Modified Cuttlefish Swarm Optimization with Machine Learning-Based Sustainable Application of Solid Waste Management in IoT

The internet of things (IoT) paradigm roles an important play in enhancing smart city tracking applications and managing procedures real time. most problem connected to has been solid waste management, which can have adverse effects on society’s health environment. Waste management developed a challenge faced by not only evolving nations but also established counties. Solid is stimulating for environments across the entire world. Therefore, there need develop effective technique that will remove these problems, or at least decreases them minimal level. This study develops modified cuttlefish swarm optimization with machine learning-based (MCSOML-SWM) cities. MCSOML-SWM aims recognize different categories wastes enable management. In model, single shot detector (SSD) model allows effectual recognition objects. Then, deep convolutional neural network-based MixNet was exploited produce feature vectors. Since trial-and-error hyperparameter tuning tedious process, MCSO algorithm applied automated tuning. For accurate classification, applies support vector (SVM) this study. A comprehensive set simulations demonstrate improved classification performance maximum accuracy 99.34%.