Transfer Learning Application for an Electronic Waste Image Classification System

Şule Öztürk-Birim () and Merve Gündüz-Cüre ()
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Şule Öztürk-Birim: Manisa Celal Bayar University
Merve Gündüz-Cüre: Manisa Celal Bayar University

Chapter Chapter 16 in The Palgrave Handbook of Sustainable Digitalization for Business, Industry, and Society, 2024, pp 349-382 from Springer

Abstract: Abstract E-waste or WEEE poses a growing environmental problem owing to the presence of toxic substances. The improper disposal of e-waste can damage the environment and human health. E-waste management involves proper collection, transportation, storage, processing, and disposal of waste. Effective e-waste management requires an accurate classification to separate recyclable items from hazardous materials. Manual categorization can be time-consuming, but automated methods such as artificial learning offer promise. This chapter explores the use of artificial learning algorithms, including machine and deep learning, for e-waste classification. Various techniques such as decision trees, random forest classifiers, support vector machines, and convolutional neural networks (CNN) are employed depending on the dataset and e-waste characteristics. CNN architectures that leverage transfer learning from large datasets, such as ImageNet, have shown promising results. This chapter presents a framework for modeling and classifying e-waste and compares the performances of different CNN architectures using metrics such as accuracy, recall, precision, and f1. A dataset of e-waste images from ten categories was collected, and transfer learning was applied to the selected architectures. The proposed intelligent classification system contributes to sustainability by automating e-waste categorization and reducing time and labor.

Keywords: E-waste classification; WEEE; Image classification; CNN algorithms; Machine learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-58795-5_16

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DOI: 10.1007/978-3-031-58795-5_16

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