Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering

The occurrence of series of events is always associated with the news report, social network, and Internet media. In this paper, a detecting system for public security events is designed, which carries out clustering operation to cluster relevant text data, in order to benefit relevant departments by evaluation and handling. Firstly, texts are mapped into three-dimensional space using the vector space model. Then, to overcome the shortcoming of the traditional clustering algorithm, an improved fuzzy -means (FCM) algorithm based on adaptive genetic algorithm and semisupervised learning is proposed. In the proposed algorithm, adaptive genetic algorithm is employed to select optimal initial clustering centers. Meanwhile, motivated by semisupervised learning, guiding effect of prior knowledge is used to accelerate iterative process. Finally, simulation experiments are conducted from two aspects of qualitative analysis and quantitative analysis, which demonstrate that the proposed algorithm performs excellently in improving clustering centers, clustering results, and consuming time.

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“…Secondly, the weight of each keyword is computed through (2). And then, texts are mapped as a character vector through vector space model (VSM).…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…With the high update speed of Internet news and its huge amount, the clustering algorithms used in TDT are generally on incremental way. In this domain, two approaches have been proposed, called single-pass algorithm and fuzzy -means (FCM) algorithm [2].…”

Section: Introductionmentioning

confidence: 99%

An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

et al. 2017

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“…Nonlinear functional analyses were confirmed suitable for solving the real scenario analyses and, exactly, multistage approach has been widely employed in simulating disaster responses [37][38][39][40][41][42]. But dangers of understanding and recognition in precision security are worthy of reconsideration to dispel EC-implications, utilizing determined EC-attention value and warning level for such implications.…”

Section: Theoretical Analysesmentioning

confidence: 99%

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

et al. 2018

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Video surveillance plays a vital role in maintaining the social security although, until now, large uncertainty still exists in danger understanding and recognition, which can be partly attributed to intractable environment changes in the backgrounds. This article presents a brain-inspired computing of attention value of surrounding environment changes (EC) with a processes-based cognition model by introducing a ratio value of EC-implications within considered periods. Theoretical models for computation of warning level of EC-implications to the universal video recognition efficiency (quantified as time cost of implication-ratio variations from to +1 , = 1, 2, . . .) are further established. Imbedding proposed models into the online algorithms is suggested as a future research priority towards precision security for critical applications and, furthermore, schemes for a practical implementation of such integration are also preliminarily discussed.

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“…Some of the most influential pioneer works on the subject are, among others, those by Ruspini (1969Ruspini ( , 1970, Tamura et al (1971), Dunn (1973Dunn ( , 1974, Bezdek (1973Bezdek ( , 1974Bezdek ( , 1980, and Bezdek et al (1984), which have inspired both applications and many further methodologies. At present, this is one of the most successful topics involving Fuzzy Sets and Statistical theories, and the number of research papers on it is unquestionably growing [among the most recent ones see, for instance, the approaches in Liu et al (2013), Gong et al (2014), Yamashita and Mayekawa (2015), Ruan et al (2016), and Nguyen-Trang and Vo- Van (2017)], and it appears often either combined with or supporting other data analysis problems. In more detail, useful references to the extensive literature on the fuzzy clustering (from both theoretical and applicative points of view) can be found in the chapter on the fuzzy clustering by D'Urso (2016), the seminal monograph by Bezdek (1981), the books by Jain and Dubes (1988), De Oliveira and Pedrycz (2007), Miyamoto et al (2008) As remarked by D'Urso (2017a), there are different uncertainty-based clustering methods that can be considered extensions, variants and alternatives of the fuzzy clustering for non-fuzzy/standard data, like -possibilistic clustering [see, for instance, Krishnapuram and Keller (1993)], -shadowed clustering [see, for instance, Pedrycz (1998) Fuzzy approaches to analyze crisp/standard data, have not been carried out as exhaustively as fuzzy clustering ones for the same data.…”

Section: On the Fuzzy Analysis And The Fuzzy Classification Of Non-fumentioning

confidence: 99%

Fuzzy data analysis and classification

D’Urso

Alvarez

2017

Adv Data Anal Classif

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In analyzing and classifying data from a statistical perspective, fuzzy sets and logic have become a valuable tool either to model and handle imprecise data or to establish flexible techniques to deal with precise data.From the very beginning of his 52 years-old theory, Professor Zadeh highlighted that "Probability theory/statistics and fuzzy logic should be viewed as complementary rather than competitive," and he anticipated and encouraged the materialization of such a complementarity. Nowadays, this assertion is a reality, as shown by the many related papers, specialized conferences, special sessions and tracks in general conferences, and so on.This special issue started in 2015, with the 50th anniversary of the seminal paper on fuzzy sets by Zadeh (1965), aiming to collect a sample of research papers about the current trends on the combination of fuzzy sets/logic and data analysis/classification. When this special issue was almost ready for publication, Zadeh unfortunately passed away at age 96 (February 4, 1921-September 6, 2017. We wish this special issue to be dedicated to Professor Zadeh, as a modest part of the many tributes that he will receive, and intending to show that fuzzy sets/logic and data analysis/classification can certainly work in synergy.

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Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering

Cited by 70 publications

References 41 publications

An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

Fuzzy data analysis and classification

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