Neural Networks (Computer)
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, ...
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An Evolutionary Approach: Analysis of Artificial Neural Networks
... Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process ...information. Neural networks, with their ...
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Recognizing Handwritten Alphabets using Neural Networks
... Neurons are the basic element of the Central Nervous System and are responsible for all the learnings we gain. Electrical impulses generated, transfer information to other neurons. The actions we perform as a response is ...
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Neural Networks
... Neural networks, with their exceptional ability to derive definition from imprecise or complicated data, and can be used to separate patterns and detect fashion which is too complex to be observed by either ...
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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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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 ...
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Sentiment Classification Via Recurrent Convolutional Neural Networks
... At present, there are some neural networks based methods that have been used in the sentiment classification task. Socher et al. [2, 3, 4] proposed the Recursive Neural Network (RecursiveNN). It has ...
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Diagnosing Thyroid Disease by Neural Networks
... of networks and also the possibility of diagnosing three new categories of thyroid disease including hyperthyroidism under treatment, hypothyroidism under treatment, and resistant thyroid is applicable and by help ...
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Data Mining using Neural Networks
... the neural networks can be used for maintaining and exploring new data sciences in order to provide encouraging frameworks in managing infinite volumes of data we have at our ...and neural ...
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Hopfield Neural Networks for Aircrafts’ Enroute Sectoring: KRISHAN-HOPES
... Hopfield neural networks or simply Hopfield nets, a widely used popular category of feedback neural network or recurrent neural networks may play a very important role in handling ...
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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 ...
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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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Unified Framework For Deep Learning Based Text Classification
... artificial neural networks, which are inspired by biological brain model made of ...convolutional neural network (CNN), deep belief networks, recurrent neural networks (RNN), ...
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Building lighting energy consumption modelling with hybrid neural-statistic approaches
... In this scenario, at present there are three different approaches [4] for modelling energy consumption in buildings : Statistic Modelling (SM), Simulation Programs (SP) and Intelligent Computer Systems (ICS). The first ...
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Attempting to Mimic the Brain Synthetically
... behind neural networks. A neural network is composed of “a set of simple computing units which influence each other through modifiable connections, or ...artificial networks were made to model ...
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Experimental Investigation of Classification Algorithms for Predicting Lesion Type on Breast DCE MR Images
... artificial neural networks (ANN) have been one of the most common approaches for researching the classification of malignant and benign breast MR lesions [1, 3-9, 20-22, 24- ...artificial neural ...
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Method of Wireless Sensor Network Data Fusion
... brain, neural network has powerful parallel pro- cessing ability ...computer networks, the most commonly used are BP neural networks and RBF neural networks, and an improved ...
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Deep Learning as a Frontier of Machine Learning: A Review
... belief networks are the example of deep learning model which are applied to such unsupervised ...Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Deep Belief Network ...
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Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the ...deep neural networks, convolutional deep neural networks, ...
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Recent trends in neuromorphic engineering
... spiking neural networks on 180nm CMOS ...spiking neural network simulator with memris- tors for computer vision ...various neural network topologies has been developed by Liu et ...
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