Property:References

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Graph Neural Networks for cardiovascular disease +
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80.  +
H
Hide-and-Seek Privacy Challenge (NeurIPS 2020) +
https://www.vanderschaar-lab.com/privacy-challenge/  +
Human Motion Analysis using Inertial Sensors +
J. Rueterbories, E. G. Spaich, B. Larsen, and O. K. Andersen, “Methods for gait event detection and analysis in ambulatory systems,” Med. Eng. & Phys., vol. 32, no. 6, pp. 545–552, 2010. J. J. Kavanagh and H. B. Menz, “Accelerometry: A technique for quantifying movement patterns during walking,” Gait & Posture, vol. 28, no. 1, pp. 1–15, 2008. D. Lai, R. Begg, and M. Palaniswami, “Computational intelligence in gait research: A perspective on current app. And future challenges,” Info. Tech. in Biomed., IEEE Trans. on, vol. 13, no. 5, pp. 687–702, 2009.  +
Human Value Detection +
https://aclanthology.org/2022.acl-long.306.pdf https://www.youtube.com/watch?v=ZAQ4LELCCY4  +
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Identification and Classification of Automotive Radar Interference using Data-driven Methods +
Radar interference: https://www.analog.com/en/resources/analog-dialogue/articles/automotive-radar-sensors-and-congested-radio-spectrum-an-urban-electronic-warfare.html Equipment: https://www.ti.com/tool/AWR2944EVM, and https://www.ti.com/tool/DCA1000EVM,  +
Identity verification of humans performing physical activities +
Mohammad Derawi, Patrick Bours, Gait and activity recognition using commercial phones, Computers & Security, Volume 39, Part B, November 2013, Pages 137-144 Ailisto, Heikki J., et al. "Identifying people from gait pattern with accelerometers." Defense and Security. International Society for Optics and Photonics, 2005.  +
Image Retrieval for Arguments +
https://touche.webis.de/clef22/touche22-web/image-retrieval-for-arguments.html  +
Improved networks for cloud-car communication +
Generative Adverserial Nets Etschberger: CAN Controller Area Network - Grundlagen, Protokolle, Bausteine, Anwendungen  +
Improving MEDication Adherence through Person Centered Care and Adaptive Interventions +
Definitions, variants, and causes of nonadherence with medication: a challenge for tailored interventions: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3711878/ Predictors of Medication Adherence Using a Multidimensional Adherence Model in Patients with Heart Failure: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2603618/ A machine learning approach for medication adherence monitoring using body-worn sensors: https://ieeexplore.ieee.org/document/7459425 Machine Learning Classification of Medication Adherence in Patients with Movement Disorders Using Non-Wearable Sensors: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5729888/  +
In-Vehicle Data Acquisition for Driving Behavior Profiling and Automotive AI Applications +
A. Dos Santos Roque, L. M. Da Silva Alves and E. P. de Freitas, "CAN-Modes: In-vehicle datasets generation and analysis in different driving situations," 2024 Workshop on Communication Networks and Power Systems (WCNPS), Brasilia, Brazil, 2024, pp. 1-7, doi: 10.1109/WCNPS65035.2024.10814379.  +
Increase Data Rate over Error-prone networks +
1) D. E. Lucani, M. V. Pedersen, D. Ruano, C. W. Sørensen, F. H. P. Fitzek, J. Heide, O. Geil, V. Nguyen, M. Reisslein — “Fulcrum: Flexible Network Coding for Heterogeneous Devices.” IEEE Access, 2018. 2) V. Nguyen, J. A. Cabrera, D. You, H. Salah, G. T. Nguyen, F. H. P. Fitzek — “Advanced Adaptive Decoder Using Fulcrum Network Codes.” IEEE Access, 2019. 3) A. Shahzad, R. Ali, A. Haider, H. S. Kim — “RS-RLNC: A Reinforcement Learning-Based Selective Random Linear Network Coding Framework for Tactile Internet.” IEEE Access, 2023.  +
Intelligible patient representation for outcome prediction of congestive heart failure patients +
1. Miotto R, Li L, Kidd BA, Dudley JT. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Scientific Reports. 2016;6:26094. doi:10.1038/srep26094. 2. Choi, Edward, et al. "Multi-layer representation learning for medical concepts." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016  +
Interactive Anomaly Detection +
- Lamba, H. and Akoglu, L., 2019, May. Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), pp. 612-620. Society for Industrial and Applied Mathematics. https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.69 - Ding, K., Li, J. and Liu, H., 2019, January. Interactive anomaly detection on attributed networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 357-365. ACM. http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf - Arnaldo, I., Veeramachaneni, K. and Lam, M., 2019. ex2: a framework for interactive anomaly detection. In ACM IUI Workshop on Exploratory Search and Interactive Data Analytics (ESIDA). http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf - Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. IEEE Access. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963  +
