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Proceedings Paper

A decision support system for fusion of hard and soft sensor information based on probabilistic latent semantic analysis technique
Author(s): Amir Shirkhodaie; Vinayak Elangovan; Amjad Alkilani; Mohammad Habibi
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Paper Abstract

This paper presents an ongoing effort towards development of an intelligent Decision-Support System (iDSS) for fusion of information from multiple sources consisting of data from hard (physical sensors) and soft (textural sources. Primarily, this paper defines taxonomy of decision support systems for latent semantic data mining from heterogeneous data sources. A Probabilistic Latent Semantic Analysis (PLSA) approach is proposed for latent semantic concepts search from heterogeneous data sources. An architectural model for generating semantic annotation of multi-modality sensors in a modified Transducer Markup Language (TML) is described. A method for TML messages fusion is discussed for alignment and integration of spatiotemporally correlated and associated physical sensory observations. Lastly, the experimental results which exploit fusion of soft/hard sensor sources with support of iDSS are discussed.

Paper Details

Date Published: 28 May 2013
PDF: 12 pages
Proc. SPIE 8758, Next-Generation Analyst, 87580A (28 May 2013); doi: 10.1117/12.2019828
Show Author Affiliations
Amir Shirkhodaie, Tennessee State Univ. (United States)
Vinayak Elangovan, Tennessee State Univ. (United States)
Amjad Alkilani, Tennessee State Univ. (United States)
Mohammad Habibi, Tennessee State Univ. (United States)

Published in SPIE Proceedings Vol. 8758:
Next-Generation Analyst
Barbara D. Broome; David L. Hall; James Llinas, Editor(s)

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