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    Seminal Quality Prediction Using Deep Learning Based on Artificial Intelligence
    (Kırıkkale Üniversitesi, 2019) Benli, Hilal; Haznedar, Bülent; Kalınlı, Adem
    Fertility rates have dramatically decreased inthe last two decades, especially in men. It has been described thatenvironmental factors, as well as life habits, may affect semen quality. Thispaper evaluates the performance of different artificial intelligence (AI)techniques for classifying fertility dataset that includes the semen sampleanalysed according to WHO 2010 criteria and publicly available on UCI datarepository. In this context, deepneural network (DNN) which involved in many studies in recent years is proposedto classify fertility dataset successfully. For the purpose of comparing theproposed method’s performance, Adaptive Neuro-Fuzzy Inference system (ANFIS) isalso used for the classification problem. The results show that the performanceof the DNN has the best with the average accuracy rate of 90.11%, and theresults of the other ANFIS methods are also satisfactory.