2024W

• 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). 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