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.

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis

Abstract: Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of great significance in controlling brucellosis and taking preventive measures. Our human brucellosis incidence data were extracted from Shanxi Provincial Center for Disease Control and Prevention. We used seasonal-trend decomposition using Loess (STL) and monthplot to analyse the seasonal characteristics of human brucellosis in Shanxi Province from 2007 to 2017. The autoregressive integrated moving average (ARIMA) model, a combined model of ARIMA and the back propagation neural network (ARIMA-BPNN), and a combined model of ARIMA and the Elman recurrent neural network (ARIMA-ERNN) were established separately to make predictions and identify the best model. Additionally, the mean squared error (MAE), mean absolute error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the performance of the model. We observed that the time series of human brucellosis in Shanxi Province increased from 2007 to 2014 but decreased from 2015 to 2017. It had obvious seasonal characteristics, with the peak lasting from March to July every year. The best fitting and prediction effect was the ARIMA-ERNN model. Compared with those of the ARIMA model, the MAE, MSE and MAPE of the ARIMA-ERNN model decreased by 18.65, 31.48 and 64.35%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 60.19, 75.30 and 64.35%, respectively. Second, compared with those of ARIMA-BPNN, the MAE, MSE and MAPE of ARIMA-ERNN decreased by 9.60, 15.73 and 11.58%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 31.63, 45.79 and 29.59%, respectively. The time series of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models. This will provide some theoretical support for the prediction of infectious diseases and will be beneficial to public health decision making.

The impact of financial development on environmental quality: evidence from Malaysia

Abstract: This paper examined the impacts of financial development on environmental quality in Malaysia, using the sum of financial access, depth, and efficiency as auxiliary variables for financial development from 1987 to 2020. The autoregressive distributed lag method was used to examine whether a level relationship (long run) existed among the variables. The paper found a long-run relationship among the variables. Financial development, population growth, economic growth, and energy usage positively significantly contribute to environmental degradation in both the short and long run, while squared economic growth significantly enhanced environmental quality in both the short and long run. Hence, environmental carbon Kuznets curve (ECKC) hold in Malaysia.

Wavelet based approach for fusing computed tomography and magnetic resonance images

Abstract: The multimodality medical image fusion plays an important role in clinical applications which can support more accurate information for physicians to diagnose diseases. In this paper, a new fusion scheme for computed tomography and magnetic resonance images based on wavelet analysis is proposed. After the images are decomposed by wavelet transform, the low frequency coefficients are performed with the maximal absolute values followed by verifying their consistency, and the high frequency coefficients are selected by a maximal local variance rule. The resultant image is then reconstructed by using the inverse wavelet transform with the combined wavelet coefficients. The performance of our method is qualitatively and quantitatively compared with some existing fusion approaches. Experimental results show that the proposed method can preserve more useful information and with higher spatial resolution.