Изображение профиля DeepNetMatlab
Флаг Turkey K?z?ltepe, Turkey
На сайте с 26 ноября 2019 г.
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I'm an electircal engineer and PhD student. I am specialized in but not limited to: - Digital signal processing - Biomedical signal (EEG, ECG, etc) processing - Pre-processing signals for machine learning algorithm - Features extraction for machine learning algorithm - Classification with k nearest neighbor (kNN) - Classification, regression, modeling and prediction with Artificial neural network (ANN) - Classification, regression, modeling and prediction with Support vector machines (SVM) - Image/signal transformation and representation for deep learn. ing algorithms - Image to label classification with CNN - Image to image regression with CNN - Object detection with R-CNN, fast R-CNN, faster R-CNN and YOLO - Image denoising with DnCNN - Image generation with GAN and VAE and etc. - Time series prediction with LSTM - Sequence to label classification with LSTM - Sequence to sequence classification with LSTM
$20 USD/hr
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  • 95%Выполненные работы
  • 93%В рамках бюджета
  • 100%Своевременность
  • 5%Рейтинг повторного найма

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Опыт работы


Feb 2016

Lecturer at Vocational School, Electricity and Energy Department

Project Engineer

Jun 2012 - Sep 2015 (3 years)

Commissioning, testing, designing, implementing and troubleshooting of several Siemens Sicam 230, Sicam WinCC systems, Sicam 1703 RTU Systems and Siprotec Relays with DIGSI software for SCMS (Substation Control and Monitoring Systems).


Bachelor of Science

2007 - 2012 (5 years)

Master of Science

2016 - 2018 (2 years)


Classification of EEG Signals Using Hilbert-Huang Transform-Based Deep Neural Networks

Hilbert-Huang transform is applied to EEG signals and they are represented as image files. Then, generated images are fed into deep neural networks with five different structures for classification. Accuracy is calculated for all cases to asses performance of proposed method. it is clear that successful results could be obtained using Hilbert-Huang transform along with deep learning networks.

Abnormal Heart Sound Detection Using Ensemble Classifiers

The goal of this study is to develop a classification method for heart sounds collected from different databases. For this purpose two level classification is employed. Firstly, recordings are segregated as per their databases. Then, in second level recordings are classified with respect to pathology by using two classifier per database. With final decision rule, proposed algorithm achieved an accuracy of 98.9%, a sensitivity of 93.75% and a specify of 99.5%.

Detection of Pathological Heart Sound by Using SVM, kNN and Ensemble Methods of Classification

In this work, Training set of [login to view URL] 2016 challenge is used to develop, train and test an algorithm that can detect pathological or abnormal heart sounds.


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