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 :
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