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Article ; Online: Deep learning approach for prediction of exergy and emission parameters of commercial high by-pass turbofan engines.

Dursun, Omer Osman / Toraman, Suat / Aygun, Hakan

Environmental science and pollution research international

2022 Volume 30, Issue 10, Page(s) 27539–27559

Abstract: Aviation emissions originated from the fuel burn have been hot topics by engineers and policy-makers due to their harmful effects on the environment and thereby human health as well as sustainability. In this study, it is tried that several emission ... ...

Abstract	Aviation emissions originated from the fuel burn have been hot topics by engineers and policy-makers due to their harmful effects on the environment and thereby human health as well as sustainability. In this study, it is tried that several emission indexes (EIs) involving CO, HC and NOx as well as fuel flow of several commercial aircraft engines (CAEs) are predicted using support vector regression (SVR) and long short-term memory (LSTM) approaches for take-off phase. Moreover, exergo-environmental parameters involving exergy efficiency (ExEFF), wasted exergy ratio (WExR) and environmental effect factor (EEF) pertinent to CAEs are computed employing thermodynamics laws. While establishing the models, rated thrust, by-pass ratio, overall pressure ratio and combustion type of the CAEs are utilized as the model inputs. According to the findings of emission modelling, the coefficient of determination (R
MeSH term(s)	Humans ; Air Pollutants/analysis ; Vehicle Emissions/analysis ; Deep Learning ; Aircraft ; Climate
Chemical Substances	Air Pollutants ; Vehicle Emissions
Language	English
Publishing date	2022-11-16
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-022-24109-y
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Article ; Online: Deep learning approach for prediction of exergy and emission parameters of commercial high by-pass turbofan engines

Dursun, Omer Osman / Toraman, Suat / Aygün, Hakan

Environ Sci Pollut Res. 2023 Feb., v. 30, no. 10 p.27539-27559

2023

Abstract	Aviation emissions originated from the fuel burn have been hot topics by engineers and policy-makers due to their harmful effects on the environment and thereby human health as well as sustainability. In this study, it is tried that several emission indexes (EIs) involving CO, HC and NOx as well as fuel flow of several commercial aircraft engines (CAEs) are predicted using support vector regression (SVR) and long short-term memory (LSTM) approaches for take-off phase. Moreover, exergo-environmental parameters involving exergy efficiency (ExEFF), wasted exergy ratio (WExR) and environmental effect factor (EEF) pertinent to CAEs are computed employing thermodynamics laws. While establishing the models, rated thrust, by-pass ratio, overall pressure ratio and combustion type of the CAEs are utilized as the model inputs. According to the findings of emission modelling, the coefficient of determination (R²) of EI NOx and EI CO of the CAEs is found as 0.929074 and 0.960277 with SVR, whereas their R² values are elevated to 0.954878 and 0.989283 with LSTM approach, respectively. However, R² of EI HC is determined lower with 0.632280 (by SVR) and 0.651749 (by LSTM). On the other hand, exergo-environmental parameters for the CAEs are estimated with high correctness at both models. Namely, R² of ExEFF and EEF regarding the CAEs are computed as 0.991748 and 0.989067 by SVR; however, these are calculated as 0.994785 and 0.992797 by LSTM method. To model these parameters with low error by using significant design variables as model inputs could help in predicting emission and environmental metrics for new engine designs.
Keywords	aircraft ; aviation ; combustion ; environmental impact ; exergy ; fuels ; human health ; neural networks ; prediction ; regression analysis
Language	English
Dates of publication	2023-02
Size	p. 27539-27559.
Publishing place	Springer Berlin Heidelberg
Document type	Article ; Online
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-022-24109-y
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Physiotherapy combined with therapeutic neuroscience education versus physiotherapy alone for patients with chronic low back pain: A pilot, randomized-controlled trial.

