2025W

• Self-Organizing Systems / 188.413 / VU

• Harnessing Information from Social Networks in Industry 4.0
  2016 – 2017 / Austrian Research Promotion Agency (FFG)

• Message ranking in a factory setting using context and user preference / Taha, A. A., Piroi, F., Hanbury, A., Tropper, T., Mutzl, T., & Shehata, H. (2017). Message ranking in a factory setting using context and user preference. In 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, Cyprus. IEEE. https://doi.org/10.1109/etfa.2017.8247708
• Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks / Jimenez del Toro, O. A., Müller, H., Krenn, M., Grünberg, K., Taha, A. A., Winterstein, M., Eggel, I., Foncubierta-Rodríguez, A., Goksel, O., Jakab, A., Kontokotsios, G., Langs, G., Menze, B. H., Fernandez, T. S., Schaer, R., Walleyo, A., Weber, M.-A., Dicente Cid, Y., Gass, T., & Hanbury, A. (2016). Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks. IEEE Transactions on Medical Imaging, 35(11), 2459–2475. https://doi.org/10.1109/tmi.2016.2578680
• An Efficient Algorithm for Calculating the Exact Hausdorff Distance / Taha, A. A., & Hanbury, A. (2015). An Efficient Algorithm for Calculating the Exact Hausdorff Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(11), 2153–2163. https://doi.org/10.1109/tpami.2015.2408351
• Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool / Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Medical Imaging. https://doi.org/10.1186/s12880-015-0068-x
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• Addressing metric challenges : bias and selection - efficient computation - hubness explanation and estimation / Taha, A. A. (2015). Addressing metric challenges : bias and selection - efficient computation - hubness explanation and estimation [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2015.34644
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• Test Data and Results of the Automatic Metric Selection Method / Taha Abdel, A., Hanbury, A., & Jimenez del Toro, O. A. (2014). Test Data and Results of the Automatic Metric Selection Method. http://hdl.handle.net/20.500.12708/38055
• A formal method for selecting evaluation metrics for image segmentation / Taha, A. A., Hanbury, A., & del Toro, O. A. J. (2014). A formal method for selecting evaluation metrics for image segmentation. In 2014 IEEE International Conference on Image Processing (ICIP). IEEE ICIP Proceedings, Austria. IEEE. https://doi.org/10.1109/icip.2014.7025187
• Visualising Class Distribution on Self-Organising Maps / Mayer, R., Taha Abdel, A., & Rauber, A. (2007). Visualising Class Distribution on Self-Organising Maps. In J. Marques de Sá, L. A. Alexandre, W. Duch, & D. P. Mandic (Eds.), Artificial Neural Networks - ICANN 2007 (pp. 359–368). Springer LNCS. https://doi.org/10.1007/978-3-540-74695-9_37
• Coloring of the SOM based on class labels / Taha, A. A. (2006). Coloring of the SOM based on class labels [Diploma Thesis, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/181390