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Improving Time Series Classification Algorithms Using Octave-Convolutional Layers
Samuel Harford, Fazle Karim, Houshang Darabi
Deep learning models utilizing convolution layers have achieved state-of-the-art performance on univariate time series classification tasks. In this work, we propose improving CNN…
Process Mining Model to Predict Mortality in Paralytic Ileus Patients
Maryam Pishgar, Martha Razo, Julian Theis +1
Paralytic Ileus (PI) patients are at high risk of death when admitted to the Intensive care unit (ICU), with mortality as high as 40\%. There is minimal research concerning PI pati…
Masking Neural Networks Using Reachability Graphs to Predict Process Events
Julian Theis, Houshang Darabi
Decay Replay Mining is a deep learning method that utilizes process model notations to predict the next event. However, this method does not intertwine the neural network with the…
On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization
Julian Theis, Ilia Mokhtarian, Houshang Darabi
Process mining algorithms discover a process model from an event log. The resulting process model is supposed to describe all possible event sequences of the underlying system. Gen…
Adversarial Attacks on Multivariate Time Series
Samuel Harford, Fazle Karim, Houshang Darabi
Classification models for the multivariate time series have gained significant importance in the research community, but not much research has been done on generating adversarial s…
A Computer-Aided System for Determining the Application Range of a Warfarin Clinical Dosing Algorithm Using Support Vector Machines with a Polynomial Kernel Function
Ashkan Sharabiani, Adam Bress, William Galanter +2
Determining the optimal initial dose for warfarin is a critically important task. Several factors have an impact on the therapeutic dose for individual patients, such as patients'…
Decay Replay Mining to Predict Next Process Events
Julian Theis, Houshang Darabi
In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent…
Adversarial Attacks on Time Series
Fazle Karim, Somshubra Majumdar, Houshang Darabi
Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples f…
Insights into LSTM Fully Convolutional Networks for Time Series Classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi
Long Short Term Memory Fully Convolutional Neural Networks (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN) have shown to achieve state-of-the-art performance on the task of classifyi…
Multivariate LSTM-FCNs for Time Series Classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi +1
Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the L…
LSTM Fully Convolutional Networks for Time Series Classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi +1
Fully convolutional neural networks (FCN) have been shown to achieve state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of f…