recurrent neural network algorithm
Review on Network Intrusion Detection using Recurrent Neural Network Algorithm
... Existing machine learning methodologies have been widely used in identifying various types of attacks, and a machine learning approach helps the network administrator take the preventive measures for intrusions. ...
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Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction
... (ABC) algorithm to optimize the Recurrent Neural Network (RNN) that is used to analyze traffic ...Deep Neural Networks are superseding the Shallow Neural Networks especially in ...
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Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network
... new neural network architecture called based on the adaptation of the shape of the sig- moid weight of the hidden layer neurons and have intro- duced its corresponding dynamic back propagation learning ...
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Image Description Using Deep Neural Network
... deep neural networks and recurrent neural networks ...Long-Term Recurrent Convolutional Network ...the Neural Image Captioning (NIC) algorithm proposed in [4] as well as ...
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Application of Artificial Intelligence for Epilepsy Disease
... and recurrent Neural Network (RNN) ...belief network [11] and restricted Boltzmann machine [10], in short it is written DBN and ...two-surface neural net accompanied by one perceptible ...
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Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation
... Since the rule sequence model belongs to the fam- ily of non-local feature (Huang, 2008), traditional testing methods like nbest reranking are not suit- able for our experiments. So we adopt hypergraph reranking (Huang ...
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Enhancing recurrent neural network-based language models by word tokenization
... Khoja’s algorithm removes any prefix or suffix that is not considered as a part of a word root and compares the rest of the word to the Arabic morphological ...stemming algorithm can be used to tokenize a ...
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Recurrent Neural Network Grammars
... The top-down transition set that RNNGs are based on lends itself to discriminative modeling as well, where sequences of transitions are modeled conditional on the full input sentence along with the incrementally ...
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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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Video Classification with Recurrent Neural Network
... RMLP neural network for classification. The RMLP neural network classify each video with its category by calculating error term and weighted sum and generate class for ...of neural ...
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PLANT GROWTH MODELING OF ZINNIA ELEGANS JACQ USING FUZZY MAMDANI AND L SYSTEM APPROACH WITH MATHEMATICA
... wavelet neural network has gradually refined description characterization of mutant function, and there is no requirement about the continuity of the function to be ...the network makes itself has a ...
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Improved Study of Side-Channel Attacks Using Recurrent Neural Networks
... Power analysis attack is a class of side-channel attacks where an attacker uses the power consumption of the device or the system as the leaked information to ex- ploit the target device or the system. The idea is that ...
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Modelling and trading the English stock market with novelty optimization techniques
... traditional recurrent neural ...GA neural network which, when com- pared with benchmark models, outperforms displaying superior accuracy and overall perfor- ...
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Research on English translation distortion detection based on image evolution
... the neural network algorithm accurately detects the English located above and constructs it into a single complete Chinese ...search algorithm of this paper, in addition to the ef- fective ...
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Deep Learning For Anticipation Of Cardiovascular Disease: A Practical Approach
... enacted Recurrent Neural Network (RNN) [12] to revise the memorization of standard feed forward neural network, which extends standard feed forward by adding internal ...Memory ...
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Research on neural network chaotic encryption algorithm in wireless network security communication
... discrete neural net- work, the chaotic neural network has more nonlinear dynamic characteristics and complexity, and its main features are reflected in chaotic ...the neural network, ...
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Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules
... deep neural networks, can automatically design the rules with little to none human ...stateful recurrent neural networks with attention-like loops and hybrid word- and character- level embeddings, ...
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3D Firework Reconstruction from a Given Videos
... different neural networks including 3D Convolution Neural Network (3D-CNN) and Recurrent Neural Network(RNN) are designed respectively to extract these parameters needed by our ...
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Robust Exponential Stability of Periodic Solutions for Static Recurrent Neural Networks with Delays
... recursive neural network with Markovian modulation and the time-delay static recurrent neural network model considering both random perturbation and Markovian switching are ...static ...
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Unsupervised Recurrent Neural Network Grammars
... Recurrent neural network grammars (RNNGs) (Dyer et ...of recurrent neural network grammars for language modeling and grammar ...
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