Gül, Hatice / Erel, Suat / Toraman, Naciye Füsun

Turkish journal of physical medicine and rehabilitation

2021 Volume 67, Issue 3, Page(s) 283–290

Abstract: Objectives: The aim of this study was to investigate the effect of therapeutic neuroscience education (TNE) combined with physiotherapy on pain, kinesiophobia, endurance, and disability in chronic low back pain (CLBP) patients.: Patients and methods: ...

Abstract	Objectives: The aim of this study was to investigate the effect of therapeutic neuroscience education (TNE) combined with physiotherapy on pain, kinesiophobia, endurance, and disability in chronic low back pain (CLBP) patients. Patients and methods: Between November 2016 and December 2017, a total of 31 patients with CLBP (5 males, 26 females; mean age: 42.3±10.8 years; range, 20 to 58 years) were randomly allocated to receive physiotherapy combined with TNE (experimental group, EG, n=16) and physiotherapy alone (control group, CG, n=15). All participants received physiotherapy consisting of five sessions per week for a total of three weeks. In addition to physiotherapy, the EG received TNE sessions consisting of two sessions per week for a total of three weeks. The primary outcomes were pain intensity as assessed by Visual Analog Scale (VAS) and kinesiophobia by Tampa Scale for Kinesiophobia (TSK), while and the secondary outcomes were trunk muscle endurance as assessed by the partial curl-up test (trunk flexor endurance [TFE]) and modified Sorensen test (trunk extensor endurance [TEE]) and disability by Roland-Morris Disability Questionnaire (RMDQ). Results: All patients completed the study. The median VAS, TSK, TFE, TEE, and RMDQ scores for the EG significantly improved after three weeks, while there was only significant improvement in the VAS, TSK, and RMDQ scores in the CG. The TSK decreased more in the EG than in the CG. The significant difference was evident in TSK and TFE in favor of the EG (p<0.05). Conclusion: These results suggest that the combination of TNE with physiotherapy can improve kinesiophobia and trunk flexor muscle endurance of patients with CLBP in the short-term.
Language	English
Publishing date	2021-09-01
Publishing country	Turkey
Document type	Journal Article
ZDB-ID	2712472-1
ISSN	2587-1250 ; 2587-1250 ; 1308-6316
ISSN (online)	2587-1250
ISSN	2587-1250 ; 1308-6316
DOI	10.5606/tftrd.2021.5556
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Article: Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks.

Toraman, Suat / Alakus, Talha Burak / Turkoglu, Ibrahim

Chaos, solitons, and fractals

2020 Volume 140, Page(s) 110122

Abstract: Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as possible to restrain the spread of the disease. The similarity of COVID- ... ...

Abstract	Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as possible to restrain the spread of the disease. The similarity of COVID-19 disease with other lung infections makes the diagnosis difficult. In addition, the high spreading rate of COVID-19 increased the need for a fast system for the diagnosis of cases. For this purpose, interest in various computer-aided (such as CNN, DNN, etc.) deep learning models has been increased. In these models, mostly radiology images are applied to determine the positive cases. Recent studies show that, radiological images contain important information in the detection of coronavirus. In this study, a novel artificial neural network, Convolutional CapsNet for the detection of COVID-19 disease is proposed by using chest X-ray images with capsule networks. The proposed approach is designed to provide fast and accurate diagnostics for COVID-19 diseases with binary classification (COVID-19, and No-Findings), and multi-class classification (COVID-19, and No-Findings, and Pneumonia). The proposed method achieved an accuracy of 97.24%, and 84.22% for binary class, and multi-class, respectively. It is thought that the proposed method may help physicians to diagnose COVID-19 disease and increase the diagnostic performance. In addition, we believe that the proposed method may be an alternative method to diagnose COVID-19 by providing fast screening.
Keywords	covid19
Language	English
Publishing date	2020-07-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2003919-0
ISSN	1873-2887 ; 0960-0779
ISSN (online)	1873-2887
ISSN	0960-0779
DOI	10.1016/j.chaos.2020.110122
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Article ; Online: Is it possible to detect cerebral dominance via EEG signals by using deep learning?

