Keynote Speaker

International Conference on Industry 4.0, Artificial Intelligence and Communications Technology (IAICT 2021)

Artificial Intelligence algorithms for the design of smart machines

Abstract

Industry 4.0 is known as the rise of new digital industrial technology. Real-time data monitoring, tracking the status and positions of products, the optimization of advanced self-driving systems are the main challenges of Industry 4.0. In order to do these tasks, a huge amount of data has to be processed by algorithms with the capability of solving industrial engineering optimization problems. On the other hand, solving optimization problems by using exhaustive search techniques is a hard task. In most cases, such problems are classified as NP-hard and have the inconvenience of huge computational costs. Therefore, the use of intelligent algorithms reduces the computational complexity and leads to an optimal solution to the problem. However, the No Free Lunch (NFL) theorem states that there is no algorithm able to solve any problem. Thus, several nature-inspired algorithms powered by neuro-fuzzy techniques have been designed. In this speech, I will illustrate the combination between evolutionary algorithms and knowledge-based systems for designing optimal smart machines.


Danilo Pelusi

Curriculum Vitae

Danilo Pelusi received the Ph.D. degree in Computational Astrophysics from the University of Teramo, Italy. He is an Associate Professor at the Department of Communication Sciences, University of Teramo. Editor of books in Springer and Elsevier, he is/was an Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Access and International Journal of Machine Learning and Cybernetics. Guest editor for Elsevier, Springer and Inderscience journals and keynote speaker in several conference, he belongs to the editorial board member of many journals. Reviewer of reputed journals such as IEEE Transactions on Fuzzy Systems and IEEE Transactions on Neural Networks and Machine Leaning, his research interests include Fuzzy Logic, Neural Networks, Information Theory, Machine Learning and Evolutionary Algorithms.