Hazardous chemicals detection and classification through millimeter wave and machine learning
College
Gokongwei College of Engineering
Department/Unit
Manufacturing Engineering and Management
Document Type
Article
Source Title
Journal of Advanced Computational Intelligence and Intelligent Informatics
Volume
28
Issue
4
First Page
753
Last Page
761
Publication Date
2024
Abstract
This paper demonstrates the effectiveness of integrat- ing computational intelligence to enhance the reliabil- ity of millimeter wave technology as a detection device for hazardous chemicals. The research explores the use of millimeter wave as an efficient and dependable alter- native technology for chemical detection with the aid of machine learning to further improve its reliability and accuracy. This advancement is crucial in enabling security agencies, and authorities to remotely identify hazardous chemicals, minimizing risks to human lives and properties. The millimeter wave relies on natu- ral non-ionizing radiation, which is of low power and considered safe for human exposure. The millimeter wave region used in this study is 77–81 GHz that offers short-pulse transmission capabilities, producing a wide spectrum of frequencies. These short pulses serve as the source for collecting the broadband spectral iden- tity of chemicals, and the subsequent detection is post- processed with machine learning to increase the level of accuracy. The result of this study shows that by us- ing com putational intelligence models such as decision tree, k-nearest neighbor, support vector machine, and random forest, enhances the overall device reliability, and achieves higher detection accuracy based on the re- ceived reflected power. This result is comparable to an X-ray system device.
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Digitial Object Identifier (DOI)
10.20965/jaciii.2024.p0753
Recommended Citation
Ilagan, L. C., & Dadios, E. P. (2024). Hazardous chemicals detection and classification through millimeter wave and machine learning. Journal of Advanced Computational Intelligence and Intelligent Informatics, 28 (4), 753-761. https://doi.org/10.20965/jaciii.2024.p0753
Disciplines
Chemical Engineering
Keywords
Hazardous substances; Millimeter waves; Machine learning; Computational intelligence
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