[PDF] Top 20 An Approach to Predict a Student’s Academic Performance using Recurrent Neural Network (RNN)
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An Approach to Predict a Student’s Academic Performance using Recurrent Neural Network (RNN)
... The proposed neural network contains three layers. The first layer is the input layer and the seventeen features are given as input in the system. The final result of the student is produced as ... See full document
5
Deep Learning For Anticipation Of Cardiovascular Disease: A Practical Approach
... Artificial Neural Network ...disease.Using RNN and GRU, Abhay Kishore et al [9] made healthcare more accurate and efficient to predict silent heart attacks and inform the user as early as ... See full document
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Dependency Recurrent Neural Language Models for Sentence Completion
... Most neural language models consider the to- kens in a sentence in the order they appear, and the hidden state representation of the network is typically reset at the beginning of each sen- ...a ... See full document
7
Dependency Recurrent Neural Language Models for Sentence Completion
... Most neural language models consider the to- kens in a sentence in the order they appear, and the hidden state representation of the network is typically reset at the beginning of each sen- ...a ... See full document
7
Automatic Target Recognition (ATR) System using Recurrent Neural Network (RNN) for Pulse Radar
... a RNN based system for prediction of target from radar return ...the performance of ATR with signal ...of RNN which enables the system to capture time variation in the inputs which generates a CRR of ... See full document
7
Deep Learning Based Visual Tracking: A Review
... first neural-network tracker that combines convolutional and recurrent networks with RL algorithm in ...an RNN constructing video memory state, and a reinforcement learning (RL) agent making ... See full document
5
Modeling Server Workloads for Campus Email Traffic Using Recurrent Neural Networks
... outperforms RNN, as shown in Table 10 (for the TUC and LJMU datasets again RNN ...probabilistic approach, as explained earlier, focuses on 98% of the traffic, excluding the ...of RNN for the ... See full document
10
A Review on Large-scale Video Classification with Recurrent Neural Network (RNN)
... Recently, Recurrent Neural Network (RNN) [2] has been express as an effective class of models for understanding image ...each approach, model for classification, features extracted for ... See full document
5
Improved Study of Side-Channel Attacks Using Recurrent Neural Networks
... is using cryptographic algorithms implementation. Using deep learning techniques, we are interested in evaluating the performance of our neural network models in predicting the power ... See full document
78
Computational Analysis of Sag and Swell in Electrical Power Supply Network
... swells) using Neural ...& Recurrent Neural Networks (RNN) for the approximation/prediction of sag and ...of RNN on PQ data demonstrates its good approximation abilities ... See full document
8
NETWORK INTRUSION DETECTION USING DEEP NEURAL NETWORKS
... reduced-size neural networks. In that paper, the author proposes a three layer RNN architecture with 41 features as inputs and four intrusion categories as outputs, and for misuse-based ...the ... See full document
9
Translation Quality Estimation using Recurrent Neural Network
... to predict if the given word/phrase is a correct/incorrect (OK/BAD) translation in the given sen- ...novel approach for word level Quality Esti- mation using Recurrent Neural ... See full document
6
A Neural Network based Approach to Automatic Post Editing
... danau et al. (2014), we use LSTMs rather than GRUs as hidden units. RNNs allow process- ing of arbitrary length sequences, however, they are susceptible to the problem of vanishing and exploding gradients (Bengio et al., ... See full document
6
Closed-form formulas for the electromagnetic parameters of inverted microstrip line
... data. Using this technique a neural network algorithm can learn by examples, and create an internal structure of rules to classify different inputs and produce one target ...output. Neural ... See full document
7
Deep Learning as a Frontier of Machine Learning: A Review
... Deep learning, a family of machine learning algorithms, is inspired by the biological process of neural networks is dominating in many applications and proving its advantage over conventional machine learning ... See full document
9
<p>Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet</p>
... ANN Neural Power ® version 3.1 was used to study the relative effects of Eudragit-S100, DCP and PVP-K30 on hardness and in-vitro dissolution for 2 – 8 h. 32 Quick propaga- tion (QP) method was employed for the ... See full document
14
Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter
... From analyzing these tweets, we found that some of the hashtags can be considered as the keyphrases of the tweet. For example: “The Warriors take Game 1 of the #NBAFinals 104-89 behind a playoff career-high 20 from Shaun ... See full document
10
Deep Temporal Recurrent Replicated Softmax for Topical Trends over Time
... a neural temporal topic model which we name as RNN-RSM, based on prob- abilistic undirected graphical topic model RSM with time-feedback connections via determinis- tic RNN, to capture temporal ... See full document
11
Egocentric Gesture Recognition for Head Mounted AR devices
... a RNN to learn temporal discrimination for recognising ego-hand gestures, while keeping the network ...stage network architecture.The first stage is Ego-Hand Mask Encoder Network (EHME ...the ... See full document
6
It may be time to perfect the neuron of artificial neural network
... We found, at the time of design, the traditional artificial neurons ignored a fact that dendrites participate in pre- calculation in a biological neuron or biological neural network. More specifically, ... See full document
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