Sensitivity Analysis of Artificial Neural Networks Identifying JWH Synthetic Cannabinoids Built with Alternative Training Strategies and Methods

This paper presents the alternative training strategies we tested for an Artificial Neural Network (ANN) designed to detect JWH synthetic cannabinoids. In order to increase the model performance in terms of output sensitivity, we used the Neural Designer data science and machine learning platform combined with the programming language Python. We performed a comparative analysis of several optimization algorithms, error parameters and regularization methods. Finally, we performed a new goodness-of-fit analysis between the testing samples in the data set and the corresponding ANN outputs in order to investigate their sensitivity. The effectiveness of the new methods combined with the optimization algorithms is discussed.

Automated summary

This paper presents the alternative training strategies tested for an Artificial Neural Network (ANN) designed to detect JWH synthetic cannabinoids. By using the Neural Designer data science and machine learning platform combined with Python, the model's output sensitivity was enhanced. Comparative analysis of various optimization algorithms, error parameters, and regularization methods was conducted, along with a new goodness-of-fit analysis.