Larsen, E. L.
, Espelund, U., Lauritsen, S. M., Thiesson, B.
, Lange, J., Friis Kjeldsen, G.
& Jørgensen, M. J. (2022).
+Priokritisk. Early identification of clinical detorioriation among hospitalized patients by explainable AI. Poster session presented at Symposium on Artificial Intelligence for Learning Health Systems.
Merhi, Y., Betancur, P. F., Ripolles, T. S., Suetta, C., Brage-Andersen, M. R., Hansen, S. K., Frydenlund, A.
, Nygaard, J. V., Mikkelsen, P. H., Boix, P. P.
& Agarwala, S. (2023).
Printed dry electrode for neuromuscular electrical stimulation (NMES) for e-textile.
Nanoscale,
15(11), 5337-5344.
https://doi.org/10.1039/d2nr06008f
Bochtis, D. D., Sørensen, C. G., Fountas, S., Moysiadis, V. & Pardalos, P. M. (2022).
Preface.
Springer optimization and its applications,
184, v-xiii.
Riishede, I., Rode, L., Sperling, L.
, Overgaard, M., Ravn, J. D.
, Sandager, P., Skov, H., Wagner, S. R., Nørgaard, P., Clausen, T. D., Jensen, C. A. J., Pihl, K., Jørgensen, F. S., Munk, J. K., Zingenberg, H. J., Pedersen, N. G., Andersen, M. R., Wright, A., Wright, D. ... Ekelund, C. K. (2023).
Pre-eclampsia Screening in Denmark (PRESIDE): National Validation Study.
Obstetrical and Gynecological Survey,
78(12), 715-717.
https://doi.org/10.1097/01.ogx.0001004636.48406.54
Riishede, I., Rode, L., Sperling, L., Ravn, J. D.
, Sandager, P., Skov, H., Wagner, S. R., Nørgaard, P., Clausen, T. D., Jensen, C. A. J., Pihl, K., Jørgensen, F. S., Munk, J. K., Zingenberg, H. J., Pedersen, N. G., Andersen, M. R., Wright, A., Wright, D., Tabor, A. & Ekelund, C. K. (2023).
Pre-eclampsia screening in Denmark (PRESIDE): national validation study.
Ultrasound in Obstetrics and Gynecology,
61(6), 682-690.
https://doi.org/10.1002/uog.26183
Mazur-Milecka, M., Kowalczyk, N., Jaguszewska, K., Zamkowska, D., Wójcik, D., Preis, K.
, Skov, H., Wagner, S., Sandager, P., Sobotka, M. & Rumiński, J. (2023).
Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data. In P. Strumiłło, A. Klepaczko, M. Strzelecki & D. Bociąga (Eds.),
The Latest Developments and Challenges in Biomedical Engineering: Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering, Lodz, Poland, September 27–29, 2023 (pp. 267-281). Springer.
https://doi.org/10.1007/978-3-031-38430-1_21
Baltakys, K., Baltakienė, M.
, Heidari, N., Iosifidis, A. & Kanniainen, J. (2023).
Predicting the trading behavior of socially connected investors: Graph neural network approach with implications to market surveillance.
Expert Systems with Applications,
228, Article 120285.
https://doi.org/10.1016/j.eswa.2023.120285
Shabani, M., Magris, M., Tzagkarakis, G., Kanniainen, J.
& Iosifidis, A. (2023).
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots.
Neural Computing and Applications,
35(25), 18519–18531.
https://doi.org/10.1007/s00521-023-08674-y
Kragh, M. F., Rimestad, J., Lassen, J. T., Berntsen, J.
& Karstoft, H. (2022).
Predicting embryo viability based on self-supervised alignment of time-lapse videos.
IEEE Transactions on Medical Imaging,
41(2), 465-475.
https://doi.org/10.1109/TMI.2021.3116986
D’Ambrosio, F., Harbo, M., Contiero, D., Bonfigli, A. R., Cicconi, D., Heuer, N., Roos, A.
