Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F25%3A106010" target="_blank" >RIV/60460709:41110/25:106010 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S1342937X24002077" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S1342937X24002077</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.gr.2024.07.014" target="_blank" >10.1016/j.gr.2024.07.014</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model

Popis výsledku v původním jazyce

The significance of waste disposal, classification, and monitoring has dramatically increased due to the increase in industrial development and the progress of intelligent urbanization. Since the last few decades, the utilization of Deep learning techniques has grown increasingly in waste management research. The efficiency of a waste reuse and recycling process relies on its capacity to restore resources to their original state, thereby minimizing pollution and promoting an ecologically sustainable framework. Selecting the optimal deep-learning method for classifying and predicting waste is challenging and time-consuming. This paper proposed intelligent garbage categorization using Bidirectional Long Short-Term Memory (Bi-LSTM) and CNN-based transfer learning to improve environmental sustainability. Organic and recyclable garbage are separated. To simplify trash categorization, a hybrid model combines TL-based CNN and Bi-LSTM. This study extensively examined the suggested technique with numerous CNN computational methods, including VGG-19, ResNet-34, AlexNet, ResNet-50, and VGG-16, using the 'TrashNet Waste' database. Key findings show that our hybrid model outperforms existing models. Our classification accuracy is 96.78 %, 5.27 % higher than the best model. Our model also reduces misclassification by 7.25 %, proving its reliability. This comprehensive examination examined the computer models' trash classification performance and provided specific viewpoints. The results explain each technique's pros and cons and show how useful they are in real-world circumstances. Waste classification is practical and sophisticated with a hybrid model. The effectiveness and cleverness of this model improve sustainable environmental practices. The proposed method's excellent performance suggests its seamless integration into practical waste management solutions.

Název v anglickém jazyce

Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model

Popis výsledku anglicky

The significance of waste disposal, classification, and monitoring has dramatically increased due to the increase in industrial development and the progress of intelligent urbanization. Since the last few decades, the utilization of Deep learning techniques has grown increasingly in waste management research. The efficiency of a waste reuse and recycling process relies on its capacity to restore resources to their original state, thereby minimizing pollution and promoting an ecologically sustainable framework. Selecting the optimal deep-learning method for classifying and predicting waste is challenging and time-consuming. This paper proposed intelligent garbage categorization using Bidirectional Long Short-Term Memory (Bi-LSTM) and CNN-based transfer learning to improve environmental sustainability. Organic and recyclable garbage are separated. To simplify trash categorization, a hybrid model combines TL-based CNN and Bi-LSTM. This study extensively examined the suggested technique with numerous CNN computational methods, including VGG-19, ResNet-34, AlexNet, ResNet-50, and VGG-16, using the 'TrashNet Waste' database. Key findings show that our hybrid model outperforms existing models. Our classification accuracy is 96.78 %, 5.27 % higher than the best model. Our model also reduces misclassification by 7.25 %, proving its reliability. This comprehensive examination examined the computer models' trash classification performance and provided specific viewpoints. The results explain each technique's pros and cons and show how useful they are in real-world circumstances. Waste classification is practical and sophisticated with a hybrid model. The effectiveness and cleverness of this model improve sustainable environmental practices. The proposed method's excellent performance suggests its seamless integration into practical waste management solutions.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

GONDWANA RESEARCH

ISSN

1342-937X

e-ISSN

1342-937X

Svazek periodika

146

Číslo periodika v rámci svazku

neuvedeno

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

14

Strana od-do

252-266

Kód UT WoS článku

001532739500001

EID výsledku v databázi Scopus

2-s2.0-85200813493