Deep Learning System for E-Waste Management

Abstract

The deep learning system for e-waste management presented in this proposal is a transformative solution designed to address the escalating challenges of garbage collection and management in urban environments. Rapid urbanization has resulted in increased waste generation, necessitating a more intelligent and efficient approach to e-waste collection and disposal. This system integrates cutting-edge technologies, primarily Artificial Intelligence (AI), to improve e-waste management processes, enhance resource utilization, and contribute to the creation of cleaner and more sustainable urban spaces. Urban areas are experiencing unprecedented growth, leading to a surge in the volume of waste generated daily; as such, traditional waste management systems struggle to cope with this influx, resulting in environmental pollution, compromised public health, and inefficient resource utilization. The proposed deep learning model with accuracy of 83% seeks to revolutionize existing practices by leveraging the capabilities of AI. The aim of this research is to develop a sequential neural network using a Keras and TensorFlow image analysis: a deep learning convolutional neural network (CNN) for e-waste management. The Python programming tool will be used to develop the deep learning model as well as a GUI that will facilitate human–computer interactions. The system will be tested and the result evaluated to assess the functionality and adequacy of the system.