An Application of Machine Learning Algorithms on the Finger Image Prediction.
In recent years, using machine learning (ML) algorithm to analyze a picture, obtain its features, and finally identify what the picture is about is getting more and more important. This is because with the popularity of the electronic equipment and high-performance computing equipment, people began...
| Publicado en: | SHS Web of Conferences Vol. 144; pp. 1 - 7 |
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| Autor principal: | |
| Formato: | Artículo |
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EDP Sciences
8/26/022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=158850583&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 158850583 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24165182 FT5R jtl: SHS Web of Conferences issn: 24165182 maglogo: N pubinfo: dt: 8/26/022 vid: 144 pid: 76090 pub: EDP Sciences artinfo: ui: 158850583 10.1051/shsconf/202214403003 ppf: 1 ppct: 6 formats: tig: atl: An Application of Machine Learning Algorithms on the Finger Image Prediction. aug: au: Liu, Yifei affil: Big Data Finance Experimental class of Chumin College, Shanxi University, Taiyuan, Shanxi Province, China sug: ab: In recent years, using machine learning (ML) algorithm to analyze a picture, obtain its features, and finally identify what the picture is about is getting more and more important. This is because with the popularity of the electronic equipment and high-performance computing equipment, people began to pursue a more convenient and automatic life. The science of the image recognition frees people's hands to a certain extent through the training of algorithms, thus making people's life more convenient. This paper presents a comparison of two ML algorithms: Multi-layer Perceptron (MLP), and Convolutional Neural Network (CNN) with three different optimization methods on the data-set by measuring their test accuracy and their running time. The said data-set consists of a training-set of 1080 pictures (64 by 64 pixels) of signs representing numbers from 0 to 5 (180 pictures per number) and a test set of 120 pictures (64 by 64 pixels) of signs representing numbers from 0 to 5 (20 pictures per number). For the implementation of the ML algorithms, the data-set was partitioned in the following fashion: 90% for training phase, and 10% for testing phase. The hyper-parameters used for all the classifiers were manually assigned. Results show that most of the presented ML algorithms performed not bad with a test accuracy over 80%, and the CNN algorithm performed best among all the implemented algorithms with a test accuracy about 91.04%. pubtype: Conference Proceedings doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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