| Περίληψη: | Nowadays, artificial intelligence and machine learning algorithms have seen an
enormous growth of interest with several researchers dedicating their lives into
seeking new algorithms or improving those already developed. Studies have shown
that they can be applied in almost every system that is required to make a decision.
Machine and deep learning algorithms are widely used in image or sound classification
tasks, in biomedical applications and language processing.
In this thesis we study the performance of Convolutional Neural Network in an
audio classification task. In particular, our classifier is fed with real time data which
are produced by recording the use of an inhaler device and it attempts to categorize
them into four classes, which namely are inhalation, exhalation, drug and noise and
other environmental sounds. Furthermore, we study the potential of pruning the
weights and how zeroing out weights with small magnitude affects the overall
performance of the classifier.
So, firstly, we describe the most common respiratory diseases and why it is
essential to monitor the medication adherence. Secondly, we introduce to the reader
the fundamentals of machine learning as well as the most important characteristics of
algorithms used for the problem of monitoring the medication adherence in
respiratory diseases through audio classification. Finally, we propose different
approaches for compressing our model and we present several metrics in order to
evaluate the performance of our classifier both before and after pruning procedure is
employed.
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