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This is a Experimental Study study.

September 12, 2025Journal of Applied Research and TechnologyOpen Access

Classification of waste images using deep learning technique

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Authors

DSDarin ShafekLatakia University HHHassan W. HilowAlsalam University College MAMohsin AhmedAlsalam University College

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Implication

This research reveals high accuracy in classifying organic and recyclable waste images, suggesting machine learning can enhance waste management processes.

Key Points

The deep learning model achieved a precision score of 0.96 for organic waste classification.
Using the VGG16 architecture, it identified recyclable images with a recall rate of 0.97, showing high effectiveness.
Transfer learning methods contributed to improved accuracy in differentiating between waste types.
The findings highlight the potential of deep learning to automate waste sorting and enhance recycling efforts.

Cite This Study

Shafek et al. (2025) studied this question.

synapsesocial.com/papers/68d44c4631b076d99fa55b53 https://doi.org/10.22201/icat.24486736e.2025.23.4.2578

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Implication

This research reveals high accuracy in classifying organic and recyclable waste images, suggesting machine learning can enhance waste management processes.

Classification of waste images using deep learning technique

Discussion

Member takes

Implication

Key Points

Cite This Study

Classification of waste images using deep learning technique

Discussion

Member takes

Implication

Key Points

Cite This Study

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