduces many facts about how the South Koreans handled these changes and adapted because underdeveloped and developing countries can use this information to make their own changes.

This book deals with evolving intelligence systems and their use in immune algorithm (IM), particle swarm optimization (PSO), bacterial foraging (BF), and hybrid intelligent system to improve plants, robots, etc. It discusses the motivation behind research on and background of evolving intelligence systems and illustrates IM-based approach for parameter estimation required for designing an intelligent system. It approaches optimal intelligent tuning using a hybrid genetic algorithmΓÇôparticle swarm optimization (GA-PSO) and illustrates hybrid GA-PSO for intelligent tuning of vector system.