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Deep Consensus Network for Recycling Waste Detection in Smart Cities

Manar Ahmed Hamza1,*, Hanan Abdullah Mengash2, Noha Negm3, Radwa Marzouk2, Abdelwahed Motwakel1, Abu Sarwar Zamani1

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, 16278, AlKharj, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Science and Arts, King Khalid University, Mahayil Asir, 62529,Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email: email

Computers, Materials & Continua 2023, 75(2), 4191-4205. https://doi.org/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 (DCNWO-RWOD) 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, Naïve 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.

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Cite This Article

APA Style
Hamza, M.A., Mengash, H.A., Negm, N., Marzouk, R., Motwakel, A. et al. (2023). Deep consensus network for recycling waste detection in smart cities. Computers, Materials & Continua, 75(2), 4191-4205. https://doi.org/10.32604/cmc.2023.027050
Vancouver Style
Hamza MA, Mengash HA, Negm N, Marzouk R, Motwakel A, Zamani AS. Deep consensus network for recycling waste detection in smart cities. Comput Mater Contin. 2023;75(2):4191-4205 https://doi.org/10.32604/cmc.2023.027050
IEEE Style
M.A. Hamza, H.A. Mengash, N. Negm, R. Marzouk, A. Motwakel, and A.S. Zamani "Deep Consensus Network for Recycling Waste Detection in Smart Cities," Comput. Mater. Contin., vol. 75, no. 2, pp. 4191-4205. 2023. https://doi.org/10.32604/cmc.2023.027050



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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