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Timing and resource prediction models are presented.
Adoption of intelligent software agent such as Cognitive Agent (CA) for the accurate resource prediction has a significant potential to solve the challenges of resource prediction in MANET.
Islam et al. [24] propose resource prediction approaches based on machine learning.
Most of the published work on resource prediction in MANET focuses on either bandwidth or energy without considering mobility effects.
This article develops a method for rapid comparison of initial project viability for multiple city sites and installation options using data from wind and solar resource prediction techniques.
We relied upon the historical data budget usage behavior of the user applications in our recommendation of future Internet data budget plan following ARIMA-based resource prediction analysis.
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Most commonly used for wind resource predictions on land as well as offshore is the WAsP program.
The variability in daily totals illuminates the difficulty in short-term resource predictions, as well as the number of days with virtually zero solar availability.
Because of many reasons such as the non-renewable structure of oil and gas resources, prediction of scientists about lack of resources in the near future, etc., oil and gas conflicts occurring in the real world are often complicated; therefore, it is indispensable to describe these complex models with less-complicated game theory models.
In this paper, an ensemble cloud resource usage prediction algorithm based on Learning Automata (LA) theory is proposed that combines state of the art prediction models, and it determines weights for individual constituent models.
There are several works that apply VM resource demand prediction techniques for resource allocation in cloud computing.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com