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Pitakaso R, Srichok T, Khonjun S, Golinska-Dawson P, Gonwirat S, Nanthasamroeng N, Boonmee C, Jirasirilerd G, Luesak P. Artificial Intelligence in enhancing sustainable practices for infectious municipal waste classification. WASTE MANAGEMENT (NEW YORK, N.Y.) 2024; 183:87-100. [PMID: 38735094 DOI: 10.1016/j.wasman.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/26/2024] [Accepted: 05/04/2024] [Indexed: 05/14/2024]
Abstract
This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environmental risks. Leveraging advanced artificial intelligence techniques, we prioritize infectious waste categorization and optimization, integrating metaheuristics into optimization methods to create a robust dual-ensemble framework. Our model, the "Enhanced Artificial Intelligence for Infectious Municipal Waste Classification System," combines ensemble image segmentation methods and diverse convolutional neural network models. Innovative geometric image augmentation enhances model robustness, diversifies training data, and improves accuracy across waste types. A pivotal aspect is the integration of a reinforcement learning-differential evolution algorithm as a decision fusion strategy, optimizing classification by harmonizing outputs from ensemble methods and convolutional neural network models. Computational results, using a newly collected dataset, demonstrate our model's accuracy exceeding 96.54% while using the existing solid waste dataset we achieve the accuracy of 97.81%, outperforming advanced approaches by margins ranging from 2.02% to 8.80%. This research significantly advances sustainable waste management, showcasing artificial intelligence's transformative potential in addressing intricate waste challenges. It establishes a foundational framework prioritizing efficiency, effectiveness, and sustainability for future waste management solutions. Acknowledging the importance of diverse datasets, customization for urban contexts, and practical integration into existing infrastructures, our work contributes to the broader discourse on the role of artificial intelligence in evolving waste management practices.
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Affiliation(s)
- Rapeepan Pitakaso
- Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand.
| | - Thanatkij Srichok
- Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand.
| | - Surajet Khonjun
- Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand.
| | - Paulina Golinska-Dawson
- Institute of Logistics, Poznan University of Technology, Jacka Rychlewskiego 2 Street, 60-965 Poznan, Poland.
| | - Sarayut Gonwirat
- Department of Computer Engineering and Automation Kalasin University, Kalasin 46000, Thailand.
| | - Natthapong Nanthasamroeng
- Artificial Intelligence Optimization SMART Laboratory, Engineering Technology Department, Faculty of Industrial Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand.
| | - Chawis Boonmee
- Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
| | - Ganokgarn Jirasirilerd
- Department of Industrial and Environmental Management Engineering, Faculty of Liberal Arts and Sciences, Sisaket Rajabhat University, Sisaket 33000, Thailand.
| | - Peerawat Luesak
- Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai 57120, Thailand.
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Mosallanezhad B, Gholian-Jouybari F, Cárdenas-Barrón LE, Hajiaghaei-Keshteli M. The IoT-enabled sustainable reverse supply chain for COVID-19 Pandemic Wastes (CPW). ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023; 120:105903. [PMID: 36712822 PMCID: PMC9874057 DOI: 10.1016/j.engappai.2023.105903] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 12/03/2022] [Accepted: 01/21/2023] [Indexed: 05/29/2023]
Abstract
Supply chains have been impacted by the COVID-19 pandemic, which is the most recent worldwide disaster. After the world health organization recognized the latest phenomena as a pandemic, nations became incapacitated to provide the required medical supplies. In the current situation, the world seeks an essential solution for COVID-19 Pandemic Wastes (CPWs) by pushing the pandemic to a stable condition. In this study, the development of a supply chain network is contrived for CPWs utilizing optimization modeling tools. Also, an IoT platform is devised to enable the proposed model to retrieve real-time data from IoT devices and set them as the model's inputs. Moreover, sustainability aspects are appended to the proposed IoT-enabled model considering its triplet pillars as objective functions. A real case of Puebla city and 15 experiments are used to validate the model. Furthermore, a combination of metaheuristic algorithms utilized to solve the model and also seven evaluation indicators endorse the selection of efficient solution approaches. The evaluation indicators are appointed as the inputs of statistical and multicriteria decision-making hybridization to prioritize the algorithms. The result of the Entropy Weights method and Combined Compromise Solution approach confirms that MOGWO has better performance for the medium-sizes, case study and an overall view. Also, NSHHO outclasses the small-size and large-size experiments.
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Affiliation(s)
- Behzad Mosallanezhad
- Department of Industrial Engineering, School of Engineering and Science, Tecnologico de Monterrey, Puebla, Mexico
| | - Fatemeh Gholian-Jouybari
- Department of Industrial Engineering, School of Engineering and Science, Tecnologico de Monterrey, Puebla, Mexico
| | | | - Mostafa Hajiaghaei-Keshteli
- Department of Industrial Engineering, School of Engineering and Science, Tecnologico de Monterrey, Puebla, Mexico
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