Conceptualizing Core Aspects in Circular Economy with Waste Recycling in Smart Cities Based on LSTM and Auto Encoder Approach

Information and communication technology (ICT) enables the smart city to share data with the public and provide superior services to its residents. Smart technology has the ability to greatly improve the efficiency and quality of rubbish collection on a global scale, which is why waste collection is an integral part of smart city services. Garbage is taken out even though it's not full, city services are overused, and petrol is wasted. Garbage collection in smart cities has two major flaws: high prices and low efficiency. Recycling is the answer since it lessens the amount of trash that needs to be disposed of and saves valuable space in landfills. Estimates suggest that humans can produce more rubbish than ever before, despite the fact that recycling rates are on the rise. Creating algorithms that can learn from data and apply that data to new situations is a major goal in machine learning. Preprocessing, feature selection, and model training are the three components of the proposed method. The proposed method employs normalization for preprocessing. It uses a clustering-based approach to extract and select features. The LSTM-AE technique is used to assess the model's efficiency. When compared to conventional approaches like LSTM and GRU, the proposed method performs exceptionally well.