Hybrid technique for heart diseases diagnosis based on convolution neural network and long short-term memory

2021 ◽  
pp. 261-280
Author(s):  
Abdelmegeid Amin Ali ◽  
Hassan Shaban Hassan ◽  
Eman M. Anwar ◽  
Ashish Khanna
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Heart Diseases ◽  
Convolution Neural Network ◽  
Hybrid Technique ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Quantification of Mental Workload Using a Cascaded Deep One-dimensional Convolution Neural Network and Bi-directional Long Short-Term Memory Model

2021 ◽  
Author(s):  
Vipul Sharma ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Mental Workload ◽  
Binary Classification ◽  
Convolution Neural Network ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Long Short Term Memory

In this paper, a new cascade one-dimensional convolution neural network (1DCNN) and bidirectional long short-term memory (BLSTM) model has been developed for binary and ternary classification of mental workload (MWL). MWL assessment is important to increase the safety and efficiency in Brain-Computer Interface (BCI) systems and professions where multi-tasking is required. Keeping in mind the necessity of MWL assessment, a two-fold study is presented, firstly binary classification is done to classify MWL into Low and High classes. Secondly, ternary classification is applied to classify MWL into Low, Moderate, and High classes. The cascaded 1DCNN-BLSTM deep learning architecture has been developed and tested over the Simultaneous task EEG workload (STEW) dataset. Unlike recent research in MWL, handcrafted feature extraction and engineering are not done, rather end-to-end deep learning is used over 14 channel EEG signals for classification. Accuracies exceeding the previous state-of-the-art studies have been obtained. In binary and ternary classification accuracies of 96.77% and 95.36% have been achieved with 7-fold cross validation, respectively.

scholarly journals Quantification of Mental Workload Using a Cascaded Deep One-dimensional Convolution Neural Network and Bi-directional Long Short-Term Memory Model

2021 ◽  
Author(s):  
Vipul Sharma ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Mental Workload ◽  
Binary Classification ◽  
Convolution Neural Network ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Long Short Term Memory

In this paper, a new cascade one-dimensional convolution neural network (1DCNN) and bidirectional long short-term memory (BLSTM) model has been developed for binary and ternary classification of mental workload (MWL). MWL assessment is important to increase the safety and efficiency in Brain-Computer Interface (BCI) systems and professions where multi-tasking is required. Keeping in mind the necessity of MWL assessment, a two-fold study is presented, firstly binary classification is done to classify MWL into Low and High classes. Secondly, ternary classification is applied to classify MWL into Low, Moderate, and High classes. The cascaded 1DCNN-BLSTM deep learning architecture has been developed and tested over the Simultaneous task EEG workload (STEW) dataset. Unlike recent research in MWL, handcrafted feature extraction and engineering are not done, rather end-to-end deep learning is used over 14 channel EEG signals for classification. Accuracies exceeding the previous state-of-the-art studies have been obtained. In binary and ternary classification accuracies of 96.77% and 95.36% have been achieved with 7-fold cross validation, respectively.

Automatic Lip Reading Using Convolution Neural Network and Bidirectional Long Short-term Memory

2019 ◽  
Vol 34 (01) ◽  
pp. 2054003 ◽  
Author(s):  
Yuanyao Lu ◽  
Jie Yan
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Active Contour Model ◽  
Video Clip ◽  
Convolution Neural Network ◽  
Short Term ◽  
Term Memory ◽  
Lip Reading ◽  
Long Short Term Memory

Traditional automatic lip-reading systems generally consist of two stages: feature extraction and recognition, while the handcrafted features are empirical and cannot learn the relevance of lip movement sequence sufficiently. Recently, deep learning approaches have attracted increasing attention, especially the significant improvements of convolution neural network (CNN) applied to image classification and long short-term memory (LSTM) used in speech recognition, video processing and text analysis. In this paper, we propose a hybrid neural network architecture, which integrates CNN and bidirectional LSTM (BiLSTM) for lip reading. First, we extract key frames from each isolated video clip and use five key points to locate mouth region. Then, features are extracted from raw mouth images using an eight-layer CNN. The extracted features have the characteristics of stronger robustness and fault-tolerant capability. Finally, we use BiLSTM to capture the correlation of sequential information among frame features in two directions and the softmax function to predict final recognition result. The proposed method is capable of extracting local features through convolution operations and finding hidden correlation in temporal information from lip image sequences. The evaluation results of lip-reading recognition experiments demonstrate that our proposed method outperforms conventional approaches such as active contour model (ACM) and hidden Markov model (HMM).

scholarly journals Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1314
Author(s):  
Kecheng Peng ◽  
Xiaoqun Cao ◽  
Bainian Liu ◽  
Yanan Guo ◽  
Chaohao Xiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Polar Vortex ◽  
Convolution Neural Network ◽  
Short Term ◽  
Gaussian Smoothing ◽  
Term Memory ◽  
Intensity Prediction ◽  
Long Short Term Memory

The variation of polar vortex intensity is a significant factor affecting the atmospheric conditions and weather in the Northern Hemisphere (NH) and even the world. However, previous studies on the prediction of polar vortex intensity are insufficient. This paper establishes a deep learning (DL) model for multi-day and long-time intensity prediction of the polar vortex. Focusing on the winter period with the strongest polar vortex intensity, geopotential height (GPH) data of NCEP from 1948 to 2020 at 50 hPa are used to construct the dataset of polar vortex anomaly distribution images and polar vortex intensity time series. Then, we propose a new convolution neural network with long short-term memory based on Gaussian smoothing (GSCNN-LSTM) model which can not only accurately predict the variation characteristics of polar vortex intensity from day to day, but also can produce a skillful forecast for lead times of up to 20 days. Moreover, the innovative GSCNN-LSTM model has better stability and skillful correlation prediction than the traditional and some advanced spatiotemporal sequence prediction models. The accuracy of the model suggests important implications that DL methods have good applicability in forecasting the nonlinear system and vortex spatial–temporal characteristics variation in the atmosphere.

scholarly journals Quantification of Mental Workload Using a Cascaded Deep One-dimensional Convolution Neural Network and Bi-directional Long Short-Term Memory Model

2021 ◽  
Author(s):  
Vipul Sharma ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Mental Workload ◽  
Binary Classification ◽  
Convolution Neural Network ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Long Short Term Memory

In this paper, a new cascade one-dimensional convolution neural network (1DCNN) and bidirectional long short-term memory (BLSTM) model has been developed for binary and ternary classification of mental workload (MWL). MWL assessment is important to increase the safety and efficiency in Brain-Computer Interface (BCI) systems and professions where multi-tasking is required. Keeping in mind the necessity of MWL assessment, a two-fold study is presented, firstly binary classification is done to classify MWL into Low and High classes. Secondly, ternary classification is applied to classify MWL into Low, Moderate, and High classes. The cascaded 1DCNN-BLSTM deep learning architecture has been developed and tested over the Simultaneous task EEG workload (STEW) dataset. Unlike recent research in MWL, handcrafted feature extraction and engineering are not done, rather end-to-end deep learning is used over 14 channel EEG signals for classification. Accuracies exceeding the previous state-of-the-art studies have been obtained. In binary and ternary classification accuracies of 96.77% and 95.36% have been achieved with 7-fold cross validation, respectively.

Sign in / Sign up

Export Citation Format

Share Document