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Goal Programming (GP) is an important analytical approach devised to solve many realworld problems.
Such a DSS has been defined and implemented exploiting a novel approach devised to facilitate the design of fuzzy DSSs for medical problems.
To prepare metaparticles, we instead adopted an approach devised to build finite clusters of identical particles, modified in such a way that different monomer types can be incorporated.
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Particular emphasis is placed on the novel targeted approaches devised to control plant disease and plant damage caused by fungi, oomycetes, bacteria, nematodes, and insects.
The problems arise in practice, and the combination of approaches devised to tackle them is itself becoming intricate.
A new approach was devised to correct the spatial offset between historical and contemporary imagery.
An iterative approach is devised to handle the complex interdependency between multicell interference profiles in the throughput analysis.
To increase the control performance, the Takagi Sugeno fuzzy-based approach is devised to provide the appropriate coefficients.
A robust mixed possibilistic-flexible programming approach is devised to cope with imprecise parameters and soft constraints.
Furthermore, an approach was devised to deal with time-varying operating conditions to make them applicable in various loading environment [21].
In this paper a coevolutionary genetic approach is devised to support hierarchical, collaborative relations between individuals representing different parameters of Takagi Sugeno fuzzy models.
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