Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices

Memiş, Samet; Enginoğlu, Serdar; Erkan, Uğur

doi:10.1109/access.2021.3089849

Cited by 33 publications

(19 citation statements)

References 27 publications

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“…Definition 4. [25] Let 𝐷 𝑡𝑟𝑎𝑖𝑛 with 𝑚 1 × 𝑛 and 𝐶 𝑚 1 ×1 be a training matrix and the class column vector of 𝐷 𝑡𝑟𝑎𝑖𝑛 . Then, 𝑓𝑤 is called the feature weight vector based on the Pearson correlation coefficient of 𝐷 𝑡𝑟𝑎𝑖𝑛 and is denoted by…”

Section: Preliminariesmentioning

confidence: 99%

Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices

PARMAKSIZ¹,

Arslan

Memiş³

et al. 2022

Journal of New Theory

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In the fight against the COVID-19 pandemic, it is vital to rapidly diagnose possible contagions, treat patients, plan follow-up procedures with correct and effective use of resources and ensure the formation of herd immunity. The use of machine learning and statistical methods provides great convenience in dealing with too many data produced during research. Since access to the PCR test used for the diagnosis of COVID-19 may be limited, the test is relatively too slow to yield results, the cost is high, and its reliability is controversial; thus, making a symptomatic classification before the PCR is timesaving and far less costly. In this study, by modifying a state-of-the-art classification method, namely Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC), an effective method is developed for a rapid diagnosis of COVID-19. The paper then presents the accuracy, sensitivity, specificity, and F1-score values that represent the diagnostic performances of the modified method. The results show that the modified method can be adopted as a competent and accurate diagnosis procedure. Afterwards, a tirage study is performed by calculating the patients’ risk scores to manage inpatient overcrowding in healthcare institutions. In the subsequent section, a vaccine priority algorithm is proposed to be used in the case of a possible crisis until the supply shortage of a newly developed vaccine is over if a possible variant of COVID-19 that is highly contagious is insensitive to the vaccine. The accuracy of the algorithm is tested with real-life data. Finally, the need for further research is discussed.

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Section: Preliminariesmentioning

confidence: 99%

Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices

PARMAKSIZ¹,

Arslan

Memiş³

et al. 2022

Journal of New Theory

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“…In these models, fuzzy parameters are taken as elements in the domain of soft approximate mapping and fuzzy subsets are taken as elements in its codomain. Recently, the researchers [ 49 , 50 , 51 , 52 , 53 , 54 ] discussed the concept of fuzzy parameterization in matrices under soft set environment. They characterized various new properties and operations with matrix setting and applied them in decision-making, spaces, and numerical data classification.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Parameterized Fuzzy Hypersoft Set and Riesz Summability Approach Based Decision Support System for Diagnosis of Heart Diseases

et al. 2022

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Fuzzy parameterized fuzzy hypersoft set (Δ-set) is more flexible and reliable model as it is capable of tackling features such as the assortment of attributes into their relevant subattributes and the determination of vague nature of parameters and their subparametric-valued tuples by employing the concept of fuzzy parameterization and multiargument approximations, respectively. The existing literature on medical diagnosis paid no attention to such features. Riesz Summability (a classical concept of mathematical analysis) is meant to cope with the sequential nature of data. This study aims to integrate these features collectively by using the concepts of fuzzy parameterized fuzzy hypersoft set (Δ-set) and Riesz Summability. After investigating some properties and aggregations of Δ-set, two novel decision-support algorithms are proposed for medical diagnostic decision-making by using the aggregations of Δ-set and Riesz mean technique. These algorithms are then validated using a case study based on real attributes and subattributes of the Cleveland dataset for heart-ailments-based diagnosis. The real values of attributes and subattributes are transformed into fuzzy values by using appropriate transformation criteria. It is proved that both algorithms yield the same and reliable results while considering hypersoft settings. In order to judge flexibility and reliability, the preferential aspects of the proposed study are assessed by its structural comparison with some related pre-developed structures. The proposed approach ensures that reliable results can be obtained by taking a smaller number of evaluating traits and their related subvalues-based tuples for the diagnosis of heart-related ailments.

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“…Machine learning algorithms adopt a mathematical model that relies on data samples, called “training data,” to make predictions or decisions without being explicitly programmed to perform a specific task. Several researchers have developed relevant classification algorithms to make the appropriate decision (Memiş et al 2021 ). There are three crucial types of machine learning algorithms, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

A framework for predicting academic orientation using supervised machine learning

Mrabet

Moussa

2022

J Ambient Intell Human Comput

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School guidance is declared an integral part of the education and training process, as it accompanies students in their educational and professional choices. Accordingly, the current situation in light of the Covid-19 epidemic requires a reconsideration of school guidance together with the methods of accompanying the student to choose the field that suits his/her personality, knowledge qualifications, perceptual and intellectual skills in order to achieve an excellent educational level that enables the learner to work in future professions. The current study aims to predict a student's potential and provide support for academic guidance. This paper emphasizes the importance of supervised machine learning and classification algorithms to predict the personality type based on student traits. Based on the information gathered, the results of this study indicate that it contributes significantly to providing a comprehensive approach to support academic self-orientation.

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Numerical Data Classification via Distance-Based Similarity Measures of Fuzzy Parameterized Fuzzy Soft Matrices

Cited by 33 publications

References 27 publications

Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices

Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices

A Novel Fuzzy Parameterized Fuzzy Hypersoft Set and Riesz Summability Approach Based Decision Support System for Diagnosis of Heart Diseases

A framework for predicting academic orientation using supervised machine learning

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