Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
GONDWANA RESEARCH
ISSN
1342-937X
e-ISSN
1342-937X
Volume of the periodical
146
Issue of the periodical within the volume
neuvedeno
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
14
Pages from-to
252-266
UT code for WoS article
001532739500001
EID of the result in the Scopus database
2-s2.0-85200813493