Research Article
A Feature-Fusion Transfer Learning Method as a Basis to Support Automated Smartphone Recycling in a Circular Smart City
@INPROCEEDINGS{10.1007/978-3-030-76063-2_29, author={Nermeen Abou Baker and Paul Szabo-M\'{y}ller and Uwe Handmann}, title={A Feature-Fusion Transfer Learning Method as a Basis to Support Automated Smartphone Recycling in a Circular Smart City}, proceedings={Science and Technologies for Smart Cities. 6th EAI International Conference, SmartCity360°, Virtual Event, December 2-4, 2020, Proceedings}, proceedings_a={SMARTCITY}, year={2021}, month={5}, keywords={Feature fusion Transfer learning Smartphone recycling Circular economy Automation systems Smart city Sustainability E-waste management Circular city}, doi={10.1007/978-3-030-76063-2_29} }- Nermeen Abou Baker
Paul Szabo-Müller
Uwe Handmann
Year: 2021
A Feature-Fusion Transfer Learning Method as a Basis to Support Automated Smartphone Recycling in a Circular Smart City
SMARTCITY
Springer
DOI: 10.1007/978-3-030-76063-2_29
Abstract
In this paper, we present how Artificial Intelligence (AI) could support automated smartphone recycling, hence, act as an enabler for Circular Smart Cities (CSC), where the Smart City paradigm could be linked to the Circular Economy (CE), which is a leading concept of the sustainable economy. While business and society strive to gain benefits from automation, the ongoing rapid digitalization, in turn, accelerates the mass production of Waste Electric and Electronic Equipment (WEEE), often called E-Waste. Therefore, E-Waste is the fastest growing waste stream in the world and comes up with several negative environmental and social impacts. In our research, we show an AI technique (particularly, Transfer Learning) that could become an enabler for the CSC and the CE in general and supporter of automated recycling, specifically. However, research on this topic is emerging only recently, and practical applications are lacking even more. For instance, object recognition has extensive research, whereas smartphone classification nevertheless has rare attention. Our main contribution is a Transfer Learning (TL) approach based on visual-feature extraction to classify smartphones; as a result, it supports automated smartphone recycling independently of brands and even without any ex-ante information about product designs. Our findings show that the main advantages of using TL, are reducing the size of the training-set, computation time, and significant enhancements without designing a completely new network from scratch. This may ease the automated recycling of smartphones as well as other E-Waste, hence, contribute to the development of the CE and CSC.
