2018
DOI: 10.1007/s00500-018-3086-0
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C-means clustering and deep-neuro-fuzzy classification for road weight measurement in traffic management system

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Cited by 28 publications
(23 citation statements)
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“…All these attributes are combined with varying ranges, as presented in Table I. The selected attributes from GUI are placed to generate the weight value by C-means clustering and Deep-Neuro-Fuzzy software [8]. The weight output range is 0-10.…”
Section: B Integrating Road Weight Modulementioning
confidence: 99%
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“…All these attributes are combined with varying ranges, as presented in Table I. The selected attributes from GUI are placed to generate the weight value by C-means clustering and Deep-Neuro-Fuzzy software [8]. The weight output range is 0-10.…”
Section: B Integrating Road Weight Modulementioning
confidence: 99%
“…A new intelligence traffic management system (ITMS) [8] [11] [20] [21] [22] [30] was proposed and implemented to find an optimum route from source to destination. Each route is segmented and called as road segment.…”
Section: Introductionmentioning
confidence: 99%
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