Toraman, Suat / Tuncer, Seda Arslan / Balgetir, Ferhat

Medical hypotheses

2019 Volume 131, Page(s) 109315

Abstract: Each brain hemisphere is dominant for certain functions such as speech. The determination of speech laterality prior to surgery is of paramount importance for accurate risk prediction. In this study, we aimed to determine speech laterality via EEG ... ...

Abstract	Each brain hemisphere is dominant for certain functions such as speech. The determination of speech laterality prior to surgery is of paramount importance for accurate risk prediction. In this study, we aimed to determine speech laterality via EEG signals by using noninvasive machine learning techniques. The retrospective study included 67 subjects aged 18-65 years who had no chronic diseases and were diagnosed as healthy based on EEG examination. The subjects comprised 35 right-hand dominant (speech center located in the left hemisphere) and 32 left-hand dominant individuals (speech center located in the right hemisphere). A spectrogram was created for each of the 18 EEG channels by using various Convolutional Neural Networks (CNN) architectures including VGG16, VGG19, ResNet, MobileNet, NasNet, and DenseNet. These architectures were used to extract features from the spectrograms. The extracted features were classified using Support Vector Machines (SVM) and the classification performances of the CNN models were evaluated using Area Under the Curve (AUC). Of all the CNN models used in the study, VGG16 had a higher AUC value (0.83 ± 0.05) in the determination of speech laterality compared to all other models. The present study is a pioneer investigation into the determination of speech laterality via EEG signals with machine learning techniques, which, to our knowledge, has never been reported in the literature. Moreover, the classification results obtained in the study are promising and lead the way for subsequent studies though not practically feasible.
MeSH term(s)	Adolescent ; Adult ; Aged ; Area Under Curve ; Broca Area/physiology ; Deep Learning ; Diagnosis, Computer-Assisted/methods ; Dominance, Cerebral ; Electroencephalography/methods ; Female ; Fourier Analysis ; Humans ; Male ; Middle Aged ; Reference Values ; Retrospective Studies ; Support Vector Machine ; Wernicke Area/physiology ; Young Adult
Language	English
Publishing date	2019-07-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	193145-3
ISSN	1532-2777 ; 0306-9877
ISSN (online)	1532-2777
ISSN	0306-9877
DOI	10.1016/j.mehy.2019.109315
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Article ; Online: A deep learning-based decision support system for diagnosis of OSAS using PTT signals.

Arslan Tuncer, Seda / Akılotu, Beyza / Toraman, Suat

Medical hypotheses

2019 Volume 127, Page(s) 15–22

Abstract: Sleep disorders, which negatively affect an individual's daily quality of life, are a common problem for most of society. The most dangerous sleep disorder is obstructive sleep apnea syndrome (OSAS), which manifests itself during sleep and can cause the ... ...

Abstract	Sleep disorders, which negatively affect an individual's daily quality of life, are a common problem for most of society. The most dangerous sleep disorder is obstructive sleep apnea syndrome (OSAS), which manifests itself during sleep and can cause the sudden death of patients. Many important parameters related to the diagnosis and treatments of such sleep disorders are simultaneously examined. This process is exhausting and time-consuming for experts and also requires experience; thus, it can cause difference of opinion among experts. Because of this, automatic sleep staging systems have been designed. In this study, a decision support system was developed to determine OSAS patients. In the developed decision support system, unlike in the available published literature, patient and healthy individual classification was performed using only the Pulse Transition Time (PTT) parameter rather than other parameters obtained from polysomnographic data like ECG (Electrocardiogram), EEG (Electroencephalography), carbon dioxide measurement and EMG (Electromyography). The suggested method can perform feature extraction from PTT signals by means of a deep-learning method. AlexNet and VGG-16, which are two Convolutional Neural Network (CNN) models, have been used for feature extraction. With the features obtained, patients and healthy individuals were classified by the Support Vector Machine (SVM) and the k-nearest neighbors (k-NN) algorithms. When the performance of the study was compared with other studies in published literature, it was seen that satisfactory results were obtained.
MeSH term(s)	Adult ; Aged ; Algorithms ; Carbon Dioxide/chemistry ; Decision Support Systems, Clinical ; Deep Learning ; Electrocardiography ; Electroencephalography ; Electromyography ; Female ; Healthy Volunteers ; Heart Rate ; Humans ; Male ; Medical Informatics ; Middle Aged ; Motivation ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Sleep Apnea, Obstructive/diagnosis ; Sleep Wake Disorders/diagnosis ; Support Vector Machine
Chemical Substances	Carbon Dioxide (142M471B3J)
Language	English
Publishing date	2019-03-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	193145-3
ISSN	1532-2777 ; 0306-9877
ISSN (online)	1532-2777
ISSN	0306-9877
DOI	10.1016/j.mehy.2019.03.026
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Article ; Online: Convolutional capsnet

