Student Mobility in Higher Education Explained by Cultural and Technological Awareness in Taiwan

Book chapter[[abstract]]This chapter explores the factors that might influence the intention to study abroad and determines which requirement can be used to attract international students. Fuzzy statistics was used to determine the influencing factors related to student mobility in terms of outbound and inbound study. Exploring students' intentions to study abroad and their readiness may provide a better conception on the issue. The result reveals that study abroad is a better choice for many college students. Since the government provided a menu driven program for universities, various universities have been found to enhance their learning programs to attract more international students. The findings suggest that maintaining cheaper tuition, enhancing culture-related programs, and providing good environment and equipment will attract more international students. However, the factors influencing the international students coming vary in different countries.[[booktype]]紙

Forecasting of Electricity Demand by Hybrid ANN-PSO Models

Developing economies need to invest in energy projects. Because the gestation period of the electric projects is high, it is of paramount importance to accurately forecast the energy requirements. In the present paper, the future energy demand of the state of Tamil Nadu in India, is forecasted using an artificial neural network (ANN) optimized by particle swarm optimization (PSO) and by General Algorithm (GA). Hybrid ANN Models have the potential to provide forecasts that perform well compared to the more traditional modelling approaches. The forecasted results obtained using the hybrid ANN-PSO models are compared with those of the ARIMA, hybrid ANN-GA, ANN-BP and linear models. Both PSO and GA have been developed in linear and quadratic forms and the hybrid ANN models have been applied to five-time series. Amongst all the hybrid ANN models, ANN-PSO models are the best fit models in all the time series based on RMSE and MAPE