Dear scientist, good morning!

**Would you like to co-author some eprints with me? **

Since I am against peer-reviewed publishing (you can see why in **Bye Bye Peer-Reviewed Publishing**), I can only collaborate if we don´t publish our work in peer-reviewed journals.

**My role**

Developing Artificial Neural Network (ANN) models and write that part of the papers, for any possible nonlinear function (continuous or discrete/qualitative) you may wish to approximate. I will use a deterministic (non-stochastic) ANN-based software of my own, valid for interpolation-based approximations only (e.g., it does not handle time series forecasting). All research carried out with that tool can be read at https://www.researchgate.net/lab/Abambres-lab-Lisbon-Portugal-Abambres-M.

**What are ANNs? **

You can see this technique (belonging to Artificial Intelligence) as the most powerful regression model in the World, whose formulation is inspired by the way any brain processes information. Each designed ANN will give us a matrix-based formula to estimate the variable or function of interest.

**How to select a suitable problem? **

You just have to imagine situations where the accurate determination of a variable of interest is quite costly and unfeasible in current practice (i.e., when employing the traditional experimental or numerical methods), meaning that the existing analytical models (if any) are too conservative or unsafe.

Imagine you have a variable or function Y = Y (X1, X2, X3, ..., Xn), either qualitative/discrete or quantitative/numerical/continuous, that can be accurately determined (experimentally or numerically) as function of the independent variables X1, X2, X3, ..., Xn (you can mix quantitative and qualitative Xi variables, if needed).

**The most important result of our work**

My software will determine a formula that in less than a second is able to compute Y with high accuracy for any given (X1,..., Xn) provided as input.

**Minimum amount of data**

As in any regression technique, we need data . You will have to send me at least max{2^n, 400} data points (X1,..., Xn; Y).

**Where to write the data?**

I´ll send you (along with instructions) a proper excel template for you to write your collected data.

**FINAL NOTE** (IMPORTANT, though NOT COMPULSORY)

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In order to assure the generalization ability**

of the proposed ANN for virtually any case, would be important that the dataset you provide has the following features:

(i) For at least 1 (the more the better) input variable, have ALL values DISTINCT WITHIN that variable(ie, as many as the no. of data points)

(ii) Not include points too close (almost coincident) to each other in the corresponding hyperspace

**i.e. better accuracy for data points where at least 1 input variable take values inexistent (among all original values)

in the original dataset you provide

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If you use MATLAB, this is the way to check the number of distinct values (ndv) in a vector of values (vv):

vv = [define the vector of values];

ndv = length(unique(vv));

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