Neural Network (NN)
Developing a Neural Network based Index for Sentiment Classification
... of neural networks, some input nodes might be considered as irrelevant and then be ...a neural network based on the sum of absolute multiplication values of the weights between the ...
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Neural Network Based Parking via Google Map Guidance
... Neural Network (NN) contains collection of traffic data along a given ...Prediction Neural Network System (SPNNS) and the Dynamic Traversing Speed Profile (DTSP) ...
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Sentence Recognition Using Hopfield Neural Network
... and Neural Network (NN) Model have been combined for the ...Binary Neural Networks have also been successfully used in the task of pattern ...Hamming Neural Network has been applied to ...
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Neural network pricing of american put options
... using neural networks as the main or complementary ...resulting network formula could be used successfully to both price and delta-hedge options ...feedforward neural network model and obtain ...
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A robust deep convolutional neural network model for text categorization
... The Torch7 Neural Network (NN) package provides an easy and modular way to build and train neural networks. Each module implements the fundamental methods and the necessary state variables for ...
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How to Classify a Government? Can a Neural Network do it?
... consider neural networks as learning mechanisms used by voters to perform a classification of the incumbent in order to distinguish opportunistic (electorally motivated) from benevolent (non-electorally motivated) ...
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Neural Network to Failure Classification in Robotic Systems
... Lastly, it is possible to confirm the behavior of the network by the results obtained. Even as a preliminary study, this work has already shown good results. This way, it is proven that errors from robot ...
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3D Convolutional Neural Network for Liver Tumor Segmentation
... As discussed above, large datasets were hard to come by. In 2009, ImageNet [8] was presented as a solution to this problem. This project aimed to help the scientific community have a centralized dataset which could be a ...
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Spiking neural network Based on cusp catastrophe Theory
... key neural network effectivenes measure is the NU (Number of Units) parameter defining the number of neurons necessary to perform a specific task and the NC (Number of Channels) defining the number of ...
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A convolutional neural network approach for speech quality assesment
... Artificial neural networks have emerged as models that attempts to mimic the process- ing in human brain and have become popular over the years, being applied to a host of practical ...Fully-connected ...
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Computational Neural Network for Global Stock Indexes Prediction
... Probabilistic Neural Network (PNN) for forecasting stock index with a view that training PNN is faster and therefore, it enables the user to develop a frequently updated training ...
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Accelerating the training of convolutional neural network
... This issue can be mitigated by reducing the desired range of applications and only considering problems that deal with images. By applying some techniques from image processing, such as convolutions, which use a reduced ...
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A Neural Network for Shortest Path Computation
... A new method to solve the shortest path problem was proposed using a two-layer Hopfield Neural Network. This solution aims to achieve an increased number of succeeded and valid convergences, which is one of ...
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An Artificial Neural Network for Data Forecasting Purposes
... The research reported in this paper focuses on a comparative analysis of NARX neural network against standard ARIMA models. The study was developed on a dataset consisting in 300 historical weekly ...
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DYNAMIC APERIODIC NEURAL NETWORK FOR TIME SERIES PREDICTION
... We ran through all eight markets' stock index data using out myopic KAII neural network and we got different results from different markets. Most of the predicti[r] ...
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AUTOMATED EDGE DETECTION USING CONVOLUTIONAL NEURAL NETWORK
... Exploring properties of this application we assume: The edge detection should work the same way anywhere the input image is placed. This class of problem is called Translation Invariant Problem. The translation invariant ...
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Prediction of users’ future requests using neural network
... In this paper, we propose an experimental system, which uses web usage mining and could result in a more accurate classification of user navigation patterns, and consequently lead to a more accurate prediction of users’ ...
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EPOXY INSULATORSâLIFETIME PREDICTION IMPLEMENTING NEURAL NETWORK TECHNIQUE
... a neural network with an appropriate input and output signals is trained, it will contain the non-linearity of the desired mapping in the neural network, avoiding the knowledge of complex ...
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Prediction of Skin Penetration using Artificial Neural Network
... Abraham descriptors were used for predicting skin permeability. A database of skin permeability containing 20 data points was compiled from literature 19 . These parameters were used to train the artificial ...
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A neural network model of ventriloquism effect and aftereffect.
... underlying neural mechanisms remain ...The network accounts for the ventriloquism effect, ascribing it to a positive feedback between the visual and auditory neurons, triggered by residual auditory activity ...
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