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http://dx.doi.org/10.6109/jkiice.2021.25.7.903

A Method for the Classification of Water Pollutants using Machine Learning Model with Swimming Activities Videos of Caenorhabditis elegans  

Kang, Seung-Ho (Department of Information Security, Dongshin University)
Jeong, In-Seon (Department of Software Engineering, Chonnam National University)
Lim, Hyeong-Seok (Department of Software Engineering, Chonnam National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.7, 2021 , pp. 903-909 More about this Journal
Abstract
Caenorhabditis elegans whose DNA sequence was completely identified is a representative species used in various research fields such as gene functional analysis and animal behavioral research. In the mean time, many researches on the bio-monitoring system to determine whether water is contaminated or not by using the swimming activities of nematodes. In this paper, we show the possibility of using the swimming activities of C. elegans in the development of a machine learning based bio-monitoring system which identifies chemicals that cause water pollution. To characterize swimming activities of nematode, BLS entropy is computed for the nematode in a frame. And, BLS entropy profile, an assembly of entropies, are classified into several patterns using clustering algorithms. Finally these patterns are used to construct data sets. We recorded images of swimming behavior of nematodes in the arenas in which formaldehyde, benzene and toluene were added at a concentration of 0.1 ppm, respectively, and evaluate the performance of the developed HMM.
Keywords
Bio-monitoring system; Caenorhabditis elegans; Hidden Markov model; Machine learning; Water pollution;
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