A novel hybrid decision-making model for team building in cloud service environment

Fan, Jiashuang; Yu, Shuhui; Chu, Jianjie; Cheng, Fangmin; Fan, Hongyu; Wang, Long; Wang, Hui; Li, Jie

doi:10.1080/0951192x.2019.1686172

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…The product CMF design knowledge adjacency network is to extract the CMF of the same semantic design resource in the product family and sort out the correlation between the three design elements of the CMF in the semantic family. For example, Yu et al (2019) proposed a color adjacency network model to assist designers in color matching using single-layer networks and color extraction techniques. The imagery knowledge gene mentioned in this paper is a combination of design knowledge that produces the same or similar imagery.…”

Section: Adjacent Network For Cmf Design Of Productsmentioning

confidence: 99%

“…In this paper, we set up perceptual experiments to obtain user evaluation data between each product in the product family and the preferred vocabulary, based on the degree of correlation between the two in the user's perceptual perception of preference. In this paper, we set up Table 1 based on references (Fan et al, 2019) to realize the interconversion between users' evaluation language and intuitionistic fuzzy numbers and achieve the acquisition of the user evaluation data matrix.…”

mentioning

confidence: 99%

“…To facilitate subsequent work and ensure data compatibility, this paper uses the linear model proposed by Fan et al (Fan et al, 2019) to process the weighting operator calculated by Eq. ( 1) and converts it into real numbers using Eq.…”

mentioning

confidence: 99%

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Construction and application of data-driven knowledge adjacency network for product CMF design

Liu¹,

KIM²

2023

JAMDSM

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To solve the problems of over-reliance on personal experience, lack of thinking about users' perceptual preferences, and neglect of the overall coupling characteristics of CMF design, the construction and application methods of product CMF design knowledge adjacency network are proposed based on the multi-layer network, intuitionistic fuzzy set, and gray correlation method theory. Firstly, the construction of the product CMF design multilayer network is completed by extracting and selecting the design resources of product families with similar imagery. Secondly, considering the product CMF design characteristics, a multi-layer network association division method based on leader nodes is proposed, and the construction of the CMF design knowledge adjacency network is further completed. Thirdly, based on intuitionistic fuzzy set theory, user preferences are considered to further improve the science of evaluation data. Finally, the calculation of association coefficients is combined with the gray correlation method, and the optimal matching between the adjacency network and the product surface assignment solution space is completed to assist designers to improve the design efficiency. The proposed method is applied to the CMF design work of a smart tractor, and perceptual experiments are set up to evaluate the design results, and the results show that this design meets the target requirements, which proves the usefulness of the method to assist the designer's work.

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Section: Adjacent Network For Cmf Design Of Productsmentioning

confidence: 99%

mentioning

confidence: 99%

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Construction and application of data-driven knowledge adjacency network for product CMF design

Liu¹,

KIM²

2023

JAMDSM

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“…Relevant scholars have proposed a clustering method based on fuzzy relation composition, but due to the disadvantages of this clustering method that is not suitable for large data sets, people rarely study it [27]- [29]. Slowly, people are trying to study fuzzy clustering using graph theory [30], [31]. In order to improve the classic fuzzy c-means' ability to cluster non-linear data, related scholars introduced a kernel function, which used the kernel function to map the data to a high-dimensional feature space and then calculate the inner product [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

et al. 2020

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Due to the rapid development of information technology and network technology, there is a lot of data, but the phenomenon of lack of knowledge is becoming more and more serious. Data mining technology has developed vigorously in this environment, and it has shown more and more vitality. Based on Spark programming model, this paper designs the parallel extension of fuzzy c-means. In order to enhance the performance of fuzzy c-means parallel expansion, the improvement strategy of k-means during the initialization phase is borrowed, and k-means// is extended to fuzzy c-means to obtain better clustering performance. Combined with Spark's programming model, this paper can obtain extended parallel fuzzy c-means algorithm. Several experiments on the data set of the algorithm proposed in this paper have shown good scalability and parallelism, effectively expanding fuzzy c-means clustering to distributed applications, greatly increasing the scale of the data processed by the algorithm. This improves the robustness of the algorithm and the adaptability of the algorithm to the shape and structure of the data, so that the parallel and scalable clustering algorithm can more effectively perform cluster analysis on big data. Three algorithms were simulated on MATLAB platform. We use simple data sets and complex two-dimensional data sets, and compare with the traditional fuzzy c-means algorithm and fuzzy c-means algorithm based on fuzzy entropy. Experiments show that the scalable parallel fuzzy c-means algorithm not only greatly improves the anti-noise performance, but also improves the convergence speed, and it can automatically determine the optimal number of clusters. INDEX TERMS Artificial intelligence, data mining, cluster analysis, scalable parallel fuzzy c-means, cloud computing.

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The Possible Equivalent Value Set for Incomplete Data Set

Mamat,

Mustafa,

Nor

et al. 2023

Lecture Notes in Computer Science

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A novel hybrid decision-making model for team building in cloud service environment

Cited by 9 publications

References 45 publications

Construction and application of data-driven knowledge adjacency network for product CMF design

Construction and application of data-driven knowledge adjacency network for product CMF design

Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

The Possible Equivalent Value Set for Incomplete Data Set

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