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- W4385585903 abstract "With the increase in crime, the issue of deception identification has become more significant. The main task at hand is to separate the innocent and culpable groups in the EEG data for lie detection. To categorise EEG data, various techniques have been created; deep belief networks are infrequently used. In order to extract the time and frequency domain characteristics of the data, this study employs a deep learning method that combines a constrained Boltzmann machine with a wavelet. Four RBMs stacked together lead to the formation of a dense belief network. Considering legal and security aspects, analysing deceit behaviour of humans is a major issue. For classification of EEG data, deep belief networks are rarely used. Our work will propose the techniques to perform analysis on EFG data. First, apply a pre-processing technique to utilize only a small fragment of the EEG image instead of the whole image. The model describes a temporal feature map of the EEG signals measured during the lie detection test. It will improve the performance by providing smaller size input data. Different classifiers are used for identifying deception. On CIT-based EEG data, an unstructured deep belief network method has been used. Four constrained Boltzmann machines are stacked and softmax regression is applied at the output layer to create a DBN. Time frequency components that are derived from EEG data are used for learning because they contain more information than unprocessed EEG data. EEG data was captured through the use of a deception detection test." @default.
- W4385585903 created "2023-08-05" @default.
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- W4385585903 date "2023-01-01" @default.
- W4385585903 modified "2023-09-24" @default.
- W4385585903 title "Comprehensive Review of Lie Detection in Subject Based Deceit Identification" @default.
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- W4385585903 doi "https://doi.org/10.1007/978-981-99-3177-4_7" @default.
- W4385585903 hasPublicationYear "2023" @default.
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