Addressing big data issues using RNN based techniques

Das, Tanuja; Saha, Goutam Kumar

doi:10.1080/02522667.2019.1703268

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…RNN training performs a frequent update of the input in its neural network. Then, it estimates the current state based in the latest input and the previous state [18]. RNN can perform huge training sets to generalise the model and increase the probability of exact predictions.…”

Section: Process In Rnnmentioning

confidence: 99%

“…The data retrieval from the RNN's memory may be complicated and cause some delay for a long sequence of the data queue. It cannot process very long sequences if using tanh or relu as an activation function [18]. The exploding gradient is an issue that occurred in RNN and in most of neural networks during the model's training.…”

Section: Disadvantages Of Recurrent Neural Networkmentioning

confidence: 99%

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Enhanced Recurrent Neural Network (RNN) For Heart Disease Risk Prediction Using Framingham Datasets

Krishnan

Magalingam²,

Ibrahim³

2023

OIJI

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Heart disease is one of the leading causes of death globally, which takes 17.9 million lives each year. The existing heart disease prediction techniques have a gap that does not consider the smoking attributes from the heart disease data. So, the accuracy is based on the limited number of medical data and the deep learning model. The existing deep learning models which use the Recurrent Neural Network (RNN) for heart disease prediction consume more processing and analysing time, mainly due to the delay of data retrieval. This delay causes the prediction process to become slower and leads to a moderate prediction only. The backpropagation of the RNN with an update gate and internal memory to carry the updated data cause a minor data glitch that leads to lower accuracy. Therefore, an efficient heart disease prediction model is very crucial to provide early detection among patients. This research proposes a Heart Disease Risk Prediction Model (HDRPM) with an enhanced RNN to improve the prediction accuracy using Framingham heart disease datasets. The specificity and sensitivity are imposed to improve the quality of the predictions. Sensitivity measure is used for detecting patients with heart disease perfectly and specificity measure is used for detecting patients without the disease perfectly. Besides the accuracy and quality of the prediction problem, the imbalance of minority classes in the dataset occurred in most deep learning prediction fields. This research aims to improve the quality of imbalanced Framingham datasets using Synthetic Minority Over-sampling Technique (SMOTe), which will synthetic instances in a small class to be equalized. The existing RNN model faces vanishing gradients that impede the learning of long data sequences. These gradients that carry information in the RNN cells will become smaller gradually till it minimises the parameter updates and leads to poor learning. For this purpose, the presence of multiple Gated Recurrent Unit (GRU) is used to overcome the vanishing gradients and ensure the hidden layers. The neurons of RNN rapidly cater for the essential information during the training and validation phase of the HDRPM. The integration of multiple GRU with the RNN, operating on the Tensorflow as back-end and Keras as the core for the neural network library has increased the performance of the proposed model. The proposed model provides up to 98.78%, the highest accuracy achieved compared to related previous work, which is a quantum neural network model with 98.57. This HDRPM is expected to significantly contribute to early detection of heart disease patients.

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Section: Process In Rnnmentioning

confidence: 99%

Section: Disadvantages Of Recurrent Neural Networkmentioning

confidence: 99%

Enhanced Recurrent Neural Network (RNN) For Heart Disease Risk Prediction Using Framingham Datasets

Krishnan

Magalingam²,

Ibrahim³

2023

OIJI

View full text Add to dashboard Cite

show abstract

“…A recursive process is applied on hidden states, accurately on H weights and remembering H and values. The exact process is used on deep RNNs, weights are recursively updated in each iteration, and then more iterations are running, we go deep in learning, the reason we call it deep RNNs [18], [27]- [29].…”

Section: ( ( (mentioning

confidence: 99%

“…There are a lot of neural network types. The convolutional neural network, which belongs to the deep learning class, is used for visual imagery [24]- [26], and the recurrent neural network, which performs in natural language processing [18], [27]- [29]. In our case, we are dealing with a dataset of time series data (Financial market prices) processing and produce a binary decision: Place a profitable trade or Place none profitable trade [30], [31].…”

Section: Introductionmentioning

confidence: 99%

A Channeled Multilayer Perceptron as Multi-Modal Approach for Two Time-Frames Algo-Trading Strategy

Fikri¹,

Moussaid²,

Rida³

et al. 2022

IJACSA

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FOREX (Foreign Exchanges) is a 24H open market with an enormous daily volume. Most of the used Trading strategies, used individually, are not providing accurate signals. In this paper, we are proposing an automated trading strategy that fits random market behaviors. It is based on neural networks applying triple exponential weighted moving average (EMA) as a trend indicator, Bollinger bands as a volatility indicator, and stochastic RSI as a momentum reversal indicator to prevent false indications in a short time frame. This approach is based on trend, volatility, and momentum reversal patterns combined with a market adaptive and a distributed multi-layer perceptron (MLP). It is called channeled multi-layer perceptron (CMLP) that is a neural network using channels and routines trained by previous profit/loss earned by triple EMA crossover, Bollinger Bands, and Stochastic RSI signals. Instead of using classic computations and Back-propagation for adjusting MLP parameters, we established a channeled multi-layer perceptron inspired by a multi-modal learning approach where each group of modalities (Channel) has its That stands for a dynamic channel coefficient to produce a multi-processed feed-forward neural network that prevents uncertain trading signals depending on trend-volatility-momentum random patterns. CMLP has been compared to Multi-Modal GARCH-ARIMA and has proven its efficiency in unstable markets.

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Automated sleep apnea detection in snoring signal using long short-term memory neural networks

Cheng

Wang

Yue

et al. 2022

Biomedical Signal Processing and Control

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Addressing big data issues using RNN based techniques

Cited by 7 publications

References 8 publications

Enhanced Recurrent Neural Network (RNN) For Heart Disease Risk Prediction Using Framingham Datasets

Enhanced Recurrent Neural Network (RNN) For Heart Disease Risk Prediction Using Framingham Datasets

A Channeled Multilayer Perceptron as Multi-Modal Approach for Two Time-Frames Algo-Trading Strategy

Automated sleep apnea detection in snoring signal using long short-term memory neural networks

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