scholarly journals Notes on: “Interval-valued intuitionistic fuzzy soft sets and their properties” [Comput. Math. Appl. 60 (2010) 906–918]

2012 ◽  
Vol 64 (9) ◽  
pp. 2954-2960 ◽  
Author(s):  
Jinyan Wang ◽  
Minghao Yin ◽  
Wenxiang Gu
2021 ◽  
pp. 1-12
Author(s):  
Admi Nazra ◽  
Yudiantri Asdi ◽  
Sisri Wahyuni ◽  
Hafizah Ramadhani ◽  
Zulvera

This paper aims to extend the Interval-valued Intuitionistic Hesitant Fuzzy Set to a Generalized Interval-valued Hesitant Intuitionistic Fuzzy Soft Set (GIVHIFSS). Definition of a GIVHIFSS and some of their operations are defined, and some of their properties are studied. In these GIVHIFSSs, the authors have defined complement, null, and absolute. Soft binary operations like operations union, intersection, a subset are also defined. Here is also verified De Morgan’s laws and the algebraic structure of GIVHIFSSs. Finally, by using the comparison table, a different approach to GIVHIFSS based decision-making is presented.


Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1061
Author(s):  
Hongwu Qin ◽  
Huifang Li ◽  
Xiuqin Ma ◽  
Zhangyun Gong ◽  
Yuntao Cheng ◽  
...  

The model of interval-valued intuitionistic fuzzy soft sets is a novel excellent solution which can manage the uncertainty and fuzziness of data. However, when we apply this model into practical applications, it is an indisputable fact that there are some missing data in many cases for a variety of reasons. For the purpose of handling this problem, this paper presents new data processing approaches for an incomplete interval-valued intuitionistic fuzzy soft set. The missing data will be ignored if percentages of missing degree of membership and nonmember ship in total degree of membership and nonmember ship for both the related parameter and object are below the threshold values; otherwise, it will be filled. The proposed filling method fully considers and employs the characteristics of the interval-valued intuitionistic fuzzy soft set itself. A case is shown in order to display the proposed method. From the results of experiments on all thirty randomly generated datasets, we can discover that the overall accuracy rate is up to 80.1% by our filling method. Finally, we give one real-life application to illustrate our proposed method.


2017 ◽  
Vol 11 (4) ◽  
pp. 999-1009 ◽  
Author(s):  
Hongwu Qin ◽  
Ahmad ShukriMohd Noor ◽  
Xiuqin Ma ◽  
Haruna Chiroma ◽  
Tutut Herawan

2013 ◽  
Vol 240 ◽  
pp. 95-114 ◽  
Author(s):  
Yuncheng Jiang ◽  
Yong Tang ◽  
Hai Liu ◽  
Zhenzhou Chen

2016 ◽  
Vol 06 (03) ◽  
pp. 1224-1230 ◽  
Author(s):  
Anita Shanthi S ◽  
◽  
Thillaigovindan N ◽  
Vadivel Naidu J ◽  
◽  
...  

2010 ◽  
Vol 60 (3) ◽  
pp. 906-918 ◽  
Author(s):  
Yuncheng Jiang ◽  
Yong Tang ◽  
Qimai Chen ◽  
Hai Liu ◽  
Jianchao Tang

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