Investigating Robustness of DNNs +
Szegedy, Christian, et al. "Intriguing properties of neural networks." arXiv preprint arXiv:1312.6199 (2013). Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. "Reducing the dimensionality of data with neural networks." Science 313.5786 (2006): 504-507. Hinton, Geoffrey E. "Learning multiple layers of representation." Trends in cognitive sciences 11.10 (2007): 428-434. Nguyen, Anh, Jason Yosinski, and Jeff Clune. "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images." arXiv preprint arXiv:1412.1897 (2014). Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.  +
Investigation of spread spectrum techniques to reduce the electromagnetic interference in switch mode power supply +
(1) Gamoudi, Rabiaa, Dhia Elhak Chariag, and Lassaad Sbita. "A review of spread-spectrum-based PWM techniques—A novel fast digital implementation." IEEE Transactions on Power Electronics 33.12 (2018): 10292-10307. (2) Perotti, M., and F. Fiori. "Software based control of the EMI generated in BLDC motor drives." 2016 International Symposium on Electromagnetic Compatibility-EMC EUROPE. IEEE, 2016. (3) Blank, Mathias, et al. "Digital slew rate and s-shape control for smart power switches to reduce EMI generation." IEEE Transactions on Power Electronics 30.9 (2014): 5170-5180  +
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Knowledge graphs 4 XAI in Healthcare +
https://www.sciencedirect.com/science/article/pii/S1566253523001148 https://www.sciencedirect.com/science/article/abs/pii/S1532046425000905 https://www.sciencedirect.com/science/article/pii/S0004370221001788 https://www.sciencedirect.com/science/article/pii/S1532046423001247 https://arxiv.org/abs/2402.12608  +
Knowledge graphs in healthcare +
https://dl.acm.org/doi/10.1145/3447772 https://arxiv.org/abs/2306.04802  +
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Leveraging LLMs for Clinical Note Annotation and Uncertainty Estimation +
Yang, Zhichao, et al. "Multi-label few-shot ICD coding as autoregressive generation with prompt." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 4. 2023. Liu, Leibo, et al. "Automated icd coding using extreme multi-label long text transformer-based models." Artificial Intelligence in Medicine (2023): 102662. Hu, Edward J., et al. "Lora: Low-rank adaptation of large language models." arXiv preprint arXiv:2106.09685 (2021). Sensoy, Murat, Lance Kaplan, and Melih Kandemir. "Evidential deep learning to quantify classification uncertainty." Advances in neural information processing systems 31 (2018).  +
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Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses +
For general understanding: https://www.youtube.com/watch?v=8PQO4P8pR8o&t=839s Papers: 1. The strong gravitational lens finding challenge - Metcalf, R. B., Meneghetti, M., Avestruz, C., et al. 2019, A&A, 625, A119 2. Testing convolutional neural networks for finding strong gravitational lenses in KiDS - Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2019a, MNRAS, 482, 807 3. Finding strong gravitational lenses through self-attention - Study based on the Bologna Lens Challenge - Thuruthipilly, H., Adam Zadrozny, Agnieszka Pollo, and Marek Biesiada. A&A, 664:A4 4. The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples - Pearson, J., Li, N., & Dye, S. 2019, MNRAS, 488, 991 5. Deep convolutional neural networks as strong gravitational lens detectors - Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J.-P. 2018, A&A, 611, A2 6. Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks - K. Rojas, E. Savary, B. Clément, M. Maus, F. Courbin, C. Lemon, J. H. H. Chan, G. Vernardos, R. Joseph, R. Cañameras, A. Galan, DOI: 0.1051/0004-6361/202142119  +
Machine Unlearning Faithfulness with XAI +
Beierle, Christoph, and Ingo J. Timm. "Intentional forgetting: An emerging field in AI and beyond." KI-Künstliche Intelligenz 33.1 (2019): 5-8. Bourtoule, Lucas, et al. "Machine unlearning." 2021 IEEE symposium on security and privacy (SP). IEEE, 2021. Cadet, Xavier F., et al. "Deep Unlearn: Benchmarking Machine Unlearning for Image Classification." 2025 IEEE 10th European Symposium on Security and Privacy (EuroS&P). IEEE, 2025. Golatkar, Aditya, Alessandro Achille, and Stefano Soatto. "Eternal sunshine of the spotless net: Selective forgetting in deep networks." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020. Vidal, Àlex Pujol, et al. "Verifying machine unlearning with explainable AI." International Conference on Pattern Recognition. Cham: Springer Nature Switzerland, 2024  +
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