Fuzzy experts on recreational vessels, a risk modelling approach for marine invasions

Acosta, Hernando Sáenz; Wu, Dongrui; Forrest, Barrie M.

doi:10.1016/j.ecolmodel.2009.11.025

Cited by 44 publications

(29 citation statements)

References 73 publications

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“…The proposed methodology is a synthesis of perceptual computing (Per-C) [25][26][27][28][29] and a fuzzy ranking algorithm that uses fuzzy preference relations [30]. The rationale for the proposed methodology hinges on a number of imperatives.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the relative importance of the outcomes, i.e., the contribution of each student with respect to those from other students, is examined in detail, which is yet to be investigated in the literature, e.g. [25][26][27][28][29]. In this aspect, our preliminary work [30], as discussed in Section 2.3, is further extended to serve this purpose.…”

Section: Introductionmentioning

confidence: 99%

“…2. It consists of three components [25][26][27][28][29], i.e., an encoder, a computing-withwords (CWW) engine, and a decoder. Linguistic grades or words from humans are converted into IT2FSs through the encoder.…”

Section: Review On Perceptual Computingmentioning

confidence: 99%

“…Indeed, Per-C has been successfully implemented to undertake a number of fuzzy multiple criteria hierarchical decision making problems [25][26][27][28][29]. In [25][26][27][28][29], Per-C focuses on ranking the sequence of outcomes, mapping the outcomes into words and/or classifying the outcomes into different categories. Nevertheless, the use of Per-C in peer assessment is still new.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, Per-C is adopted owing to its effectiveness in handling inherent uncertainties in words [25]. Specifically, Per-C is able to handle subjectivity, vagueness, imprecision, and uncertainty while achieving tractability and robustness in modelling human decision-making behaviours [25][26][27][28][29]. Comparing with type-1 fuzzy models [9,[18][19][20][21][22][23], Per-C adopts interval type-2 fuzzy sets (IT2FSs) in tackling a decision making problem [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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A new fuzzy peer assessment methodology for cooperative learning of students

Chai

Tay

Lim

2015

Applied Soft Computing

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a b s t r a c tIn this paper, a new fuzzy peer assessment methodology that considers vagueness and imprecision of words used throughout the evaluation process in a cooperative learning environment is proposed. Instead of numerals, words are used in the evaluation process, in order to provide greater flexibility. The proposed methodology is a synthesis of perceptual computing (Per-C) and a fuzzy ranking algorithm. Per-C is adopted because it allows uncertainties of words to be considered in the evaluation process. Meanwhile, the fuzzy ranking algorithm is deployed to obtain appropriate performance indices that reflect a student's contribution in a group, and subsequently rank the student accordingly. A case study to demonstrate the effectiveness of the proposed methodology is described. Implications of the results are analyzed and discussed. The outcomes clearly demonstrate that the proposed fuzzy peer assessment methodology can be deployed as an effective evaluation tool for cooperative learning of students.

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