108 citations · 147 across the 4 of their papers we have counts for
6 papers
From multiscale biophysics to digital twins of tissues and organs: future opportunities for in silico pharmacology
Michael Taynnan Barros, Michelangelo Paci, Aapo Tervonen +4
With many advancements in in silico biology in recent years, the paramount challenge is to translate the accumulated knowledge into exciting industry partnerships and clinical appl…
Feature transforms for image data augmentation
Loris Nanni, Michelangelo Paci, Sheryl Brahnam +1
A problem with Convolutional Neural Networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many method…
Comparison of different convolutional neural network activation functions and methods for building ensembles
Loris Nanni, Gianluca Maguolo, Sheryl Brahnam +1
Recently, much attention has been devoted to finding highly efficient and powerful activation functions for CNN layers. Because activation functions inject different nonlinearities…
An Ensemble of Convolutional Neural Networks for Audio Classification
Loris Nanni, Gianluca Maguolo, Sheryl Brahnam +1
In this paper, ensembles of classifiers that exploit several data augmentation techniques and four signal representations for training Convolutional Neural Networks (CNNs) for audi…
Data augmentation approaches for improving animal audio classification
Loris Nanni, Gianluca Maguolo, Michelangelo Paci
In this paper we present ensembles of classifiers for automated animal audio classification, exploiting different data augmentation techniques for training Convolutional Neural Net…
Audiogmenter: a MATLAB Toolbox for Audio Data Augmentation
Gianluca Maguolo, Michelangelo Paci, Loris Nanni +1
Audio data augmentation is a key step in training deep neural networks for solving audio classification tasks. In this paper, we introduce Audiogmenter, a novel audio data augmenta…