DocumentCode
640875
Title
Cognitive diversity in perceptive informatics and affective computing
Author
Hsu, D. Frank
Author_Institution
Dept. of Comput. & Inf. Sci., Fordham Univ., New York, NY, USA
fYear
2013
fDate
16-18 July 2013
Firstpage
6
Lastpage
7
Abstract
The advent of sensor technologies and imaging modalities has greatly increased our ability to map the brain structure and understand its cognitive function. In order for the acquired Big Data (with large volume, wide variety, and high velocity) to be valuable, innovative data-centric algorithms and systems in machine learning, data mining and artificial intelligence have been developed, designed and implemented. Due to the complexity of the brain system and its cognitive processes, new data-driven paradigm is needed to recognize patterns in Big Data, to fuse information from different sources (systems and sensors), and to extract useful knowledge for actionable decisions.
Keywords
brain models; cognition; data handling; data mining; learning (artificial intelligence); pattern recognition; sensor fusion; affective computing; artificial intelligence; big data; brain cognitive function; brain structure; brain system complexity; cognitive diversity; cognitive process; data mining; data-driven paradigm; imaging modalities; information fusion; innovative data-centric algorithms; machine learning; pattern recognition; perceptive informatics; sensor technologies; Abstracts;
fLanguage
English
Publisher
ieee
Conference_Titel
Cognitive Informatics & Cognitive Computing (ICCI*CC), 2013 12th IEEE International Conference on
Conference_Location
New York, NY
Print_ISBN
978-1-4799-0781-6
Type
conf
DOI
10.1109/ICCI-CC.2013.6622219
Filename
6622219
Link To Document