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Sima Siami-Namini
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2020 – today
- 2024
- [j4]Saroj Gopali, Sima Siami-Namini, Faranak Abri, Akbar Siami Namin:
A Comparative Multivariate Analysis of VAR and Deep Learning-Based Models for Forecasting Volatile Time Series Data. IEEE Access 12: 155423-155436 (2024) - [c6]Denish Omondi Otieno, Faranak Abri, Sima Siami-Namini, Akbar Siami Namin:
The Accuracy of Domain Specific and Descriptive Analysis Generated by Large Language Models. COMPSAC 2024: 1739-1746 - [i7]Denish Omondi Otieno, Faranak Abri, Sima Siami-Namini, Akbar Siami Namin:
The Accuracy of Domain Specific and Descriptive Analysis Generated by Large Language Models. CoRR abs/2405.19578 (2024) - 2021
- [c5]Saroj Gopali
, Faranak Abri
A Comparison of TCN and LSTM Models in Detecting Anomalies in Time Series Data. IEEE BigData 2021: 2415-2420 - [i6]Saroj Gopali, Faranak Abri, Sima Siami-Namini, Akbar Siami Namin:
A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models. CoRR abs/2112.09293 (2021) - 2020
- [c4]Luis Felipe Gutiérrez
A Concern Analysis of Federal Reserve Statements: The Great Recession vs. The COVID-19 Pandemic. IEEE BigData 2020: 2079-2086 - [i5]Neda Tavakoli, Sima Siami-Namini, Mahdi Adl Khanghah, Fahimeh Mirza Soltani, Akbar Siami Namin:
Clustering Time Series Data through Autoencoder-based Deep Learning Models. CoRR abs/2004.07296 (2020) - [i4]Luis Felipe Gutiérrez, Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin:
A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic. CoRR abs/2012.02098 (2020)
2010 – 2019
- 2019
- [c3]Faranak Abri, Sima Siami-Namini, Mahdi Adl Khanghah, Fahimeh Mirza Soltani, Akbar Siami Namin:
Can Machine/Deep Learning Classifiers Detect Zero-Day Malware with High Accuracy? IEEE BigData 2019: 3252-3259 - [c2]Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin:
The Performance of LSTM and BiLSTM in Forecasting Time Series. IEEE BigData 2019: 3285-3292 - [i3]Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin:
A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM. CoRR abs/1911.09512 (2019) - [i2]Faranak Abri, Sima Siami-Namini, Mahdi Adl Khanghah, Fahimeh Mirza Soltani, Akbar Siami Namin:
The Performance of Machine and Deep Learning Classifiers in Detecting Zero-Day Vulnerabilities. CoRR abs/1911.09586 (2019) - 2018
- [j3]Alaa Darabseh, Sima Siami-Namini, Akbar Siami Namin:
Continuous Authentications Using Frequent English Terms. Appl. Artif. Intell. 32(1): 13-47 (2018) - [j2]Xiaozhen Xue, Sima Siami-Namini, Akbar Siami Namin:
Assessing the Effectiveness of Coverage-Based Fault Localizations Using Mutants. Int. J. Softw. Eng. Knowl. Eng. 28(8): 1091-1119 (2018) - [j1]Xiaozhen Xue, Sima Siami-Namini, Akbar Siami Namin:
Testing Multi-Threaded Applications Using Answer Set Programming. Int. J. Softw. Eng. Knowl. Eng. 28(8): 1151-1175 (2018) - [c1]Sima Siami-Namini, Neda Tavakoli
A Comparison of ARIMA and LSTM in Forecasting Time Series. ICMLA 2018: 1394-1401 - [i1]Sima Siami-Namini, Akbar Siami Namin:
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM. CoRR abs/1803.06386 (2018)
Coauthor Index
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last updated on 2024-11-08 21:27 CET by the dblp team
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