There are three main disadvantages including time-consuming task, high cost and complex detection procedures in the semen quality measurement to heighten the roosters' reproductive capability in breeder flocks. Another solution is to select the breeder roosters with fine phenotypic characteristics by humans, while it is also a considerably labor-intensive task and even increases the risk of zoonoses at a poultry farm. To solve these problems, this paper proposes a strategy that effective promoting factors applied to Progressive Multi-Granularity (PMG) network ensures the accuracy of entire image and improves the accuracy of finegrained image. This strategy allows the basic networks boost the classification performance in the case of specific combination. Given the same images inputted into our model, two groups of questionnaires for practitioners and non-practitioners judging the fertility by the rooster's phenotypic traits, the experimental results show that our method has raised the accuracy by almost 10% by comparison with the results of questionnaire survey.
With the improvement of living standards, the problem of human obesity has been getting worse. It is important to classify human obesity and determine the relevant obesity factors. In this paper, Lasso feature selection method is used for feature selection of data set to further reduce the data dimension. In addition, because the traditional LightGBM algorithm has a certain randomness in parameter selection, it is difficult to determine the optimal combination of parameters. This paper uses genetic algorithm to optimize the parameters of LightGBM algorithm. It is worth mentioning that the use of data standardization reduces the runtime of LightGBM. The LightGBM based on GA optimization compared with other common machine learning algorithms obtains good results, compared with the traditional LightGBM algorithm, the average accuracy and the average runtime are improved by 0.5% and decreased by 72.12% respectively.
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