Abstract
Deception is a well-known term that involves acting in a way that it leads another person to believe something that is not true. Deception is a very common occurrence in daily life, and many times it becomes a big problem for national security. To cope with this problem, deception detection has gained a lot of attention recently. In this paper, we have tried to come up with a deceit identification system where we have used electroencephalograph (EEG) data collected by performing a concealed information test. To improve the performance of the system, first we have tried to select the optimal subset of the EEG channels using binary particle swarm optimization and secondly performed support vector machine hyper-parameter optimization using continuous version of PSO. The proposed model is validated using EEG dataset. The performance of the proposed system has resulted in increase of accuracy from 76.98 to 96.45% which is a significant improvement. Also, the proposed approach outperformed in terms of sensitivity, specificity, F1-score and G-measure when compared with state-of-the-art models.
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19 August 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00500-024-10084-8
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Acknowledgements
Authors would like to thank Dr. Damoder Reddy Edla form NIT Goa and Dr. Anushree Bablani from IIIT Sri City, Andhra Pradesh, for their support.
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00500-024-10084-8"
The authors Vijayasree Boddu and Prakash Kodali have contributed equally to this work.
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Boddu, V., Kodali, P. RETRACTED ARTICLE: PSO-based optimization for EEG data and SVM for efficient deceit identification. Soft Comput 27, 9835–9843 (2023). https://doi.org/10.1007/s00500-023-08476-3
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DOI: https://doi.org/10.1007/s00500-023-08476-3