Aydin Akan

Prof. Dr. Aydin Akan

Electrical and Electronics Engineering, İzmir University of Economics, İzmir, Turkey (Türkiye)
orcid

Research Focus & Metrics

Academic Career Length

30Years (1997-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
29 June 2016 in Pattern Analysis and Applications
Verified
Journal Article
Closed access
PDF file
AMAhmet Mert
Ahmet Mert
Aydin AkanAydin Akan
Aydin Akan

1 October 2019
Verified
Conference Paper
Closed access
EIElif Izci
Elif Izci
MOMehmet Akif Ozdemir
Mehmet Akif Ozdemir
MDMurside Degirmenci
Murside Degirmenci
Aydin AkanAydin Akan
Aydin Akan
Arrhythmia is irregular changes of normal heart rhythm and effective manual identifying of them require a lot of time and depends on experience of clinicians. This paper proposes deep learning-based novel 2-D convolutional neural network (CNN) approach for accurate classification of five different arrhythmia types. The performance of the proposed architecture is tested on Electrocardiogram (ECG) signals that are taken from MIT-BIH arrhythmia benchmark database. ECG signals was segmented into heartbeats and each of the heartbeats was converted into 2-D grayscale images as an input data for CNN structure. The accuracy of the proposed architecture was found as 97.42% on the training results revealed that the proposed 2-D CNN architecture with transformed 2-D ECG images can achieve highest accuracy without any preprocessing and feature extraction and feature selection stages for ECG signals.

1 September 2014 in Digital Signal Processing
Verified
Journal Article
Closed access
AMAhmet Mert
Ahmet Mert
Aydin AkanAydin Akan
Aydin Akan

1 October 2019
Verified
Conference Paper
Closed access
MOMehmet Akif Ozdemir
Mehmet Akif Ozdemir
BEBerkay Elagoz
Berkay Elagoz
AAAysegul Alaybeyoglu
Aysegul Alaybeyoglu
RSReza Sadighzadeh
Reza Sadighzadeh
Aydin AkanAydin Akan
Aydin Akan
Emotion is an important topic in different fields such as biomedical engineering, psychology, neuroscience and health. Emotion recognition could be useful for diagnosis of brain and psychological disorders. In recent years, deep learning has progressed much in the field of image classification. In this study, we proposed a Convolutional Neural Network (CNN) based LeNet architecture for facial expression recognition. First of all, we merged 3 datasets (JAFFE, KDEF and our custom dataset). Then we trained our LeNet architecture for emotion states classification. In this study, we achieved accuracy of 96.43% and validation accuracy of 91.81% for classification of 7 different emotions through facial expressions.


1 October 2022 in IRBM
Verified
Journal Article
Closed access
MDM. Degirmenci
M. Degirmenci
MOMehmet Akif Ozdemir
Mehmet Akif Ozdemir
EIE. Izci
E. Izci
Aydin AkanAydin Akan
Aydin Akan