## 2 Literature Review
The best input variables were selected by comparing various environmental and socioeconomic factors. Based on the performance criteria of the ANN structures, the result was chosen. Therefore, the multilayer perception approach gives better results when compared to the existing method. [PERSON] et al. [29] proposed waste management solutions for Tehran Municipal Office, Iran, by assessing the energy flow using ANN. Recycling wastes provided an entry point for energy challenges. Authors used the Levenberg-Marquardt (LM) training algorithm and 5-7-7-11 topology for predicting recycled items, which therefore influence environmental emissions. They achieved better performance with accuracy, which ranges between 0.92 and 0.978. [PERSON] et al. [30] analyzed the solid waste ignition in Taiwan using a multilayer perceptron for the dry and wet wastes, and the heating value was low, which is the crucial parameter for incinerator capacity. [PERSON] et al. [31] developed a model for producing energy from the solid waste collected in European and Balkan countries. Sensitivity analyses and correlation techniques were used to enhance their model. [PERSON] et al. [32] proposed a method for sustaining the economy through waste recycling, and they discovered the most efficient method using deep convolutional neural networks. The major disadvantage of such systems is slow prediction. Therefore, to enhance the performance of the projection, dense block skip connections were modified. In a layered network, the number of parameters was reduced to 3 million out of 7 million using the RecycleNet model. By incorporating advanced decision support techniques into smart cities, the authors [33] implemented a useful waste disposal model.For providing the evidence to the authorities as well as monitoring the process periodically, surveillance cameras were incorporated into this model. Solid wastes such as unused electronic and electric equipment are identified as an essential aspect of e-waste disposal because they contain toxic chemicals. In Malaysia, they successfully developed a smart system to collect household waste electronics [34]. [PERSON] et al. [35] proposed two modules: the first module helps to monitor the volume of waste, and the second module implements dynamic scheduling of vehicles. The authors [36] categorized the waste as hazardous waste, recyclable waste, and compostable waste. Different CNN-based classifier techniques such asDenseNet-121, VGG-16, MobileNet V2, and ResNet-50 for 9200 were applied and attained a maximum accuracy of 94.86% from the ResNet-50 classifier [36]. [PERSON] et al. [37] presented a technique to classify the wastes after eating food by using the image classification model so that they can be disposed of in separate containers. A CompostNet model was deployed, which was a CNN for image classification. This application sorts the food waste according to biodegradable, recyclable, and non-biodegradable wastes and therefore can be a potential game-changer in implementing proper food disposal techniques. The limitations of computational cost and in-field application were also considered, and hence AlexNet was deployed. [PERSON] et al. [38] developed a classification model by integrating three pre-trained CNN models together for incredible accuracy. [PERSON] et al. [39] developed a multilayer hybrid deep learning system that efficiently disposes of unwanted objects scattered by the public in open space. CNN and MLP are used for extracting and consolidating the features to classify waste as recyclable or not. The cameras in the open spaces then detect the garbage, identify the type and disposal procedure, and alert the authorities accordingly to maintain a clean environment. [PERSON] et al. [40] mapped and estimated the amount of agricultural plastic waste by using satellite images to capture the images. The areas that are pollution-free are differentiated from the ones that have plastic pollution using support vector machines (SVMs). [PERSON] et al.