Titlebook: Handbuch Studentisches Gesundheitsmanagement - Perspektiven, Impulse und Praxiseinblicke; Mareike Timmann,Tatjana Paeck,Max Sprenger Book

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書目名稱Handbuch Studentisches Gesundheitsmanagement - Perspektiven, Impulse und Praxiseinblicke影響因子(影響力)




書目名稱Handbuch Studentisches Gesundheitsmanagement - Perspektiven, Impulse und Praxiseinblicke影響因子(影響力)學科排名




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書目名稱Handbuch Studentisches Gesundheitsmanagement - Perspektiven, Impulse und Praxiseinblicke網絡公開度學科排名




書目名稱Handbuch Studentisches Gesundheitsmanagement - Perspektiven, Impulse und Praxiseinblicke被引頻次




書目名稱Handbuch Studentisches Gesundheitsmanagement - Perspektiven, Impulse und Praxiseinblicke被引頻次學科排名




書目名稱Handbuch Studentisches Gesundheitsmanagement - Perspektiven, Impulse und Praxiseinblicke年度引用




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書目名稱Handbuch Studentisches Gesundheitsmanagement - Perspektiven, Impulse und Praxiseinblicke讀者反饋




書目名稱Handbuch Studentisches Gesundheitsmanagement - Perspektiven, Impulse und Praxiseinblicke讀者反饋學科排名

未成熟

使堅硬

Jan Fischer,Mareike Timmannpirical results suggest that the forecasting accuracy of RNN models critically depends on hyperparameter calibration and that the two hidden layer RNN-GRU networks outperform the RSVD model. RNNs can generate mortality schedules that are biologically plausible and fit well the mortality schedules ac

懶惰人民

rmine several factors including NDVI, DEM, distance to residential areas, distance to road that are highly associated with malaria risks. The results of this study help measuring the incidence of malaria in the context of climate change and under the impact of change in people’s livelihoods.

描述

學術討論會

Edgar Voltmer entropy and Hurst exponent to detect PPI. Here, features are extracted directly from primary sequences, and the Support Vector Machine algorithm is used as a classifier. The proposed model on the DIP (Database of Interacting Proteins) dataset gives 96.71% accuracy with 94.94% precision and 98.58% r

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Christina Nill,Joachim Birzelec mixed logit (MDML) model as a generalization of the BDML model and derive the basic properties such as non-stationary mean, variance and correlations for the repeated multinomial responses. Next, to understand these basic properties, we develop step by step likelihood estimating equations for the

GONG

Pastry

Julia Burian,Jennifer Lehnchen,Eileen Heumann,Stefanie Maria Helmer,Christiane Stockc mixed logit (MDML) model as a generalization of the BDML model and derive the basic properties such as non-stationary mean, variance and correlations for the repeated multinomial responses. Next, to understand these basic properties, we develop step by step likelihood estimating equations for the

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