This paper demonstrates the effectiveness of integrating computational intelligence to enhance the reliability of millimeter wave technology as a detection device for hazardous chemicals. The research explores the use of millimeter wave as an efficient and dependable alternative 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 natural 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 identity 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 using computational 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 received reflected power. This result is comparable to an X-ray system device.

1. [1] K. Sakurada and H. Ohta, “No promising antidote 25 years after the Tokyo subway sarin attack: A Review,” Legal Medicine, Vol.47, Article No.101761, 2020. https://doi.org/10.1016/j.legalmed.2020.101761
2. [2] Z. Bielecki et al., “Sensors and systems for the detection of explosive devices – an overview,” Metrology and Measurement Systems, Vol.19, No.1, pp. 3-28, 2012. http://doi.org/10.2478/v10178-012-0001-3
3. [3] S. Prabhakaran, S. S. Rosy, and S. S. Grace, “Detecting and spotting bombs using wireless sensors and expert systems,” Proc. of 2010 Int. Conf. on Communication Control and Computing Technologies, pp. 366-371, 2010. https://doi.org/10.1109/ICCCCT.2010.5670579
4. [4] S. Thomas and A. Devi, “Design and Implementation of Unmanned Ground Vehicle (UGV) for Surveillance and Bomb Detection Using Haptic Arm Technology,” Proc. of 2017 Int. Conf. on Innovations in Green Energy and Healthcare Technologies (IGEHT), 2017. https://doi.org/10.1109/IGEHT.2017.8094106
5. [5] R. Tu et al., “Amine-capped ZnS-Mn²⁺ nanocrystals for fluorescence detection of trace TNT explosive,” Analytical Chemistry, Vol.80, No.9, pp. 3458-3465, 2008. https://doi.org/10.1021/ac800060f
6. [6] J. Yinon, “Peer reviewed: Detection of explosives by electronic noses,” Analytical Chemistry, Vol.75, No.5, pp. 98A-105A, 2003. https://doi.org/10.1021/ac0312460
7. [7] F. Röck, N. Barsan, and U. Weimar, “Electronic nose: current status and future trends,” Chemical Reviews, Vol.108, No.2, pp. 705-725, 2008. https://doi.org/10.1021/cr068121q
8. [8] G. J. Lockwood, S. L. Shope, J. C. Wehlburg, M. M. Selph, and J. Jacobs, “Bomb detection using backscattered X-rays,” SPIE Proc., 1999.
9. [9] National Research Council, “Existing and Potential Standoff Explosive Detection Techniques,” National Academic Press, 2004.
10. [10] National Research Council, “Assessment of Millimeter-Wave and Terahertz Technology for Detection and Identification of Concealed Explosives and Weapons,” The National Academies Press, 2007. https://doi.org/10.17226/11826
11. [11] I. Breiman and A. Cutler, “What is random forest?,” IBM. https://www.ibm.com/topics/random-forest [Accessed November 30, 2023]
12. [12] H. Meng and W. Dou, “Analysis and Design of Radome in Millimeter Wave Band,” Microwave and Millimeter Wave Technologies from Photonic Bandgap Devices to Antenna and Applications, 2010. https://doi.org/10.5772/9054
13. [13] M. A. Aranda, “Simulation of the Propagation of Terahertz Waves in Linear and Nonlinear Isotropic Media,” Worcester Polytechnic Institute, 2018.
14. [14] L. Vanfretti and V. S. N. Arava, “Decision tree-based classification of multiple operating conditions for power system voltage stability assessment,” Int. J. of Electrical Power & Energy Systems, Vol.123, Article No.106251, 2020. https://doi.org/10.1016/j.ijepes.2020.106251
15. [15] B. Taha and A. A. Mohsin, “Classification Based on Decision Tree Algorithm for Machine Learning,” J. for Applied Science and Technology Trends, Vol.2, No.1, pp. 20-28, 2021. https://doi.org/10.38094/jastt20165
16. [16] A. Christmann and I. Steinwart, “Support Vector Machines,” Springer, 2008. https://doi.org/10.1007/978-0-387-77242-4
17. [17] W. Gao, S. Oh, and P. Viswanath, “Demystifying Fixed k-Nearest Neighbor Information Estimators,” IEEE Trans. on Information Theory, Vol.64, No.8, pp. 5629-5661, 2018. https://doi.org/10.1109/TIT.2018.2807481
18. [18] X. Zhou, P. Lu, Z. Zheng, D. Tolliver, and A. Keramati, “Accident prediction accuracy assessment for highway-rail grade crossings using random forest algorithm compared with decision tree,” Reliability Engineering & System Safety, Vol.200, Article No.106931, 2020. https://doi.org/10.1016/j.ress.2020.106931
19. [19] Z. Yang and A. Mani, “Interference Mitigation for AWR/IWR Devices,” Texas Instruments, 2022.
20. [20] T. Bangira et al., “Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water,” Remote Sensing, Vol.11, No.11, Article No.1351, 2019. https://doi.org/10.3390/rs11111351
21. [21] M. Bischoff, L. Owens, A. Cooney, D. T. Petkie, and J. A. Deibel, “Characterization of composite materials using millimeter-wave techniques,” Proc. of 2011 Int. Conf. on Infrared, Millimeter, and Terahertz Waves, 2011. https://doi.org/10.1109/irmmw-THz.2011.6105152
22. [22] R. A. Nugrahaeni and K. Mutijarsa, “Comparative Analysis of Machine Learning, KNN, SVM and Random Forest for Facial Expression Classification,” Proc. of Int. Seminar on Application for Technology of Information and Communication (ISemantic), pp. 163-168, 2016. https://doi.org/10.1109/ISEMANTIC.2016.7873831
23. [23] I. Ahmad, M. Basheri, M. J. Iqbal, and A. Rahim, “Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection,” IEEE Access, Vol.6, pp. 33789-33795, 2018. https://doi.org/10.1109/ACCESS.2018.2841987
24. [24] “scikit-learn.” https://www.scikit-learn.org [Accessed November 29, 2023]