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Titlebook: Computational Intelligence, Communications, and Business Analytics; Second International Jyotsna Kumar Mandal,Somnath Mukhopadhyay,Kousik D

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Jyotsna Kumar Mandal,Somnath Mukhopadhyay,Kousik D
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neue betriebswirtschaftliche forschung (nbf)or resources. For this reason, service providers must have to select a proper node that may complete user’s task in between the deadline. So, providers must have to distribute a dynamic workload evenly across multiple nodes which ensure that no nodes are overloaded or under loaded which helps to red
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https://doi.org/10.1007/978-3-322-99285-7ignificant amount of feature dimension is reduced without compromising on recognition accuracy. Comparison of the present feature selection method with two of its ancestors also reveals that the present method outperforms the others.
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https://doi.org/10.1007/978-3-658-11464-0ich is followed by a type reduction. As vague set resembles an interval valued set, therefore type reduction essentially performs the defuzzification. We have compared the performance of our proposed VCA with the general Fuzzy C means (FCM) and Shadowed C means (SCM). To evaluate the performance, we
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https://doi.org/10.1007/978-3-8350-9455-0re defined) are changed. This will in turn change the mid points of the interval which is used to measure variation of transition. This procedure is repeated until optimum positions of cuts corresponding to the lowest forecasting error are obtained. The experiment is carried out on standard datasets
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