Gender Classification
The biographies_EN dataset contains 1000 biographies of literature writers retrieved from the english version of Wikipedia.
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Several fields of study can benefit from a large, structured, and accurate dataset of historical figures. Due to a lack of such a dataset, in this paper, we aim to use machine learning and text mining models to collect, predict, and cleanse online data with a focus on age and gender. We developed a five-step method and inferred birth and death years, binary gender, and occupation from community-submitted data to all language versions of the Wikipedia project.
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Lists of male and female names have been drawn up to determine the gender affiliation of the authors of scientific publications. Feminine Given Names and Masculine Given Names were taken from Wikipedia's free encyclopedia for the listing of the lists. These lists are supplemented by names specific to the research of countries. As a result, the list of male names contains 2515 records and a list of female names - 1461 records. The following was to check the lists for the coincidence of names. 218 names coincided withdrawn from both lists.
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Iris recognition has been an interesting subject for many research studies in the last two decades and has raised many challenges for the researchers. One new and interesting challenge in the iris studies is gender recognition using iris images. Gender classification can be applied to reduce processing time of the identification process. On the other hand, it can be used in applications such as access control systems, and gender-based marketing and so on. To the best of our knowledge, only a few numbers of studies are conducted on gender recognition through analysis of iris images.
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