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All Journal Bulletin of Electrical Engineering and Informatics
Thanh-Long Cung
Hanoi University of Science and Technology
Electrical & Electronics Engineering

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An approach of re-organizing input dataset to enhance the quality of emotion recognition using the bio-signals dataset of MIT Van-Dung Pham; Thanh-Long Cung
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3248

Abstract

The purpose of this paper is to propose an approach of re-organizing input data to recognize emotion based on short signal segments and increase the quality of emotional recognition using physiological signals. MIT's long physiological signal set was divided into two new datasets, with shorter and overlapped segments. Three different classification methods (support vector machine, random forest, and multilayer perceptron) were implemented to identify eight emotional states based on statistical features of each segment in these two datasets. By re-organizing the input dataset, the quality of recognition results was enhanced. The random forest shows the best classification result among three implemented classification methods, with an accuracy of 97.72% for eight emotional states, on the overlapped dataset. This approach shows that, by re-organizing the input dataset, the high accuracy of recognition results can be achieved without the use of EEG and ECG signals.
Improving intrusion detection in SCADA systems using stacking ensemble of tree-based models Duc-Duong Nguyen; Minh-Thuy Le; Thanh-Long Cung
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3334

Abstract

This paper introduces a stacking ensemble model, which combines three single models, to improve intrusion detection in supervisory control and data acquisition (SCADA) systems. The first layer of the proposed model is the combination of random forest, light boosting gradient machine, and eXtreme gradient boosting models. We use an multilayer perceptron (MLP) network as a meta-classifier of the model. The proposed model is optimized and tested on an international dataset (gas pipeline dataset). The tested results show an accuracy of 99.72% with the f1-score of 99.72% for binary classification tasks (attacked or non-attacked detection). For categorical tasks, the detection rates of almost all attack types are higher than 97.55% (except for denial of service (DoS)-95.17%), with an overall accuracy of 99.62%.
Co-Authors Duc-Duong Nguyen Minh-Thuy Le Van-Dung Pham