scholarly journals HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

2020 ◽  
Vol 104 ◽  
pp. 187-200 ◽  
Author(s):  
Shreshth Tuli ◽  
Nipam Basumatary ◽  
Sukhpal Singh Gill ◽  
Mohsen Kahani ◽  
Rajesh Chand Arya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Healthcare System ◽  
Heart Diseases ◽  
Fog Computing ◽  
Automatic Diagnosis ◽  
Smart Healthcare ◽  
Computing Environments

scholarly journals OPTIMIZING TELEMEDICINE FRAMEWORK USING FOG COMPUTING FOR SMART HEALTHCARE SYSTEMS

2021 ◽  
Author(s):  
Michael Enbibel
Keyword(s):  
Data Management ◽  
Healthcare System ◽  
Data Flow ◽  
System Organization ◽  
Fog Computing ◽  
Healthcare Systems ◽  
Distributed Data ◽  
Smart Healthcare ◽  
Network Latency

This research is done for optimizing telemedicine framework by using fogging or fog computing for smart healthcare systems. Fog computing is used to solve the issues that arise on telemedicine framework of smart healthcare system like Infrastructural, Implementation, Acceptance, Data Management, Security, Bottleneck system organization, and Network latency Issues. we mainly used Distributed Data Flow (DDF) method using fog computing in order to fully solve the listed issues.

scholarly journals OPTIMIZING TELEMEDICINE FRAMEWORK USING FOG COMPUTING FOR SMART HEALTHCARE SYSTEMS

2021 ◽  
Author(s):  
Michael Enbibel
Keyword(s):  
Data Management ◽  
Healthcare System ◽  
Data Flow ◽  
System Organization ◽  
Fog Computing ◽  
Healthcare Systems ◽  
Distributed Data ◽  
Smart Healthcare ◽  
Network Latency

This research is done for optimizing telemedicine framework by using fogging or fog computing for smart healthcare systems. Fog computing is used to solve the issues that arise on telemedicine framework of smart healthcare system like Infrastructural, Implementation, Acceptance, Data Management, Security, Bottleneck system organization, and Network latency Issues. we mainly used Distributed Data Flow (DDF) method using fog computing in order to fully solve the listed issues.

scholarly journals Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Benzhen Guo ◽  
Yanli Ma ◽  
Jingjing Yang ◽  
Zhihui Wang
Keyword(s):  
Deep Learning ◽  
Health Status ◽  
Internet Of Things ◽  
Healthcare System ◽  
Learning Model ◽  
Gray Scale ◽  
Data Set ◽  
Smart Healthcare ◽  
Cloud Server ◽  
Deep Learning Model

Introduction. Health monitoring and remote diagnosis can be realized through Smart Healthcare. In view of the existing problems such as simple measurement parameters of wearable devices, huge computing pressure of cloud servers, and lack of individualization of diagnosis, a novel Cloud-Internet of Things (C-IOT) framework for medical monitoring is put forward. Methods. Smart phones are adopted as gateway devices to achieve data standardization and preprocess to generate health gray-scale map uploaded to the cloud server. The cloud server realizes the business logic processing and uses the deep learning model to carry out the gray-scale map calculation of health parameters. A deep learning model based on the convolution neural network (CNN) is constructed, in which six volunteers are selected to participate in the experiment, and their health data are marked by private doctors to generate initial data set. Results. Experimental results show the feasibility of the proposed framework. The test data set is used to test the CNN model after training; the forecast accuracy is over 77.6%. Conclusion. The CNN model performs well in the recognition of health status. Collectively, this Smart Healthcare System is expected to assist doctors by improving the diagnosis of health status in clinical practice.

scholarly journals OPTIMIZING TELEMEDICINE FRAMEWORK USING FOG COMPUTING FOR SMART HEALTHCARE SYSTEMS

2021 ◽  
Author(s):  
Michael Enbibel
Keyword(s):  
Data Management ◽  
Healthcare System ◽  
Data Flow ◽  
System Organization ◽  
Fog Computing ◽  
Healthcare Systems ◽  
Distributed Data ◽  
Smart Healthcare ◽  
Network Latency

This research is done for optimizing telemedicine framework by using fogging or fog computing for smart healthcare systems. Fog computing is used to solve the issues that arise on telemedicine framework of smart healthcare system like Infrastructural, Implementation, Acceptance, Data Management, Security, Bottleneck system organization, and Network latency Issues. we mainly used Distributed Data Flow (DDF) method using fog computing in order to fully solve the listed issues.

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