A Swarm Intelligence (SI) based algorithm, which is known as Particle Swarm Optimization (PSO), has been used to generate error estimation functions.
In order to reduce RLFs and unnecessary handovers in femtocells, we propose a cost function based algorithm, which is defined as follows: C = ∑ i = 1 5 α i N i (3).
Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative.
Incorporating the algorithms in the sensor networks creates new diffusion-based algorithms, which are shown to perform much better than their non-diffusion-based counterparts.
In order to verify the null space condition, we proposed novel and simple enumeration-based algorithms, which are called the basic and optimized pick-l algorithms, to obtain upper bounds of α k.
In this article, we consider the uniform circular arrays (UCAs) with the number of antenna elements insufficient to apply the traditional beamspace-based algorithms, which are labeled as sparse UCAs.
Netflix has used big data to improve its content recommendation engine, first via a crowd-sourced algorithm using customer rating, then, through machine-learning-based algorithms which are able to develop new insights from the mash ups of a wide range of data (show features, social data from other Netflix users, or box-office).
Causality assessment was based on algorithms which are relatively simple to apply but where the weighting of each criterion can be somewhat arbitrary.
These web-based computational tools are based on different algorithms which are based on several parameters calculated individually for each miRNA.
In this paper, we introduce a Mixed Integer Linear Programming (MILP) based algorithm, which can be efficiently used to improve an existing feasible, but non-optimal, production schedule or to reschedule jobs in the case of changed operational parameters.
