I have a sort of mathematical problem and I'm not sure which model I should choose to make an LSTM neural network.
Currently in my country, there is a system in which certain groups of researchers upload information on products of scientific interest, such as research articles, books, patents, software, among others. Depending on the number of products, the system assigns a classification to each group, which can be A1, A, B and C, where A1 is the highest classification and C is the minimum.
The classification is done through a mathematical model whose entries are, the total number of each product, the total sum of all products, number of authors, among other indices that are calculated with the previous values.
Once the entries are obtained, these values are processed by a set of formulas and the final result is a single number.
This number is located in a range provided by the mathematical model and this is how the group is classified.
What I want to do is given the current classification of a group, give suggestions of different values to improve their classification.
For example, if there is a group with classification C, suggest how many products it should have, how many authors, what value should its indexes have, so that its category would be finally B.
I think the structure of my network should be: -1 input, which would be the classification you want to get. -Multiple output, one for each product and indexes.
But I do not understand how to make the network take into account the current classification of the group, in addition to the number of products and the value of the current indexes.
If you have further questions about the problem, please feel free to ask.
I appreciate your suggestions.