Future for Cleaner Environment: A Hybrid Machine Learning Approach for Intelligent Waste Management

Intelligent waste management is becoming increasingly vital in addressing the challenges posed by escalating waste volumes and environmental concerns. The standard methods for handling waste are frequently economical and ineffective thus sophisticated technology alternatives must be used. Using the strengths of both convolutional & recurrent neural networks, this research suggests a hybrid machine learning strategy for intelligent trash management. The study highlights the opportunity of machine learning to transform the effective management of waste and also clarifies the shortcomings of traditional approaches through an extensive analysis of current approaches. Specifically, the focus is on RNN-based algorithms for sequential data processing. The experimental evaluation demonstrates the superiority of RNN-based architectures, particularly gated recurrent units (GRU) and bidirectional GRU models, in classifying waste images into distinct categories. These models exhibit robust performance and outperform other RNN and CNN algorithms, validating the efficacy of the proposed hybrid approach.