Heart attack prediction using deep neuro fuzzy Inputs will be in the datafile that will go to the Simulink model and the output will be in an oscilloscope. Web application should be made for deep fuzzy system
Budget: $30 – $250 USD
Requirement :
1. 6 input divided into 3 (2 input = 1 output) and 3 will be the output. 2 layer deep fuzzy
2. From the 3 outputs, 1 fuzzy output should be extracted which will show whether it is risk or no risk
3. 3 part model to be transferred to Simulink
4. 3 of the inputs will be in the datafile that will go to the Simulink model and the output will be in an oscilloscope
5. 2 file should be kept in which data paper or plot can be done from 2 files
6. . By combining 3 Simulink models, 3 outputs should be fitted to 1 neural network, 1 output from the neural network will be output that the patient has risk or no risk.
7. Web application should be made for deep neuro fuzzy system
This paper should be given as follows and our input will be 6 and output will be 3. These 3 outputs will be implemented again and 1 will be output which done by deep fuzzy.
1. 6 input divided into 3 (2 input = 1 output) and 3 will be the output. 2 layer deep fuzzy
2. From the 3 outputs, 1 fuzzy output should be extracted which will show whether it is risk or no risk
3. 3 part model to be transferred to Simulink
4. 3 of the inputs will be in the datafile that will go to the Simulink model and the output will be in an oscilloscope
5. 2 file should be kept in which data paper or plot can be done from 2 files
6. . By combining 3 Simulink models, 3 outputs should be fitted to 1 neural network, 1 output from the neural network will be output that the patient has risk or no risk.
7. Web application should be made for deep neuro fuzzy system
This paper should be given as follows and our input will be 6 and output will be 3. These 3 outputs will be implemented again and 1 will be output which done by deep fuzzy.
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