Improved Particle Swarm Optimization with Deep Learning-Based Municipal Solid Waste Management in Smart Cities

• Published in: RGSA : Revista de Gestão Social e Ambiental Vol. 17; no. 4; pp. e03561 - 20
• Main Authors: Udayakumar, R., Elankavi, R., R. Vimal, V., Sugumar, R.
• Format: Journal Article
• Language: English
• Published: São Paulo Centro Universitário da FEI, Revista RGSA 27.06.2023
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Cities; Classification; Deep learning; Developed countries; Genetic algorithms; Interdisciplinary subjects; Internet of Things; Machine learning; Municipal solid waste; Municipal waste management; Neural networks; Object recognition; Optimization techniques; Particle swarm optimization; Smart cities; Solid waste management; Solid wastes; Support vector machines; Sustainability management; Sustainable waste management; Tuning; Waste management; Waste materials
• ISSN: 1981-982X; 1981-982X
• DOI: 10.24857/rgsa.v17n4-022

Objectives: The Internet of Things (IoT) framework is crucial for improving monitoring applications for smart cities and controlling municipal operations in real time. The most significant issue with applications to smart cities has been the handling of solid waste, which may have negative consequences on the health and well-being of people. Waste management has become a problem that developing and developed nations must face. The management of solid waste is a significant and exciting issue that affects habitats all around the world. Thus, it is necessary to create an efficient method to eliminate these issues or, at the very least, reduce them to a manageable level.   Theoretical framework: This work proposed an Improved Particle Swarm Optimization with Deep Learning-based Municipal Solid Waste Management (IPSODL-MSWM) in smart cities.   Methods: The IPSODL-MSWM approach aims to identify various types of solid waste materials and enable sustainable waste management. A Single Shot Detection (SSD) model enables efficient object detection in the IPSODL-MSWM paradigm. Then, feature vectors were generated using the MobileNetV2 model based on a deep Convolutional Neural Network (CNN). IPSO has been obtained by using a hybrid Genetic Algorithm (GA) and PSO algorithm.   Results and Conclusion: The IPSODL method has been employed for automatic hyperparameter tuning since manual trial-and-error hyperparameter tuning is time-consuming.   Implications of the research: The IPSODL-MSWM approach uses Support Vector Machine (SVM) for accurate municipal excess categorization in this work. This implies sustainable waste management model for better smart city development.   Originality/value: With an optimal accuracy of 99.45%, many simulations show the IPSODL-MSWM model's enhanced capability for classification.