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Houshang Darabi
University of Illinois at Chicago
75Publications
10H-index
2,691Citations
Publications 75
Newest
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 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. However, such deep learning models by themselves lack a clear representation of the process states. At the same time, recent methods have neglected the time feat...
#1Ashkan Sharabiani (UIC: University of Illinois at Chicago)H-Index: 3
#2Adam P. Bress (UofU: University of Utah)H-Index: 15
Last.Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 10
view all 5 authors...
#1Fazle KarimH-Index: 4
#2Somshubra MajumdarH-Index: 3
Last.Samuel HarfordH-Index: 3
view all 4 authors...
Abstract Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classification model by augmenting the fully convolutional block with a squeeze-and-excitation block to further improve accuracy. Our proposed models outperform most state-of-the-art...
#1Julian Theis (UIC: University of Illinois at Chicago)H-Index: 1
#2Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 10
Process Mining is a famous technique which is frequently applied to Software Development Processes, while being neglected in Human-Computer Interaction (HCI) recommendation applications. Organizations usually train employees to interact with required IT systems. Often, employees, or users in general, develop their own strategies for solving repetitive tasks and processes. However, organizations find it hard to detect whether employees interact efficiently with IT systems or not. Hence, we have d...
#1Samuel Harford (UIC: University of Illinois at Chicago)H-Index: 3
#2Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 10
Last.Dennis P. Watson (UIC: University of Illinois at Chicago)
view all 8 authors...
Abstract Background Out-of-hospital cardiac arrest (OHCA) affects nearly 400,000 people each year in the United States of which only 10% survive. Using data from the Cardiac Arrest Registry to Enhance Survival (CARES), and machine learning (ML) techniques, we developed a model of neurological outcome prediction for OHCA in Chicago, Illinois. Methods Rescue workflow data of 2639 patients with witnessed OHCA were retrieved from Chicago’s CARES. An Embedded Fully Convolutional Network (EFCN) classi...
#1Fazle KarimH-Index: 4
#2Somshubra MajumdarH-Index: 3
Last.Houshang DarabiH-Index: 10
view all 3 authors...
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 classifying time series signals on the old University of California-Riverside (UCR) time series repository. However, there has been no study on why LSTM-FCN and ALSTM-FCN perform well. 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...
#1Julian TheisH-Index: 1
#2Houshang Darabi (UIC: University of Illinois at Chicago)H-Index: 10
In complex processes, various events can happen in different sequences. The prediction of the next event activity given an a-priori process state is of importance in such processes. Recent methods leverage deep learning techniques such as recurrent neural networks to predict event activities from raw process logs. However, deep learning techniques cannot efficiently model logical behaviors of complex processes. In this paper, we take advantage of Petri nets as a powerful tool in modeling logical...
#1Fazle KarimH-Index: 4
#2Somshubra MajumdarH-Index: 3
Last.Houshang DarabiH-Index: 10
view all 3 authors...
Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models. The proposed attack on the classification model utilizes a distilled model as a surrogate th...
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