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Publikationen

Suchbegriffe: machine learning, . Treffer: 20


** = Publikationen gelistet in SCI/SSCI (veröffentlicht im Web of Science)
* = Publikationen in sonstigen peer-reviewten Journalen (ggf. noch nicht im WoS veröffentlicht)
Filter: Originalarbeit, Übersichtsarbeit, Konferenzband Originalarbeit

2021

** Bayer, B; Diaz, RD; Melcher, M; Striedner, G; Duerkop, M Digital Twin Application for Model-Based DoE to Rapidly Identify Ideal Process Conditions for Space-Time Yield Optimization.

PROCESSES. 2021; 9(7), 1109 WoS FullText FullText_BOKU

** Creutzig, F; Callaghan, M; Ramakrishnan, A; Javaid, A; Niamir, L; Minx, J; Muller-Hansen, F; Sovacool, B; Afroz, Z; Andor, M; Antal, M; Court, V; Das, N; Diaz-Jose, J; Dobbe, F; Figueroa, MJ; Gouldson, A; Haberl, H; Hook, A; Ivanova, D; Lamb, WF; Maizi, N; Mata, E; Nielsen, KS; Onyige, CD; Reisch, LA; Roy, J; Scheelbeek, P; Sethi, M; Some, S; Sorrell, S; Tessier, M; Urmee, T; Virag, D; Wan, C; Wiedenhofer, D; Wilson, C Reviewing the scope and thematic focus of 100 000 publications on energy consumption, services and social aspects of climate change: a big data approach to demand-side mitigation*.

ENVIRON RES LETT. 2021; 16(3), 033001 WoS FullText FullText_BOKU

** Lasser, J; Matzhold, C; Egger-Danner, C; Fuerst-Waltl, B; Steininger, F; Wittek, T; Klimek, P Integrating diverse data sources to predict disease risk in dairy cattle-a machine learning approach.

J ANIM SCI. 2021; 99(11), skab294 WoS PubMed FullText FullText_BOKU

** Woeber, W; Mehnen, L; Sykacek, P; Meimberg, H Investigating Explanatory Factors of Machine Learning Models for Plant Classification.

PLANTS-BASEL. 2021; 10(12), 2674 WoS PubMed FullText FullText_BOKU

2020

** Baumgartner, J; Gruber, K; Simoes, SG; Saint-Drenan, YM; Schmidt, J Less Information, Similar Performance: Comparing Machine Learning-Based Time Series of Wind Power Generation to Renewables.ninja.

ENERGIES. 2020; 13(9): WoS FullText FullText_BOKU

** Bayer, B; Striedner, G; Duerkop, M Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization.

BIOTECHNOL J. 2020; 15(9), 2000121 WoS PubMed FullText FullText_BOKU

** Feigl, M; Herrnegger, M; Klotz, D; Schulz, K Function Space Optimization: A Symbolic Regression Method for Estimating Parameter Transfer Functions for Hydrological Models.

WATER RESOUR RES. 2020; 56(10), e2020WR027385 WoS PubMed FullText FullText_BOKU

** Hintze, S; Maulbetsch, F; Asher, L; Winckler, C Doing nothing and what it looks like: inactivity in fattening cattle.

PEERJ. 2020; 8, e9395 WoS PubMed PUBMED Central FullText FullText_BOKU

** Jin, XL; Li, ZH; Atzberger, C Editorial for the Special Issue "Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery".

REMOTE SENS-BASEL. 2020; 12(6), 940 WoS FullText FullText_BOKU

** Salcedo-Sanz, S; Ghamisi, P; Piles, M; Werner, M; Cuadra, L; Moreno-Martinez, A; Izquierdo-Verdiguier, E; Munoz-Mari, J; Mosavi, A; Camps-Valls, G Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources.

INFORM FUSION. 2020; 63: 256-272. WoS FullText FullText_BOKU

** Waldmann, P; Pfeiffer, C; Meszaros, G Sparse Convolutional Neural Networks for Genome-Wide Prediction.

FRONT GENET. 2020; 11, 25 WoS PubMed FullText FullText_BOKU

2019

** Kratzert, F; Klotz, D; Herrnegger, M; Sampson, AK; Hochreiter, S; Nearing, GS Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning.

WATER RESOUR RES. 2019; 55(12): 11344-11354. WoS FullText FullText_BOKU

** Mihaylov, I; Kandula, M; Krachunov, M; Vassilev, D A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models.

BIOL DIRECT. 2019; 14(1), 22 WoS PubMed FullText FullText_BOKU

** Oner, T; Thiam, P; Kos, G; Krska, R; Schwenker, F; Mizaikoff, B Machine learning algorithms for the automated classification of contaminated maize at regulatory limits via infrared attenuated total reflection spectroscopy.

WORLD MYCOTOXIN J. 2019; 12(2): 113-122. WoS FullText FullText_BOKU

** Rammer, W; Seidl, R Harnessing Deep Learning in Ecology: An Example Predicting Bark Beetle Outbreaks.

FRONT PLANT SCI. 2019; 10, 1327 WoS PubMed FullText FullText_BOKU

** Schuwirth, N; Borgwardt, F; Domisch, S; Friedrichs, M; Kattwinkel, M; Kneis, D; Kuemmerlen, M; Langhans, SD; Martinez-Lopez, J; Vermeiren, P How to make ecological models useful for environmental management.

ECOL MODEL. 2019; 411, 108784 WoS FullText FullText_BOKU

2017

** Melcher, M; Scharl, T; Luchner, M; Striedner, G; Leisch, F; Boosted structured additive regression for Escherichia coli fed-batch fermentation modeling..

Biotechnol Bioeng. 2017; 114(2):321-334 WoS PubMed FullText FullText_BOKU

2015

** Heiser, M; Scheidl, C; Eisl, J; Spangl, B; Hubl, J Process type identification in torrential catchments in the eastern Alps.

GEOMORPHOLOGY. 2015; 232: 239-247. WoS FullText FullText_BOKU

** Olsen, L; Oostenbrink, C; Jorgensen, FS Prediction of cytochrome P450 mediated metabolism.

ADV DRUG DELIVER REV. 2015; 86: 61-71. WoS PubMed FullText FullText_BOKU

2008

** Stjernschantz, E; Vermeulen, NP; Oostenbrink, C Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450..

Expert Opin Drug Metab Toxicol. 2008; 4(5):513-527 WoS PubMed FullText FullText_BOKU

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