A novel neural network architecture utilizing parametric-logarithmic-modulus-based activation function: Theory, algorithm, and applications

Abstract

This paper introduces a novel parametric-logarithmic-modulus-based activation function (PLM-AF) designed to significantly enhance the nonlinear expression capabilities of high-dimensional spectroscopy data. A one-dimensional CNN-LSTM (1D-CNN-BiLSTM) model is subsequently developed to capture long-term dependencies within glucose Raman spectroscopy. To the best of our knowledge, this is the first work to simultaneously optimize the predictive performance of the model from the perspectives of both network architecture and activation functions. The effectiveness of the model is comprehensively evaluated against state-of-the-art methods using a public Raman spectroscopy dataset. Compared to the sub-optimal glucose prediction models, the proposed model improves the training root mean square error (RMSE) by 41.89%. The improved prediction accuracy demonstrates that the proposed regression model with the novel PLM-AF can significantly facilitate non-invasive glucose concentration prediction, thereby advancing the auxiliary diagnosis and healthcare industry.