Neural Networks (NN)
Application of Neural Networks with CSD Coefficients for Human Face Recognition
... of neural networks classifier with face recognition application become very large because of the large database of face ...for neural networks with face recognition application in real time ...
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Diagnosing Thyroid Disease by Neural Networks
... as T3UR, FTI, FT4, FT3, T4, T3, and TSH, were obtained by considering age of the patient for classifying eight categories of thyroid diseases (30 subjects with healthy thyroid, 30 subjects suffering from hyperthyroidism, ...
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Data Mining using Neural Networks
... IEEE Neural Networks Society is on the way to become a Computational Intelligence Society and this reflects the trend to integrate neural computation into hybrid methods also known as soft computing ...
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Neural Networks
... Neural networks take a dissimilar approach in solving a problem as compared to conventional computers. Conventional computers utilize an algorithmic approach i.e. the computer observes a set of instructions ...
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Application of Ensemble Neural Networks for Different Time Scale Wind Speed Prediction
... ensemble neural network is constructed with the amalgamation of improved back propagation network, radial basis function network, multi-layer perceptron network and ELMAN ...Ensemble neural networks ...
8
Recognizing Handwritten Alphabets using Neural Networks
... Artificial Neural Networks which mimics the neurons of the human ...by Neural Networks. Neural Networks have been applied to a range of problems like stock market prediction, ...
5
Hopfield Neural Networks for Aircrafts’ Enroute Sectoring: KRISHAN-HOPES
... feedback neural network’s category Hopfield nets to manage the problem of sectoring ...of neural network which can store memory ...these networks are also known as recurrent or dynamic ...(ART1) ...
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Sentiment Classification Via Recurrent Convolutional Neural Networks
... We use one trick to train phrases, which is widely used when training neural networks with a stochastic gradient. We call initialization parameters from the uniform distribution of the neural ...
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An Evolutionary Approach: Analysis of Artificial Neural Networks
... Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or ...
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An Introduction to Neural Networks - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... Neural networks are often used for statistical analysis and data modelling, in which their role is perceived as an alternative to standard nonlinear regression or cluster analysis techniques (Cheng & ...
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Neural Networks - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... biological neural networks, the connection strengths are sometimes also called synaptic ...in neural network ...vector. Neural networks are often classified with respect to their ...
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Creating building energy prediction models with convolutional recurrent neural networks
... This is where Convolutional Neural Networks (CNN) can aid. CNNs can automate feature learning from raw inputs in a systematic way [31] and has the potential to outper- form NNs in which features are ...
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Correlation analysis and prediction of personality traits using graphic data collections
... social networks are ...artificial neural networks are used as a ...convolutional neural network, trained on the images and results of the NEO-FFI questionnaire, was used to predict personality ...
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The Application of Neural Network in Multiple Object Tracking
... of neural networks in MOT, such as feature extraction, model formulation of both appearance and ...in neural networks has shown competitive superiority and drawn wide attention from ...
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Intrusion Detection System Using Hybrid Approach by MLP and K-Means Clustering
... Here author proposed a technique in which first step is to divide the large amount of data into small subset of data. For this k-means clustering is used. Once the cluster is formed for classification or labeling ...
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An agent based method for predicting monthly maximum & minimum quote prices
... why neural networks are mainly used to solve this ...Elman networks is presented. Section 3 focuses on Elman neural network general architecture and the specific structure used in the proposed ...
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A Comprehensive Review on McCulloch Pitts Neuron Model
... Artificial Neural networks resembles the brain of a human in Two different aspects ...Artificial Neural Network is characterised by its Architecture, Training and Activation ...
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Artificial Neural network for Data mining –A study
... Abstract— Data mining is defined as the extraction of hidden predictive information from large databases. It finds its application in real world situations such as business, science, technology, and government .A data ...
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Method of Wireless Sensor Network Data Fusion
... RBF neural networks, a heterogeneous RBF neural network information fusion algorithm in wireless sensor networks is presented ...sensor networks, wireless transmission only needs to ...
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Gao, Huaien (2009): Distributed learning in sensor networks: an online-trained spiral recurrent neural network, guided by an evolution framework, making duty-cycle reduction more robust. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... recurrent neural networks (RNN) cannot be easily adapted to such co-evolution concept in sensor network, either because of the massive-junction of the hidden layer ...
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