Classification Algorithm for Gene Expression Graph and Manhattan Distance

Sevugapandi, N; Chandran, C. P.

doi:10.11591/ijeecs.v5.i2.pp472-478

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Complementary base pairing between the sample cell and gene sequences on the chip produces different colours based on the expression level of the gene. The introduction of microarray technology allows researchers to analyse thousands of gene expression profiles simultaneously [2][3][4][5]. The datasets produced by microarray technology is known as gene expression dataset [2].…”

Section: Introductionmentioning

confidence: 99%

An Effective Pre-Processing Phase for Gene Expression Classification

Seah¹,

Kasim²,

Fudzee³

et al. 2018

IJEECS

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A raw dataset prepared by researchers comes with a lot of information. Whether the information is usefull or not, completely depends on the requirement and purposes. In machine learning, data pre-processing is the very initial stage. It is a must to make sure the dataset is totally suitable for the requirement. In significant directed random walk (sDRW), there are three steps in data pre-processing stage. First, we remove unwanted attributes, missing value and proper arrangement, followed by normalization of the expression value and lastly, filtering method is applied. The first two steps are completed by Bioconductor package while the last step is works in sDRW.

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Section: Introductionmentioning

confidence: 99%

An Effective Pre-Processing Phase for Gene Expression Classification

Seah¹,

Kasim²,

Fudzee³

et al. 2018

IJEECS

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“…To increase the accuracy of speaker recognition, we can use a data mining approach. One method in the data mining approach is the Manhattan distance method [5]. The Manhattan distance method is the methods for image matching using distance measurements on two speakers.…”

Section: Introductionmentioning

confidence: 99%

Speaker Recognition in Content-based Image Retrieval for a High Degree of Accuracy

et al. 2018

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The purpose of this research is to measure the speaker recognition accuracy in Content-Based Image Retrieval. To support research in speaker recognition accuracy, we use two approaches for recognition system: identification and verification, an identification using fuzzy Mamdani, a verification using Manhattan distance. The test results in this research. The best of distance mean is size 32x32. The best of the verification for distance rate is 965, and the speaker recognition system has a standard error of 5% and the system accuracy is 95%. From these results, we find that there is an increase in accuracy of almost 2.5%. This is due to a combination of two approaches so the system can add to the accuracy of speaker recognition.

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