Speakers

Prof. Dongshu Wang, IEEE Senior Member

Zhengzhou University, China

Brief Introduction: Prof. Wang Dongju, professor and doctoral supervisor of School of Electrical and Information Engineering, Zhengzhou University, Visiting scholar of Department of Computer Science and Engineering, Michigan State University, USA.

Presided over and participated in 4 projects of the National Natural Science Foundation, 2 projects of the National key research and development Plan of the Ministry of Science and Technology, presided over 4 provincial scientific research projects, and participated in more than 10 enterprise cooperation projects. As the first author and corresponding author, he has published more than 70 SCI/EI papers in well-known journals such as IEEE TNNLS, IEEE TCDS, IEEE TAI, IEEE TITS, Information science, pattern recognition, control theory and application, control and decision, etc. Among them, IEEE Transactions has 9 long articles, H-index 18, and 5000+ cited papers. He is also the editor of the IEEE CDS newsletter and deputy editor of the International Journal of Humanoid Robotics.

Research areas: Robot autonomous mind development, machine learning, artificial intelligence, brain-inspired computing, robot intelligent control, etc.

Prof. Zike Zhang

Zhejiang University, China

Zi-Ke Zhang (Member, IEEE) received the Doctoral degree in physics from the University of Fribourg, Fribourg, Switzerland, in 2011. He is currently a Professor with the College of Media and International Culture, Zhejiang University, Hangzhou, China. He has authored or coauthored more than 70 SCI-indexed papers in Physics and Computer Science, including Physics Reports, Information Sciences. His research interests include complex networks and machine learning

Assoc. Prof. Fengming Du

Dalian Maritime University, China

Brief Introduction: Du Fengming, Associate professor, doctoral supervisor, Postdoctoral fellow of Ship and Ocean Engineering Mobile Station, Dalian Maritime University. His main research areas are engine optimization and inspection, material design, artificial intelligence algorithm, machine vision, etc.

At present, he has published more than 60 academic papers, been authorized by national invention patents and other intellectual property rights 4, presided over more than 10 national and provincial scientific research projects, won the honorary titles of Liaoning Province Hundred million Talent Engineering talent, Dalian Young Science and Technology Star, Dalian High-level talent - Young talent, and won the Liaoning Province Natural Science Academic Achievement Award (ranked first). He is also a senior member of the Chinese Society of Mechanical Engineering, an industrial technical expert of Dalian Bureau of Industry and Information Technology, and a review expert of Dalian Invention Association. He is The author of IET Image Processing, Journal of Materials Processing Technology and The International Journal of Advanced Reviewer of Manufacturing Technology, International Journal of Engine Research and other journals. Served as guest editor of Lubricants, Coatings and other periodicals.

Assoc. Prof. Francesco Fontanella

University of Cassino and Southern Lazio, Italy

Francesco Fontanella is currently an Associate Professor at the Department of Electrical and Information Engineering at the University of Cassino and Southern Lazio, Italy. His main research interests are machine learning, data science, complex systems, and in particular evolutionary computation.
In the field of Machine Learning, his research focuses on the development of applications that address real-world problems. These problems include detecting and predicting neurodegenerative diseases, identifying authors in ancient manuscripts, and detecting water pollution.
In these areas, he has co-authored over 100 peer-reviewed publications and contributed to several research projects. He is actively involved in organising workshops at some of the leading conferences in the field of computational intelligence. He also serves as guest editor and editorial board member for several international scientific journals.

TITLE: Machine Learning for Early Diagnosis of Neurodegenerative Diseases: Advances and Challenges 

ABSTRACT 
Alzheimer's and Parkinson's being the most common, and their incidence is expected to increase dramatically over the next few decades. Unfortunately, these diseases cannot be cured, but early diagnosis can help to better manage their symptoms and progression. These aspects explain the importance of developing support systems for the early diagnosis of neurodegenerative diseases. For this reason, in the last few years, researchers have been growing interested in applying machine learning (ML) based approaches to effectively identify people affected by ND. Many ML approaches are currently being used for this purpose, analysing different types of data, including brain images, genetic data and, more recently, handwriting.
In this talk, I will discuss the latest results in diagnosing ND using ML tools. I will also discuss the open issues and future challenges that need to be addressed in order for ML to be widely used in ND diagnosis.