Sima Siami-Namini

According to our database1, Sima Siami-Namini authored at least 14 papers between 2018 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2021
A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models.
CoRR, 2021

A Comparison of TCN and LSTM Models in Detecting Anomalies in Time Series Data.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020
A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic.
CoRR, 2020

Clustering Time Series Data through Autoencoder-based Deep Learning Models.
CoRR, 2020

A Concern Analysis of Federal Reserve Statements: The Great Recession vs. The COVID-19 Pandemic.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019
The Performance of Machine and Deep Learning Classifiers in Detecting Zero-Day Vulnerabilities.
CoRR, 2019

A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM.
CoRR, 2019

The Performance of LSTM and BiLSTM in Forecasting Time Series.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Can Machine/Deep Learning Classifiers Detect Zero-Day Malware with High Accuracy?
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018
Testing Multi-Threaded Applications Using Answer Set Programming.
Int. J. Softw. Eng. Knowl. Eng., 2018

Assessing the Effectiveness of Coverage-Based Fault Localizations Using Mutants.
Int. J. Softw. Eng. Knowl. Eng., 2018

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM.
CoRR, 2018

Continuous Authentications Using Frequent English Terms.
Appl. Artif. Intell., 2018

A Comparison of ARIMA and LSTM in Forecasting Time Series.
Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018


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