| A comparative performance analysis of different activation functions in LSTM networks for classification A Farzad, H Mashayekhi, H Hassanpour Neural Computing and Applications 31, 2507-2521, 2019 | 142 | 2019 |
| Unsupervised log message anomaly detection A Farzad, TA Gulliver ICT Express 6 (3), 229-237, 2020 | 103 | 2020 |
| Log message anomaly detection and classification using auto-B/LSTM and auto-GRU A Farzad, TA Gulliver arXiv preprint arXiv:1911.08744, 2019 | 21 | 2019 |
| Oversampling Log Messages Using a Sequence Generative Adversarial Network for Anomaly Detection and Classification A Farzad, TA Gulliver International Conference on Artificial Intelligence and Machine Learning …, 2019 | 7 | 2019 |
| Log message anomaly detection and classification using auto-B A Farzad, TA Gulliver LSTM and auto-GRU, 1-28, 2019 | 7 | 2019 |
| Two class pruned log message anomaly detection A Farzad, TA Gulliver SN Computer Science 2 (5), 391, 2021 | 5 | 2021 |
| Log Message Anomaly Detection with Oversampling A Farzad, TA Gulliver International Journal of Artificial Intelligence and Applications (IJAIA) 11 …, 2020 | 5 | 2020 |
| Log message anomaly detection with fuzzy C-means and MLP A Farzad, TA Gulliver Applied Intelligence 52 (15), 17708-17717, 2022 | 4 | 2022 |
| Log message anomaly detection using machine learning A Farzad | | 2021 |
| Evaluation the Role of Activation Function in LSTM Networks A Farzad, H Mashayekhi Conference of Interdisciplinary Research in Computer, Electrical, Mechanical …, 2016 | | 2016 |
| Sentiment Classification on the Cloud A Farzad, MA Shehab https://www.researchgate.net/publication …, 0 | | |
