Applying Neural Networks in Polygraph Testing

Derevyagin, L. A.; Makarov, V. V.; Molchanov, A. Yu.; Tsurkov, V. I.; Yakovlev, A. N.

doi:10.1134/s1064230722040050

Cited by 1 publication

(2 citation statements)

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“…In a novel avenue of research, Ref. [49] suggests the utilization of machine learning to automate the functions of a polygraph examiner, employing neural network architectures from the scikit-learn library. Specifically, this approach advocates the use of the Voting Classification architecture and a transformer to enhance the efficiency of polygraph testing, align features, and diminish instances of erroneous conclusions regarding the subject's responses.…”

Section: Resultsmentioning

confidence: 99%

“…The overall accuracy rate of neural networks used in the reviewed studies ranged from 60.5% to 100%, with a mean accuracy rate of 88.7%. For example, in [49], the accuracy of binary learning (strong and weak responses to a given question) was found to be: for the plethysmogram, 86.8% ± 3%, for the galvanic skin response, 95.3% ± 3%, and for respiratory rhythms, 72.7% ± 3%. In [55], the accuracies of the models ranged from 61.4% to 71.9%.…”

Section: Accuracy Resultsmentioning

confidence: 99%

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Neural Network Applications in Polygraph Scoring—A Scoping Review

Rad,

Paraschiv,

Kiss

2023

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Polygraph tests have been used for many years as a means of detecting deception, but their accuracy has been the subject of much debate. In recent years, researchers have explored the use of neural networks in polygraph scoring to improve the accuracy of deception detection. The purpose of this scoping review is to offer a comprehensive overview of the existing research on the subject of neural network applications in scoring polygraph tests. A total of 57 relevant papers were identified and analyzed for this review. The papers were examined for their research focus, methodology, results, and conclusions. The scoping review found that neural networks have shown promise in improving the accuracy of polygraph tests, with some studies reporting significant improvements over traditional methods. However, further research is needed to validate these findings and to determine the most effective ways of integrating neural networks into polygraph testing. The scoping review concludes with a discussion of the current state of the field and suggestions for future research directions.

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Section: Resultsmentioning

confidence: 99%

Section: Accuracy Resultsmentioning

confidence: 99%