Classification of Helitron’s Types in the C.elegans Genome based on Features Extracted from Wavelet Transform and SVM Methods

Touati, Rabeb; Messaoudi, Imen; ElloumiOueslati, Afef; Lachiri, Zied

doi:10.5220/0006631001270134

Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies 2018

DOI: 10.5220/0006631001270134

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Classification of Helitron’s Types in the C.elegans Genome based on Features Extracted from Wavelet Transform and SVM Methods

Rabeb Touati¹,

Imen Messaoudi²,

Afef ElloumiOueslati³

et al.

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Cited by 4 publications

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“…The basic task of SVM is to find the best hyperplane with the maximum distance of each class [20]. The hyperplane is a function that can be split into classes with high dimensions [21].…”

mentioning

confidence: 99%

Comparison of Machine Learning Approach for Waste Bottle Classification

et al. 2022

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The use of machine learning for the image classification process is growing all the time. Many methods can be used to classify an image with good accuracy. Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are popular methods for this case. The two approaches have differences in the data training process to achieve classification objectives. Although there are some differences between these approaches, there are some advantages to both of them. This research explores the comparison of the two CNN and SVM methods by comparing the training process carried out and the accuracy results of the classification. The process stages are divided into pre-processing, training, and testing. The objects used are ten waste plastic bottles with different brands of medium size with a total data of 1100 images. Based on the observations, both methods have advantages and disadvantages in the data training and classification process. However, from the results, CNN's accuracy is better than SVM. The accuracy of both networks is 99% for CNN and 74% for SVM, respectively. So, from the results of experiments that have been carried out in the study, it was found that CNN was still better than SVM. Doi: 10.28991/ESJ-2022-06-05-011 Full Text: PDF

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“…The basic task of SVM is to find the best hyperplane with the maximum distance of each class [20]. The hyperplane is a function that can be split into classes with high dimensions [21].…”

mentioning

confidence: 99%

Comparison of Machine Learning Approach for Waste Bottle Classification

et al. 2022

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A combined support vector machine-FCGS classification based on the wavelet transform for Helitrons recognition in C.elegans

Touati

Messaoudi

Oueslati

et al. 2018

Multimed Tools Appl

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The Helitron family classification using SVM based on Fourier transform features applied on an unbalanced dataset

Touati

Oueslati

Messaoudi

et al. 2019

Med Biol Eng Comput

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Classification of Helitron’s Types in the C.elegans Genome based on Features Extracted from Wavelet Transform and SVM Methods

Cited by 4 publications

References 0 publications

Comparison of Machine Learning Approach for Waste Bottle Classification

Comparison of Machine Learning Approach for Waste Bottle Classification

A combined support vector machine-FCGS classification based on the wavelet transform for Helitrons recognition in C.elegans

The Helitron family classification using SVM based on Fourier transform features applied on an unbalanced dataset

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