图像模糊环境下的降维技术及其在决策中的应用

IF 0.6 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Knowledge-Based and Intelligent Engineering Systems Pub Date : 2023-07-13 DOI:10.3233/kes-230031
Preeti Devi, Bartłomiej Kizielewicz, A. Guleria, A. Shekhovtsov, N. Gandotra, Namita Saini, W. Sałabun
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引用次数: 4

摘要

软矩阵的概念在许多工程应用以及社会经济和金融问题中起着至关重要的作用。在人类观点建模中,使用图像模糊集来处理不确定性数据。在这项工作中,我们回顾了图像模糊软矩阵的概念及其不同的后续类。此外,还提供了对所提矩阵的不同类型的二进制运算。本文的主要贡献是利用选择矩阵及其加权形式和得分矩阵的概念,提出了一种考虑模糊软矩阵图像的决策新算法。当前决策问题面临的挑战是涉及许多定性和定量标准。因此,降维技术在决策过程中具有简化和广泛适用性的重要作用。我们提出了一个约简过程的算法,使用提出的对象和面向参数的图像模糊软矩阵的定义以及为所提供的信息找到阈值的技术。然后,给出了每种算法的数值算例。还对拟议的技术进行了详细的比较研究,并与其他现有技术进行了对比。
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Dimensionality reduction technique under picture fuzzy environment and its application in decision making
The notion of soft matrix plays a vital role in many engineering applications and socio-economic and financial problems. A picture fuzzy set has been used to handle uncertainty data in modeling human opinion. In this work, we recall the picture fuzzy soft matrix concept and its different subsequent classes. Also, different kinds of binary operations over the proposed matrices have been provided. The main contribution of this paper is that using the concept of choice matrix and its weighted form and the score matrix, a new algorithm for decision-making has been outlined by considering the picture of fuzzy soft matrices. The current challenge In the decision-making problems is that many qualitative and quantitative criteria are involved. Hence, the dimensionality reduction technique plays an essential role in simplicity and broader applicability in the decision-making processes. We present an algorithm for the reduction process using the proposed definitions of the object and parameter-oriented picture fuzzy soft matrix and the technique to find the threshold value for the provided information. Then, illustrative numerical examples have also been provided for each proposed algorithm. A detailed comparative study of the proposed techniques has also been carried out in contrast with other existing techniques.
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来源期刊
CiteScore
2.10
自引率
0.00%
发文量
22
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