Browsing by Author "Sen Koktas, Nigar"

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Citation - WoS: 19
Citation - Scopus: 28
A Multi-Classifier for Grading Knee Osteoarthritis Using Gait Analysis
(Elsevier Science Bv, 2010) Sen Koktas, Nigar; Yalabik, Nese; Yavuzer, Guenes; Duin, Robert P. W.
This study presents a system for detecting and scoring of a knee disorder, namely, osteoarthritis (OA). Data used for training and recognition is mainly data obtained through computerized gait analysis, which is a numerical representation of the mechanical measurements of human walking patterns. History and clinical characteristics of the subjects such as age, body mass index and pain level are also included in decision-making. Subjects are allocated into four OA-severity categories, formed in accordance with the Kellgren-Lawrence scale: "Normal", "Mild", "Moderate", and "Severe". Different types of classifiers are combined to incorporate the different types of data and to make the best advantages of different classifiers for better accuracy. A decision tree is developed with Multilayer Perceptrons (MLP) at the leaves. This gives an opportunity to use neural networks to extract hidden (i.e. implicit) knowledge in gait measurements and use it back into the explicit form of the decision trees for reasoning. The approach is similar to the Mixture of Experts method. Individual feature selection is applied using the Mahalanobis distance measure and most discriminatory features are used for each expert MLP. The system is tested by a separate set and a success rate of about 80% is achieved on the average. (c) 2010 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 3
An Application of Principal Component Analysis - Artificial Neural Network for the Simultaneous Quantitative Analysis of a Binary Mixture System
(Chiminform Data S A, 2009) Dinc, Erdal; Baleanu, Dumitru; Sen Koktas, Nigar; Baleanu, Dumitru; 6981; Matematik
Artificial neural networks (ANNs) based on the use of principal components and the original absorbance data were proposed for the simultaneous quantitative analysis of amlodipine (AML) and atorvastatin (ATO) in tablets. A concentration set of mixtures containing ATO and AML in different concentration composition between 0.0-20.0 mu g/mL was prepared in methanol. The measured absorbance data matrix for the concentration data set was obtained and the principal components were extracted. In the next step five principal components were selected as an input data for the artificial neural network. This combined approach was named principal components-artificial neural network (PCA-ANN). The same problem was solved by using the application of the artificial neural network to the original absorbance data matrix. This approach was denoted as ANN. The classical ANN approach was used as a comparison method. Both PCA-ANN and ANN methods were tested by analyzing various synthetic mixtures corresponding to the validation set of AML and ATO compounds. The proposed methods were successfully applied to the quantitative analysis of the commercial tablets and a coincidence was reported between the proposed methods.
Citation - WoS: 2
Citation - Scopus: 3
New Spectral Approaches To the Simultaneous Quantitative Resolution of A Combined Veterinary Formulation By Ann and Pca-Ann Methods
(Walter de Gruyter & Co, 2011) Dinc, Erdal; Baleanu, Dumitru; Baleanu, Dumitru; Sen Koktas, Nigar; 56389; Matematik
The simultaneous spectral prediction of levamisole (LEV) and triclabendazole (TRI) in combined veterinary formulation was performed by the new chemometric methods, artificial neural network (ANN) and principal component analysis-artificial neural network (PCA-ANN). Despite the overlapping spectra of LEV and TRI in the same wavelength region, the proposed methods do not use any separation procedure for the analysis of the related compounds. Good precision and accuracy were observed for the applications of the proposed artificial neural network models to an independent binary mixture set consisting of the active compounds. These methods were successfully applied for the chemometric quantitation of a veterinary formulation of LEV and TRI.
Citation - WoS: 2
Citation - Scopus: 4
Statistical Analysis of Gait Data To Assist Clinical Decision Making
(Springer-verlag Berlin, 2010) Sen Koktas, Nigar; Duin, Robert P. W.
Gait analysis is used for non-automated and automated diagnosis of various neuromuskuloskeletal abnormalities. Automated systems are important in assisting physicians for diagnosis of various diseases. This study presents preliminary steps of designing a clinical decision support system for semi-automated diagnosis of knee illnesses by using temporal gait data. This study compares the gait of Ill patients with 110 age-matched normal subjects. Different feature reduction techniques, (FFT, averaging and PCA) are compared by the Mahalanobis Distance criterion and by performances of well known classifiers. The feature selection criteria used reveals that the gait measurements for different parts of the body such as knee or hip to be more effective for detection of the illnesses. Then, a set of classifiers is tested by a ten-fold cross validation approach on all datasets. It is observed that average based datasets performed better than FFT applied ones for almost all classifiers while PCA applied dataset performed better for linear classifiers. In general, nonlinear classifiers performed quite well (best error rate is about 0.035) and better than the linear ones.
Statistical Analysis of Gait Data to Assist Clinical Decision Making
(Springer-Verlag Berlin, 2010) Sen Koktas, Nigar
Gait analysis is used for non-automated and automated diagnosis of various neuromuskuloskeletal abnormalities. Automated systems are important in assisting physicians for diagnosis of various diseases. This study presents preliminary steps of designing a clinical decision support system for semi-automated diagnosis of knee illnesses by using temporal gait data. This study compares the gait of Ill patients with 110 age-matched normal subjects. Different feature reduction techniques, (FFT, averaging and PCA) are compared by the Mahalanobis Distance criterion and by performances of well known classifiers. The feature selection criteria used reveals that the gait measurements for different parts of the body such as knee or hip to be more effective for detection of the illnesses. Then, a set of classifiers is tested by a ten-fold cross validation approach on all datasets. It is observed that average based datasets performed better than FFT applied ones for almost all classifiers while PCA applied dataset performed better for linear classifiers. In general, nonlinear classifiers performed quite well (best error rate is about 0.035) and better than the linear ones.