An enhanced control solutions for efficient urban waste management using deep learning algorithms

Authors

  • Gabriel James Department of Computing, Topfaith University, Nigeria
  • Anietie Ekong Department of Computing, Topfaith University, Nigeria
  • Etimbuk Abraham Department of Computer Science, Akwa Ibom State University, Nigeria
  • Enobong Oduobuk Department of Electrical Electronics Engineering, Topfaith University, Nigeria
  • Nseobong Michael Department of Physics, Topfaith University, Nigeria
  • Victor Ufford Department of Mathematics and Computer Science, Ritman University, Ikot Ekpene, Nigeria
  • Oscar Ebong Department of Computer and Robotics Education, University of Uyo, Uyo, Nigeria

Keywords:

Waste management, Deep learning algorithms, IoT, Support vector machine

Abstract

This study focuses on developing an efficient urban waste management system using deep learning algorithms and Internet of Things (IoT) technology. The goal is to improve waste management in Ikot Ekpene municipality by enabling quick disposal responses to prevent environmental pollution. The researchers employed an Object-Oriented Analysis and Design (OOAD) methodology to develop a software system that integrates mobile GIS techniques and IoT sensors to monitor and manage waste. The system was trained using a dataset of 510 images of six classes of waste-related scenarios, with an 84% training set and 16% validation set. The results showed that the garbage bin half-full class had the highest F1 score (above 80%) at a confidence score of 0.4, indicating accurate detection. The average F1 score for all classes was 0.45 at a confidence score of 0.238. The system’s API was designed using Python, supporting both web-based and Android-based applications. The integration of IoT sensors and mobile GIS techniques enables real-time monitoring and efficient waste management. This study demonstrates the potential of deep learning algorithms and IoT technology in improving waste management services, contributing to a cleaner and healthier environment. The system’s effectiveness in detecting waste levels and triggering timely disposal responses can help prevent environmental pollution and enhance the well-being of citizens.

Dimensions

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Published

2024-09-02

How to Cite

An enhanced control solutions for efficient urban waste management using deep learning algorithms. (2024). African Scientific Reports, 3(3), 183. https://doi.org/10.46481/asr.2024.3.3.183

Issue

Volume 3, Issue 3, December 2024 (In Progress)

Section

MATHEMATICAL SCIENCES SECTION
Copyright & Licensing

Copyright (c) 2024 Gabriel James, Anietie Ekong, Etimbuk Abraham, Enobong Oduobuk, Nseobong Michael, Victor Ufford, Oscar Ebong

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

An enhanced control solutions for efficient urban waste management using deep learning algorithms. (2024). African Scientific Reports, 3(3), 183. https://doi.org/10.46481/asr.2024.3.3.183