@inproceedings{79a87bc9006e4cd292b209e9457a70fa,
title = "Predicting Seminal Quality Using Back-Propagation Neural Networks with Optimal Feature Subsets",
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.",
author = "Jieming Ma and Aiyan Zhen and Guan, {Sheng Uei} and Chun Liu and Xin Huang",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018 ; Conference date: 07-07-2018 Through 08-07-2018",
year = "2018",
doi = "10.1007/978-3-030-00563-4_3",
language = "English",
isbn = "9783030005627",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "25--33",
editor = "Cheng-Lin Liu and Jinchang Ren and Amir Hussain and Bin Luo and Huimin Zhao and Jiangbin Zheng and Xinbo Zhao",
booktitle = "Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings",
}