A Comparison of ARIMA and LSTM in Forecasting Time Series. - Sima Siami-Namini; Neda Tavakoli; Akbar Siami Namin (ICMLA, 2018)

Continuous Authentications Using Frequent English Terms. - Alaa Darabseh; Sima Siami-Namini; Akbar Siami Namin (Appl. Artif. Intell., 2018)

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM. - Sima Siami-Namini; Akbar Siami Namin (CoRR, 2018)

Assessing the Effectiveness of Coverage-Based Fault Localizations Using Mutants. - Xiaozhen Xue; Sima Siami-Namini; Akbar Siami Namin (Int. J. Softw. Eng. Knowl. Eng., 2018)

Testing Multi-Threaded Applications Using Answer Set Programming. - Xiaozhen Xue; Sima Siami-Namini; Akbar Siami Namin (Int. J. Softw. Eng. Knowl. Eng., 2018)

Can Machine/Deep Learning Classifiers Detect Zero-Day Malware with High Accuracy? - Faranak Abri; Sima Siami-Namini; Mahdi Adl Khanghah; Fahimeh Mirza Soltani; Akbar Siami Namin (IEEE BigData, 2019)

The Performance of LSTM and BiLSTM in Forecasting Time Series. - Sima Siami-Namini; Neda Tavakoli; Akbar Siami Namin (IEEE BigData, 2019)

A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM. - Sima Siami-Namini; Neda Tavakoli; Akbar Siami Namin (CoRR, 2019)

The Performance of Machine and Deep Learning Classifiers in Detecting Zero-Day Vulnerabilities. - Faranak Abri; Sima Siami-Namini; Mahdi Adl Khanghah; Fahimeh Mirza Soltani; Akbar Siami Namin (CoRR, 2019)

A Concern Analysis of Federal Reserve Statements: The Great Recession vs. The COVID-19 Pandemic. - Luis Felipe Gutiérrez; Sima Siami-Namini; Neda Tavakoli; Akbar Siami Namin (IEEE BigData, 2020)

Clustering Time Series Data through Autoencoder-based Deep Learning Models. - Neda Tavakoli; Sima Siami-Namini; Mahdi Adl Khanghah; Fahimeh Mirza Soltani; Akbar Siami Namin (CoRR, 2020)

A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic. - Luis Felipe Gutiérrez; Sima Siami-Namini; Neda Tavakoli; Akbar Siami Namin (CoRR, 2020)

A Comparison of TCN and LSTM Models in Detecting Anomalies in Time Series Data. - Saroj Gopali; Faranak Abri; Sima Siami-Namini; Akbar Siami Namin (IEEE BigData, 2021)

A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models. - Saroj Gopali; Faranak Abri; Sima Siami-Namini; Akbar Siami Namin (CoRR, 2021)

The Accuracy of Domain Specific and Descriptive Analysis Generated by Large Language Models. - Denish Omondi Otieno; Faranak Abri; Sima Siami-Namini; Akbar Siami Namin (COMPSAC, 2024)

A Comparative Multivariate Analysis of VAR and Deep Learning-Based Models for Forecasting Volatile Time Series Data. - Saroj Gopali; Sima Siami-Namini; Faranak Abri; Akbar Siami Namin (IEEE Access, 2024)

The performance of the LSTM-based code generated by Large Language Models (LLMs) in forecasting time series data. - Saroj Gopali; Sima Siami-Namini; Faranak Abri; Akbar Siami Namin (Nat. Lang. Process. J., 2024)