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

G. G. James, A. E. Okpako, C. Ituma & J. E. Asuquo, “Development of hybrid intelligent based information retrieval technique”, Int. J. Comput. Appl. 184 (2022) 1. https://doi.org/10.5120/ijca2022922401.

O. A. S. Carpinteiro, I. Lima, J. M. C. Assis, A. C. Z. De Souza, E. M. Moreira & C. A. M. Pinheiro, “A neural model in anti-spam systems”, in Artificial Neural Networks – ICANN 2006, vol. 4132, S. Kollias, A. Stafylopatis, W. Duch, and E. Oja, Eds., in Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin Heidelberg 4132 (2006) 847. https://doi.org/10.1007/11840930 88.

G. G. James, U. A. Umoh, U. G. Inyang & O. M. Ben, “File allocation in a distributed processing environment using Gabriel’s allocation models”, Int. J. Eng. Tech. Math. 5 (2012) 56. https://www.researchgate.net/publication/380365618 File Allocation in a Distributed Processing Environment Using Gabriel’s Allocation Model.

C. Ituma, G. G. James & F. U. Onu, “A neuro-fuzzy based document tracking & classification system”, Int. J. Eng. Appl. Sci. Technol. 4 (2020) 414. https://doi.org/10.33564/IJEAST.2020.v04i10.075.

C. Ituma, G. G. James & F. U. Onu, “Implementation of intelligent document retrieval model using neuro-fuzzy technology”, Int. J. Eng. Appl. Sci. Technol. 4 (2020) 65. https://doi.org/10.33564/IJEAST.2020.v04i10.013.

T. Subramanya, D. Harutyunyan & R. Riggio, “Machine learning-driven service function chain placement and scaling in MEC-enabled 5G networks”, Comput. Netw. 166 (2020) 106980. https://doi.org/10.1016/j.comnet.2019.106980.

M. Rosén & J. E. Gustafsson, “Is computer availability at home causally related to reading achievement in grade 4? A longitudinal difference in different approaches to IEA data from 1991 to 2006”, Large-Scale Assess. Educ. 4 (2016) 5. https://doi.org/10.1186/s40536-016-0020-8.

G. G. James, G. J. Ekanem, E. A. Okon & O. M. Ben, “The design of e-cash transfer system for modern bank using generic algorithm”, Int. J. Sci. Technol. Res. 9 (2012) 234. https://www.researchgate.net/publication/380433463_THE_DESIGN_OF_E-CASH_TRANSFER_SYSTEM_FOR_MODERN_BANK_USING_GENERICALGORITHM#-fullTextFileContent.

M. El-Fadel, A. N. Findikakis & J. O. Leckie, “Environmental impacts of solid waste landfilling”, J. Environ. Manage 50 (1997) 25. https://doi.org/10.1006/jema.1995.0131.

S. Bigirimana, “Managing waste through the internet of things (IoT)”, in Proceedings of the EAI International Conference for Research, Innovation and Development for Africa, Victoria Falls, Zimbabwe: EAI, 2018. https://doi.org/10.4108/eai.20-6-2017.2270818.

L. Huang, X. Dong & T. Edward, “A scalable deep learning platform for identifying geological features from seismic attributes”, The Leading Edge, Jan. 2017. https://mc.manuscriptcentral.com/tle.https://doi.org/10.1190/tle360302491.

E. Mati Asefa, K. Bayu Barasa & D. Adare Mengistu, “Application of geographic information system in solid waste management”, in Geographic Information Systems and Applications in Coastal Studies, Y. Zhang and Q. Cheng, Eds., IntechOpen, 2022. https://doi.org/10.5772/intechopen.103773.

D. A. Saldana, L. Starck, P. Mougin, B. Rousseau & B. Creton, “Prediction of flash points for fuel mixtures using machine learning and a novel equation”, Energy Fuels 27 (2013) 3811. https://doi.org/10.1021/ef4005362.

N. Kundariya et al., “A review on integrated approaches for municipal solid waste for environmental and economical relevance: Monitoring tools, technologies, and strategic innovations”, Bioresour. Technol. 342 (2021) 125982. https://doi.org/10.1016/j.biortech.2021.125982.

R. Boutaba, N. Shahriar, M. A. Salahuddin & N. Limam, “Managing virtualized networks and services with machine learning”, in Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning, 1st ed., N. Zincir-Heywood, M. Mellia & Y. Diao, Eds., Wiley, 2021, pp. 68. https://doi.org/10.1002/9781119675525.ch3.

D. E. Mohamed-Hussein, “A survey on sentiment analysis challenges”, J. King Saud Univ. - Eng. Sci. 1 (2016) 34. [Online]. www.ksu.edu.sa.

S. Akbulut, I. Balikci-Cicek & C. Colak, “Classification of breast cancer on the strength of potential risk factors with boosting models: a public health informatics application”, Med. Bull. Haseki 60 (2022) 196. https://doi.org/10.4274/haseki.galenos.2022.8440.

H. Chen, N. Wang, X. Du, K. Mei, Y. Zhou & G. Cai, “Classification prediction of breast cancer based on machine learning”, Comput. Intell. Neurosci. 23 (2023) 9. https://doi.org/10.1155/2023/6530719.

S. Bigirimana, “Managing waste through the internet of things (IoT)”, in Proceedings of the EAI International Conference for Research, Innovation and Development for Africa, Victoria Falls, Zimbabwe: EAI 2 (2018) 67. https://doi.org/10.4108/eai.20-6-2017.2270818.

