Identifying the Risk in Lie Detection for Assessing Guilty and Innocent Subjects for Healthcare Applications
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65434-3_2
Reference35 articles.
1. Bablani A, Edla DR, Tripathi D, Venkatanareshbabu K (2018) Subject based deceit identification using empirical mode decomposition. SceinceDirect Procedia Comput Sci 132:32–39. https://doi.org/10.1016/j.procs.2018.05.056
2. Bablani A, Edla DR, Kuppili V (2018) Deceit identification test on EEG data using deep belief network. In: 2018 9th International conference on computing, communication and networking technologies (ICCCNT). https://doi.org/10.1109/ICCCNT.2018.8494124
3. Bablani A et al (2018) Classification of EEG data using k-nearest neighbor approach for concealed information test. Procedia Comput Sci 143:242–249. https://doi.org/10.1016/j.procs.2018.10.392
4. Dodia S, Edla DR, Bablani A, Ramesh D, Kuppili V (2019) An efficient EEG based deceit identification test using wavelet packet transform and linear discriminant analysis. J Neurosci Methods 314:31–40. https://doi.org/10.1016/j.jneumeth.2019.01.007.Epub 2019 Jan 17. PMID: 30660481
5. Bablani A, Edla v, Kuppili V, Ramesh D (2020) A multi stage EEG data classification using k-means and feed forward neural network. 8(3):P718–724. https://doi.org/10.1016/j.cegh.2020.01.008
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