IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i18p11704-d918125.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/18/11704/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/18/11704/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. Jiang Yao & Zhiqiang Wang & Chunhui Liu & Guichen Huang & Qingbo Yuan & Kai Xu & Wenhui Zhang, 2022. "Detection Method of Crushing Mouth Loose Material Blockage Based on SSD Algorithm," Sustainability, MDPI, vol. 14(21), pages 1-16, November.

    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. Aoqi Xu & Mehdi Darbandi & Danial Javaheri & Nima Jafari Navimipour & Senay Yalcin & Anas A. Salameh, 2023. "The Management of IoT-Based Organizational and Industrial Digitalization Using Machine Learning Methods," Sustainability, MDPI, vol. 15(7), pages 1-28, March.
    2. Sehrish Munawar Cheema & Abdul Hannan & Ivan Miguel Pires, 2022. "Smart Waste Management and Classification Systems Using Cutting Edge Approach," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    3. Vladimir Simic & Ali Ebadi Torkayesh & Abtin Ijadi Maghsoodi, 2023. "Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest recursive feature elimination algorithm," Annals of Operations Research, Springer, vol. 328(1), pages 1105-1150, September.
    4. Shaik Vaseem Akram & Rajesh Singh & Anita Gehlot & Mamoon Rashid & Ahmed Saeed AlGhamdi & Sultan S. Alshamrani & Deepak Prashar, 2021. "Role of Wireless Aided Technologies in the Solid Waste Management: A Comprehensive Review," Sustainability, MDPI, vol. 13(23), pages 1-31, November.
    5. Abdul Rehman & Muhammad Ahmed Qureshi & Tariq Ali & Muhammad Irfan & Saima Abdullah & Sana Yasin & Umar Draz & Adam Glowacz & Grzegorz Nowakowski & Abdullah Alghamdi & Abdulaziz A. Alsulami & Mariusz , 2021. "Smart Fire Detection and Deterrent System for Human Savior by Using Internet of Things (IoT)," Energies, MDPI, vol. 14(17), pages 1-30, September.
    6. Nurul Hamizah Mohamed & Samir Khan & Sandeep Jagtap, 2023. "Modernizing Medical Waste Management: Unleashing the Power of the Internet of Things (IoT)," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    7. Jae Hong Park & Phil Goo Kang & Eunseok Kim & Tae Woo Kim & Gahee Kim & Heejeong Seok & Jinwon Seo, 2021. "Introduction of IoT-Based Surrogate Parameters in the Ex-Post Countermeasure of Industrial Sectors in Integrated Permit Policy," Sustainability, MDPI, vol. 13(23), pages 1-22, December.
    8. Sabbir Ahmed & Sameera Mubarak & Jia Tina Du & Santoso Wibowo, 2022. "Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
    9. Abdallah Namoun & Ali Tufail & Muhammad Yasar Khan & Ahmed Alrehaili & Toqeer Ali Syed & Oussama BenRhouma, 2022. "Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges," Sustainability, MDPI, vol. 14(20), pages 1-32, October.
    10. Shekaina Justin & Wafaa Saleh & Maha M. A. Lashin & Hind Mohammed Albalawi, 2023. "Design of Metaheuristic Optimization with Deep-Learning-Assisted Solar-Operated On-Board Smart Charging Station for Mass Transport Passenger Vehicle," Sustainability, MDPI, vol. 15(10), pages 1-16, May.

    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:14:y:2022:i:18:p:11704-:d:918125. 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.