Results 1 - 10 of 113,364

Oil production prediction with neural network method

by Haohan Liu , Wei Li , Songlin Zhang
"... Abstract-Many kinds of method can be used to predict oil production, and the neural network method is one of the most basic methods to predict oil production. In this study a modified neural network method is proposed to predict oil production in oil field. A fuzzy cluster analysis is introduced to ..."
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Abstract-Many kinds of method can be used to predict oil production, and the neural network method is one of the most basic methods to predict oil production. In this study a modified neural network method is proposed to predict oil production in oil field. A fuzzy cluster analysis is introduced

Artificial Neural Network Method for Predicting Protein

by Ovidiu Ivanciuc, Douglas J. Klein, Sonja Nikoli, Chun Li, Jun Wang, Biochem Press, C. Li, P. He, J. Wang, Chun Li, Jun Wang
"... Motivation. The rapid growth of DNA sequences data in various DNA databanks has made analyzing these sequences, especially, finding genes in them very important, and it is even a more critical task at present to clarify the number of genes. The motivation of this paper is to suggest an artificial ne ..."
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neural network method specific for predicting protein–coding genes in the yeast genome. Method. We first obtain a 12–dimensional vector from a DNA primary sequence, and then construct a 12×21×1 three–layer feedforward neural network. After being trained in a supervised manner with the error back

Video Steganography Using Neural Network Methods

by Shalini Choubey, Dr. Ashish Bansal
"... Security is nothing new; the way that security has become a part of our daily life is unprecedented. Attacks, misuse or unauthorized access of information is of great concern today which makes the protection of documents through digital media a priority problem. This urges the researcher’s to devise ..."
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of a pixel colour is negligible. The performance of this method can be further improved with the use Neural Networks Methods (like Artificial Neural Networks approach adoption (ANN) and Full Counter propagation Neural Networks (FCNN)). In this paper we will see some of the possible ways to incorporate

Analysis of Neocognitron of Neural Network Method in the String Recognition

by Amit Kumar Gupta, Yash Pal Singh
"... Abstract: This paper aims that analysing neural network method in pattern recognition. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. The proposed solutions focus on applying Neocognitron Algorithm model for patte ..."
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Abstract: This paper aims that analysing neural network method in pattern recognition. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. The proposed solutions focus on applying Neocognitron Algorithm model

Neural Network Method for Person's Personality Recognition on the Face Image

by Tropchenko Andrey, Tropchenko Alex
"... Abstract. This article examines the neural network methods to identify a person's identity on the face image used in biometric identification systems. ..."
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Abstract. This article examines the neural network methods to identify a person's identity on the face image used in biometric identification systems.

Two Neural Network Methods for Multidimensional Scaling

by Michiel C. van Wezel, Joost N. Kok, Walter A. Kosters - In European Symposium on Arti Neural Networks (ESANN'97 , 1996
"... Multidimensional scaling (MDS) embeds points in a Euclidean space given only dissimilarity data. Only very recently MDS has gotten some attention from neural network researchers. We propose two neural network methods for MDS and evaluate them using both artificially generated and real data. Training ..."
Abstract - Cited by 4 (2 self) - Add to MetaCart
Multidimensional scaling (MDS) embeds points in a Euclidean space given only dissimilarity data. Only very recently MDS has gotten some attention from neural network researchers. We propose two neural network methods for MDS and evaluate them using both artificially generated and real data

Two Neural Network Methods for Multidimensional Scaling

by Michiel Van, Michiel C. Van Wezel, Joost N. Kok, Walter A. Kosters - In European Symposium on Arti Neural Networks (ESANN'97 , 1997
"... Multidimensional scaling (MDS) embeds points in a Euclidean space given only dissimilarity data. Only very recently MDS has gotten some attention from neural network researchers. We propose two neural network methods for MDS and evaluate them using both artificially generated and real data. Training ..."
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Multidimensional scaling (MDS) embeds points in a Euclidean space given only dissimilarity data. Only very recently MDS has gotten some attention from neural network researchers. We propose two neural network methods for MDS and evaluate them using both artificially generated and real data

Evolving Neural Networks through Augmenting Topologies

by Kenneth O. Stanley, Risto Miikkulainen - Evolutionary Computation
"... An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task ..."
Abstract - Cited by 536 (112 self) - Add to MetaCart
An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning

Imagenet classification with deep convolutional neural networks.

by Alex Krizhevsky , Ilya Sutskever , Geoffrey E Hinton - In Advances in the Neural Information Processing System, , 2012
"... Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the pr ..."
Abstract - Cited by 1010 (11 self) - Add to MetaCart
Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than

Artificial Neural Network Methods in Quantum Mechanics

by I. E. Lagaris, A. Likas, D. I. Fotiadis - COMPUTER PHYSICS COMMUNICATIONS , 1997
"... In a previous article [1] we have shown how one can employ Artificial Neural Networks (ANNs) in order to solve non-homogeneous ordinary and partial differential equations. In the present work we consider the solution of eigenvalue problems for differential and integrodifferential operators, using AN ..."
Abstract - Cited by 5 (2 self) - Add to MetaCart
In a previous article [1] we have shown how one can employ Artificial Neural Networks (ANNs) in order to solve non-homogeneous ordinary and partial differential equations. In the present work we consider the solution of eigenvalue problems for differential and integrodifferential operators, using
Results 1 - 10 of 113,364