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Bulunan: 10 Adet 0.002 sn
Yazar/lar : ÖZTÜRK, Ali ✕Atıf Dizini : Scopus ✕
Koleksiyon [4]
İlgili Araştırmacılar [11]
Ambargo Durumu [1]
Tam Metin [1]
Yayın Türü [2]
Yazar/lar [20]
Atıf Dizini [2]
Konu Başlıkları [20]
Yayıncı [7]
Yayın Tarihi [7]
Yayın Dili [2]
dc.description.provenance [1]
Erişime Açık

A Hybrid Model For The Prediction Of Aluminum Foil Output Thickness İn Cold Rolling Process

ÖZTÜRK, Ali | ŞEHERLİ, Rıfat

This study proposes a hybrid model composed of multiple prediction algorithms and an autoregressive moving average (ARMA) module for the thickness prediction. In order to attain higher accuracy, the prediction algorithms were globally combined by simple voting to reduce the effect of the inductive bias imposed by each algorithm on the dataset. The global multiexpert combination (GMEC) system included the multilayer perceptron neural network (MLPNN), radial basis function network (RBFN), multiple linear regression (MLR), and support vector machines (SVM) algorithms. The proposed hybrid model ex ...Daha fazlası

Makale 2019 Turkiye Klinikleri 1 Görüntülenme
Erişime Açık

Short-Term Prediction of PM2. 5 Pollution with Deep Learning Methods

AYTURAN, Yasin Akın | AYTURAN, Zeynep Cansu | ALTUN, Hüseyin Oktay | KONGOLİ, Cezar | TUNÇEZ, Fatma Didem | DURSUN, Şükrü | ÖZTÜRK, Ali

Particulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short-term prediction of PM2.5 pollution at 1, 2 and 3 hours was modelled using deep learning methods. Three deep learning algorithms and the combination thereof were evaluated: Long-short term memory units (LSTM), recurrent neural networks (RNN) and gated recurrent unit (GRU). Air Quality Monitoring Station ...Daha fazlası

Makale 2020 Global Nest Journal-GLOBAL NETWORK ENVIRONMENTAL SCIENCE & TECHNOLOGY 11 Görüntülenme
Erişime Açık

Asymptomatic dermatophyte scalp carriage in school children in Erzincan, Turkey

AYDIN, Merve | GÜLHAN, Barış | GÜMRAL, Ramazan | İLKİT, Macit | ÖZTÜRK, Ali

Background: This study aimed to investigate the prevalence of symptomatic tinea capitis infections of the scalp and its asymptomatic carriage in students attending primary schools in Erzincan, Turkey. Materials and Methods: Eighteen primary schools were visited; 1 located in the central district and 17 located in other districts of the Erzincan province. From 2015 November to 2016 April, scalp scrapings were obtained from a total of 1879 students aged 6 to 13 years (mean age: 9.37±1.69) 924 (49.2%) male and 955 (50.8%) female using sterile hairbrushes, and assessed for tinea capitis and asympt ...Daha fazlası

Konferans Bildirisi 2018 Revista Română de Medicină de Laborator 3 Görüntülenme
Erişime Açık

Early autism diagnosis of children with machine learning algorithms

BÜYÜOFLAZ, Fatiha Nur | ÖZTÜRK, Ali

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that has become one of the major health problems, and early diagnosis has a great deal of important in terms of controlling the disease. The increase in the number of autoimmune influenza and ASD cases in the world reveals an urgent need to develop easily applied and effective screening methods In this study, performance comparisons were made using three different classification methods, Naive Bayes, IBk (k-nearest neighbors), RBFN (radial basis function network), and Random Forest, on UCI 2017 Autistic Spectrum Disorder Screenin ...Daha fazlası

Konferans Bildirisi 2018 Institute of Electrical and Electronics Engineers Inc. 5 Görüntülenme
Erişime Açık

Neuro-fuzzy Classification of Transcranial Doppler Signals with Chaotic Meaures and Spectral Parameters

ÖZTÜRK, Ali | ARSLAN, Ahmet

Transcranial Doppler (TCD) is a non-invasive diagnosis method which is used in diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. In this study, chaos analysis of the TCD signals recorded from the middle arteries of the temporal region of brain of the 82 patients and 23 healthy people was investigated. Among 82 patients, 20 of them had cerebral aneurism, 10 had brain hemorrhage, 22 had cerebral oedema and the remaining 30 had brain tumor. Maximum Lyapunov exponent which is the strongest quantitative indicator of chaos was found to be positive for all ...Daha fazlası

