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Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management

Author

Listed:
  • Mesfer Al Duhayyim

    (Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Heba G. Mohamed

    (Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Mohammed Aljebreen

    (Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia)

  • Mohamed K. Nour

    (Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Mecca 24382, Saudi Arabia)

  • Abdullah Mohamed

    (Research Centre, Future University in Egypt, New Cairo 11845, Egypt)

  • Amgad Atta Abdelmageed

    (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Ishfaq Yaseen

    (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Gouse Pasha Mohammed

    (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

Abstract

Increasing waste generation has become a key challenge around the world due to the dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via the Internet of things (IoT). It is limited to controlling the bad odor of blowout gases and the spreading of overspills by using an IoT-based solution. The inadequate and poor dumping of waste produces radiation and toxic gases in the environment, creating an adversarial effect on global warming, human health, and the greenhouse system. The IoT and deep learning (DL) confer active solutions for real-time data monitoring and classification, correspondingly. Therefore, this paper presents an artificial ecosystem-based optimization with an improved deep learning model for IoT-assisted sustainable waste management, called the AEOIDL-SWM technique. The presented AEOIDL-SWM technique exploits IoT-based camera sensors for collecting information and a microcontroller for processing the data. For waste classification, the presented AEOIDL-SWM technique applies an improved residual network (ResNet) model-based feature extractor with an AEO-based hyperparameter optimizer. Finally, the sparse autoencoder (SAE) algorithm is exploited for waste classification. To depict the enhancements of the AEOIDL-SWM system, a widespread simulation investigation is performed. The comparative analysis shows the enhanced outcomes of the AEOIDL-SWM technique over other DL models.

Suggested Citation

  • Mesfer Al Duhayyim & Heba G. Mohamed & Mohammed Aljebreen & Mohamed K. Nour & Abdullah Mohamed & Amgad Atta Abdelmageed & Ishfaq Yaseen & Gouse Pasha Mohammed, 2022. "Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11704-:d:918125
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    References listed on IDEAS

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    1. Kuruva Lakshmanna & Neelakandan Subramani & Youseef Alotaibi & Saleh Alghamdi & Osamah Ibrahim Khalafand & Ashok Kumar Nanda, 2022. "Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    2. Ayaz Hussain & Umar Draz & Tariq Ali & Saman Tariq & Muhammad Irfan & Adam Glowacz & Jose Alfonso Antonino Daviu & Sana Yasin & Saifur Rahman, 2020. "Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach," Energies, MDPI, vol. 13(15), pages 1-22, August.
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    Cited by:

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