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

Basha, Sameh H.; Tharwat, Alaa; Ahmed, Kawsar; Hassanien, Aboul Ella

doi:10.1007/978-3-319-99010-1_45

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…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. And In these applications, the NRCS always had better results compared to other models.…”

Section: Neutrosophic Rule-based Classification Systemmentioning

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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Section: Neutrosophic Rule-based Classification Systemmentioning

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

Self Cite

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A Proposed Neutrosophic Probability Model for Normalized DifferenceVegetation Index Using Remote Sensing

Shan-E-Fatima,

Khan,

Javid

2024

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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In this research work, a new neutrosophic probability model named a neutrosophic Topp-Leone exponentiated generalized exponential distribution is proposed to model the normalized difference vegetation index (NDVI) of Pakistan in the year 2022. The MODIS was used in this research, which gave spatial and spectral information on NDVI in the Earth. NDVI maps were produced from the acquired data, and NDVI sensors computed the normalized difference vegetation index. Mathematical characteristics of the proposed model of NDVI are derived, including central tendency measures, dispersion estimators, inferential statistics, moment generating function, order statistics, reliability measures, quantile-based statistical measures, and renyi entropy and graphical representations of the density function are also presented. The maximum likelihood technique is used to estimate the parameters for the proposed probability model.

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

Cited by 2 publications

References 19 publications

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

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

A Proposed Neutrosophic Probability Model for Normalized DifferenceVegetation Index Using Remote Sensing

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