Ahmet Cevahir Cinar

Ahmet Cevahir Cinar

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Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

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

Representative Publications

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1 February 2018 in Applied Soft Computing
Verified
Journal Article
Closed access
ABAhmet Babalik
Ahmet Babalik
ACAhmet Cevahir Cinar
Ahmet Cevahir Cinar
Mustafa Servet KiranMustafa Servet Kiran
Mustafa Servet Kiran

2 September 2020 in Arabian Journal for Science and Engineering
Verified
Journal Article
Closed access
PDF file
ACAhmet Cevahir Cinar
Ahmet Cevahir Cinar

1 January 2018 in Computers & Industrial Engineering
Verified
Journal Article
Closed access
ACAhmet Cevahir Cinar
Ahmet Cevahir Cinar
Mustafa Servet KiranMustafa Servet Kiran
Mustafa Servet Kiran

1 August 2020 in Engineering Science and Technology, an International Journal
Verified
Journal Article
Open access
ACAhmet Cevahir Cinar
Ahmet Cevahir Cinar
SKSedat Korkmaz
Sedat Korkmaz
MKMustafa Servet Kiran
Mustafa Servet Kiran

30 January 2023 in Multimedia Tools and Applications
Verified
Journal Article
Closed access
PDF file
ACAhmet Cevahir Cinar
Ahmet Cevahir Cinar
TKTurkan Beyza Kara
Turkan Beyza Kara

1 November 2019 in IOP Conference Series: Materials Science and Engineering
Verified
Conference Paper
Open access
PDF file
AYA Yasar
A Yasar
ISI Saritas
I Saritas
MSM A Sahman
M A Sahman
ACAhmet Cevahir Cinar
Ahmet Cevahir Cinar
An artificial neural network system has been developed to detect Parkinson’s Disease (PD). Three samples were taken from each patient and included in the system. The importance of the study is based on the development and use of a new subject-based ANN approach that takes into account the dependent nature of the data in a replicated measure-based design. In order to evaluate the performance of the proposed system, an audio replication-based experiment was performed to differentiate healthy people from PD patients. The UCI Experiment consisted of 80 subjects, half of whom were affected by PD. Although the proposed system has a reduced number of subjects, the system is able to distinguish people with PD from an acceptable degree of healthy people with an accuracy rate of 94.93% in an artificial neural network.