|
11441 |
Predicting the duration of motorway incidents using machine learning Enthalten in European transport research review Bd. 16, 19.2.2024, Nr. 1, date:12.2024: 1-12
|
|
|
11442 |
Predicting the effect of climate change on the spatiotemporal distribution of two endangered plant species, Silene leucophylla Boiss. and Silene schimperiana Boiss., using machine learning, in Saint Catherine Protected Area, Egypt Enthalten in Ǧāmiʿat Banī-Suwaif: Beni-Suef University Journal of Basic and Applied Sciences Bd. 13, 30.9.2024, Nr. 1, date:12.2024: 1-23
|
|
|
11443 |
Predicting the effects of winter water warming in artificial lakes on zooplankton and its environment using combined machine learning models Enthalten in Scientific reports Bd. 12, 27.9.2022, Nr. 1, date:12.2022: 1-14
|
|
|
11444 |
Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning Enthalten in Scientific reports Bd. 15, 8.5.2025, Nr. 1, date:12.2025: 1-12
|
|
|
11445 |
Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning Enthalten in Scientific reports Bd. 14, 20.2.2024, Nr. 1, date:12.2024: 1-10
|
|
|
11446 |
PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer’s Disease With machine learning: the PREVIEW study protocol Enthalten in BMC neurology Bd. 23, 12.8.2023, Nr. 1, date:12.2023: 1-13
|
|
|
11447 |
Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method Enthalten in Communications materials Bd. 3, 6.7.2022, Nr. 1, date:12.2022: 1-10
|
|
|
11448 |
Predicting the in-game status in soccer with machine learning using spatiotemporal player tracking data Enthalten in Scientific reports Bd. 12, 29.9.2022, Nr. 1, date:12.2022: 1-10
|
|
|
11449 |
Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm Enthalten in BMC medical informatics and decision making Bd. 22, 22.9.2022, Nr. 1, date:12.2022: 1-11
|
|
|
11450 |
Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach Enthalten in Translational Psychiatry Bd. 8, 5.11.2018, Nr. 1, date:12.2018: 1-11
|
|