The book presents the proceedings of two conferences: The 22nd International Conference on Artificial Intelligence (ICAI'20) and The 4th International Conference on Applied Cognitive Computing (ACC'20). The conferences took place in Las Vegas, NV, USA, July 27-30, 2020, and are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Topics include: deep learning; neural networks; brain models; cognitive science; natural language processing; fuzzy logic and soft computing (ICAI) and novel computationally intelligent algorithms; bio inspired cognitive algorithms; modeling human brain processing systems (ACC); and more. Authors include academics, researchers, and professionals.

• Presents the proceedings of two conferences as part of the 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20);
• Includes the tracks: artificial intelligenceand applied cognitive computing;
• Features papers from the 22nd International Conference on AI (ICAI'20) and the 4th International Conference on Applied Cognitive Computing (ACC'20).

Presents the proceedings of two conferences as part of the 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20) Includes the tracks: artificial intelligence and applied cognitive computing Features papers from the 22nd International Conference on AI (ICAI'20) and the 4th International Conference on Applied Cognitive Computing (ACC'20)

Autorentext
Professor Hamid R. Arabnia received a Ph.D. degree in Computer Science from the University of Kent (England) in 1987. He is currently a Professor Emeritus of Computer Science at University of Georgia, USA, where he has been since October 1987. His research interests include parallel and distributed processing techniques & algorithms, supercomputing, Data Science, imaging science, and other compute intensive problems. Applications of interest include: medical imaging and security. Most recent activities include: Studying ways to promote legislation that would prevent cyber-stalking, cyber-harassment, and cyber-bullying. As a victim of cyber-harassment and cyber-bullying, in 2017 he won a lawsuit with damages awarded to him for a total of $2.3 Million and $656K attorney costs. Since this court case was one of the few cases of its kind in the United States, this ruling is considered to be important; Final Judgement for damages was issued in Leon County Courthouse of Tallahassee in Floridaby Circuit Court Judge. Prof. Arabnia is Editor-in-Chief of The Journal of Supercomputing (Springer). He is also on the editorial and advisory boards of 30 other journals. He is the book series editor-in-chief of "Transactions of Computational Science and Computational Intelligence" (Springer). He has won 12 distinguished awards, including the "Outstanding Research Contributions to the Field of Supercomputing" (President of IEEE/SMC) and "Distinguished Research Award" for Outstanding Contributions to Adaptable Communication Systems (ACM SIGAPP IMCOM). Dr. Arabnia is Fellow and Advisor of Center of Excellence in Terrorism, Resilience, Intelligence & Organized Crime Research (CENTRIC). He has been a PI/Co-PI on about $8 Million externally funded projects, about $200K internally funded projects, and about $4 Million equipment grants. During his tenure as Director of Graduate Programs, Dr. Arabnia secured the largest level of funding in the history of the department for supporting the research and education of graduate students (PhD, MS). Dr. Arabnia has delivered a number of keynote and plenary lectures at international conferences; most recently at: The 14th IEEE International Conference on Parallel and Distributed Systems (ICPADS, Australia); International Conference on Future Generation Communication and Networking (FGCN / IEEE CS, Sanya); The 10th IEEE International Conference on High Performance Computing and Communications (HPCC, Dalian); and ACM IMCOM International Conference. He has also delivered a number of "distinguished lectures" at various universities and research units/centers (USA, Spain, South Korea, Japan, Iran, Saudi Arabia, UK, Canada, Turkey, China, Ireland, Australia, ...); his distinguished lectures were funded and sponsored by US Department of Defense, SERSC of Republic of Korea, US National Science Foundation, H2020 of Europe, and others.

Ken Ferens is an Assistant Professor at the Department of Electrical and Computer Engineering (ECE), University of Manitoba, Canada. He is the Computer Engineering Champion in the Centre for Engineering Professional Practice and Engineering Education. He is the director of the Applied Cognitive Intelligence (ACI) Research Group in the Department of ECE. He obtained his Ph.D. in 1996 from the University of Manitoba, and is a member of The Association of Professional Engineers and Geoscientists of the Province of Manitoba. He has authored over 100 publications in his research areas. Dr. Ferens is a senior member of the Institute of Electrical & Electronics Engineers (IEEE); a member of the Canadian Engineering Education Association; the Chair of the EduManCom Chapter of the IEEE; the Vice-Chair of the Computer and Computational Intelligence Chapter of the IEEE; and the Chair of the Industry, Teaching Assistants, and Student Forums, for Engineering Curriculum Review and Improvement.

David de la Fuente is a Full Professor of Oviedo Univers

Inhalt

Introduction.- PART I: ICAI'20.- Brain models, Brain mapping, Cognitive science.- Natural language processing.- Fuzzy logic and soft computing.- Social impact of AI.- Emerging technologies.- Statistical learning theory.- Unsupervised and Supervised Learning.- Multivariate analysis.- Hierarchical learning models.- Relational learning models.- Bayesian methods.- Meta learning.- Stochastic optimization.- Heuristic optimization techniques.- Neural networks and variations.- Reinforcement learning.- PART II: ACC 2020.- Novel Computationally Intelligent algorithms.- Bio Inspired Cognitive Algorithms.- Improving Cognition in machine learning systems.- Modeling Human Brain processing systems.- Multimodal learning systems.- Autonomous learning systems.- Reinforced learning.- Conclusion.