Combine MCDM Methods and PSO to Evaluate Economic Benefits of High-Tech Zones in China

Yu, Xiaobing; Wu, Xuejing; Tongzhao, Huo

doi:10.3390/su12187833

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…This innovative design could help the policymakers to understand how the policy inputs are relevant with the different types of outputs. This innovative perspective also expands the previous efficiency evaluation in the high-tech zones in China [ 12 , 43 , 44 , 49 ].…”

Section: Discussionsupporting

confidence: 59%

Evaluating economic efficiency of the national high-tech industrial development districts in the Yangtze River Delta by stochastic frontier analysis

Yang

2024

Heliyon

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Section: Discussionsupporting

confidence: 59%

Evaluating economic efficiency of the national high-tech industrial development districts in the Yangtze River Delta by stochastic frontier analysis

Yang

2024

Heliyon

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“…This characteristic enables the creation of numerous matrices distributed between local federated hospital clients and central servers. However, the classical version of the TOP-SIS method has some weaknesses: (1) insensitive to small values [118], (2) distorts the original information [119], (3) ranks reversal flaws [120], and (4) relatively small distance between the positive and negative ideal solutions [121][122][123]. Therefore, an improvement in TOPSIS is considerably warranted.…”

Section: Mcdm Methodsmentioning

confidence: 99%

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

Alamoodi

Zaidan

Albahri

et al. 2023

Complex Intell. Syst.

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When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic’s main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID‐19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.

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“…However, the classical TOPSIS method's use of Euclidean distance is associated with certain limitations, including the insensitivity to small values, the distortion of original information, The distance between the positive-ideal-solution and negative-ideal-solution relatively small [40], and the rank reversal flaws [41]. Several enhanced forms of TOPSIS have been introduced in scholarly works to address these limitations [38][39][40][41][42][43][44][45][46]. For instance, [42] used the Canberra distance as an alternative to the Euclidean distance.…”

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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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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Combine MCDM Methods and PSO to Evaluate Economic Benefits of High-Tech Zones in China

Cited by 7 publications

References 22 publications

Evaluating economic efficiency of the national high-tech industrial development districts in the Yangtze River Delta by stochastic frontier analysis

Evaluating economic efficiency of the national high-tech industrial development districts in the Yangtze River Delta by stochastic frontier analysis

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

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

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