Counterpropagation neural networks were applied to the fast prediction of 1H NMR chemical shifts of CHn groups in organic compounds. The training set consisted of 744 examples of protons that were represented by physicochemical, topological, and geometric descriptors. The selection of descriptors was performed by genetic algorithms, and the models obtained were compared to those containing all ...

The propagation of errors through the uniform disk visibility function is examined. Implications of those errors upon measures of absolute visibility through optical and near-infrared interferometers are considered within the context of using calibration stars to establish system visibilities for these instruments. We suggest a simple ratio test to establish empirically whether or not the measu...

In this paper artificial neural network (ANN) models were developed to predict the mechanical properties and machinability of Cu–Sn–Pb–Si–Ni– Fe–Zn–Al alloys on the basis of the chemical composition (wt%) of alloying elements. The multi-layer perceptron architecture was used for developing ANN models. Two ANN training approaches, namely, the classical gradient descent back propagation (BP) and ...

Accurately estimating software effort is probably the biggest challenge facing software developers. Estimates done at the proposal stage has high degree of inaccuracy, where requirements for the scope are not defined to the lowest details, but as the project progresses and requirements are elaborated, accuracy and confidence on estimate increases. It is important to choose the right software ef...

Because of the defects of a posteriori and experience-depended traditional method, a prediction model of the length of garment marking based on dynamic fuzzy neural network (DFNN) combined with Quantum-behaved Particle Swarm Optimization (QPSO) was proposed. The salient characteristics of the method are: 1) hierarchical on-line selforganizing learning is used; 2) neurons can be recruited or del...

Relative location prediction in computed tomography (CT) scan images is a challenging problem. In this paper, a regression model based on one-dimensional convolutional neural networks is proposed to determine the relative location of a CT scan image both robustly and precisely. A public dataset is employed to validate the performance of the study’s proposed method using a 5-fold cross validatio...

BACKGROUND This study aimed to evaluate the prediction accuracy of postoperative refractions using partial coherence interferometry (IOL-Master) and applanation ultrasound (AL-3000) assisted with corneal topography (TMS-4) in eyes that had undergone myopic laser-assisted in situ keratomileusis (LASIK). METHODS Haigis-L formula, Koch-Maloney method using Haigis formula, Shammas clinically deri...

The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samp...

Due to the reduction of renewable energy resources such as fossil fuels, the energy crisis is one of the most critical issues in today’s world. The application of these resources brings about many environmentalpollutionsthatleadtoglobalwarming. Therefore,variouscountrieshaveattemptedto reducepotentialdamageanduserenewableenergiesbytheintroductionandpromotionofrenewable energies as an essential ...

the purpose of the present study was to examine the effects of self – control modeling , that is presenting moded to learner whenever they asked for it, on learning a timing task.twenty four right handed subjects (12 males, 12 females, aged 19-20) voluntarily participated in the study and were assigned randomly to two groups of self – control and yoked.the task was pressing the keys number 1, 4...