AIWLO-WMO Model Based an Artificial Intelligence to Smart Waste Management for Smart Cities Environment

Abstract

Smart waste management is one of the world's massive challenges, either in the advanced or started economies. Regularly, the technologies of artificial intelligence obtained recognition in presenting different computer techniques to solving smart waste challenge. The smart waste management of specified problems, events and doubts and partial statistics were capable for AI. Although this task did operate lots of findings, very few assessments proved the impact of AI to determine numerous difficulties of intelligent organization of waste. Perfect assessment of waste amount and condition is explained to Smart waste management technique development and model. Currently, heavy quantity of waste resources has become substantial enhanced with increasing population. Appropriate management of waste resources becomes necessary to decrease environmental degradation and recover value of life in smart cities. Smart Waste management supports to gather and heal waste resources from society. Suitable arrangement of waste objects needs the model of automated waste category models based on artificial intelligence and image processing-based methods. The aim of this paper, an automated artificial intelligence with world larva optimization supports waste management and organization (AIWLO-WMO) model is proposed for smart cities environment. The proposed model mainly develops a RetinaNet built object finding segment to recognize the presence of smart waste objects in the views. To better the category operation, Adagrad optimizer has used. To confirming the invented outcome of the AIWLO-WMO method, complete testing is performed on normal dataset and the found rates implied the authority of AIWLO-WMO model throughout other methods with expanded accuracy of 99.62%