Dr. Ahmad Aghaebrahimian

Projects

• Advancing Information Accessibility in Hospitals Employing Large Language Models / Project leader / ongoing
• Radio Signal Unsupervised and Transfer Learning / Team member / completed
• Towards Enhancing Large Language Models with SNOMED CT for Multi-document Patient Records Summarization / Project leader / completed
• Boosting the Development of Plant Cell Cultures with Deep Learning Through Literature Mining / Project leader / completed
• AI for colorectal cancer: towards the improved CMS classification and interpretability / Team member / completed
• AuSuM – Automatic Supply Chain Monitoring / Deputy project leader / completed

Publications

Articles in scientific journal, peer-reviewed

• Glüge, S. et al. (2024) 'Robust low-cost drone detection and classification using convolutional neural networks in low SNR environments', IEEE Journal of Radio Frequency Identification, 8, pp. 821–830. doi: 10.1109/JRFID.2024.3487303.
• Lardos, A. et al. (2022) 'Computational literature-based discovery for natural products research : current state and future prospects', Frontiers in Bioinformatics, 2(827207). doi: 10.3389/fbinf.2022.827207.

Written conference contributions, peer-reviewed

• Aghaebrahimian, A., Anisimova, M. and Gil, M. (2022) 'Ontology-aware biomedical relation extraction', in Sojka, P. et al. (eds) Text, Speech, and Dialogue. Cham: Springer, pp. 160–171. doi: 10.1007/978-3-031-16270-1_14.
• Tuggener, D. and Aghaebrahimian, A. (2021) 'The Sentence End and Punctuation Prediction in NLG text (SEPP-NLG) shared task 2021', in Proceedings of the Swiss Text Analytics Conference 2021. CEUR Workshop Proceedings. doi: 10.21256/zhaw-23258.
• Aghaebrahimian, A. and Cieliebak, M. (2020) 'Named entity disambiguation at scale', in Schilling, F.-P. and Stadelmann, T. (eds) Artificial Neural Networks in Pattern Recognition. Cham: Springer, pp. 102–110. doi: 10.1007/978-3-030-58309-5_8.
• Aghaebrahimian, A. and Cieliebak, M. (2019) 'Towards integration of statistical hypothesis tests into deep neural networks', in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 5551–5557. doi: 10.18653/v1/P19-1557.
• Aghaebrahimian, A., Ustaszewski, M. and Stauder, A. (2019) 'The TransBank Aligner : cross-sentence alignment with Deep Neural Networks', in Ekstein, K. (ed.) Text, Speech, and Dialogue. Cham: Springer, pp. 185–196. doi: 10.1007/978-3-030-27947-9_16.
• Aghaebrahimian, A. and Cieliebak, M. (2019) 'Hyperparameter tuning for deep learning in natural language processing', in 4th Swiss Text Analytics Conference (SwissText 2019), Winterthur, June 18-19 2019. Swisstext. doi: 10.21256/zhaw-18993.

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