Intelligent Bio-Inspired Detection of Food Borne Pathogen by DNA Barcodes: The Case of Invasive Fish Species Lagocephalus Sceleratus

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)

Abstract

Climate change combined with the increase of extreme weather phenomena, has significantly influenced marine ecosystems, resulting in water overheating, increase of sea level and rising of the acidity of surface waters. The potential impacts in the biodiversity of sensitive ecosystems (such as Mediterranean sea) are obvious. Many organisms are under extinction, whereas other dangerous invasive species are multiplied and thus they are destroying the ecological equilibrium. This research paper presents the development of a sophisticated, fast and accurate Food Pathogen Detection (FPD) system, which uses the biologically inspired Artificial Intelligence algorithm of Extreme Learning Machines. The aim is the automated identification and control of the extremely dangerous for human health invasive fish species “Lagocephalus Sceleratus”. The matching is achieved through extensive comparisons of protein and DNA sequences, known also as DNA barcodes following an ensemble learning approach.

Keywords

Extreme learning machines Ensemble learning Food pathogen detection DNA barcoding Lagocephalus sceleratus Invasive species Climate change 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Forestry & Management of the Environment & Natural ResourcesDemocritus University of ThraceOrestiadaGreece

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