  Global gender disparity in science is an unsolved problem. Predicting gender
has an important role in analysing the gender gap through online data. We study
this problem within the UK, Malaysia and China. We enhance the accuracy of an
existing gender prediction tools of names that can predict the sex of Chinese
characters and English characters simultaneously and with more precision.
During our research, we found that there is no free gender forecasting tool to
predict an arbitrary number of names. We addressed this shortcoming by
providing a tool that can predict an arbitrary number of names with free
requests. We demonstrate our tool through a number of experimental results. We
show that this tool is better than other gender prediction tools of names for
analysing social problems with big data. In our approach, lists of data can be
dynamically processed and the results of the data can be displayed with a
dynamic graph. We present experiments of using this tool to analyse the gender
disparity in computer science in the UK, Malaysia and China.
