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...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2022/14/shsconf_stehf2022_03003.pdf |
| _version_ | 1811185689590497280 |
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| author | Liu Yifei |
| author_facet | Liu Yifei |
| author_sort | Liu Yifei |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-04-11T13:33:29Z |
| format | Article |
| id | doaj.art-a3a8b13e426247458e519750c85c7a46 |
| institution | Directory Open Access Journal |
| issn | 2261-2424 |
| language | English |
| last_indexed | 2024-04-11T13:33:29Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj.art-a3a8b13e426247458e519750c85c7a462022-12-22T04:21:42ZengEDP SciencesSHS Web of Conferences2261-24242022-01-011440300310.1051/shsconf/202214403003shsconf_stehf2022_03003An Application of Machine Learning Algorithms on the Finger Image PredictionLiu Yifei0Big Data Finance Experimental class of Chumin College, Shanxi UniversityIn 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%.https://www.shs-conferences.org/articles/shsconf/pdf/2022/14/shsconf_stehf2022_03003.pdf |
| spellingShingle | Liu Yifei An Application of Machine Learning Algorithms on the Finger Image Prediction SHS Web of Conferences |
| title | An Application of Machine Learning Algorithms on the Finger Image Prediction |
| title_full | An Application of Machine Learning Algorithms on the Finger Image Prediction |
| title_fullStr | An Application of Machine Learning Algorithms on the Finger Image Prediction |
| title_full_unstemmed | An Application of Machine Learning Algorithms on the Finger Image Prediction |
| title_short | An Application of Machine Learning Algorithms on the Finger Image Prediction |
| title_sort | application of machine learning algorithms on the finger image prediction |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2022/14/shsconf_stehf2022_03003.pdf |
| work_keys_str_mv | AT liuyifei anapplicationofmachinelearningalgorithmsonthefingerimageprediction AT liuyifei applicationofmachinelearningalgorithmsonthefingerimageprediction |