Neutrosophic rule-based prediction system for assessment of pollution on Benthic Foraminifera in Burullus Lagoon in Egypt

Basha, Sameh H.; Sahlol, Ahmed T.; Baz, Sherif M. El; Hassanien, Aboul Ella

doi:10.1109/icces.2017.8275388

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…An element e is represented by the triple e(T ; I ; F) to mathematically indicate the element's belongingness to a set as follows: t is its degree of belongingness, i is its indeterminacy , and f is its falsity degree, where t, i, and f take real values in T, I, and F, respectively (Smarandache 2003;Basha et al 2017).…”

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RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic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

confidence: 99%

“…There is no constraint on their sum, i.e., − 0 ≤ supT S (x)+supI S (x)+supF S (x) ≤ 3 + . NS operators could be constructed using different ways (Ansari et al 2013;Basha et al 2017).…”

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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“…T, I , and F are called neutrosophic components, and these components represent the truth, indeterminacy, and falsehood values respectively in studying neutrosophy, neutrosophic logic, neutrosophic set, neutrosophic probability, neutrosophic statistics ( Ashbacher, 2002 ). In real world applications, it is easier to use standard real interval [0,1] for T, I , and F instead of the non-standard unit interval ] − 0 , 1 + [ ( Ansari et al, 2013;Basha, Sahlol, El Baz, & Hassanien, 2017 ).…”

Section: Neutrosophic Logicmentioning

confidence: 99%

“…The NRCS first appeared in 2016, by Basha et al (2016b) . An application of this system was presented in Basha et al (2017) to predict the pollution status of the Burullus lagoon, in Egypt, according to the concentrations of trace metals. In 2018, another application introduced also by Basha, Tharwat, Ahmed, and Hassanien (2018) , for building a predictive model to estimate the sperm quality based on personal lifestyle and environmental factors using NRCS.…”

Section: Neutrosophic Rule-based Classification Systemmentioning

confidence: 99%

“…Tharwat, Gabel, https://doi.org/10.1016/j.eswa.2018.12.014 0957-4174/© 2018 Elsevier Ltd. All rights reserved. and Hassanien (2017) , Tharwat, Moemen, and Hassanien (2017) introduced two models for predicting the toxicity of drugs, on the same dataset used in this paper. They used the Whale Optimization and Dragonfly algorithms for finding the optimal parameters of the Support Vector Machine (SVM) classifier, and their model obtained competitive results.…”

Section: Introductionmentioning

confidence: 99%

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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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A Predictive Model for Seminal Quality Using Neutrosophic Rule-Based Classification System

Basha

Tharwat

Ahmed

et al. 2018

Advances in Intelligent Systems and Computing

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Neutrosophic rule-based prediction system for assessment of pollution on Benthic Foraminifera in Burullus Lagoon in Egypt

Cited by 5 publications

References 12 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 prediction system for toxicity effects assessment of biotransformed hepatic drugs

A Predictive Model for Seminal Quality Using Neutrosophic Rule-Based Classification System

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