Arif Mehmood¹, Saleem Abdullah², Choonkil Park^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 411-428, 2022, DOI:10.32604/cmes.2022.016967 - 24 January 2022

Abstract Fuzzy soft topology considers only membership value. It has nothing to do with the non-membership value. So an extension was needed in this direction. Vague soft topology addresses both membership and non-membership values simultaneously. Sometimes vague soft topology (single structure) is unable to address some complex structures. So an extension to vague soft bi-topology (double structure) was needed in this direction. To make this situation more meaningful, a new concept of vague soft bi-topological space is introduced and its structural characteristics are attempted with a new definition. In this article, new concept of vague soft… More >

Arif Mehmood¹, Muhammad Aslam², Muhammad Imran Khan³, Humera Qureshi³, Choonkil Park^4,*, Jung Rye Lee⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1565-1585, 2022, DOI:10.32604/cmes.2022.018518 - 30 December 2021

Abstract In this article, new generalized neutrosophic soft * _b open set is introduced in neutrosophic soft bi-topological structurers (NSBTS) concerning soft points of the space. This new set is produced by making the marriage of soft semi-open set with soft pre-open set in neutrosophic soft topological structure. An ample of results are investigated in NSBTS on the basis of this new neutrosophic soft * _b open set. Proper examples are settled for justification of these results. The non-validity of some results is vindicated with examples. More >

Shouzhen Zeng^1,2, Shahbaz Ali^3,*, Muhammad Khalid Mahmood⁴, Florentin Smarandache⁵, Daud Ahmad⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 581-606, 2022, DOI:10.32604/cmes.2022.017397 - 29 November 2021

Abstract Fuzzy models are present everywhere from natural to artificial structures, embodying the dynamic processes in physical, biological, and social systems. As real-life problems are often uncertain on account of inconsistent and indeterminate information, it seems very demanding for an expert to solve those problems using a fuzzy model. In this regard, we develop a hybrid new model m-polar Diophantine neutrosophic N-soft set which is based on neutrosophic set and soft set. Additionally, we define several different sorts of compliments on the proposed set. A proposed set is a generalized form of fuzzy, soft, Pythagorean fuzzy, More >

M. A. El Safty^1,*, Samirah Al Zahrani¹, M. K. El-Bably², M. El Sayed³

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 267-285, 2022, DOI:10.32604/cmc.2022.019345 - 07 September 2021

Abstract In this paper, we present a proposed method for generating a soft rough approximation as a modification and generalization of Zhaowen et al. approach. Comparisons were obtained between our approach and the previous study and also. Eventually, an application on Coronavirus (COVID-19) has been presented, illustrated using our proposed concept, and some influencing results for symptoms of Coronavirus patients have been deduced. Moreover, following these concepts, we construct an algorithm and apply it to a decision-making problem to demonstrate the applicability of our proposed approach. Finally, a proposed approach that competes with others has been More >

S. A. Al blowi¹, M. El Sayed², M. A. El Safty^3,*

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 961-972, 2021, DOI:10.32604/iasc.2021.018242 - 20 August 2021

Abstract Real-world applications are now dealing with a huge amount of data, especially in the area of high-dimensional features. Trait reduction is one of the major steps in decision making problems. It refers to the determination of a minimum subset of attributes which preserves the final decision based on the entire set of attributes. Unfortunately, most of the current features are irrelevant or redundant, which makes these systems unreliable and imprecise. This paper proposes a new paradigm based on fuzzy soft relationship and level fuzzy soft relationship, called Union - Intersection decision making method. Using these More >

Atiqe Ur Rahman^1,*, Muhammad Saeed¹, Sultan S. Alodhaibi², Hamiden Abd El-Wahed Khalifa^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 743-777, 2021, DOI:10.32604/cmes.2021.016736 - 22 July 2021

Abstract Hypersoft set is an extension of soft set as it further partitions each attribute into its corresponding attribute-valued set. This structure is more flexible and useful as it addresses the limitation of soft set for dealing with the scenarios having disjoint attribute-valued sets corresponding to distinct attributes. The main purpose of this study is to make the existing literature regarding neutrosophic parameterized soft set in line with the need of multi-attribute approximate function. Firstly, we conceptualize the neutrosophic parameterized hypersoft sets under the settings of fuzzy set, intuitionistic fuzzy set and neutrosophic set along with More >

Rahmat Hidayat^1,2,*, Iwan Tri Riyadi Yanto^1,3, Azizul Azhar Ramli¹, Mohd Farhan Md. Fudzee¹, Ansari Saleh Ahmar⁴

Computer Systems Science and Engineering, Vol.38, No.1, pp. 119-130, 2021, DOI:10.32604/csse.2021.015628 - 01 April 2021

Abstract

Classification is one of the data mining processes used to predict predetermined target classes with data learning accurately. This study discusses data classification using a fuzzy soft set method to predict target classes accurately. This study aims to form a data classification algorithm using the fuzzy soft set method. In this study, the fuzzy soft set was calculated based on the normalized Hamming distance. Each parameter in this method is mapped to a power set from a subset of the fuzzy set using a fuzzy approximation function. In the classification step, a generalized normalized Euclidean

… More >

R.V.V. Krishna^1,*, S. Srinivas Kumar²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 281-290, 2020, DOI:10.31209/2019.100000121

Abstract In this paper, a color image segmentation algorithm is proposed by extracting both texture and color features and applying them to the one -against-all multi class support vector machine (MSVM) classifier for segmentation. Local Binary Pattern is used for extracting the textural features and L*a*b color model is used for obtaining the color features. The MSVM is trained using the samples obtained from a novel soft rough fuzzy c-means (SRFCM) clustering. The fuzzy set based membership functions capably handle the problem of overlapping clusters. The lower and upper approximation concepts of rough sets deal well More >

Nazan Çakmak Polat, Gözde Yaylali, Bekir Tanay

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 305-311, 2019, DOI:10.31209/2018.100000006

Abstract Soft set theory, which was defined by D. Molodtsov, has a rich potential for applications in several fields of life. One of the successful application of the soft set theory is to construct new methods for Decision Making problems. In this study, we are introducing a method using graph representation of soft set relations to solve Decision Making problems. We have successfully applied this method to various examples. More >