Idea – You can measure magnetic field noise and the time it takes for the virus to die in a hot water saline solution and in a hot water solution. It will be label 1 for saline and label 0 for hot water only. Collect a lot of data X_train and y_train = 0,0,0, … 1,1,1 … which is a label truth> 1 min. Randomly mix data and then practice the ML function. Then run the model on any substance in water to calculate the probability it takes before the virus dies.

You can calculate everything with machine learning as long as you know a relevant truth. Just to do experiments that the matrix function model then has to adapt to. Assume all
input data can affect the virus. Then you insert a truth in the process that becomes label = 1 and without label = 0. Then train the model so that it grasps when something has been
inserted. It corresponds to 0.0.93,1.22, -0.01,1.01,1,0.1,0 from random mix of data. Remove the manual deposit and see what the model classifies for similar problems.

Idea – You can measure magnetic field noise and the time it takes for the virus to die in a hot water saline solution and in a hot water solution. It will be label 1 for saline and
label 0 for hot water only. Collect a lot of data X_train and y_train = 0,0,0, … 1,1,1 … which is a label truth> 1 min. Randomly mix data and then practice the ML function.
Then run the model on any substance in water to calculate the probability it takes before the virus dies.