| A comparison of ARIMA and LSTM in forecasting time series S Siami-Namini, N Tavakoli, AS Namin 2018 17th IEEE international conference on machine learning and applications …, 2018 | 1541 | 2018 |
| The performance of LSTM and BiLSTM in forecasting time series S Siami-Namini, N Tavakoli, AS Namin 2019 IEEE International conference on big data (Big Data), 3285-3292, 2019 | 1385 | 2019 |
| Using mutation analysis for assessing and comparing testing coverage criteria JH Andrews, LC Briand, Y Labiche, AS Namin IEEE Transactions on Software Engineering 32 (8), 608-624, 2006 | 652 | 2006 |
| Forecasting economics and financial time series: ARIMA vs. LSTM S Siami-Namini, AS Namin arXiv preprint arXiv:1803.06386, 2018 | 369 | 2018 |
| Sufficient mutation operators for measuring test effectiveness A Siami Namin, JH Andrews, DJ Murdoch Proceedings of the 30th international conference on Software engineering …, 2008 | 270 | 2008 |
| The influence of size and coverage on test suite effectiveness AS Namin, JH Andrews Proceedings of the eighteenth international symposium on Software testing …, 2009 | 208 | 2009 |
| A comparative analysis of forecasting financial time series using arima, lstm, and bilstm S Siami-Namini, N Tavakoli, AS Namin arXiv preprint arXiv:1911.09512, 2019 | 156 | 2019 |
| Detecting phishing websites through deep reinforcement learning M Chatterjee, AS Namin 2019 IEEE 43rd annual computer software and applications conference (COMPSAC …, 2019 | 130 | 2019 |
| Predicting vulnerable software components through n-gram analysis and statistical feature selection Y Pang, X Xue, AS Namin 2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015 | 114 | 2015 |
| The core cyber-defense knowledge, skills, and abilities that cybersecurity students should learn in school: Results from interviews with cybersecurity professionals KS Jones, AS Namin, ME Armstrong ACM Transactions on Computing Education (TOCE) 18 (3), 1-12, 2018 | 109 | 2018 |
| A survey on the moving target defense strategies: An architectural perspective J Zheng, AS Namin Journal of Computer Science and Technology 34, 207-233, 2019 | 108 | 2019 |
| The use of mutation in testing experiments and its sensitivity to external threats AS Namin, S Kakarla Proceedings of the 2011 International Symposium on Software Testing and …, 2011 | 98 | 2011 |
| Can machine/deep learning classifiers detect zero-day malware with high accuracy? F Abri, S Siami-Namini, MA Khanghah, FM Soltani, AS Namin 2019 IEEE international conference on big data (Big Data), 3252-3259, 2019 | 77 | 2019 |
| Forecasting economics and financial time series: ARIMA vs S Siami-Namini, AS Namin LSTM. arXiv 1803, 2018 | 72 | 2018 |
| An autoencoder-based deep learning approach for clustering time series data N Tavakoli, S Siami-Namini, M Adl Khanghah, F Mirza Soltani, ... SN Applied Sciences 2, 1-25, 2020 | 67 | 2020 |
| A survey of privacy concerns in wearable devices P Datta, AS Namin, M Chatterjee 2018 IEEE international conference on big data (big data), 4549-4553, 2018 | 60 | 2018 |
| A comparison of TCN and LSTM models in detecting anomalies in time series data S Gopali, F Abri, S Siami-Namini, AS Namin 2021 IEEE International Conference on Big Data (Big Data), 2415-2420, 2021 | 59 | 2021 |
| How social engineers use persuasion principles during vishing attacks KS Jones, ME Armstrong, MKK Tornblad, A Siami Namin Information & Computer Security 29 (2), 314-331, 2021 | 57 | 2021 |
| Forecasting economic and financial time series: Arima vs. LSTM SS Namin, AS Namin arXiv preprint arXiv:1803.06386, 2018 | 57 | 2018 |
| Finding sufficient mutation operators via variable reduction AS Namin, JH Andrews Second Workshop on Mutation Analysis (Mutation 2006-ISSRE Workshops 2006), 5-5, 2006 | 52 | 2006 |
Sima Siami-NaminiJohns Hopkins University在 jhu.edu 的电子邮件经过验证