Adaptability and advanced services for ambient intelligence require an
intelligent technological support for understanding the current needs and
the desires of users in the interactions with the environment for their
daily use, as well as for understanding the current status of the
environment also in complex situations. This infrastructure constitutes an
essential base for smart living. Various technologies are nowadays
converging to support the creation of efficient and effective
infrastructures for ambient intelligence.
Artificial intelligence can provide flexible techniques for designing and
implementing monitoring and control systems, which can be configured from
behavioral examples or by mimicking approximate reasoning processes to
achieve adaptable systems. Machine learning can be effective in extracting
knowledge from data and learn the actual and desired behaviors and needs
of individuals as well as the environment to support informed decisions in
managing the environment itself and its adaptation to the people’s needs.
Biometrics can help in identifying individuals or groups: their profiles
can be used for adjusting the behavior of the environment. Machine
learning can be exploited for dynamically learning the preferences and
needs of individuals and enrich/update the profile associated either to
such individual or to the group. Biometrics can also be used to create
advanced human-computer interaction frameworks.
Cloud computing environments will be instrumental in allowing for
worldwide availability of knowledge about the preferences and needs of
individuals as well as services for ambient intelligence to build
This talk will analyze the opportunities offered by these technologies to
support the realization of adaptable operations and intelligent services
for smart living in an ambient intelligent infrastructure.
Prof. Vincenzo Piuri has received his Ph.D. in computer engineering at
Politecnico di Milano, Italy (1989). He is Full Professor in computer
engineering at the Università degli Studi di Milano, Italy (since 2000).
He has been Associate Professor at Politecnico di Milano, Italy and
Visiting Professor at the University of Texas at Austin and at George
Mason University, USA.
His main research interests are artificial intelligence, computational
intelligence, intelligent systems, machine learning, pattern analysis and
recognition, signal and image processing, biometrics, intelligent
measurement systems, industrial applications, digital processing
architectures, fault tolerance, dependability, and cloud computing
infrastructures. Original results have been published in more than 400
papers in international journals, proceedings of international
conferences, books, and book chapters.
He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior
Member of INNS. He has been IEEE Vice President for Technical Activities
(2015), IEEE Director, President of the IEEE Computational Intelligence
Society, Vice President for Education of the IEEE Biometrics Council, Vice
President for Publications of the IEEE Instrumentation and Measurement
Society and the IEEE Systems Council, and Vice President for Membership of
the IEEE Computational Intelligence Society.
He is Editor-in-Chief of the IEEE Systems Journal (2013-19), and Associate
Editor of the IEEE Transactions on Computers and the IEEE Transactions on
Cloud Computing, and has been Associate Editor of the IEEE Transactions on
Neural Networks and the IEEE Transactions on Instrumentation and
He received the IEEE Instrumentation and Measurement Society Technical
Award (2002). He is Honorary Professor at Obuda University, Hungary;
Guangdong University of Petrochemical Technology, China; Northeastern
University, China; Muroran Institute of Technology, Japan; and the Amity
The aim of this talk is comprehensive coverage of Evolutionary algorithms
one of the growing area of research in field of Computational Intelligence.
Many real world problems have multiple objectives, where instead of exact
solution a set of optimal solutions is required. Evolutionary algorithm is
a highly effective way of finding multiple effective solutions in a single
Prof. Deb is Koenig Endowed Chair Professor at Department of Electrical and
Computer Engineering in Michigan State University (MSU), USA. He also holds
joint appointments at Department of Mechanical Engineering and at Department of
Computer Science and Engineering at MSU. Prior to his joining MSU, he was at
Indian Institute of Technology (IIT) Kanpur. Prof. Deb's research interests are
in Evolutionary Optimization and their application in optimization, Meta-
modeling, Constraint Handling, Engineering Design, Neural Networks, Data-
mining and Machine learning.
He is awarded Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur
Mamdani Prize, Distinguished Alumni Award from IIT Kharagpur, Edgeworth-
Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research
award from Germany. He is fellow of IEEE, ASME, and three science academies
in India. He has published over 375 research papers with Google Scholar citation
of 63,500 with h-index 84. He is in the editorial board on 20 major international