| Title |
Deep Consensus Network for Recycling Waste Detection in Smart Cities |
| ID_Doc |
41580 |
| Authors |
Hamza, MA; Mengash, HA; Negm, N; Marzouk, R; Motwakel, A; Zamani, A |
| Title |
Deep Consensus Network for Recycling Waste Detection in Smart Cities |
| Year |
2023 |
| Published |
Cmc-Computers Materials & Continua, 75, 2 |
| DOI |
10.32604/cmc.2023.027050 |
| Abstract |
Recently, urbanization becomes a major concern for developing as well as developed countries. Owing to the increased urbanization, one of the important challenging issues in smart cities is waste management. So, automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management. Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials. This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWORWOD) in Smart Cities. The goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable ones. The proposed DCNWO-RWOD technique involves the design of deep consensus network (DCN) to detect waste objects in the input image. For improving the overall object detection performance of the DCN model, the whale optimization algorithm (WOA) is exploited. Finally, Naive Bayes (NB) classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones. The performance validation of the DCNWO-RWOD technique takes place using the open access dataset. The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures. |
| Author Keywords |
Smart city; waste management; object detection; recycling; deep consensus network |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000992747000007 |
| WoS Category |
Computer Science, Information Systems; Materials Science, Multidisciplinary |
| Research Area |
Computer Science; Materials Science |
| PDF |
https://www.techscience.com/cmc/v75n2/52018/pdf
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