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.     

A self‐correction technique for real‐time simulation of regulated rivers

An automatic self‐correction scheme is presented for online adjustment of stream‐flow forecasts during simulation of river systems with regulating structures, when actual flow data for interior points are available in real time. Another scheme is presented which allows for the modelling of channel losses due to physical effects such as evapotranspiration, bank storage and diversions. A previously developed river simulation/reservoir management model has been modified to include both schemes. The model is particularly useful for real‐time, short‐term flood forecasting and reservoir operation. The model application is made to the Lower Colorado River in Texas.     

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.     

Cost-conscious comparison of supervised learning algorithms over multiple data sets

In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi²Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from “best” to “worst” where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi²Test generates orderings using pairwise tests on error and different types of cost using time and space complexity of the learning algorithms.     

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.     

Resource Leveling in Line‐of‐Balance Scheduling

Resource leveling involves minimizing resource fluctuations without changing the completion time of a project. A smooth distribution of resources minimizes logistical problems and results in cost savings. Line‐of‐balance (LOB) is a resource‐based scheduling system that is used in projects that exhibit repetitive characteristics, performs resource allocation as a matter of course, but does not deal with resource leveling. In the past, researchers experienced declines in productivity whenever they leveled resources in different linear scheduling models by adjusting activities’ production rates. The objective of this research is to develop a genetic algorithm‐based resource leveling model for LOB schedules that does not impact productivity negatively. This model is based on the “natural rhythm” principle, according to which a crew of optimum size will be able to complete an activity in the most productive way. The “natural rhythm” principle allows shifting the start time of an activity at different units by adjusting the number of crews without changing the duration of the activity in any one unit and without violating the precedence relationships between activities. An LOB schedule is established for a pipeline project and is used to illustrate the proposed resource leveling model. It was observed that the model provides a smoother resource utilization histogram. Performing resource leveling in LOB scheduling without sacrificing productivity is the major contribution of the proposed model.