Konferans Bildirisi 2015 Institute of Electrical and Electronics Engineers Inc. 3 Görüntülenme
Erişime Açık

Comparison of machine learning algorithms on different datasets

UYSAL, Elif | ÖZTÜRK, Ali

Machine learning algorithms are methods used to classify data. Aim of this study is comparison of machine learning algorithms on different datasets. For this study, 9 different machine learning algorithms with 10 fold cross validation method in WEKA is classified on 3 different datasets. As a result of classification, machine learning algorithm which has high accuracy rate is different for 3 datasets. Multilayer Perceptron algorithm for Car Evaluation dataset, Random Forest algorithm for Image Segmentation dataset and Simple Logistic algorithm for User Knowledge Modeling dataset were obtained. ...Daha fazlası

Konferans Bildirisi 2018 Institute of Electrical and Electronics Engineers Inc. 6 Görüntülenme
Erişime Açık

Nonlinear Short-term Prediction of Aluminum Foil Thickness via Global Regressor Combination

ÖZTÜRK, Ali | ŞEHERLİ, Rıfat

In this study, short-term prediction of aluminum foil thickness time-series data recorded during cold-rolling process was investigated. The locally projective nonlinear noise reduction was applied in order to improve the predictability of the time series. The higher-order statistics methods (bispectrum and bicoherence) were used to detect the nonlinearity. The embedding vectors with appropriate embedding dimension and time delay were obtained via the false nearest neighbors and mutual information methods, respectively. The maximum prediction horizon was determined depending on the maximal Lyap ...Daha fazlası

Makale 2017 Taylor and Francis Inc. 4 Görüntülenme
Erişime Açık

Short term prediction of aluminium strip thickness via Support Vector Machines

ÖZTÜRK, Ali | ŞEHERLİ, Rıfat

The fundamental principle of cold rolling process is the tension produced by the coiling and uncoiling motors of the rolling machine. If the tension is not properly regulated, the strip thickness will not be homogenous over the surface and even ruptures may occur. Therefore, short-term prediction of the aluminium strip thickness is important to control the tension. In this study, nonlinear time series analysis methods were applied to the recorded thickness data in order to obtain the embedding vectors with appropriate embedding dimension and time delay. For various prediction horizons, the emb ...Daha fazlası

Konferans Bildirisi 2015 Institute of Electrical and Electronics Engineers Inc. 3 Görüntülenme
Erişime Açık

In vitro antifungal and antibiofilm activities of novel sulfonyl hydrazone derivatives against Candida spp.

AYDIN TERZİOĞLU, Merve | ÖZTÜRK, Ali | DURAN, Tuğçe | ÖZMEN, Ümmühan Özdemir | ŞUMLU, Esra | AYAN, Esra Bilen | KORUCU, Emine Nedime

Background: The aim of this study was to investigate the antifungal and antibiofilm activity of the new sulfonyl hydrazones compound derived from sulphonamides. Methods: In this study, new sulfonyl hydrazone series were synthesized via a green chemistry method. The structures of the synthesized compounds were characterized by elemental analyses and spectroscopic methods. The antifungal activities of the Anaf compounds against Candida strains under planktonic conditions were tested. The biofilm-forming ability of Candida strains was determined and the inhibitory effects of Anaf compounds on Can ...Daha fazlası

Makale 2023 J Mycol Med 0 Görüntülenme
Erişime Açık

Chaos analysis of Transcranial Doppler signals for feature extraction

ÖZTÜRK, Ali

In this study, chaos theory tools were used for feature extraction from Transcranial Doppler (TCD) signals. The surrogates data sets of the TCD signals which were used for the nonlinearity analysis were extracted as the first feature set. The nonlinear cross prediction errors which were used for the stationary analysis were also extracted for the TCD signals as another feature set. The chaotic invariant features like correlation dimension, maximum Lyapunov exponent, recurrence quantification measures etc. give quantitative values of complexity of the TCD signals. The correlation dimension and ...Daha fazlası

Konferans Bildirisi 2016 SciTePress 3 Görüntülenme
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