MOPTA 2019 aims at bringing together a diverse group of people from both discrete and continuous optimization, working on both theoretical and applied aspects. There will be a small number of invited talks from distinguished speakers and contributed talks, spread over three days. The target is to present a diverse set of exciting new developments from different optimization areas while at the same time providing a setting which will allow increased interaction among the participants. Organizers aim to bring together researchers from both the theoretical and applied communities who do not usually have the chance to interact in the framework of a medium-scale event.
My presentation will focus on the isolation of the waveform data, the noise filtration via normalization, Butterworth and forward-backward filter application, and Fast Fourier transformation. It will also discuss one of the projects I worked on that has the trained Artificial Neural Network (ANN) contained 23 dense layers with the number of neurons decreasing per two layers with ReLU activation, how Multiclass activation was used along with AdamOptimizer.