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This study proposes three effective debris-flow prediction models and an inference engine to predict and decide the debris-flow occurrence in Taiwan.
This study is deals with artificial neural network (ANN) and fuzzy expert system (FES) modelling of a gasoline engine to predict engine power, torque, specific fuel consumption and hydrocarbon emission.
These features are extracted from the data and are used in the AI and ML engine to predict a behavior of the system or classify new signals into desired categories to prompt a certain action or behavior from the system.
First, we used the diabetes-specific UKPDS risk engine to predict relative probabilities for CHD in persons with pre-diabetes.
Hence, we assessed the discrimination and calibration of the UKPDS risk engine to predict 4, 5, 6 and 8 year cardiovascular risk in patients with type 2 diabetes.
There are several explanations for the poor to moderate performance of the UKPDS risk engine to predict CHD and CVD risk in this population.
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These outfits use superior recommendation engines to predict what people might want to watch.
The Framingham Study [ 3] and the United Kingdom Prospective Diabetes Study (UKPDS) developed risk scores or engines to predict CHD-related events [ 4] and stroke [ 5].
Finally, an improved Elman neural network (IENN) based forecast engine proposed to predict the load signal in this procedure.
The SLA framework was applied further for the management of security-based SLA (in [50 53]) and has been integrated in simulation engines able to predict the evolution of the developed application [54, 55].
The main goal of the considered engine model is to predict emissions formed by in-cylinder combustion during the closed-volume part of the engine cycle, employing detailed sub-models for the chemical kinetics of the fuel, turbulent mixing, and convective heat transfer.
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