Jiangxi University of Finance and Economics

About: Jiangxi University of Finance and Economics is a education organization based out in Nanchang, China. It is known for research contribution in the topics: Fuzzy logic & China. The organization has 2865 authors who have published 3556 publications receiving 41567 citations.

A Novel Concealed Information Test Method Based on Independent Component Analysis and Support Vector Machine

Abstract: The concealed information test (CIT) has drawn much attention and has been widely investigated in recent years. In this study, a novel CIT method based on denoised P3 and machine learning was proposed to improve the accuracy of lie detection. Thirty participants were chosen as the guilty and innocent participants to perform the paradigms of 3 types of stimuli. The electroencephalogram (EEG) signals were recorded and separated into many single trials. In order to enhance the signal noise ratio (SNR) of P3 components, the independent component analysis (ICA) method was adopted to separate non-P3 components (ie, artifacts) from every single trial. In order to automatically identify the P3 independent components (ICs), a new method based on topography template was proposed to automatically identify the P3 ICs. Then the P3 waveforms with high SNR were reconstructed on Pz electrodes. Second, the 3 groups of features based on time,frequency, and wavelets were extracted from the reconstructed P3 waveforms. Finall...

Hedges or safe havens—revisit the role of gold and USD against stock: a multivariate extended skew-t copula approach

Abstract: Our paper concerns the question of whether there exist hedge assets during extreme market conditions, which has become increasingly important since the recent financial crisis. This paper develops a novel extended skew-t copula model to examine the effectiveness of gold and US dollar (USD) as hedge or safe haven asset against stock prices for seven developed markets over the 2000–2013 period. Our results indicate the existence of skewness and heavy/thin tails in the distributions of all three types of assets in most of the developed markets, lending support to the employment of flexible distributions to evaluate the tail dependences among assets. We find that USD is preferred to gold as a hedge asset during normal market conditions, while both assets can serve as safe haven assets for most countries when stock markets crash. Our simultaneous analysis of the three assets advises against a joint hedge strategy of gold and USD due to the high tail dependence between them during extreme market conditions. Thi...

Fingerprint matching based on extreme learning machine

Abstract: Considering fingerprint matching as a classification problem, the extreme learning machine (ELM) is a powerful classifier for assigning inputs to their corresponding classes, which offers better generalization performance, much faster learning speed, and minimal human intervention, and is therefore able to overcome the disadvantages of other gradient-based, standard optimization-based, and least squares-based learning techniques, such as high computational complexity, difficult parameter tuning, and so on. This paper proposes a novel fingerprint recognition system by first applying the ELM and Regularized ELM (R-ELM) to fingerprint matching to overcome the demerits of traditional learning methods. The proposed method includes the following steps: effective preprocessing, extraction of invariant moment features, and PCA for feature selection. Finally, ELM and R-ELM are used for fingerprint matching. Experimental results show that the proposed methods have a higher matching accuracy and are less time-consuming; thus, they are suitable for real-time processing. Other comparative studies involving traditional methods also show that the proposed methods with ELM and R-ELM outperform the traditional ones.