neural network based models
Linear and Neural Network-based Models for Short-Term Heat Load Forecasting
... forecasting models for heat demand a day in advance in a district heating system is discussed in this ...is based on district heating data for the city of Ljubljana, Slovenia, for five subsequent winter ...
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Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension
... deep neural networks. More specifically, they often use recurrent neural net- works (RNNs), a special type of networks, which process input ...NLP models, is the simultaneous process- ing of input ...
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LTL UDE at SemEval 2019 Task 6: BERT and Two Vote Classification for Categorizing Offensiveness
... list- based classification, using classifiers such as SVM or logistic regression based on sentence embed- dings, and neural network-based models such as a Multi-layer Perceptron ...
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Modified Neural Network Architecture based Expert System for Automated Disease Classification and Detection using PCA Algorithm
... reference models. Various sensor models (electrical and mechanical) are already inbuilt in Matlab and are readily available for development of automotive and mechanical system ...
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Freight-Forward Agreement Time series Modelling Based on Artificial Neural Network Models
... This paper employs modular neural networks. These networks are a special class of multi-layer perceptron (MLP) feed-forward artificial neural network model corresponding to input data maps. These ...
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Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
... decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in ...these ...
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Novel Ensemble Neural Network Models for better Prediction using Variable Input Approach
... Artificial Neural Network (ANN) models, namely, Multilayer Perceptron Network (MLPN), Elman Recurrent Neural Network (ERNN), Radial Basis Function Network (RBFN), Hopfield ...
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Computational Ad Hominem Detection
... is based on two sequence models: a bidirec- tional GRU neural network for a sequence of word representations and another similar network for POS ...
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Deep Reinforcement Learning of the Model Fusion with Double Q learning
... deep neural networks. We apply what we call a double DQN. Based on the double q- learning algorithm we adds different models of neural networks to form a fusion frame module, considering the ...
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A Short Term Traffic Flow Prediction Based on Recurrent Neural Networks for Road Transportation Control in ITS
... are based on simulations and mathematical derivations to most of these studies rely on mathematical equations or simulation techniques to define the development of network ...transportation network ...
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OPTIMIZATION OF HIGH VOLTAGE POWER SUPPLY FOR INDUSTRIAL MICROWAVE GENERATORS FOR ONE MAGNETRON
... filtering models [31,6,32] have been used extensively in travel time estimation ...These models are effective in predicting travel time one or two time periods ahead, but they deteriorate with multiple time ...
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Prediction models in the design of neural network based ECG classifiers: A neural network and genetic programming approach
... one based on NNs and the other on Genetic Programming ...method based on natural selection rules ...two models based on NNs and GP techniques to estimate, giv- en a NN design for ECG ...
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The Edinburgh/JHU Phrase based Machine Translation Systems for WMT 2015
... language models on the target ...bilingual neural network models: one over the source and one over the source and target, and an NPLM language model over the tar- ...
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Deep Learning Based Sentiment Analysis for Recommender System
... products based on user ...learning models. In this paper we will discuss about the various models that are used for feature generation, which will be provided as input for the sentiment ...classifier ...
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Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection
... different network models and algorithms for data classification problems with the constant development of machine ...of network security as well as small difference between different intrusion ...
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Comparative study of static and dynamic neural network models for nonlinear time series forecasting
... decades, neural network models have been focused upon by researchers due to their more real performance and on this basis different types of these models have been used in ...these ...
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APPROXIMATE, GENERALIZED FIELD DATA BASED MATHEMATICAL MODELING AND ANN SIMULATION OF PVC PIPE MANUFACTURING PROCESS
... type neural network is chosen. (6) This network is then trained using the training ...the network is ...Artificial Neural Network (ANN). The output of this network can be ...
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Joint Multitask Learning for Community Question Answering Using Task Specific Embeddings
... Various neural models have been applied to cQA tasks such as question-question similarity (dos Santos et ...advanced neural network architectures based on convolutional neural ...
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Effort Estimation of Agile and Web-Based Software Using Artificial Neural Networks
... estimation models for agile and web-based software by using various neural networks such as Feed-Forward Neural Network (FFNN), Radial Basis Function Neural Network ...
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STOCK MARKET PREDICTION USING BIO-INSPIRED COMPUTING: A SURVEY
... various neural network models i.e functional link artificial neural network(FLANN),Radial basis function neural network(RBFNN) local linear wavelet neural ...
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