Grey relational analysis and natural language Processing

Khuman, Arjab Singh; Yang, Yingjie; Liu, Sifeng

doi:10.1109/gsis.2015.7301838

Cited by 9 publications

(9 citation statements)

References 3 publications

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“…The grey concepts were identified by Deng (1982) to handle vagueness, which can capably exemplify the incompleteness of a system (Liu et al, 2016;Khuman et al, 2016). This theory can competently hold uncertainty as discrete and imperfect information (Liu et al, 2011).…”

Section: Second Phase Of the Methodologymentioning

confidence: 99%

A merged approach for modeling qualitative characteristics of agile arena under grey domain

Sahu

Naval

Narang

et al. 2018

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Purpose Agile practices are important for executing business dealings proficiently in today’s scenario, as they thrust on implicating strategies for meeting quick market requirements. These practices are noteworthy from the point of competitiveness and for fulfilling customer’s demands speedily and promptly. The purpose of this paper is to appraise agile supplier selection dilemma based on analytical hierarchy process (AHP), which accompanied grey information. The authors drafted a group of momentous agile supplier selection measures, which can be utilized by the group of industrial and manufacturing industries to measure the status of agile parameters in their partner firms. G-TOPSIS approach to handling the case of agile supplier selection problem is presented by the authors in this work. Design/methodology/approach The conception of AHP, grey theory and TOPSIS techniques is fused in this study, under the application arena of agile supply chain management (ASCM). The AHP principals are implicated in the first phase to define the priority importance weights of agile measures and, additionally, grey theory and TOPSIS principals are fused in the second phase to fabricate a significant agile supplier selection model. Findings A merged approach accompanying multiple measures is developed for aiding decision making and for modeling qualitative characteristics of agile arena under grey domain. The present work can be utilized to access the agile performance characteristics of the organization and can define the status of their partner suppliers. The technical guidelines of AHP and G-TOPSIS approach are explained in this study to be implicated in distinguish decision fields. An educational podium for dispensing the theoretical knowledge on supply chain management, ASCM and agility is presented in this study. Originality/value A second-level hierarchical structure is built by the authors to facilitate the managers in taking effective decision pertaining to agile measure in their organizations. The lists of qualitative characteristics are catalogued from the literature review in this study. The built model can undertake risk associated in defining the nature of agile criterions as grey concept can undertake risk associated with the system. Thus, the authors implicated G-TOPSIS approach to handling the case of agile supplier selection problem in this study. The presented hierarchical structure will capably assist the industrial and manufacturing firms to react toward random and unpredictable market requirements, along with attaining organizations goals and profits.

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Section: Second Phase Of the Methodologymentioning

confidence: 99%

A merged approach for modeling qualitative characteristics of agile arena under grey domain

Sahu

Naval

Narang

et al. 2018

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“…Here, α � 0.4 is used [28]. Among them, the definition of H i ′ is calculated by the degree of grey incidence (DGI) [29] according to…”

Section: Definition Of Topic Popularity In Social Networkmentioning

confidence: 99%

Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models

Hong

Qishan

2020

Discrete Dynamics in Nature and Society

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Purpose. The purpose of this article is to predict the topic popularity on the social network accurately. Indicator selection model for a new definition of topic popularity with degree of grey incidence (DGI) is undertook based on an improved analytic hierarchy process (AHP). Design/Methodology/Approach. Through screening the importance of indicators by the deep learning methods such as recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent unit (GRU), a selection model of topic popularity indicators based on AHP is set up. Findings. The results show that when topic popularity is being built quantitatively based on the DGI method and different weights of topic indicators are obtained from the help of AHP, the average accuracy of topic popularity prediction can reach 97.66%. The training speed is higher and the prediction precision is higher. Practical Implications. The method proposed in the paper can be used to calculate the popularity of each hot topic and generate the ranking list of topics’ popularities. Moreover, its future popularity can be predicted by deep learning methods. At the same time, a new application field of deep learning technology has been further discovered and verified. Originality/Value. This can lay a theoretical foundation for the formulation of topic popularity tendency prevention measures on the social network and provide an evaluation method which is consistent with the actual situation.

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“…What should be understood firstly, is that the use of the absolute degree of grey incidence is undertaken in an unconventional manner. The notion of grey incidence for sequence comparability was first investigated by the authors in (Khuman et al, 2015b), where it was executed in a natural language processing domain.…”

Section: Grey Analysismentioning

confidence: 99%

The quantification of subjectivity: The R-fuzzy grey analysis framework

Khuman

Yang

John

2019

Expert Systems with Applications

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This paper puts forward a newly derived framework for capturing and inferring from subjective based uncertainty for any given observation. The framework is referred to as the R-fuzzy grey analysis framework (RfGAf), which itself is comprised of 3 distinct components: 1. R-fuzzy sets-to capture the uncertainty, which utilises crisp rough set bounding of uncertain possible fuzzy membership values. 2. A significance measure-to provide a means to allow for conditional probability to be undertaken, and also to allow for the translation of the data to that of a time series, allowing for the linking to that of the third component. 3. Grey analysis, more specifically, the absolute degree of grey incidence, where post-analysis can be undertaken and additional metrics obtained. The hybridisation of all three has allowed for the creation of a framework ideally suited for the quantification of perception based uncertainty, which by proxy will be inherently associated to subjectivity. It will be shown and demonstrated how such a framework can be made use of, showcasing the advantages of such an approach. By making use of R-fuzzy sets and the significance measure, an intermediary approach to that of a generalised type-2 fuzzy set can be obtained. As it is widely agreed upon that a generalised type-2 fuzzy approach is ideal for capturing higher degrees of resolution with regards to uncertainty, the associated computational burden of its complexity makes it unfavourable, hence why the interval-valued type-2 approach is favoured. The findings indicate the RfGAf can allow for the high capacity and detail one would expect when considering a type-2 fuzzy set representation, with that of the simplistic objectiveness one would associate to a typical type-1 fuzzy set. The novelty of the framework allows for one to fully capture all the nuances and individualities of a population without a single loss of information. That snapshot in time can tell an awful lot with regards to perceived perception. The framework can be deployed on varying sizes of populations, from the intrinsically small to the overtly large. Potentially, one can use that snapshot to predict how an observation could be perceived in future events. If one can forecast the perception ahead of time, one can improve drastically on efficacy and efficiency. Such a framework has a strong applicability with regards to expert and intelligent systems in allowing for more detailed inference to be utilised and acted upon.

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Grey relational analysis and natural language Processing

Cited by 9 publications

References 3 publications

A merged approach for modeling qualitative characteristics of agile arena under grey domain

A merged approach for modeling qualitative characteristics of agile arena under grey domain

Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models

The quantification of subjectivity: The R-fuzzy grey analysis framework

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