Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models
[Display omitted] •Classification of organic and recyclable wastes with deep learning models.•We extracted and combined the features from the layer of 1000-features of CNN models.•Feature selection method was applied to the combined feature set to reduce features.•The dataset was rebuilt with the Au...
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| Vydané v: | Measurement : journal of the International Measurement Confederation Ročník 153; s. 107459 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Elsevier Ltd
01.03.2020
Elsevier Science Ltd |
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| ISSN: | 0263-2241, 1873-412X |
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| Abstract | [Display omitted]
•Classification of organic and recyclable wastes with deep learning models.•We extracted and combined the features from the layer of 1000-features of CNN models.•Feature selection method was applied to the combined feature set to reduce features.•The dataset was rebuilt with the AutoEncoder model and the above steps were repeated.•The two feature sets obtained by feature selection were combined and classified.
Unless adequate measures are taken for waste litter, the ecological balance may deteriorate over time. The wastes disposed of the trash can be divided into two classes that are organic and recycling types. In recent years, artificial intelligence is frequently mentioned in all areas of technology. In this study, the dataset used for the classification of waste is reconstructed with the AutoEncoder network. The feature sets are then extracted using two datasets by Convolutional Neural Network (CNN) architectures and these feature sets are combined. The Ridge Regression (RR) method performed on the combined feature set reduced the number of features and also revealed the efficient features. Support Vector Machines (SVMs) were used as classifiers in all experiments. The most successful classification accuracy in the experiments was 99.95%. In this study, it is seen that the proposed approach is successful in the classification of waste types. |
|---|---|
| AbstractList | [Display omitted]
•Classification of organic and recyclable wastes with deep learning models.•We extracted and combined the features from the layer of 1000-features of CNN models.•Feature selection method was applied to the combined feature set to reduce features.•The dataset was rebuilt with the AutoEncoder model and the above steps were repeated.•The two feature sets obtained by feature selection were combined and classified.
Unless adequate measures are taken for waste litter, the ecological balance may deteriorate over time. The wastes disposed of the trash can be divided into two classes that are organic and recycling types. In recent years, artificial intelligence is frequently mentioned in all areas of technology. In this study, the dataset used for the classification of waste is reconstructed with the AutoEncoder network. The feature sets are then extracted using two datasets by Convolutional Neural Network (CNN) architectures and these feature sets are combined. The Ridge Regression (RR) method performed on the combined feature set reduced the number of features and also revealed the efficient features. Support Vector Machines (SVMs) were used as classifiers in all experiments. The most successful classification accuracy in the experiments was 99.95%. In this study, it is seen that the proposed approach is successful in the classification of waste types. Unless adequate measures are taken for waste litter, the ecological balance may deteriorate over time. The wastes disposed of the trash can be divided into two classes that are organic and recycling types. In recent years, artificial intelligence is frequently mentioned in all areas of technology. In this study, the dataset used for the classification of waste is reconstructed with the AutoEncoder network. The feature sets are then extracted using two datasets by Convolutional Neural Network (CNN) architectures and these feature sets are combined. The Ridge Regression (RR) method performed on the combined feature set reduced the number of features and also revealed the efficient features. Support Vector Machines (SVMs) were used as classifiers in all experiments. The most successful classification accuracy in the experiments was 99.95%. In this study, it is seen that the proposed approach is successful in the classification of waste types. |
| ArticleNumber | 107459 |
| Author | Cömert, Zafer Toğaçar, Mesut Ergen, Burhan |
| Author_xml | – sequence: 1 givenname: Mesut surname: Toğaçar fullname: Toğaçar, Mesut email: mtogacar@firat.edu.tr organization: Department of Computer Technology, Firat University Elazig, Turkey – sequence: 2 givenname: Burhan surname: Ergen fullname: Ergen, Burhan email: bergen@firat.edu.tr organization: Department of Computer Engineering, Faculty of Engineering, Firat University Elazig, Turkey – sequence: 3 givenname: Zafer surname: Cömert fullname: Cömert, Zafer email: zcomert@samsun.edu.tr organization: Department of Software Engineering, Faculty of Engineering, Samsun University Samsun, Turkey |
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| Keywords | Deep learning Recycling waste AutoEncoder network Feature selection |
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•Classification of organic and recyclable wastes with deep learning models.•We extracted and combined the features from the layer of... Unless adequate measures are taken for waste litter, the ecological balance may deteriorate over time. The wastes disposed of the trash can be divided into two... |
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| Title | Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models |
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