neural networks (NNs)
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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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
... Many cryptographers would agree that, it is only because of the concept of semaphore‘s design invention that, the exploration of Internet QoS might have came into existence. The notion that cyberneticists collaborate ...
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Neural Networks
... A neural network is data processing system which consists of large number of simple & highly interconnected elements which are used for processing in a framework which is inspired by the ...hand, neural ...
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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 ...
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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, ...
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Hopfield Neural Networks for Aircrafts’ Enroute Sectoring: KRISHAN-HOPES
... artificial neural networks are biologically ...artificial neural networks perform computational tasks by modeling the human brain ...the neural networks are divided in two ...
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Application of Neural Networks with CSD Coefficients for Human Face Recognition
... Human face recognition is one of the active area of research and it is a preliminary step to a wide range of practical applications which includes image processing, pattern recognition, personal identity verification, ...
72
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 Introduction to Neural Networks - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... This book grew out of a set of course notes for a neural networks module given as part of a Masters degree in "Intelligent Systems". The people on this course came from a wide variety of ...
317
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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Neural Networks - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... to neural networks, this indicates that we search for a neural net- work and an adaptation algorithm that improves continuously when being trained in a stationary environment but, when confronted ...
111
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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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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Building lighting energy consumption modelling with hybrid neural-statistic approaches
... Abstract. In the proposed work we aim at modelling building lighting energy consumption. We compared several classical methods to the latest Artificial Intelligence modelling technique : Artificial Neural ...
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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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FORECASTING ENERGY DEMAND FOR A HOUSE USING ARTIFICIAL NEURAL NETWORK
... The Neural networks are widely using for short-term, and to a lesser degree medium and long term, demand ...both neural network and abductive network for modeling, and comparing their ...
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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 ...
183
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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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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