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

Disciplines

Chemical Engineering

Keywords

Hazardous substances; Millimeter waves; Machine learning; Computational intelligence

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