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Malaria can be forecasted using an assortment of methods and significant malaria predictors have been identified in a variety of settings.
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The Standard Precipitation Index (SPI 12 and SPI 24) was forecasted using a traditional stochastic model (ARIMA) and compared to machine learning techniques such as artificial neural networks (ANNs), and support vector regression (SVR).
Sea surface temperature (SST) was forecasted using a slab ocean model to allow diurnal variation and nudged to the persistent SST anomaly at the initial time with an e-folding time (that is, the time in which SST is nudged to specified values) of 7 days.
Cognitive status after six months (C6) in the absence of pharmacologic treatment is forecast using a regression equation based on baseline cognitive status (C0).
Next, the set of modes are forecasted using the ELM.
Moreover, CODeff was forecasted using another effluent parameter.
In addition, future values of the network response time are forecasted using the prediction framework.
Finally, the FN values of the different additive mixtures were forecasted using the models.
The WLAN cell capacity is forecasted using the multirate/multicodec (MR/MC) capacity index explained and validated in Section 5.1.
Trends for permafrost temperature were forecast using large-scale periods of variation in permafrost temperature.
In the absence of empirical data, such effects have been forecast using models [6] [8] parameterized from observations of natural and experimental prion infections in captive deer [4], [8] [10].
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CEO of Professional Science Editing for Scientists @ prosciediting.com