Combining grey relational analysis and TOPSIS concepts for evaluating the technical innovation capability of high technology enterprises with fuzzy information

Ran, Rong; Wang, Bangjun

doi:10.3233/ifs-141380

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…In pursuit of the same objective, additional groups of scholars have incorporated grey relational analysis with TOPSIS, as demonstrated in sources [42][43][44][45][46]. The primary advantages of the Grey relational analysis method include its simplicity in calculations, reliance on original data, and effectiveness in making business decisions [52]. In addition, Grey relational analysis can handle uncertain, incomplete, or vague data by using grey numbers, which makes it suitable for situations where precise data is not available [52,53] assess and compare the daily performance of the electricity market, enhancing the Grey-TOPSIS method by employing an optimisation model to determine the final proximity between the different samples and the ideal one [49].…”

Section: Related Workmentioning

confidence: 99%

“…The primary advantages of the Grey relational analysis method include its simplicity in calculations, reliance on original data, and effectiveness in making business decisions [52]. In addition, Grey relational analysis can handle uncertain, incomplete, or vague data by using grey numbers, which makes it suitable for situations where precise data is not available [52,53] assess and compare the daily performance of the electricity market, enhancing the Grey-TOPSIS method by employing an optimisation model to determine the final proximity between the different samples and the ideal one [49]. Later, [18] adopted this version of the Grey-TOPSIS method and combined it with the Bald-Eagle-Search (BES) algorithm [54] to rank IoT healthcare systems.…”

Section: Related Workmentioning

confidence: 99%

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A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

Qahtan,

Yatim,

Osman

et al. 2023

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E-health Industry 4.0 systems ranking based on Blockchain is a multi-criteria decision-making (MCDM) problem, considering the multiple evaluation properties, their significance, and data variety. The final closeness between the evaluation sample and ideal solutions also constitutes an optimisation problem. To the authors’ knowledge, no study has provided a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems based on Blockchain. Consequently, this study proposes and discusses a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems, utilising the SFS-FWZIC method to address the significance of properties issue and the Grey-TOPSIS method to tackle data variation, multiple evaluation properties, and the ideal solutions optimisation problem. The methodology of the proposed approach consists of three sections. First, three decision matrices are constructed based on the intersection of E-health systems with the mentioned properties, intersecting Electronic Health Records (EHRs), Electronic Medical Records (EMRs), and Personal Health Records (PHRs) with seven properties. Second, the weights of each privacy and security property are calculated using the SFS-FWZIC method. Finally, the weights determined by the SFS-FWZIC method and the three decision matrices are input into the Grey-TOPSIS method to rank E-health systems across the three categories. The findings reveal the following: (1) The SFS-FWZIC method effectively assigned weights to privacy and security properties, with access control achieving the highest significance weight (0.1934) and Secure-search receiving the lowest weight (0.0603). (2) The Grey-TOPSIS method efficiently ranked E-health systems across the three categories based on various parameters, including α=0.1,α=0.3,α=0.5,α=0.7, and α=0.9. Sensitivity and correlation analysis were conducted to evaluate the results, revealing high correlation results based on each α value across all discussed scenarios of changing property weights. The implications of this study can lead to better decision-making, improved security and privacy, increased competition, and widespread adoption, ultimately contributing to more efficient and effective healthcare delivery.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

Qahtan,

Yatim,

Osman

et al. 2023

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“…The grey forecasting model, as one of the significant constituents of grey system theory, has attracted attention and has been actively studied by many scholars. The GM(1,1) model is the most important part of grey prediction theory; however, many scholars discovered that the prediction precision of this model is unstable, and extensive research mainly on the following topics has been conducted: Converting an original sequence for improvement of smoothness [12]; enhancing the computational methods of parameters [13][14][15][16]; modifying residuals of models [17][18][19]; reforming the modelling fashion, and performing some preparations for expanding GM(1,1) models [20]; investigating the modelling conditions [21]; extending the structure of traditional grey prediction models [22]; optimizing the parameters of grey prediction models, such as initial value [23,24], background value [25,26], and the order of accumulating generation operators [27,28]; and combining the grey prediction model with other modelling methods [29,30].…”

Section: Figurementioning

confidence: 99%

Forecasting Electricity Demand Using a New Grey Prediction Model with Smoothness Operator

Zhao

Zhou

2018

Symmetry

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A stable electricity supply is the basis for ensuring the healthy and sustained development of a regional economy. Reasonable electricity prediction is the key to guaranteeing the stability and efficiency of electricity supply. To this end, we used a reformative grey prediction model to forecast electricity demand. In order to effectively improve the smoothness of a raw modelling sequence, we employed an existing smoothing algorithm that significantly compressed the amplitude of the random oscillation sequence. Then, an improved grey forecasting model with three parameters (IGFM_TP) was deduced. In the end, a new model was used to forecast the demand for electricity of one city in the western region of China, and comparisons of simulation values and errors with those of GFM_TP, GM(1,1), DGM(1,1) and SAIGM were conducted. The findings show that the mean absolute simulation percentage error of IGFM_TP was 7.8%, and those of the other four models were 12.1%, 12.3%, 11.1%, and 10.1%, respectively. Therefore, the simulation precision of the new model achieved an optimal effect. The proposed new grey model provides is an effective method for electricity demand prediction.

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“…Xu et al [20] explored deep into the aspects of the integration of computer technology, the innovation mode, and the I&E ability training of computer professionals. For the purpose of cultivating students' I&E ability, Ran and Wang [21] optimized the teaching mode of computer network experimental courses. Scholar Li [22] proposed an all-around mechanism for cultivating innovative talents majoring in computer science in local colleges and universities, and integrated it into the construction of professional curriculum and the talent training mode.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Innovation and Entrepreneurship Ability of Computer Majors Based on Neural Network Optimized by Particle Swarm Optimization

Ding¹,

Chai²,

Zeng³

2021

Int. J. Emerg. Technol. Learn.

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The current evaluation index systems (EISs) of innovation and entrepreneurship (I&E) ability are not sufficiently systematic, scientific, or practical. To solve the problem, this paper tries to evaluate the I&E ability of computer majors, using neural networks improved by particle swarm optimization (PSO). Firstly, an EIS of 22 second-level indexes under 5 first-level indexes was designed to evaluate the I&E ability of college computer majors. Next, an evaluation model was developed based on fuzzy neural network (FNN), and the corresponding training algorithm was created. Moreover, an improved PSO was introduced to optimize the FNN, and the optimization process was detailed. The proposed model was proved effective through experiments.

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Combining grey relational analysis and TOPSIS concepts for evaluating the technical innovation capability of high technology enterprises with fuzzy information

Cited by 12 publications

References 14 publications

A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

Forecasting Electricity Demand Using a New Grey Prediction Model with Smoothness Operator

Evaluation of Innovation and Entrepreneurship Ability of Computer Majors Based on Neural Network Optimized by Particle Swarm Optimization

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