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

Mesfer Al Duhayyim (), Heba G. Mohamed, Mohammed Aljebreen, Mohamed K. Nour, Abdullah Mohamed, Amgad Atta Abdelmageed, Ishfaq Yaseen and Gouse Pasha Mohammed
Additional contact information
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

Sustainability, 2022, vol. 14, issue 18, 1-17

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.

Keywords: sustainability; artificial intelligence; Internet of things; waste classification; quality assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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