Abstract
Lie detection identification has become a significant concern with the rising crime rate. It is a growing research field that is critical in real-time applications. Recognizing lies is an essential technique in human-computer interaction. The primary job is to determine whether the EEG data gathered for the lie detection is guilty or innocent. This paper proposes that EEG channels data for the human brain learn from EEG signals. The 16-channel electrode for the human brain to capture EEG channels data from the concealed information test. EEG channel imbalanced data handled with the SMOTE technique and machine learning methods will aid in the analysis of large amounts of data to lie detection. This system used the SMOTE method to remove the imbalanced EEG channel data set. These balanced data sets are trained to lie detection and increase system performance using several classifiers such as the KKN, DT, LR, RM, and SVM approaches. This algorithm compares performance parameters such as accuracy, F1 score, specificity, precision, and sensitivity. According to the trial outcomes, the SVM technique achieves the most incredible accuracy of 98.3% on the EEG data set.
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Ramesh, M., Edla, D.R. (2023). Lie Detection with the SMOTE Technique and Supervised Machine Learning Algorithms. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_74
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