Liu, Jiangang, et al. “Transdermal optical imaging revealed different spatiotemporal patterns of facial cardiovascular activities.” Scientific Reports, vol. 8, no. 1, 2018.
Bae, S., Borac, S., Emre, Y. et al. Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms. Commun Med 2, 40 (2022).
Gupta, Pankaj. (2023). Leveraging Machine Learning and Artificial Intelligence for Fraud Prevention. 10. 47-52.
Constaˆncio AS, Tsunoda DF, Silva HdFN, Silveira JMd, Carvalho DR (2023)
Deception detection with machine learning: A systematic review and statistical analysis. PLoS ONE 18(2): e0281323.
Peleg, D., Ayal, S., Ariely, D., & Hochman, G. (2019). The Lie Deflator – The effect of polygraph test feedback on subsequent (dis)honesty. Judgment and Decision Making, 14(6), 728-738.
Farah, Martha & Hutchinson, J. & Phelps, Elizabeth & Wagner, Anthony. (2014). Functional MRI-based lie detection: Scientific and societal challenges. Nature reviews. Neuroscience. 15. 123-31.
Herbig, Friedo JW, and Anthony Minaar. “Polygraph’s Relationship with Afrocentricity in South African Workplace: Deception Detection Parity, Or Parody?”
International Journal of Criminal Justice Science, vol. 17, no. 1, January -June 2022.
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