In recent years, the focus on sustainability has grown by everyone, including policymakers, companies, and consumers. In this perspective, recycling plays an important role because it allows to reduce the amount of waste to be disposed of, at the same time reducing the need for raw materials. This paper presents ScanBage, a web application designed and developed to support users in separating waste collection. It exploits two machine learning algorithms to automatically classify garbage categories and it employs Gamification elements with the aim of increasing user involvement.

Encouraging users in waste sorting using deep neural networks and gamification / Delnevo G.; Aguzzi G.; Letizi S.; Luffarelli M.; Petreti A.; Mirri S.. - ELETTRONICO. - (2021), pp. 230-235. (Intervento presentato al convegno 1st Conference on Information Technology for Social Good, GoodIT 2021 tenutosi a ita nel 2021) [10.1145/3462203.3477056].

Encouraging users in waste sorting using deep neural networks and gamification

Delnevo G.;Aguzzi G.;Letizi S.;Luffarelli M.;Mirri S.
2021

Abstract

In recent years, the focus on sustainability has grown by everyone, including policymakers, companies, and consumers. In this perspective, recycling plays an important role because it allows to reduce the amount of waste to be disposed of, at the same time reducing the need for raw materials. This paper presents ScanBage, a web application designed and developed to support users in separating waste collection. It exploits two machine learning algorithms to automatically classify garbage categories and it employs Gamification elements with the aim of increasing user involvement.
2021
GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good
230
235
Encouraging users in waste sorting using deep neural networks and gamification / Delnevo G.; Aguzzi G.; Letizi S.; Luffarelli M.; Petreti A.; Mirri S.. - ELETTRONICO. - (2021), pp. 230-235. (Intervento presentato al convegno 1st Conference on Information Technology for Social Good, GoodIT 2021 tenutosi a ita nel 2021) [10.1145/3462203.3477056].
Delnevo G.; Aguzzi G.; Letizi S.; Luffarelli M.; Petreti A.; Mirri S.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/838556
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