Intelligent Waste Management Using WasteIQNet With Hierarchical Learning and Meta-Optimization
Effective waste management remains a critical pillar for sustainable urban development, particularly in rapidly growing regions like Delhi-NCR, where heterogeneous waste streams complicate classification. This study presents WasteIQNet, an intelligent, hierarchy-aware deep hybrid model for fine-grai...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11015996/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849426050512060416 |
|---|---|
| author | Sakshi Tiwari Snigdha Bisht Kanchan Sharma |
| author_facet | Sakshi Tiwari Snigdha Bisht Kanchan Sharma |
| author_sort | Sakshi Tiwari |
| collection | DOAJ |
| description | Effective waste management remains a critical pillar for sustainable urban development, particularly in rapidly growing regions like Delhi-NCR, where heterogeneous waste streams complicate classification. This study presents WasteIQNet, an intelligent, hierarchy-aware deep hybrid model for fine-grained waste classification across 18 categories structured under Wet and Dry waste types. The model integrates MobileNetV3 for semantic feature extraction with GraphSAGE to capture structural relationships among image representations. To address class imbalance and feature sparsity, the architecture incorporates Feature-wise Attention (FWA), Top-K Mixture of Experts (TopK-MoE), and advanced regularization techniques including Dynamic Sparse Training (DST) and Model-Agnostic Meta-Learning (MAML). We introduce a novel Hierarchical Tree Loss function to penalize semantically distant misclassifications by leveraging domain-specific waste hierarchy paths. The proposed framework is trained and evaluated on the WEDR dataset, a curated collection of 360,000 images reflecting real-world waste conditions across Delhi’s major landfill zones. WasteIQNet achieves a peak classification accuracy of 97.87%, demonstrating substantial improvements in both interpretability and generalization. This work contributes a scalable, robust, and deployment-ready solution tailored for real-time smart city waste segregation initiatives. |
| format | Article |
| id | doaj-art-6ff4dff059fa4a2fb37f78aa6e45c8e6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6ff4dff059fa4a2fb37f78aa6e45c8e62025-08-20T03:29:34ZengIEEEIEEE Access2169-35362025-01-011310641610643410.1109/ACCESS.2025.357409511015996Intelligent Waste Management Using WasteIQNet With Hierarchical Learning and Meta-OptimizationSakshi Tiwari0https://orcid.org/0009-0004-2613-9809Snigdha Bisht1https://orcid.org/0009-0001-9577-4652Kanchan Sharma2https://orcid.org/0000-0001-6519-4887Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, IndiaDepartment of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, IndiaDepartment of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, IndiaEffective waste management remains a critical pillar for sustainable urban development, particularly in rapidly growing regions like Delhi-NCR, where heterogeneous waste streams complicate classification. This study presents WasteIQNet, an intelligent, hierarchy-aware deep hybrid model for fine-grained waste classification across 18 categories structured under Wet and Dry waste types. The model integrates MobileNetV3 for semantic feature extraction with GraphSAGE to capture structural relationships among image representations. To address class imbalance and feature sparsity, the architecture incorporates Feature-wise Attention (FWA), Top-K Mixture of Experts (TopK-MoE), and advanced regularization techniques including Dynamic Sparse Training (DST) and Model-Agnostic Meta-Learning (MAML). We introduce a novel Hierarchical Tree Loss function to penalize semantically distant misclassifications by leveraging domain-specific waste hierarchy paths. The proposed framework is trained and evaluated on the WEDR dataset, a curated collection of 360,000 images reflecting real-world waste conditions across Delhi’s major landfill zones. WasteIQNet achieves a peak classification accuracy of 97.87%, demonstrating substantial improvements in both interpretability and generalization. This work contributes a scalable, robust, and deployment-ready solution tailored for real-time smart city waste segregation initiatives.https://ieeexplore.ieee.org/document/11015996/Class imbalance handlingdeep learningdynamic sparse trainingfeature extractiongraph neural networkshierarchical classification |
| spellingShingle | Sakshi Tiwari Snigdha Bisht Kanchan Sharma Intelligent Waste Management Using WasteIQNet With Hierarchical Learning and Meta-Optimization IEEE Access Class imbalance handling deep learning dynamic sparse training feature extraction graph neural networks hierarchical classification |
| title | Intelligent Waste Management Using WasteIQNet With Hierarchical Learning and Meta-Optimization |
| title_full | Intelligent Waste Management Using WasteIQNet With Hierarchical Learning and Meta-Optimization |
| title_fullStr | Intelligent Waste Management Using WasteIQNet With Hierarchical Learning and Meta-Optimization |
| title_full_unstemmed | Intelligent Waste Management Using WasteIQNet With Hierarchical Learning and Meta-Optimization |
| title_short | Intelligent Waste Management Using WasteIQNet With Hierarchical Learning and Meta-Optimization |
| title_sort | intelligent waste management using wasteiqnet with hierarchical learning and meta optimization |
| topic | Class imbalance handling deep learning dynamic sparse training feature extraction graph neural networks hierarchical classification |
| url | https://ieeexplore.ieee.org/document/11015996/ |
| work_keys_str_mv | AT sakshitiwari intelligentwastemanagementusingwasteiqnetwithhierarchicallearningandmetaoptimization AT snigdhabisht intelligentwastemanagementusingwasteiqnetwithhierarchicallearningandmetaoptimization AT kanchansharma intelligentwastemanagementusingwasteiqnetwithhierarchicallearningandmetaoptimization |