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- W2894840052 abstract "Many studies have shown that there is a decline in seminal quality during the past two decades. Seminal quality may be affected by environmental factors and health status, as well as life habits. Artificial intelligence (AI) technology has been recently applied to recognize this effect. However, conventional AI algorithms are not prepared to cope with the class-imbalanced fertility dataset. To this end, a back-propagation neural network (BPNN) is used to predict the seminal profile of an individual from the dataset. A neural-genetic algorithm (N-GA) is employed to select optimal feature subsets and optimize the parameters of the used neural network. Results indicate that the proposed method outperforms other AI methods on seminal quality prediction in terms of precision and accuracy." @default.
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- W2894840052 date "2018-01-01" @default.
- W2894840052 modified "2023-09-27" @default.
- W2894840052 title "Predicting Seminal Quality Using Back-Propagation Neural Networks with Optimal Feature Subsets" @default.
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- W2894840052 doi "https://doi.org/10.1007/978-3-030-00563-4_3" @default.
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