Generalization of Fuzzy C-Means based on Neutrosophic Logic

Hassanien, Aboul Ella; Basha, Sameh H.; Abdalla, Areeg

doi:10.24846/v27i1y201805

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…T , I , and F could be dynamically defined as vector functions or operators of set values depending on parameters like: space, time (Smarandache 2003 ; Hassanien et al. 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…The sets T , I , and F do not have to be intervals, rather, they may be real values: discrete or continuous; finite or not; union or intersection of various subsets (Smarandache 2003;Basha et al 2016b). T , I , and F could be dynamically defined as vector functions or operators of set values depending on parameters like: space, time (Smarandache 2003;Hassanien et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

et al. 2021

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The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19.

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“…T , I , and F could be dynamically defined as vector functions or operators of set values depending on parameters like: space, time (Smarandache 2003 ; Hassanien et al. 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

et al. 2021

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“…Neutrosophic logic has been constructed to serve mathematically building models containing uncertainty of many different types ambiguity or vagueness, inconsistency or contradiction, redundancy or incompleteness, and incompleteness [36,38]. Neutrosophica logic is constructed such that each hypothesis is assumed to have a proportion of the truth in the T subset, a proportion of the falsification in the F subset, and a proportion of the indeterminacy in the I subset, where T, F, I are real subsets of ] − 0, 1 + [, where supT = t_sup, infT = t_inf, supF = f _sup, infF = f _inf, supI = i_sup, infI = i_inf, n_sup = t_sup + f _sup + i_sup, and n_inf = t_inf + f _inf + i_inf [39][40][41].…”

Section: Neutrosophic Logicmentioning

confidence: 99%

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

Hefny¹,

Hassanien²,

Basha³

2021

Computers, Materials &Amp; Continua

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Identity verification using authenticity evaluation of handwritten signatures is an important issue. There have been several approaches for the verification of signatures using dynamics of the signing process. Most of these approaches extract only global characteristics. With the aim of capturing both dynamic global and local features, this paper introduces a novel model for verifying handwritten dynamic signatures using neutrosophic rule-based verification system (NRVS) and Genetic NRVS (GNRVS) models. The neutrosophic Logic is structured to reflect multiple types of knowledge and relations among all features using three values: truth, indeterminacy, and falsity. These three values are determined by neutrosophic membership functions. The proposed model also is able to deal with all features without the need to select from them. In the GNRVS model, the neutrosophic rules are automatically chosen by Genetic Algorithms. The performance of the proposed system is tested on the MCYT-Signature-100 dataset. In terms of the accuracy, average error rate, false acceptance rate, and false rejection rate, the experimental results indicate that the proposed model has a significant advantage compared to different well-known models.

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“…Neutrosophic logic was developed to represent the mathematical models that contains uncertainty, vagueness, ambiguity, imprecision, incompleteness, inconsistency, redundancy as well as contradiction ( Hassanien, Basha, & Abdalla, 2018;Smarandache, 2003 ). Neutrosophic logic is a logic in which each proposition is estimated to have a percentage of truth in a subset T , a percentage of indeterminacy in a subset I , and a percentage of falsity in a subset F , where T, I, F are standard or non-standard real subsets of ] − 0 , 1 + [ where ] − 0 , 1 + [ is a non-standard unit interval ( Ansari et al, 2013;Robinson, 2003 ).…”

Section: Neutrosophic Logicmentioning

confidence: 99%

Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Basha

Tharwat

Abdalla

et al. 2019

Expert Systems with Applications

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a b s t r a c tMeasuring toxicity is an important step in drug development. However, the current experimental methods which are used to estimate the drug toxicity are expensive and need high computational efforts. Therefore, these methods are not suitable for large-scale evaluation of drug toxicity. As a consequence, there is a high demand to implement computational models that can predict drug toxicity risks. In this paper, we used a dataset that consists of 553 drugs that biotransformed in the liver. In this data, there are four toxic effects, namely, mutagenic, tumorigenic, irritant and reproductive effects. Each drug is represented by 31 chemical descriptors. This paper proposes two models for predicting drug toxicity risks. The proposed models consist of three phases. In the first phase, the most discriminative features are selected using rough set-based methods to reduce the classification time and improve the classification performance. In the second phase, three different sam pling algorithms, namely, Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique (SMOTE) are used to obtain balanced data. In the third phase, the first proposed model employs the Neutrosophic Rule-based Classification System (NRCS), and the second model uses Genetic NRCS (GNRCS) to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed models obtained high sensitivity (89-93%), specificity (91-97%), and GM (90-94%) for all toxic effects. Overall, the results of the proposed models indicate that it could be utilized for the prediction of drug toxicity in the early stages of drug development.

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Generalization of Fuzzy C-Means based on Neutrosophic Logic

Cited by 7 publications

References 19 publications

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

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