Shuyuti, Nur Aini Syakimah Ahmad and Salami, Erfan and Dahari, Mahidzal and Arof, Hamzah and Ramiah, Harikrishnan (2024) Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey. IEEE Access, 12. pp. 31498-31514. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3351858.
Full text not available from this repository.Abstract
With the rapid development of artificial intelligence (AI), especially in machine learning and deep learning technologies, the particle and impurity detection and removal processes employed in many industries have been improved. Particles and impurities of any size, shape and in any condition can be detected using advanced technology in both areas. This paper presents a comprehensive overview of research papers that discuss the application of AI techniques for the detection and removal of particles and impurities. The publications featured in this review were mainly retrieved from the Web of Science (WoS) database, covering the timeframe from 2000 to 2023. This paper also covers the review on the impurity detection and removal specifically in edible bird's nest (EBN). The aim of this paper is to provide a valuable resource for the future development of AI applications in particle and impurity detection and removal technologies that have not been addressed in this study. Through the review and analysis of AI for particle and impurity detection and removal techniques in recent years, this paper includes the following parts: research trend in particle and impurity detection in general and AI methods in particle and impurity detection, applications of AI in particle and impurity detection in related industries including in EBN and AI applications in particle and impurity removal. This review study will offer advantages to researchers engaged in the field of AI with regards to the detection and removal of particles and impurities.
| Item Type: | Article |
|---|---|
| Funders: | Public Service Department of Malaysia |
| Uncontrolled Keywords: | Impurities; Artificial intelligence; Industries; Biomedical imaging; Deep learning; Market research; Manufacturing processes; Machine learning; Radiation detectors; machine learning; deep learning; impurity detection; particle detection; particle removal; edible bird's nest |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Nov 2024 03:04 |
| Last Modified: | 14 Nov 2024 03:04 |
| URI: | http://eprints.um.edu.my/id/eprint/45892 |
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