Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Anesthetic gas control with neuro-fuzzy system in anesthesia
Expert Systems with Applications · Mart 2010
YÖKSİS Kayıtları
Anesthetic gas control with neuro fuzzy system in anesthesia
Expert Systems with Applications · 2010 SCI-Expanded
Prof. Dr. RÜŞTÜ GÜNTÜRKÜN →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Game-theoretic DEA Optimization for Sustainable Agricultural Carbon Trading: Evidence from Türkiye’s Maize Production
2026 ISSN: 0957-4174 SCI-Expanded Q1
Prof. Dr. ZEKİ BAYRAMOĞLU →
Web based medical decision support system application of Coronary Heart Disease diagnosis with Boolean functions minimization method
2011 ISSN: 09574174 SCI
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Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method
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A fuzzy clustering approach for finding similar documents using a novel similarity measure
2007 ISSN: 0957-4174 SCI-Expanded 22 atıf
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A new approach on search for similar documents with multiple categories using fuzzy clustering
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Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS
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Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
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Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
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2009 ISSN: 0957-4174 SCI-Expanded 29 atıf Q1
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Prognosis of prostate cancer by artificial neural networks
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Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
2011 ISSN: 0957-4174 SCI-Expanded 24 atıf Q1
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Anesthetic gas control with neuro fuzzy system in anesthesia
2010 ISSN: 09574174 SCI-Expanded
Prof. Dr. RÜŞTÜ GÜNTÜRKÜN →
An Adaptive Network Based Fuzzy Inference System ANFIS for the prediction of stock market return The case of the Istanbul Stock Exchange
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Predicting direction of stock price index movement using artificial neural networks and support vector machines The sample of the Istanbul Stock Exchange
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Makale Bilgileri
ISSN09574174
Yayın TarihiMart 2010
Cilt / Sayfa37 · 2690-2695
Scopus ID2-s2.0-70449524324
Özet
In this study, power spectrum of the EEG data and the heartbeat data obtained from 25 patients has been applied to the designed neuro-fuzzy system. The designed system has composed of two parts; one is an artificial neural network and the other is a fuzzy system. A back-propagation artificial neural network has been developed which contains 53 nodes in the input layer, 27 nodes in the hidden and 1 node in the output layer. In the artificial neural network inputs, the power spectral density values corresponding 1-50 Hz frequency interval of the EEG slices which has 10 s of time interval, the ratio of the total of the PSD values of current EEG slice to the total PSD values of EEG slice of pre-anesthesia, the ratio of the total PSD values of the EEG data to the total PSD values of the previous EEG data, and the previous anesthetic gas ratio values have been applied and the network has been educated. At the end of the education total error has been found as 10<sup>- 17</sup>. In the fuzzy system block, the ratio of current heartbeat to the previous one, the ratio of the current heartbeat to the pre-operation heartbeat, the ratio of the output of the artificial neural network to the previous applied anesthetic gas have been applied as variables and in the system output gas ratio prediction has been obtained as percentage. The designed neuro-fuzzy system has been tested by using 10 data set obtained from four different patients. In the anesthetic gas prediction according to the anesthesia level, successful results have been obtained with the designed system. © 2009 Elsevier Ltd. All rights reserved.
Yazarlar (2)
1
Mustafa Tosun
2
R. Güntürkün
Anahtar Kelimeler
Depth of anesthesia
EEG power spectrum
Neuro-fuzzy control
Kurumlar
Dumlupinar Üniversitesi
Kutahya Turkey
Scimago Dergi (ISSN Eşleşmesi)
Expert Systems with Applications
Q1
SJR Skoru1,854
H-Index290
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Computer Science Applications (Q1)
Engineering (miscellaneous) (Q1)
Metrikler
6
Atıf
2
Yazar
3
Anahtar Kelime