Moth Monarch Optimization-Based Deep Belief Network in Deception Detection System

Abstract

Deception is the action of causing a person to believe something, which is known to be lying with the provision of evidence to support such false beliefs with certain intensions. Identification of the deceptive characteristics manually is a challenging problem for the researchers. Thus, an automatic deception detector is necessary to be developed in order to ensure higher accuracy. Accordingly, this paper proposes a novel deception detector method called Moth Monarch optimization-based Deep Belief Neural Network (MMO-DBN). The proposed MMO-DBN classifier undergoes the phases of feature extraction and classification. Initially, the input speech signals are pre-processed to remove the noise present in the signal and subjected to feature extraction to extract the significant features, such as Mel Frequency Cepstral Coefficients (MFCC), Spectral Kurtosis, Spectral Spread, Spectral Centroid, minimum blood pressure, maximum blood pressure, respiration rate, and Tonal Power Ratio. Then, these extracted features are subjected to classification using Deep Belief Neural Network (DBN), which is trained with the proposed Moth Monarch optimization (MMO) algorithm that is the integration of Monarch Butterfly Optimization (MBO) and Moth Search (MS) algorithm. The performance of the proposed MMO-DBN is analyzed using the metrics, namely accuracy, sensitivity, and specificity. The proposed method obtained the higher accuracy, sensitivity, and specificity of 0.984, 0.9836, and 0.9375, respectively that shows the superiority of the proposed MMO-DBN in deception detection.