Toraman, Suat / Alakus, Talha Burak / Turkoglu, Ibrahim

Chaos, Solitons & Fractals

A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

2020 Volume 140, Page(s) 110122

Keywords	General Mathematics ; covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ZDB-ID	2003919-0
ISSN	1873-2887 ; 0960-0779
ISSN (online)	1873-2887
ISSN	0960-0779
DOI	10.1016/j.chaos.2020.110122
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: KAZANÇ YÖNETİMİ ve YÖNETİM KARAKTERİSTİKLERİ İLİŞKİSİNİN BORSA İSTANBUL’DA TEST EDİLMESİ

Şakir SAKARYA / Suat KARA / Ahmet Mesut TORAMAN

Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Vol 21, Iss 2, Pp 126-

2019 Volume 139

Abstract: ÖZ Son yıllarda yaşanan ve dünyada oldukça geniş yankı uyandıran muhasebe skandalları ile ortaya çıkan ekonomik kayıplar, ilginin kazanç yönetimi üzerine yoğunlaşmasına sebep olmuştur. Bu durum şirketlerin kazanç yönetimi vasıtasıyla finansal ... ...

Abstract	ÖZ Son yıllarda yaşanan ve dünyada oldukça geniş yankı uyandıran muhasebe skandalları ile ortaya çıkan ekonomik kayıplar, ilginin kazanç yönetimi üzerine yoğunlaşmasına sebep olmuştur. Bu durum şirketlerin kazanç yönetimi vasıtasıyla finansal tablolarında olması gerekenden farklı veriler sunması yatırımcıları yeniden düşünmeye sevk etmiştir. Yatırımcı düşüncelerinde oluşan bu yeni soru işaretleri kazanç yönetiminin tespiti ve yöntemlerinin uygulanması konusunda geniş bir literatür oluşmasına olanak sağlamıştır. Akademik çalışmalarda şirketin yapısı, denetimi ve yönetim karakteristikleri kapsamında birçok etkenin kazanç yönetimi ile ilişkisi araştırılmaktadır. Bu çalışmada, öncelikle BIST’ te işlem gören şirketlerin kazanç yönetimine başvurup başvurmadıkları Beneish modeli kullanılarak tespit edilmeye çalışılmış ve daha sonra da kazanç yönetimine başvurdukları düşünülen şirketler ile yönetim karakteristikleri arasındaki ilişki araştırılmıştır. Şirketlerin 2014-2017 yılları finansal tablolarından yararlanılarak kazanç yönetimine etkisi olan yönetim karakteristikleri lojistik regresyon analizi kullanılarak ölçülmüştür. Analiz sonuçlarına göre, yönetim karakteristiklerinden Tepe Yöneticiliği İkilemi, Yönetim Kurulu Üye Sayısı, Bağımsız Denetçi Görüşü ve Ortaklık Yapısındaki Yabancı Yatırımcı Yüzdesinin, şirketlerde manipülasyona açık değişkenler olduğu sonucuna varılmıştır.
Keywords	kazanç yönetimi ; yönetim karakteristikleri ; beneish ; bist ; earnings management ; management characteristics ; Social Sciences ; H ; Business ; HF5001-6182
Language	English
Publishing date	2019-12-01T00:00:00Z
Publisher	Afyon Kocatepe University
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Toraman, Suat / Alakus, Talha Burak / Turkoglu, Ibrahim

