IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i9p7321-d1135099.html
   My bibliography  Save this article

Modified Cuttlefish Swarm Optimization with Machine Learning-Based Sustainable Application of Solid Waste Management in IoT

Author

Listed:
  • Mesfer Al Duhayyim

    (Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia)

Abstract

The internet of things (IoT) paradigm roles an important play in enhancing smart city tracking applications and managing city procedures in real time. The most important problem connected to smart city applications has been solid waste management, which can have adverse effects on society’s health and environment. Waste management has developed a challenge faced by not only evolving nations but also established and developed counties. Solid waste management is an important and stimulating problem for environments across the entire world. Therefore, there is the need to develop an effective technique that will remove these problems, or at least decreases them to a minimal level. This study develops a modified cuttlefish swarm optimization with machine learning-based solid waste management (MCSOML-SWM) in smart cities. The MCSOML-SWM technique aims to recognize different categories of solid wastes and enable smart waste management. In the MCSOML-SWM model, a single shot detector (SSD) model allows effectual recognition of objects. Then, a deep convolutional neural network-based MixNet model was exploited to produce feature vectors. Since trial-and-error hyperparameter tuning is a tedious process, the MCSO algorithm was applied for automated hyperparameter tuning. For accurate waste classification, the MCSOML-SWM technique applies support vector machine (SVM) in this study. A comprehensive set of simulations demonstrate the improved classification performance of the MCSOML-SWM model with maximum accuracy of 99.34%.

Suggested Citation

  • Mesfer Al Duhayyim, 2023. "Modified Cuttlefish Swarm Optimization with Machine Learning-Based Sustainable Application of Solid Waste Management in IoT," Sustainability, MDPI, vol. 15(9), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7321-:d:1135099
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/9/7321/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/9/7321/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Meena Malik & Sachin Sharma & Mueen Uddin & Chin-Ling Chen & Chih-Ming Wu & Punit Soni & Shikha Chaudhary, 2022. "Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models," Sustainability, MDPI, vol. 14(12), pages 1-18, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alejandro Valencia-Arias & Juana Ramírez Dávila & Wilmer Londoño-Celis & Lucia Palacios-Moya & Julio Leyrer Hernández & Erica Agudelo-Ceballos & Hernán Uribe-Bedoya, 2024. "Research Trends in the Use of the Internet of Things in Sustainability Practices: A Systematic Review," Sustainability, MDPI, vol. 16(7), pages 1-23, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sujal Goel & Anannya Mishra & Garima Dua & Vandana Bhatia, 2024. "SEFWaM–deep learning based smart ensembled framework for waste management," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 22625-22653, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7321-:d:1135099. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.