Neural plasticity in adults with amblyopia

Amblyopia is a neuronal abnormality of vision that is often considered irreversible in adults. We found strong and significant improvement of Vernier acuity in human adults with naturally occurring amblyopia following practice. Learning was strongest at the trained orientation and did not transfer to an untrained task (detection), but it did transfer partially to the untrained eye (primarily at the trained orientation). We conclude that this perceptual learning reflects alterations in early neural processes that are localized beyond the site of convergence of the two eyes. Our results suggest a significant degree of plasticity in the visual system of adults with amblyopia.     

On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks

Engineering with Computers - A novel Harris hawks optimization algorithm is applied to microchannel heat sinks for the minimization of entropy generation. In the formulation of the heat transfer...     

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.     

The Small and Silent Force Multiplier: A Swarm UAV—Electronic Attack

At the last two decades, according to UAVs concepts and technological advances, there have been lots of unimagined improvements. Nowadays there are serious works and researches about the usage of UAVs in military operations at electronic warfare (EW) missions. But most of the work on UAV platforms is based upon the advantages of a single, big, expensive, and non-expendable platform. In this study, to get rid off the disadvantages of a stand alone platform a new concept is developed consisting of multiple UAVs with smaller dimensions, at a cheaper price and a wider coverage. According to clarify the study, firstly the EW and RADAR systems and then the swarm UAV concepts are explained. In this manner the current and previous works are pointed out and then the use of the swarm UAVs for EA in military operations is stated. Objectively, the swarm UAV concept’s advantages and some outstanding challenges to the intra-theater space have been put forward regarding the information mentioned above. As a result it is considered that the swarm UAV systems will be tasked important EW missions in the future operation theatres, as soon as the technical handicaps are solved.     

A Flexible Classifier Based on Optimum Curve Fitting Approach

ABSTRACT

This study proposes a curve fitting approach for classification problems. The different classification data sets are utilized to test and evaluate the suggested method. For tested classification problems, the Gaussian curve fitting models are used. In the curve fitting stage, the number of curves equals the number of attributes in the related classification problem. For example, there are 4 attributes for iris dataset, thus four Gaussian curves are fitted for this problem. Then, output values of these fitted curves are calculated to average values, and this average value is rounded to the nearest integers. The same procedure is applied to the other dataset with having different number of features. In optimization stage, for each of classification application, the optimum values of constants of Gaussian function are determined by using genetic algorithm. For all used classification dataset, a part of the set is used during the optimization phase, and then the proposed model is validated with the remainder of the dataset. Furthermore, the optimal valuesof each of the attributes in tested classification application are determined by optimization algorithm. It is a valuable property of the proposed method that the accuracy of high classification can be achieved with a low number of reference data by the stage of determination of optimal feature set. Simulation results show that proposed classification approach with optimum values of constants and optimal feature set based on curve fitting has high accuracy rate. The proposed approach can be used for different classification problems.     

High-Performance Liquid Chromatography Analysis and Assessment of Benzoic Acid in Yogurt, Ayran, and Cheese in Turkey

Among food sources, particular attention is paid to milk and dairy products, due to its nutritional importance. Benzoic acid is extensively used in the preservation of foods. A reliable method for the determination of benzoic acid in some dairy products, such as yogurt, ayran, and cheese using high-performance liquid chromatography and UV detection was validated. The peak of benzoic acid was measured at a wavelenght of 226 nm. Samples were purchased from supermarkets in Turkey in the period 2009–2010. All dairy product samples were produced from cow’s milk. The levels of benzoic acid in cheese, yogurt, and ayran samples were in the range of 3.17 to 56.77 mg kg⁻¹, 8.94 to 28.30 mg kg⁻¹, and 1.54 to 16.57 mg L⁻¹, respectively. The results show that benzoic acid widely occurs in milk products in Turkey at the low levels.     

Vitamin A status of healthy children in Manisa,Turkey

Background  

Vitamin A deficiency is a major public health nutrition problem in the developing world. Even subclinical Vitamin A deficiency is associated with increased childhood mortality. Severe maternal vitamin A deficiency may cause increased mortality in the first months of life. There have been a limited number of studies regarding vitamin A status in Turkey. The aim of this study was to assess vitamin A status of healthy children in Manisa, Turkey.