Big data ensemble clinical prediction for healthcare data by using deep learning model

2018 ◽  
Vol 5 (4) ◽  
pp. 258 ◽  
Author(s):  
Sreekanth Rallapalli ◽  
R.R. Gondkar
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Learning Model ◽  
Clinical Prediction ◽  
Healthcare Data ◽  
Deep Learning Model

A hybrid deep learning model for efficient intrusion detection in big data environment

2020 ◽  
Vol 513 ◽  
pp. 386-396 ◽  
Author(s):  
Mohammad Mehedi Hassan ◽  
Abdu Gumaei ◽  
Ahmed Alsanad ◽  
Majed Alrubaian ◽  
Giancarlo Fortino
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Learning Model ◽  
Data Environment ◽  
Deep Learning Model

scholarly journals A High-Order CFS Algorithm for Clustering Big Data

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Fanyu Bu ◽  
Zhikui Chen ◽  
Peng Li ◽  
Tong Tang ◽  
Ying Zhang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Clustering Algorithm ◽  
Big Data Analytics ◽  
Learning Model ◽  
Heterogeneous Data ◽  
High Order ◽  
Tensor Model ◽  
Industrial Internet ◽  
Deep Learning Model

With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.

scholarly journals Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks

2020 ◽  
Vol 10 (24) ◽  
pp. 8934
Author(s):  
Yan He ◽  
Bin Fu ◽  
Jian Yu ◽  
Renfa Li ◽  
Rucheng Jiang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Learning Model ◽  
Streaming Data ◽  
Smart Devices ◽  
Generative Adversarial Network ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Iot Devices ◽  
Deep Learning Model

Wireless and mobile health applications promote the development of smart healthcare. Effective diagnosis and feedbacks of remote health data pose significant challenges due to streaming data, high noise, network latency and user privacy. Therefore, we explore efficient edge and cloud design to maintain electrocardiogram classification performance while reducing the communication cost. These contributions include: (1) We introduce a hybrid smart medical architecture named edge convolutional neural networks (EdgeCNN) that balances the capability of edge and cloud computing to address the issue for agile learning of healthcare data from IoT devices. (2) We present an effective deep learning model for electrocardiogram (ECG) inference, which can be deployed to run on edge smart devices for low-latency diagnosis. (3) We design a data enhancement method for ECG based on deep convolutional generative adversarial network to expand ECG data volume. (4) We carried out experiments on two representative datasets to evaluate the effectiveness of the deep learning model of ECG classification based on EdgeCNN. EdgeCNN shows superior to traditional cloud medical systems in terms of network Input/Output (I/O) pressure, architecture cost and system high availability. The deep learning model not only ensures high diagnostic accuracy, but also has advantages in aspect of inference time, storage, running memory and power consumption.

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