Refined Neutrosophy and Lattices vs. Pair Structures and YinYang Bipolar Fuzzy Set

Smarandache, Florentín

doi:10.3390/math7040353

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…In order to meet the demands of describing fuzzy and indeterminate information at the same time from nature and society, Smarandache [7,8] founded the notion of neutrosophic sets (NSs), which can be regarded as many generalizations of extended fuzzy sets [9] and used in plenty of meaningful areas [10][11][12]. An NS contains three types of membership functions (the truth, indeterminacy and falsity ones), and all of them take values in ]0 − , 1 + [.…”

Section: A Brief Review Of Interval-valued Neutrosophic Informationmentioning

confidence: 99%

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Zhang

Kang

et al. 2020

Mathematics

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In plenty of realistic situations, multi-attribute group decision-making (MAGDM) is ubiquitous and significant in daily activities of individuals and organizations. Among diverse tools for coping with MAGDM, granular computing-based approaches constitute a series of viable and efficient theories by means of multi-view problem solving strategies. In this paper, in order to handle MAGDM issues with interval-valued neutrosophic (IN) information, we adopt one of the granular computing (GrC)-based approaches, known as multigranulation probabilistic models, to address IN MAGDM problems. More specifically, after revisiting the related fundamental knowledge, three types of IN multigranulation probabilistic models are designed at first. Then, some key properties of the developed theoretical models are explored. Afterwards, a MAGDM algorithm for merger and acquisition target selections (M&A TSs) with IN information is summed up. Finally, a real-life case study together with several detailed discussions is investigated to present the validity of the developed models.

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Section: A Brief Review Of Interval-valued Neutrosophic Informationmentioning

confidence: 99%

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Zhang

Kang

et al. 2020

Mathematics

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“…The possibilities of fuzzy sets to apply crisp numbers influenced newly proposed extensions of the fuzzy set theory, such as interval-valued fuzzy (IVF) sets [ 3 ], intuitionistic fuzzy (IF) sets [ 4 ], neutrosophic set theory [ 5 ], and others. Based on the IVF and IF sets, Atanassov and Gargov [ 6 ] introduced interval-valued intuitionistic fuzzy (IVIF) sets.…”

Section: Introductionmentioning

confidence: 99%

An Intuitionistic Extension of the Simple WISP Method

Zavadskas

Stanujkić

Turskis

et al. 2022

Entropy

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“…Neutrosophy is a branch of philosophy, introduced by F. Smarandache in 1980, which generalized dialectics and studied the origin, nature, and scope of neutralities, in addition to their interactions with numerous ideational spectra [ 24 ]. In neutrosophy theory, every event has a definite degree of truth ( T ), falsity ( F ), and indeterminacy ( I ) that have to be considered independently from each other [ 23 , 25 – 28 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic‐Slantlet Transform Domain for Statistical Texture Feature Extraction

Wady

Yousif

Hasan

2020

BioMed Research International

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Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.

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Refined Neutrosophy and Lattices vs. Pair Structures and YinYang Bipolar Fuzzy Set

Cited by 4 publications

References 15 publications

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

An Intuitionistic Extension of the Simple WISP Method

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic‐Slantlet Transform Domain for Statistical Texture Feature Extraction

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