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Found 14 papers, 6 papers with code
Improving Time Series Classification Algorithms Using Octave-Convolutional Layers
In this paper, we experimentally show that by substituting convolutions with OctConv, we significantly improve accuracy for time series classification tasks for most of the benchmark datasets.
Process Mining Model to Predict Mortality in Paralytic Ileus Patients
There is a need for more accurate prediction modeling for ICU patients diagnosed with PI.
Masking Neural Networks Using Reachability Graphs to Predict Process Events
This paper proposes an approach to further interlock the process model of Decay Replay Mining with its neural network for next event prediction.
On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization
"Adversarial System Variant Approximation to Quantify Process Model Generalization."
Adversarial Attacks on Multivariate Time Series
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 samples for these models.
Adversarial System Variant Approximation to Quantify Process Model Generalization
Sequence Generative Adversarial Networks are trained on the variants contained in an event log with the intention to approximate the underlying variant distribution of the system behavior.
A Computer-Aided System for Determining the Application Range of a Warfarin Clinical Dosing Algorithm Using Support Vector Machines with a Polynomial Kernel Function
Using a sample of 4, 237 patients, we have proposed a companion classification model to one of the most popular dosing algorithms (International Warfarin Pharmacogenetics Consortium (IWPC) clinical model), which identifies the appropriate cohort of patients for applying this model.
Decay Replay Mining to Predict Next Process Events
Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event from a process state.
Insights into LSTM Fully Convolutional Networks for Time Series Classification
In this paper, we perform a series of ablation tests (3627 experiments) on LSTM-FCN and ALSTM-FCN to provide a better understanding of the model and each of its sub-module.
Adversarial Attacks on Time Series
In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models.
Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments
Based on users' behavior logs tracked by a Java application suitable for multi-application and multi-instance environments, we demonstrate the applicability for a specific task in a common Windows environment utilizing realistic simulated behaviors of users.
Pathological Voice Classification Using Mel-Cepstrum Vectors and Support Vector Machine
Vocal disorders have affected several patients all over the world.
Multivariate LSTM-FCNs for Time Series Classification
Over the past decade, multivariate time series classification has received great attention.
Ranked #1 on
Time Series Classification
on CharacterTrajectories
LSTM Fully Convolutional Networks for Time Series Classification
We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification.
Ranked #2 on
Outlier Detection
on ECG5000
