The main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrection strategy supported by a recurrent neural network for finding a near optimal solution of a given objective function. Recently there have been attempts for using artificial neural networks (ANNs) in optimization problems and some types of ANNs such as Hopfield network and Bol...

The main problem with modern quality control of sound speakers is that the process is conducted manually. This manual checking of the quality of sound speakers is time consuming. In order to find an automated way of doing this, this paper presents an intelligent system for automated quality control in sound speaker manufacturing, which fuses Fractal Dimension (FD) into Artificial Neural Network...

Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. However, ANNs remain challenging to use for non-expert users. In this paper, we present NeuroNER, an easyto-use named-entity recognition tool based on ANNs. Users can annotate entities using a graphical web-based user i...

Purpose – We describe an intelligent video categorization engine (IVCE) that uses the learning capability of artificial neural networks (ANNs) to classify suitably preprocessed video segments into a predefined number of semantically meaningful events (categories). Design/methodology/approach – We provide a survey of existing techniques that have been proposed, either directly or indirectly, tow...

This paper applies artificial neural networks (ANNs) to the survival analysis problem. Because ANNs can easily consider variable interactions and create a non-linear prediction model, they offer more flexible prediction of survival time than traditional methods. This study compares ANN results on two different breast cancer datasets, both of which use nuclear morphometric features. The results ...

The artificial neural networks (ANNs) are well suitable to solve a variety class of problems in a knowledge discovery field (e.g., in natural language processing) because the trained networks are more accurate at classifying the examples that represent a problem domain. However, the neural networks that consist of large number of weighted connections (called also links) and activation units oft...

This paper describes the implementation of multivariate data analysis: NEURODOC applies the axial k-means method for automatic, non-hierarchical cluster analysis and a Principal Component Analysis (PCA) for representing the clusters on a map. We next introduce Artificial Neural Networks (ANNs) to extend NEURODOC into a neural platform for the cluster analysis and cartography of bibliographic da...

Analog Artificial Neural Networks (ANNs) could be the core of an "intelligent" signal processor, with the today used digital processing replaced by a raw "data driven" methodology. Characteristic of this approach is: analog input/output: signals don't need A/D and D/A converters; speed: an analog system can be faster than a digital or a mixed-mode one; effectiveness: ANNs already demonstrated t...

It seems obvious that the massively parallel computations inherent in artificial neural networks (ANNs) can only be realized by massively parallel hardware. However, the vast majority of the many ANN applications simulate their ANNs on sequential computers which, in turn, are not resource-efficient. The increasing availability of parallel standard hardware such as FPGAs, graphics processors, an...