Chaos Solitons Fractals

Abstract	Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as possible to restrain the spread of the disease. The similarity of COVID-19 disease with other lung infections makes the diagnosis difficult. In addition, the high spreading rate of COVID-19 increased the need for a fast system for the diagnosis of cases. For this purpose, interest in various computer-aided (such as CNN, DNN, etc.) deep learning models has been increased. In these models, mostly radiology images are applied to determine the positive cases. Recent studies show that, radiological images contain important information in the detection of coronavirus. In this study, a novel artificial neural network, Convolutional CapsNet for the detection of COVID-19 disease is proposed by using chest X-ray images with capsule networks. The proposed approach is designed to provide fast and accurate diagnostics for COVID-19 diseases with binary classification (COVID-19, and No-Findings), and multi-class classification (COVID-19, and No-Findings, and Pneumonia). The proposed method achieved an accuracy of 97.24%, and 84.22% for binary class, and multi-class, respectively. It is thought that the proposed method may help physicians to diagnose COVID-19 disease and increase the diagnostic performance. In addition, we believe that the proposed method may be an alternative method to diagnose COVID-19 by providing fast screening.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #640817
Database	COVID19

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Article ; Online: Investigation of the discrimination and characterization of blood serum structure in patients with opioid use disorder using IR spectroscopy and PCA-LDA analysis.

Guleken, Zozan / Ünübol, Başak / Bilici, Rabia / Sarıbal, Devrim / Toraman, Suat / Gündüz, Oğuzhan / Erdem Kuruca, Serap

Journal of pharmaceutical and biomedical analysis

2020 Volume 190, Page(s) 113553

Abstract: Harmful illicit drug use, such as opioid use disorder (OUD), causes multiple diseases that result in physiological, pathological, and structural changes in serum biochemical parameters based on the period of use. Fourier-transform infrared (FTIR) ... ...

Abstract	Harmful illicit drug use, such as opioid use disorder (OUD), causes multiple diseases that result in physiological, pathological, and structural changes in serum biochemical parameters based on the period of use. Fourier-transform infrared (FTIR) spectrometry is a noninvasive optical technique that can provide accurate evidence about the biochemical compounds of analytical samples. This technique is based on the detection of functional groups and the spectral analysis of the region of the selected bands, which provides a reliable and accurate tool for evaluating changes in the biochemical parameters of OUD patients. In the present study, the Attenuated Total Reflection (ATR)-FTIR technique and clinical laboratory biochemical results were used to investigate the phospholipid-protein balance in the blood serum of participants with OUD by comparing their data to that of healthy controls. To compare the biochemical laboratory results with serum vibrational spectroscopy, we used infrared (IR) spectroscopy to distinguish the serum of the OUD patients, who had an average duration of use of 7.31 ± 3.8 years (ranging from 6 to 15 years). We aimed to compare the clinical reports with findings from IR spectroscopy coupled with chemometrics analysis, principal component analysis (PCA), and linear discriminant analysis (LDA). The serum samples of the OUD male patients (n = 20) and healthy male individuals (n = 14) were evaluated using FTIR spectroscopy (range of 4000 cm
MeSH term(s)	Discriminant Analysis ; Humans ; Male ; Opioid-Related Disorders ; Principal Component Analysis ; Serum ; Spectroscopy, Fourier Transform Infrared
Language	English
Publishing date	2020-08-15
Publishing country	England
Document type	Journal Article
ZDB-ID	604917-5
ISSN	1873-264X ; 0731-7085
ISSN (online)	1873-264X
ISSN	0731-7085
DOI	10.1016/j.jpba.2020.113553
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