ICMLCI 20-21 OCT,2018
International Conference on Machine Learning and Computational Intelligence
(ICMLCI-2018) Venue: RELC, Singapore
International Advisory Committee

Prof. Ishwar Sethi
Department of Computer Science and Engineering, Oakland University, Rochester, USA

Prof. Xin-She Yang
School of Science and Technology, Middlesex University, Hendon Campus, London NW4 4BT, UK

Prof. Janusz Kacprzyk
Polish Academy of Sciences, Warsaw, Poland

Prof. Lakhmi C. Jain
University of Canberra, Canberra, Australia

Prof. Florin Popenţiu-Vlădicescu
Academy of Romanian Scientists, UNESCO Chair Department, University of Oradea, 1, University Street, City – Oradea, 410087, Romania

Prof. Kazumi Nakamatsu
School of Human Science and Environment, University of Hyogo, Shinzaike-hon-cho, Himeji 670-0092, Japan

Prof. Yeon-Mo Yang
National Institute of Technology; School of Electronic Engineering; Gyeongbuk, Gumi; Republic of Korea

Prof. Jae Moon Lee
Hansung University, Republic of Korea

Professor Baojiang Zhong
School of Computer Science and Technology; Soochow University, Suzhou 215006, China

Prof. Fatos Xhafa
Department of Languages and Informatics Systems, Technical University of Catalonia, Campus Nord, Ed. Omega, C/Jordi Girona 1-3, 08034 Barcelona, Spain

Prof. Madjid Tavana
Professor and Distinguished Chair of Business Analytics, Chairman of the Business Systems and Analytics Department, La Salle University, Philadelphia, Pennsylvania 19141, USA

Professor Zhengtao Yu
Dean, School of Information Engineering and Automation, Kunming University of Science and Technology, 650500, China.

Dr. Xiaolong Li
Department of Electronics and Computer Engineering Technology, College of Technology, Indiana State University, USA.

Dr. Chan-Su Lee
Associate Professor, Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 712-749, Rep. of Korea (South Korea)

Sponsored by

IIMT, Bhubaneswar (INDIA)


Submission Guidelines
  • The submissions may be of any form out of the following:
  • New algorithms with empirical, theoretical, psychological, or biological justification.
  • Experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems.
  • Applications of existing techniques that shed light on the strengths and weaknesses of the methods.
  • New learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks.
  • Development of new analytical frameworks that advance theoretical studies of practical learning methods.
  • Computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
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