Sima Siami-Namini

22 papers B 2C 1Journal 15Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	CO2 (Co-Compliance Officer): An LLM-Based Ontology-Driven Methodology for Generating Knowledge Graphs and AI Compliance Checking. IEEE Access Venkata Sai Prathyush Turaga, Timea Pahi, Simon Tjoa, Sima Siami-Namini, Akbar Siami Namin
2025	B	`conf`	In-Context and Few-Shots Learning for Forecasting Time Series Data Based on Large Language Models. IEEE Big Data Saroj Gopali, Bipin Chhetri, Deepika Giri, Sima Siami-Namini, Akbar Siami Namin
2025	J	`jnl`	In-Context and Few-Shots Learning for Forecasting Time Series Data based on Large Language Models. CoRR Saroj Gopali, Bipin Chhetri, Deepika Giri, Sima Siami-Namini, Akbar Siami Namin
2024	J	`jnl`	A Comparative Multivariate Analysis of VAR and Deep Learning-Based Models for Forecasting Volatile Time Series Data. IEEE Access Saroj Gopali, Sima Siami-Namini, Faranak Abri, Akbar Siami Namin
2024	B	`conf`	The Accuracy of Domain Specific and Descriptive Analysis Generated by Large Language Models. COMPSAC Denish Omondi Otieno, Faranak Abri, Sima Siami-Namini, Akbar Siami Namin
2024	J	`jnl`	The Accuracy of Domain Specific and Descriptive Analysis Generated by Large Language Models. CoRR Denish Omondi Otieno, Faranak Abri, Sima Siami-Namini, Akbar Siami Namin
2024	J	`jnl`	The Performance of the LSTM-based Code Generated by Large Language Models (LLMs) in Forecasting Time Series Data. CoRR Saroj Gopali, Sima Siami-Namini, Faranak Abri, Akbar Siami Namin
2024	J	`jnl`	The performance of the LSTM-based code generated by Large Language Models (LLMs) in forecasting time series data. Nat. Lang. Process. J. Saroj Gopali, Sima Siami-Namini, Faranak Abri, Akbar Siami Namin
2021	J	`jnl`	A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models. CoRR Saroj Gopali, Faranak Abri, Sima Siami-Namini, Akbar Siami Namin
2021	—	`conf`	A Comparison of TCN and LSTM Models in Detecting Anomalies in Time Series Data. IEEE BigData Saroj Gopali, Faranak Abri, Sima Siami-Namini, Akbar Siami Namin
2020	J	`jnl`	A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic. CoRR Luis Felipe Gutiérrez, Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin
2020	—	`conf`	A Concern Analysis of Federal Reserve Statements: The Great Recession vs. The COVID-19 Pandemic. IEEE BigData Luis Felipe Gutiérrez, Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin
2020	J	`jnl`	Clustering Time Series Data through Autoencoder-based Deep Learning Models. CoRR Neda Tavakoli, Sima Siami-Namini, Mahdi Adl Khanghah, Fahimeh Mirza Soltani, Akbar Siami Namin
2019	J	`jnl`	A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM. CoRR Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin
2019	—	`conf`	Can Machine/Deep Learning Classifiers Detect Zero-Day Malware with High Accuracy? IEEE BigData Faranak Abri, Sima Siami-Namini, Mahdi Adl Khanghah, Fahimeh Mirza Soltani, Akbar Siami Namin
2019	—	`conf`	The Performance of LSTM and BiLSTM in Forecasting Time Series. IEEE BigData Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin
2019	J	`jnl`	The Performance of Machine and Deep Learning Classifiers in Detecting Zero-Day Vulnerabilities. CoRR Faranak Abri, Sima Siami-Namini, Mahdi Adl Khanghah, Fahimeh Mirza Soltani, Akbar Siami Namin
2018	C	`conf`	A Comparison of ARIMA and LSTM in Forecasting Time Series. ICMLA Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin
2018	J	`jnl`	Assessing the Effectiveness of Coverage-Based Fault Localizations Using Mutants. Int. J. Softw. Eng. Knowl. Eng. Xiaozhen Xue, Sima Siami-Namini, Akbar Siami Namin
2018	J	`jnl`	Continuous Authentications Using Frequent English Terms. Appl. Artif. Intell. Alaa Darabseh, Sima Siami-Namini, Akbar Siami Namin
2018	J	`jnl`	Forecasting Economics and Financial Time Series: ARIMA vs. LSTM. CoRR Sima Siami-Namini, Akbar Siami Namin
2018	J	`jnl`	Testing Multi-Threaded Applications Using Answer Set Programming. Int. J. Softw. Eng. Knowl. Eng. Xiaozhen Xue, Sima Siami-Namini, Akbar Siami Namin