Sürdürülebilir Kalkınma Amaçları

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• BildiriAçık Erişim2019/r/munzur/3440
  Bigailab-4race-50K: Race Classification with a New Benchmark Dataset

  Talo, Muhammed; Ay, Betul; Makinist, Semiha; Aydin, Galip

  Face analysis is the process of extracting useful information such as gender, age and race from a face image. In this study, we look at ways of closing the gap between the capabilities of automatic facial recognition and race classification methods. For this purpose, we take the following two steps. (1) We offer a public race dataset consisting of labeled images to overcome the challenges of real-world race estimation tasks. (2) We present a benchmark study to find race of a human face using a pre-trained Convolutional Neural Network (CNN) model on the large-scale face dataset. We are able to achieve a remarkable accuracy of 97.6% in race classification task.

  AtıfScopus3·OpenAlex5

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• MakaleAçık Erişim2019Scopus SJRQ2/r/munzur/3201
  Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals

  Ay, Betul; Yildirim, Ozal; Talo, Muhammed; Baloglu, Ulas Baran; Aydin, Galip; Puthankattil, Subha D.; Acharya, U. Rajendra

  Depression affects large number of people across the world today and it is considered as the global problem. It is a mood disorder which can be detected using electroencephalogram (EEG) signals. The manual detection of depression by analyzing the EEG signals requires lot of experience, tedious and time consuming. Hence, a fully automated depression diagnosis system developed using EEG signals will help the clinicians. Therefore, we propose a deep hybrid model developed using convolutional neural network (CNN) and long-short term memory (LSTM) architectures to detect depression using EEG signals. In the deep model, temporal properties of the signals are learned with CNN layers and the sequence learning process is provided through the LSTM layers. In this work, we have used EEG signals obtained from left and right hemispheres of the brain. Our work has provided 99.12% and 97.66% classification accuracies for the right and left hemisphere EEG signals respectively. Hence, we can conclude that the developed CNN-LSTM model is accurate and fast in detecting the depression using EEG signals. It can be employed in psychiatry wards of the hospitals to detect the depression using EEG signals accurately and thus aid the psychiatrists.

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  AtıfScopus279·OpenAlex291

  İncele