Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...

image classification is an issue which utilizes image processing, pattern recognition and classification methods. automatic medical image classification is a progressive area in image classification and it expected to be more developed in the future. due to this fact that automatic diagnosis which use intelligent methods such as medical image classification can assist pathologists by providing ...

this paper presents the application of radial basis neural networks to the development of a novel method for the condition monitoring and fault diagnosis of synchronous generators. in the proposed scheme, flux linkage analysis is used to reach a decision. probabilistic neural network (pnn) and discrete wavelet transform (dwt) are used in design of fault diagnosis system. pnn as main part of thi...

Semi-supervised learning has achieved remarkable success in the past few years at harnessing the power of unlabeled data and tackling domains where few labeled data examples exist. We test the hypothesis that deep semisupervised architectures learn general representations. We combine two well-known techniques for semi-supervised and transfer learning, ladder networks and progressive neural netw...

A parametric study was carried out in order to investigate the buckling capacity of the vertically stiffened cylindrical shells. To this end ANSYS software was used. Cylindrical steel shells with different yield stresses, diameter-to-thickness ratios (D/t) and number of stiffeners were modeled and their buckling capacities were calculated by displacement control nonlinear static analysis.  Radi...

to determine the amount of food amino acid and to spend time in the laboratories are expensive & time-consuming due to a chemical analysis. in the current laboratories, digestion nirs method is widely used for this purpose. but this method has technical limitation. therefor is important find appropriate method for estimate amount of amino acids. artificial neural network (ann) can provide a bet...

Research into regularization techniques is motivated by the tendency of neural networks to to learn the specifics of the dataset it was trained on rather than learning general features that are applicable to unseen data. This is known as overfitting. The goal of any supervised machine learning task is to approximate a function that maps inputs to outputs, given a dataset of examples and labels....

In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental questio...

Compressive strength and concrete slump are the most important required parameters for design, depending on many factors such as concrete mix design, concrete material, experimental cases, tester skills, experimental errors etc. Since many of these factors are unknown, and no specific and relatively accurate formulation can be found for strength and slump, therefore, the concrete properties ca...

Multi-instance learning originates from the investigation on drug activity prediction, where the task is to predict whether an unseen molecule could be used to make some drug. Such a problem is difficult because a molecule may have many alternative shapes with low energy, yet only one of those shapes may be responsible for the qualification of the molecule to make the drug. Because of its uniqu...