Development of a complex adaptive pnn system for the rapid detection of E.coli

Yinglei Li, William Ford, Kun Xiang, Walker Land, Robert Congdon, Omowunmi Sadik

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations


The objective of this research is to develop a complex adaptive piecewise linear regression / probabilistic neural network (PNN) intelligent system for the rapid detection and classification of Escherichia coli (E.coli). The rapid detection and classification of E.coli is important because current methods require a long period of analysis before a classification can be determined. The objective of this paper is to describe the design and preliminarily evaluate an Intelligent Decision Support System (IDSS) that will validate the following hypotheses: An intelligent decision support system (IDSS) to allow the rapid collection and classification of E.coli can be designed and preliminarily evaluated, which will significantly decrease detection and classification times for E.coli bacteria, thereby addressing the food spoilage problem. The research in this paper provides a preliminary answer to: What performance improvement percentage can be realized against the 16 to 48 hours required for the conventional multistep methods of detection of microorganisms (using E.coli data as a baseline)? For the 16 hour period we have a 6.7% reduction in the time-to-detect period ((16-15)/15 × 100% = 6.7%) and for the 48 hour period we have a 220% reduction in time ((48- 15)/15×100% = 220%).

Original languageEnglish (US)
Pages (from-to)342-347
Number of pages6
JournalProcedia Computer Science
StatePublished - 2013
Externally publishedYes
Event2013 Complex Adaptive Systems Conference, CAS 2013 - Baltimore, MD, United States
Duration: Nov 13 2013Nov 15 2013

All Science Journal Classification (ASJC) codes

  • General Computer Science


  • Classification
  • Escherichia coli
  • Probabilistic neural network (PNN)


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