, Pedersen, C. F., Fabbietti, P. & Gagliardi, C. (2024).
Preact to lower the risk of falling by customized rehabilitation across Europe: the feasibility study protocol of the PRECISE project in Italy.
Frontiers in Public Health,
12, Article 1293621.
https://doi.org/10.3389/fpubh.2024.1293621
Rasmussen, S. M.
, Nielsen, T., Hody, S., Hager, H. & Schousboe, L. P. (2021).
Photoplethysmography for demarcation of cutaneous squamous cell carcinoma.
Scientific Reports,
11, Article 21467.
https://doi.org/10.1038/s41598-021-00645-4
Benatto, G. A. D. R., Chi, M., B Jensen, O., Santamaria Lancia, A. A., Riedel-Lyngskær, N., Iandolo, B.
, Davidsen, R. S., Hansen, O., Thorsteinsson, S. & Behrensdorff Poulsen, P. (2018).
Photoluminescence imaging induced by laser line scan: Study for outdoor field inspections. In
2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (pp. 395-399)
https://doi.org/10.1109/PVSC.2018.8547416
Ferrarini, B., Ehsan, S., Leonardis, A.
, Rehman, N. U. & McDonald-Maier, K. D. (2018).
Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database.
IEEE Access,
6, 8564-8573.
https://doi.org/10.1109/access.2018.2795460
Craig, M. T., Wohland, J., Stoop, L. P.
, Kies, A., Pickering, B., Bloomfield, H. C., Browell, J., De Felice, M., Dent, C. J., Deroubaix, A., Frischmuth, F., Gonzalez, P. L. M., Grochowicz, A., Gruber, K., Härtel, P., Kittel, M., Kotzur, L., Labuhn, I., Lundquist, J. K. ... Brayshaw, D. J. (2022).
Overcoming the disconnect between energy system and climate modeling.
Joule,
6(7), 1405-1417.
https://doi.org/10.1016/j.joule.2022.05.010
Rasmussen, I. A., Moeskops, B., Micheloni, C., Gócs, K.
, Jensen, A. L., Jørgensen, M. S., Kristensen, H., Willer, H. & Padel, S. (2018).
Organic Knowledge Network Arable: D.4.3 Final report on evaluation of end-user material.
https://orgprints.org/id/eprint/37847/2/OK_Net_WP4_D.4.3_final.pdf
Anagnostis, A., Tagarakis, A. C., Kateris, D., Moysiadis, V.
, Sørensen, C. G., Pearson, S.
& Bochtis, D. (2021).
Orchard mapping with deep learning semantic segmentation.
Sensors,
21(11), Article 3813.
https://doi.org/10.3390/s21113813
Pham, X. H., Heiß, M., Tran, D., Nguyen, M. A., Nguyen, A. Q.
& Kayacan, E. (2023).
ORB-Net: End-to-end Planning Using Feature-based Imitation Learning for Autonomous Drone Racing. In
ISR Europe 2023: 56th International Symposium on Robotics, in cooperation with Fraunhofer IPA September 26 – 27, 2023 in Stuttgart (pp. 16-21). VDE Verlag GmbH.
https://ieeexplore.ieee.org/document/10363048
Mukherjee, M., Kumar, V.
, Zhang, Q., Mavromoustakis, C. X. & Matam, R. (2022).
Optimal Pricing for Offloaded Hard- and Soft-Deadline Tasks in Edge Computing.
IEEE Transactions on Intelligent Transportation Systems,
23(7), 9829-9839.
https://doi.org/10.1109/TITS.2021.3117973
Li, C.
, Kies, A., Zhou, K., Schlott, M., Sayed, O. E., Bilousova, M. & Stöcker, H. (2024).
Optimal Power Flow in a highly renewable power system based on attention neural networks.
Applied Energy,
359, Article 122779.
https://doi.org/10.1016/j.apenergy.2024.122779