Easy Python software with neural network model
Budget: ₹600 – ₹5,000 INR
This project consists of 6
of tasks .
I experimentally measured 13 different Nano-composites in Electrical (dielectric strength ) and Mechanical test (tensile strength) for each composite.
The dielectric strength test was performed 10 times for each Nano-composite type to improve the accuracy in 6 different conditions ; the software should calculate the average of the 10 results and work on the average value for each condition.
The Tensile Strength test was performed 3 times for each Nano-composite, the software should calculate the average results and work on the average value.
I need a python software that can analyze the data using Feed Forward Neural Network (FFNN) to predict or forecast the results for the dielectric strength and the tensile strength of the other concentrations that were not tested in the same filler concentration wt% range (The filler wt% range from 0 to 7). The experimental results only tested on filler wt% for each nano-composite : 0, 1, 3, 5, 7. The software should forecast any others wt% required that were not experimentally measured in the laboratory.
The attached files are :
1- The brief explanation of the project and the 5 tasks that you should do.
2- The Electrical experimental results.
3- The mechanical Experimental results.
4- A published paper that used the FFNN technique using Matlab for other type of material ( as a guide for you)
of tasks .
I experimentally measured 13 different Nano-composites in Electrical (dielectric strength ) and Mechanical test (tensile strength) for each composite.
The dielectric strength test was performed 10 times for each Nano-composite type to improve the accuracy in 6 different conditions ; the software should calculate the average of the 10 results and work on the average value for each condition.
The Tensile Strength test was performed 3 times for each Nano-composite, the software should calculate the average results and work on the average value.
I need a python software that can analyze the data using Feed Forward Neural Network (FFNN) to predict or forecast the results for the dielectric strength and the tensile strength of the other concentrations that were not tested in the same filler concentration wt% range (The filler wt% range from 0 to 7). The experimental results only tested on filler wt% for each nano-composite : 0, 1, 3, 5, 7. The software should forecast any others wt% required that were not experimentally measured in the laboratory.
The attached files are :
1- The brief explanation of the project and the 5 tasks that you should do.
2- The Electrical experimental results.
3- The mechanical Experimental results.
4- A published paper that used the FFNN technique using Matlab for other type of material ( as a guide for you)