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Titolo:
Pattern development for vessel accidents: a comparison of statistical and neural computing techniques
Autore:
Le Blanc, LA; Hashemi, RR; Rucks, CT;
Indirizzi:
Berry Coll, Campbell Sch Business, Mt Berry, GA 30149 USA Berry Coll Mt Berry GA USA 30149 ell Sch Business, Mt Berry, GA 30149 USA Univ Arkansas, Dept Comp Sci, Little Rock, AR 72204 USA Univ Arkansas Little Rock AR USA 72204 omp Sci, Little Rock, AR 72204 USA Univ Arkansas, Dept Mkt, Little Rock, AR 72204 USA Univ Arkansas Little Rock AR USA 72204 ept Mkt, Little Rock, AR 72204 USA
Titolo Testata:
EXPERT SYSTEMS WITH APPLICATIONS
fascicolo: 2, volume: 20, anno: 2001,
pagine: 163 - 171
SICI:
0957-4174(200102)20:2<163:PDFVAA>2.0.ZU;2-H
Fonte:
ISI
Lingua:
ENG
Keywords:
pattern recognition; cluster analysis; neural networks; vessel accidents;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
9
Recensione:
Indirizzi per estratti:
Indirizzo: Le Blanc, LA Berry Coll, Campbell Sch Business, Box 5024, Mt Berry, GA 30149 USA Berry Coll Box 5024 Mt Berry GA USA 30149 Berry, GA 30149 USA
Citazione:
L.A. Le Blanc et al., "Pattern development for vessel accidents: a comparison of statistical and neural computing techniques", EXPER SY AP, 20(2), 2001, pp. 163-171

Abstract

This paper describes a sample of over 900 vessel accidents that occurred on the lower Mississippi River. Two different techniques, one statistical and the other based on a neural network model, were used to build logical groups of accidents. The objective in building the groups was to maximize between-group variation and minimize within-group variation. The result was groups whose records were as homogenous as possible. A clustering algorithm (i.e., a non-inferential statistical technique) generated sets of three, four and five groups. A Kohenen neural network model (i.e., a self-organizing map) also generated sets of three, four and five groups. The two sets of parallel groups were radically different as to the relative number of records in each group. In other words, when the two sets of groups were constructed by the respective techniques, the membership of each comparable group within the two different sets was substantially different.. Not only was the respective record count in each group substantiallydifferent, so were the descriptive statistics describing each comparable set of groups. These results have significant implications for marine policy makers. Important policy variables include safety factors such as weather, speed of current, time of operation, and location of accidents, but mandatory utilization of a voluntary vessel tracking service may be subject to debate. (C) 2001 Elsevier Science Ltd. All rights reserved.

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Documento generato il 20/01/20 alle ore 05:47:48