EBiGRU: An Enhanced Bidirectional GRU-Based Multi-scale Neural Network for Intelligent Waste Segregation

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Anupriya, Ratish Kumar

10.14445/23488549/IJECE-V12I7P106

How to Cite?

Anupriya, Ratish Kumar, "EBiGRU: An Enhanced Bidirectional GRU-Based Multi-scale Neural Network for Intelligent Waste Segregation," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 7, pp. 62-73, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I7P106

Abstract:

The demand for effective waste management systems increases with evolving technology, and an intelligent system is needed to classify and segregate waste easily and optimally. With this focus, this paper proposes a novel method of waste classification that utilizes the benefits of Convolutional Neural Networks (CNN) to improve the performance of Bidirectional Gated Recurrent Units (BiGRU) and develop an Enhanced BiGRU (EBiGRU). Here, a novel Multi-Scale Linear Aggregation Network (MLAN) in CNN is a backbone for BiGRU and extracts scale-invariant features to understand the local and global context of the information in the captured images. These feature maps are then sequentially embedded with customized BiGRU to define the dependencies between forward and backwards directions. This combination not only improves the feature representation but also enhances contextual awareness and makes the model robust to scale variations. The proposed system is evaluated using two separate datasets (Waste Segregation Image and Garbage Classification dataset) and one with their combination. The performance is computed based on accuracy, loss, and correct classification rate, which declares the efficacy of the proposed method and presents its robustness.

Keywords:

Waste classification, Deep Learning, BiGRU, EBiGRU, Multi-scale features.

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