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- W4385243208 abstract "Regression modeling approaches with sufficient literature support have postulated that intellectual property rights (IPR) have a positive impact on human capital in general. However, few papers attempt to uncover the impact of human capital on IPR protections. As IPR fosters innovation, it is critical to understand how developments in education, technology, and health care affect IPR. This paper primarily focuses on the investigation of what specific human capital indicators would be good predictors of IPR and is conducted using machine learning techniques. Compared to ridge regression and pruned regression tree, the random forest model outperforms all other models, with the highest R squared score and the lowest RMSE score. The random forest model suggests that among health, technological skills, and education, university enrollment per capita and physicians per capita play a more important role for predicting intellectual property rights. Moreover, using classification modeling techniques, a neural network model with a few hidden layers and less elements in each hidden layer effectively overcomes the overfitting issue and surpasses all other more complex neural network models. This finding indicates that the concision and precision of artificial intelligence models helps simplify the degree of complexity of social science." @default.
- W4385243208 created "2023-07-26" @default.
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- W4385243208 date "2023-01-01" @default.
- W4385243208 modified "2023-09-26" @default.
- W4385243208 title "Machine Learning Prediction of Intellectual Property Rights Based on Human Capital Factors" @default.
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- W4385243208 doi "https://doi.org/10.1007/978-981-99-3243-6_28" @default.
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