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Dergiler / Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi / 2019 / Cilt: 11 - Sayı: 1

Seminal Quality Prediction Using Deep Learning Based on Artificial Intelligence

Hilal BENLİ
Bülent HAZNEDAR
Adem KALINLI
Sayfa
350–357
Yayın Tarihi
2019-01-31
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The results show that the performance of the DNN has the best with the average accuracy rate of 90.11%, and the results of the other ANFIS methods are also satisfactory.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Fertility rates have dramatically decreased in the last two decades, especially in men.
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Temel Bulgular & Veri:This paper evaluates the performance of different artificial intelligence (AI) techniques for classifying fertility dataset that includes the semen sample analysed according to WHO 2010 criteria and publicly available on UCI data repository.
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Sonuç & Katkı:The results show that the performance of the DNN has the best with the average accuracy rate of 90.11%, and the results of the other ANFIS methods are also satisfactory.
🔬Tür:Araştırma Makalesi
📍Kapsam:2010 Verileri
⚙️Değişkenler:Classification · fertility · statistical methods

Abstract

Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors, as well as life habits, may affect semen quality. This paper evaluates the performance of different artificial intelligence (AI) techniques for classifying fertility dataset that includes the semen sample analysed according to WHO 2010 criteria and publicly available on UCI data repository. In this context, deep neural network (DNN) which involved in many studies in recent years is proposed to classify fertility dataset successfully. For the purpose of comparing the proposed method’s performance, Adaptive Neuro-Fuzzy Inference system (ANFIS) is also used for the classification problem. The results show that the performance of the DNN has the best with the average accuracy rate of 90.11%, and the results of the other ANFIS methods are also satisfactory.

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Bu Makaleye Atıf Yap

BENLİ, H., HAZNEDAR, B., KALINLI, A. (2019). Seminal Quality Prediction Using Deep Learning Based on Artificial Intelligence. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, Cilt: 11 - Sayı: 1, 350–357. https://ajindex.com/articles/889427

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Kaynakça (5)

  1. Bidgoli, A.A., Komleh, H.E., & Mousavirad, S.J., (2015). Seminal quality prediction using optimized artificial neural network with genetic algorithm. 9th International Conference on Electrical and Electronics Engineering (ELECO), 695-699.
  2. Gil, D., Girela, J. L., De Juan, J., Gomez-Torres, M. J., & Johnsson, M., (2012). Predicting seminal quality with artificial intelligence methods. Expert Systems with Applications. 39, 12564-12573.
  3. Gil, D., & Girela, J.L., (accessed on 8 November 2013). UCI Machine Learning Repository: Fertility data set. Available online: http://archive.ics.uci.edu/ml/datasets/Fertility.
  4. Girela, J. L., Gil, D., Johnsson, M., Gomez-Torres, M. J., & De Juan, J., (2013). Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods. Biology of Reproduction, 88, 99. LeCun, Y., Bengio, Y., & Hinton, G., (2015). Deep learning. Nature. 521 (7553), 436–444.
  5. Lu, J., Huang, Y., & LA, N., (2010). [WHO laboratory manual for the examination and processing of human semen: Its applicability to andrology laboratories in China],” Zhonghua nan ke xue= National journal of andrology. 16 (10), 867- 871. Tieleman, T., & Hinton, G., (2012). Lecture 6.5-RMSprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4. Wang, H., Xu, Q., & Zhou, L., (2014). Seminal quality prediction using clustering-based decision forests. Algorithms. 7, 405-417.