A rough set approach for approximating differential dependencies

Tran, Anh Duy; Arch-int, Somjit; Arch-int, Ngamnij

doi:10.1016/j.eswa.2018.06.025

Cited by 16 publications

(4 citation statements)

References 51 publications

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“…2019 [8]; Zhao Dandan et al 2019 [9]; Tran Anh Duy 2018 [10]; Jing Yunge 2018 [11]).…”

Section: Early Warning Status and Challengesmentioning

confidence: 99%

Base on the Public Scientific Quality Improvement Research on Risk Early Warning of Online Shopping

Cao²,

Wang

et al. 2021

Preprint

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In order to improve the scientific quality of the public, the Chinese Association for Science and Technology has put forward a call to combine popular science education with leisure and entertainment. In view of the fact that online shopping involves a wide range of areas, and the people pay more attention to it, the paper completed the innovation of online shopping risk warning science knowledge, the design of popular science mechanism and the dissemination of popular science knowledge. The paper used complex network’s knowledge discovery methods and decision theory to design online shopping risk warning science knowledge; Using the complex network public opinion dissemination trust analysis realize the dissemination of popular science knowledge and promote the improvement of the public's quality of popular science. The spread of risk early warning science knowledge in the network shows that the risk early warning mechanism designed can achieve the purpose of improving public science knowledge when the reward and punishment measures are appropriate.

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“…2019 [8]; Zhao Dandan et al 2019 [9]; Tran Anh Duy 2018 [10]; Jing Yunge 2018 [11]).…”

Section: Early Warning Status and Challengesmentioning

confidence: 99%

Base on the Public Scientific Quality Improvement Research on Risk Early Warning of Online Shopping

Cao²,

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Tran et al [44] developed a rough set system from perspective information, and it was observed to perform better than the traditional greedy methods. Tiwari et al [45] suggested knowledge extraction in framing expert and intelligent systems which transformed fuzzy decision system (FDS) into intuitionistic FDS (IFDS) with a fixed degree of hesitancy.…”

Section: Literature Survey On Fuzzy and Rough Setmentioning

confidence: 99%

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sundararajan

Palanisamy

2020

Computational Intelligence and Neuroscience

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Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively.

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“…As one of the intelligence methods, RS theory proposed by Pawlak to cope with uncertain information in the assessment process. 42 Its most remarkable merit is that uncertainty stems from the original data set and no additional data is required. It expresses the team experts' judgment roughness by a pair of concepts, that is, the lower approximation and the upper approximation.…”

Section: Rough Number Theorymentioning

confidence: 99%

An improving approach for failure mode and effect analysis under uncertainty environment: A case study of critical function component

Huang

Xiao

Zhang

et al. 2020

Quality & Reliability Eng

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Failure mode and effect analysis (FMEA) is a powerful risk discerning technique for identifying, evaluating, and reducing possible failures of products or processes. However, the classical FMEA has been criticized for inherent limitations, such as equal weights of risk elements and lack of capability in handling inaccurate information. Although fuzzy-based modified FMEA methods are frequently utilized to handle vagueness of experts' judgments, they still have some drawbacks, for example, requiring extra assumptions, neglecting experts' bounded rationality and psychological effects, lacking consideration of randomness, and only considering three classical risk elements among most of them. Therefore, this study develops an extended risk assessment method to enhance the performance of FMEA, which integrates the superiority of rough number theory in handling subjective and inaccurate information and the advantage of cloud model theory in reflecting the randomness of qualitative evaluations. Moreover, two synthetic weighting methods are developed to determine the weights of risk elements and handle the experts' individual effects, respectively, which consider both subjective and objective aspects. In addition, maintenance is added into the classical risk elements, and then a hierarchical structure containing four risk dimensions is built to evaluate failures' risk levels comprehensively. Finally, an application case to demonstrate the effectiveness of the developed FMEA model is presented. K E Y W O R D S cloud model, failure mode and effect analysis, hierarchical TOPSIS, rough number theory, synthetic weight 1 | INTRODUCTION Failure mode and effect analysis (FMEA) is a reliable risk assessment technique to determine the known and/or potential failure modes, causes, and effects on the stability and performance of a system, design, service, and process to identify the most crucial ones and conduct actions to eliminate them. 1 In the 1960s, it was firstly used by NASA in the aerospace industry to ensure the safety and quality of products. 2 FMEA is commonly performed by a cross-functional team contained experts with different knowledge and experience to analyze and quantify the relationships between

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A rough set approach for approximating differential dependencies

Cited by 16 publications

References 51 publications

Base on the Public Scientific Quality Improvement Research on Risk Early Warning of Online Shopping

Base on the Public Scientific Quality Improvement Research on Risk Early Warning of Online Shopping

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

An improving approach for failure mode and effect analysis under uncertainty environment: A case study of critical function component

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