S. Thaseen-Ikram, V. Mohanraj, S. Ramachandran & A. Balakrishnan, “An intelligent waste management application using iot and a genetic algorithm–fuzzy inference system”, Appl. Sci. 13 (2023) 3943. https://doi.org/10.3390/app13063943.

N. G. Resmi, A. Shajan, J. Jose, J. P. George & M. Harikrishnan, “Solid waste tracking and route optimization using geotagging and k-means clustering”, Int. J. Appl. Eng. Res. 16 (2021) 633. https://doi.org/10.1016/j.comnet.2019.106980.

M. W. Rahman, R. Islam, A. Hasan, N. I. Bithi, Md. M. Hasan & M. M. Rahman, “Intelligent waste management system using deep learning with IoT”, J. King Saud Univ. - Comput. Inf. Sci. 34 (2022) 2072. https://doi.org/10.1016/j.jksuci.2020.08.016.

E. G. Chukwu, G. G. James, M. E. Benson-Emenike & N. A. Michael, “Observed and evaluated service quality on patients waiting time of university of Uyo teaching hospital using queuing models”, International Journal of Innovative Science and Research Technology 8 (2023) 2094. https://www.researchgate.net/profile/Mercy-Benson-Emenike/publication/375225789_Observed_and_Evaluated_Service_Quality_on_Patients_Waiting_Time_of_University_of_UYO_Teaching_Hospital_using_Queuing_Models/links/6543b138b6233776b74441-c1/Observed-and-Evaluated-Service-Quality-on-Patients-Waiting-Time-of-University-of-UYO-Teaching-Hospital-using-Queuing-Models.pdf.

A. D. Munguı́a-López, V. M. Zavala, J. E. Santibañez-Aguilar & J. M. Ponce-Ortega, “Optimization of municipal solid waste management using a coordinated framework”, Waste Manag. 115 (2020) 15. https://doi.org/10.1016/j.wasman.2020.07.006.

J. Wang, X. Wu & C. Zhang, “Support vector machines based on K-means clustering for real-time business intelligence systems”, Int. J. Bus. Intell. Data Min. 1 (2005) 54. https://doi.org/10.1504/IJBIDM.2005.007318.

M. Bakare et al., “Etiological explanation, treatability and preventability of childhood autism: a survey of Nigerian healthcare workers’ opinion”, Ann. Gen. Psychiatry 8 (2009) 6. https://doi.org/10.1186/1744-859X-8-6.

A. J. Russell et al., “The mental health of individuals referred for assessment of autism spectrum disorder in adulthood: A clinic report”, Autism 20 (2016) 623. https://doi.org/10.1177/1362361315604271.

G. James, I. J. Umoren, S. Inyang, S. Inyang & O. Aloysius, “Analysis of support vector machine and random forest models for classification of the impact of technostress in covid and post-covid era”, J. Niger. Soc. Phys. Sci. 6 (2024) 2102. https://doi.org/10.46481/jnsps.2024.2102.

T. C. Carabel, N. O. Martı́nez, S. A. Garcia & I. F. Suárez, “Technostress in Communication and Technology Society: Scoping Literature Review from the Web of Science”, Arch. Prev. Riesgos Laborales 21 (2018) 234. https://doi.org/10.12961/aprl.2018.21.01.4.

Anietie Ekong, Immaculata Attih, Gabriel James & Unyime Edet, “Effective classification of diabetes mellitus using support vector machine algorithm”, Res. J. Sci. Technol. 4 (2024) 34. https://www.rejost.com.ng/index.php/home.

C. I. Ituma, S. O. Iwok & G. G. James, “A model of intelligent packet switching in wireles communication networks”, Int. J. Sci. Eng. Res. 11 (2020) 64. http://www.ijser.org.

C. I. Ituma, S. O. Iwok & G. G. James, “Implementation of an optimized packet switching parameters in wireless communication networks”, Int. J. Sci. Eng. Res. 11 (2020) 58. http://www.ijser.org.

M. Fitzgerald, “The Clinical Gestalts of Autism: Over 40 years of Clinical Experience with Autism”, in Autism - Paradigms, Recent Research and Clinical Applications, M. Fitzgerald and J. Yip, Eds., InTech 23 (2017) 218. https://doi.org/10.5772/65906.

A. Grover & G. Purvis, “Technostress: Technological Antecedents and Implications”, MIS Q. 35 (2011) 831. https://doi.org/10.2307/41409963.

J. H. Kim, “Factors Associated with Smartphone Addiction Tendency in Korean Adolescents”, Int. J. Environ. Res. Public. Health 18 (2021) 11668. https://doi.org/10.3390/ijerph182111668.

A. Dimoka, P. A. Pavlou & F. D. Davis, “Research Commentary —NeuroIS: The Potential of Cognitive Neuroscience for Information Systems Research”, Inf. Syst. Res. 22 (2011) 687. https://doi.org/10.1287/isre.1100.0284.

J. Breaugh, “Too Stressed To Be Engaged? The Role of Basic Needs Satisfaction in Understanding Work Stress and Public Sector Engagement”, Public Pers. Manag. 50 (2021) 108. https://doi.org/10.1177/0091026020912516.

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

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

Similar Articles

21-30 of 34

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)