scholarly journals Enhanced Deep Learning Assisted Convolutional Neural Network for Heart Disease Prediction on the Internet of Medical Things Platform

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 189503-189512
Author(s):  
Yuanyuan Pan ◽  
Minghuan Fu ◽  
Biao Cheng ◽  
Xuefei Tao ◽  
Jing Guo
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
The Internet ◽  
Disease Prediction ◽  
Internet Of Medical Things

scholarly journals Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 5467
Author(s):  
Surenthiran Krishnan ◽  
Pritheega Magalingam ◽  
Roslina Ibrahim
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Prediction ◽  
The Neural Network ◽  
Proposed Model ◽  
Deep Learning Model

<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>

scholarly journals A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification

2018 ◽  
Vol 10 (12) ◽  
pp. 116 ◽  
Author(s):  
Yonghua Zhu ◽  
Xun Gao ◽  
Weilin Zhang ◽  
Shenkai Liu ◽  
Yuanyuan Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Business Value ◽  
The Internet ◽  
Information Analysis ◽  
Target Information ◽  
Learning Methods ◽  
Daily Lives

The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and the sentiment on these aspects are different, which makes it meaningless to give an overall sentiment polarity of the sentence. In this paper, we introduce Attention-based Aspect-level Recurrent Convolutional Neural Network (AARCNN) to analyze the remarks at aspect-level. The model integrates attention mechanism and target information analysis, which enables the model to concentrate on the important parts of the sentence and to make full use of the target information. The model uses bidirectional LSTM (Bi-LSTM) to build the memory of the sentence, and then CNN is applied to extracting attention from memory to get the attentive sentence representation. The model uses aspect embedding to analyze the target information of the representation and finally the model outputs the sentiment polarity through a softmax layer. The model was tested on multi-language datasets, and demonstrated that it has better performance than conventional deep learning methods.

scholarly journals A Deep Learning Based Prediction of Arabic Manuscripts Handwriting Style

2020 ◽  
Vol 17 (5) ◽  
pp. 702-712
Author(s):  
Manal Khayyat ◽  
Lamiaa Elrefaei
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Evaluation Metrics ◽  
The Internet ◽  
Promising Technology ◽  
Deep Learning Model

With the increasing amounts of existing unorganized images on the internet today and the necessity to use them efficiently in various types of applications. There is a critical need to discover rigid models that can classify and predict images successfully and instantaneously. Therefore, this study aims to collect Arabic manuscripts images in a dataset and predict their handwriting styles using the most powerful and trending technologies. There are many types of Arabic handwriting styles, including Al-Reqaa, Al-Nask, Al-Thulth, Al-Kufi, Al-Hur, Al-Diwani, Al-Farsi, Al-Ejaza, Al-Maghrabi, Al-Taqraa, etc. However, the study classified the collected dataset images according to the handwriting styles and focused on only six types of handwriting styles that existed in the collected Arabic manuscripts. To reach our goal, we applied the MobileNet pre-trained deep learning model on our classified dataset images to automatically capture and extract the features from them. Afterward, we evaluated the performance of the developed model by computing its recorded evaluation metrics. We reached that MobileNet convolutional neural network is a promising technology since it reached 0.9583 as the highest recorded accuracy and 0.9633 as the average F-score

scholarly journals An efficient convolutional neural network for coronary heart disease prediction

2020 ◽  
Vol 159 ◽  
pp. 113408 ◽  
Author(s):  
Aniruddha Dutta ◽  
Tamal Batabyal ◽  
Meheli Basu ◽  
Scott T. Acton
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Disease Prediction

scholarly journals Heart Diseases Prediction using Deep Learning Neural Network Model

2020 ◽  
Vol 9 (3) ◽  
pp. 2244-2248
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Heart Diseases ◽  
Medical Science ◽  
Disease Prediction ◽  
Health Issues ◽  
Deep Learning Neural Network

Deep learning plays an important role in the field of medical science in solving health issues and diagnosing various diseases. So in this paper, we will discuss heart disease. We proposed a model for heart disease prediction. Heart Disease is on of key area where Deep Neural Network can be used so we can improve the overall quality of the classification of heart disease. The classification can be performed on the various ways like KNN, SVM, Naïve Bayes, Random Forest. Heart Disease UCI dataset will be used to demonstrate Talos Hyper-parameter optimization is more efficient than others.

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