Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.     

A new feature selection method on classification of medical datasets: Kernel F-score feature selection

In this paper, we have proposed a new feature selection method called kernel F-score feature selection (KFFS) used as pre-processing step in the classification of medical datasets. KFFS consists of two phases. In the first phase, input spaces (features) of medical datasets have been transformed to kernel space by means of Linear (Lin) or Radial Basis Function (RBF) kernel functions. By this way, the dimensions of medical datasets have increased to high dimension feature space. In the second phase, the F-score values of medical datasets with high dimensional feature space have been calculated using F-score formula. And then the mean value of calculated F-scores has been computed. If the F-score value of any feature in medical datasets is bigger than this mean value, that feature will be selected. Otherwise, that feature is removed from feature space. Thanks to KFFS method, the irrelevant or redundant features are removed from high dimensional input feature space. The cause of using kernel functions transforms from non-linearly separable medical dataset to a linearly separable feature space. In this study, we have used the heart disease dataset, SPECT (Single Photon Emission Computed Tomography) images dataset, and Escherichia coli Promoter Gene Sequence dataset taken from UCI (University California, Irvine) machine learning database to test the performance of KFFS method. As classification algorithms, Least Square Support Vector Machine (LS-SVM) and Levenberg–Marquardt Artificial Neural Network have been used. As shown in the obtained results, the proposed feature selection method called KFFS is produced very promising results compared to F-score feature selection.     

A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS)

The forecasting of air pollution is important for living environment and public health. The prediction of SO₂ (sulfur dioxide), which is one of the indicators of air pollution, is a significant part of steps to be done in order to decrease the air pollution. In this study, a novel feature scaling method called neighbor-based feature scaling (NBFS) has been proposed and combined with artificial neural network (ANN) and adaptive network–based fuzzy inference system (ANFIS) prediction algorithms in order to predict the SO₂ concentration value is from air quality metrics belonging to Konya province in Turkey. This work consists of two stages. In the first stage, SO₂ concentration dataset has been scaled using neighbor-based feature scaling. In the second stage, ANN and ANFIS prediction algorithms have been used to forecast the SO₂ value of scaled SO₂ concentration dataset. SO₂ concentration dataset was obtained from Air Quality Statistics database of Turkish Statistical Institute. To constitute dataset, the mean values belonging to seasons of winter period have been used with the aim of watching the air pollution changes between dates of December, 1, 2003 and December, 30, 2005. In order to evaluate the performance of the proposed method, the performance measures including mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and IA (Index of Agreement) values have been used. After NBFS method applied to SO₂ concentration dataset, the obtained RMSE and IA values are 83.87–0.27 (IA) and 93–0.33 (IA) using ANN and ANFIS, respectively. Without NBFS, the obtained RMSE and IA values are 85.31–0.25 (IA) and 117.71–0.29 (IA) using ANN and ANFIS, respectively. The obtained results have demonstrated that the proposed feature scaling method has been obtained very promising results in the prediction of SO₂ concentration values.     

Wear behavior of as-deposited and oxidized ternary (Zr,Hf)N coatings

In the present study, the tribological performance of ternary (Zr,Hf)N coatings, whose Hf content was varied between 0 and 21 wt pct, has been systematically examined in the as-deposited and oxidized conditions. Coatings were applied on AISI D2 tool steel plates by arc physical vapor deposition (PVD). Oxidation was conducted at 400 °C for times up to 12 hours in air. Wear tests revealed pronounced improvement in the wear resistance with increasing Hf content in the as-deposited coatings. Oxidation for 12 hours yielded more than a fourfold increase in wear resistance. The beneficial effect of Hf on wear resistance decreased upon oxidation.     

Sliding wear behaviour of ZrN and (Zr, 12 wt% Hf)N coatings

In the present study, the sliding wear resistances of ZrN and (Zr, 12 wt% Hf)N coatings deposited on a hardened AISI D2 tool steel by arc-physical vapor deposition (PVD) technique were examined by a ball-on-disc wear tester. Alloying of ZrN coating with 12 wt% Hf did not change the hardness significantly, but achieved an improvement on adhesion strength and dry sliding wear resistance against steel (AISI 52100-55HRC) and Al₂O₃ balls.     

The effect of magnetic field on the impurity binding energy of shallow donor impurities in a Ga1?xInxNyAs1?y/GaAs quantum well

Using a variational approach, we have investigated the effects of the magnetic field, the impurity position, and the nitrogen and indium concentrations on impurity binding energy in a Ga_1−xIn_xN_yAs_1−y/GaAs quantum well. Our calculations have revealed the dependence of impurity binding on the applied magnetic field, the impurity position, and the nitrogen and indium concentrations.