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Ibrahim, Mohammed Hussein
Prof. Dr. Ismail SARITAS
Computer Science & IT

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Human Gender Prediction on Facial Mobil Images using Convolutional Neural Networks Hacibeyoglu, Mehmet; Ibrahim, Mohammed Hussein
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 3 (2018)
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018644778

Abstract

The interest in automatic gender classification has increased rapidly, especially with the growth of online social networking platforms, social media applications, and commercial applications. Most of the images shared on these platforms are taken by mobile phone with different expressions, different angles and low resolution. In recent years, convolutional neural networks have become the most powerful method for image classification. Many researchers have shown that convolutional neural networks can achieve better performance by modifying different network layers of network architecture. Moreover, the selection of the appropriate activation function of neurons, optimizer and the loss function directly affects the performance of the convolutional neural networks. In this study, we propose a gender classification system from facial images taken by mobile phone using convolutional neural networks. The proposed convolutional neural networks have a simple network architecture with appropriate parameters can be used when rapid training is needed with the amount of limited training data. In the experimental study, the Adience benchmark dataset was used with 17492 different images with different gender and ages. The classification process was carried out by 10-fold cross validation. According the experimental results, the proposed convolutional neural networks predicted the gender of the images 98.8% correctly for training and 89.1% for testing.
Improved Nelder-Mead Optimization Method in Learning Phase of Artificial Neural Network Merdan, Mustafa Adnan; Kocer, Hasan Erdinc; Ibrahim, Mohammed Hussein
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 4 (2018)
Publisher : Prof. Dr. Ismail SARITAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018448461

Abstract

It is difficult to find the optimum weight values of artificial neural networks for optimization problem. In this study, Nelder-Mead optimization method [17] has been improved and used for determining the optimal values of weights. The results of the proposed improved Nelder-Mead method are compared with results of the standard Nelder-Mead method which is used in ANNs learning algorithm.  The most common data sets are taken from UCI machine learning repository.  According to the experimental results, in this study better results are achieved in terms of speed and performance.
Co-Authors Hacibeyoglu, Mehmet Kocer, Hasan Erdinc Merdan, Mustafa Adnan