Design of Waste Management System Using Ensemble Neural Networks

Abstract

Waste management is an essential societal issue, and the classical and manual waste auditing methods are hazardous and time-consuming. In this paper, we introduce a novel method for waste detection and classification to address the challenges of waste management. The method uses a collection of deep neural networks to allow for accurate waste detection, classification, and waste size quantification. The trained neural network model is integrated into a mobile-based application for trash geotagging based on images captured by users on their smartphones. The tagged images are then connected to the cleaners’ database, and the nearest cleaners are notified of the waste. The experimental results using publicly available datasets show the effectiveness of the proposed method in terms of detection and classification accuracy. The proposed method achieved an accuracy of at least 90%, which surpasses that reported by other state-of-the-art methods on the same datasets.

Photocatalytic degradation of drugs and dyes using a maching learning approach

“…[52][53][54][55][56][57] Machine learning algorithms help in identifying the most appropriate treatment technologies and strategies for different types of waste, considering factors such as waste composition, environmental impact, and costeffectiveness. 25,[58][59][60][61][62] Because machine learning algorithms are suitable for depicting complex nonlinear processes, they are gradually being adopted to better manage waste and facilitate sustainable environmental development. [63][64][65][66][67] These algorithms can process massive datasets and discover previously hidden patterns and discernible relationships through traditional analytical methods.…”

“…52–57 Machine learning algorithms help in identifying the most appropriate treatment technologies and strategies for different types of waste, considering factors such as waste composition, environmental impact, and cost-effectiveness. 25,58–62…”

“… 24 A novel algorithm has been steadily adopted to better manage waste and encourage environmentally sound expansion because of its demonstrated ability to display complex nonlinear phenomena. 25 Artificial intelligence techniques have also achieved remarkable results in developing forecasts for waste control owing to their capacity to simulate intricate mechanisms. 19,26 Forecasting models for waste control, specifically concerning the catalytic deterioration efficacy of natural contaminants, are crucial for ensuring efficient and sustainable wastewater treatment procedures.…”