ICMLCI 14th-15th, December, 2019

International Conference on Machine Learning and Computational Intelligence
ICMLCI

Organized by: IRNet, Bhubaneswar, Odisha, India

Organizing Committee

Keynote Speakers

Prof. Dr. Tao Shen
Vice Dean,
School of Information Engineering and Automation,
Kunming University of Science and Technology
Kunming,China.

Short Bio-data:
Prof. Tao Shen is a doctoral supervisor in School of Information Engineering and Automation, Kunming University of Science and Technology. He had received his master’s degree and Ph.D. from Illinois Institute of Technology in USA, bachelor degree from University of Electronic Science and Technology of China. He is an executive director and Deputy Secretary-General of Youth Committee of Chinese Materials Institute, a fellow of Institute of Electrical and Electronics Engineers(IEEE) and China Computer Federation(CCF). He is a peer-reviewer with National Natural Science Foundation of China, a high-caliber talent accredited by provincial government of Yunnan province. He participated in over 10 research projects, including projects supported by the National Natural Science Foundation of China, Applied Basic Research Programs of Yunnan Province, and Science Foundation of Department of Education, Yunnan Province. He has published more than 40 papers indexed by SCI/EI, 2 monographs, and held numerous patents and copyrights. His main research fields including: Terahertz technologies and their applications, Artificial Intelligence, Data Mining and Smart Power Grids.

Sponsored by
IIMT, BHUBANESWAR

IIMT, Bhubaneswar (INDIA)

IRNET

IRNet (INDIA)

Publication Partners
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.
Venue

Interscience Institute of Management Technology
Kantabada, Bhubaneswar, India

Visitors
knowledge management