• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.972-980
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• 2022

The butterfly species recognition technology based on machine learning using images has the effect of reducing a lot of time and cost of those involved in the related field to understand the diversity, number, and habitat distribution of butterfly species. In order to improve the accuracy and time efficiency of butterfly species classification, various features used as the inputs of machine learning models have been studied. Among them, branch length similarity(BLS) entropy or color intensity entropy methods using the concept of entropy showed higher accuracy and shorter learning time than other features such as Fourier transform or wavelet. This paper proposes a feature extraction algorithm using RGB color intensity entropy for butterfly color images. In addition, we develop butterfly recognition systems that combines the proposed feature extraction method with representative ensemble models and evaluate their performance.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.624-632
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• 2017

Automatic butterfly identification using images is one of the interesting research fields because it helps the related researchers studying species diversity and evolutionary and development process a lot in this field. The performance of the butterfly species identification system is dependent heavily on the quality of selected features. In this paper, we propose color intensity (CI) entropy by using the distribution of color intensities in a butterfly image. We show color intensity entropy can increase the recognition rate by 10% if it is used together with previously suggested branch length similarity entropy. In addition, the performance comparison with other features such as Eigenface, 2D Fourier transform, and 2D wavelet transform is conducted against several well known machine learning methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.903-909
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• 2021

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.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.318-329
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• 2013

This paper proposes a DNA watermarking method for the privacy protection and the prevention of illegal copy. The proposed method allocates codons to random circular angles by using random mapping table and selects triplet codons for embedding target with the help of the Lipschitz regularity value of local modulus maxima of codon circular angles. Then the watermark is embedded into circular angles of triplet codons without changing the codes of amino acids in a DNA. The length and location of target triplet codons depend on the random mapping table for 64 codons that includes start and stop codons. This table is used as the watermark key and can be applied on any codon sequence regardless of the length of sequence. If this table is unknown, it is very difficult to detect the length and location of them for extracting the watermark. We evaluated our method and DNA-crypt watermarking of Heider method on the condition of similar capacity. From evaluation results, we verified that our method has lower base changing rate than DNA-crypt and has lower bit error rate on point mutation and insertions/deletions than DNA-crypt. Furthermore, we verified that the entropy of random mapping table and the locaton of triplet codons is high, meaning that the watermark security has high level.