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Samet Memiş

Bandirma Onyedi Eylul University, Marine Engineering, Faculty Member

Istanbul Rumeli University, Computer Engineering, Faculty Member

Canakkale Onsekiz Mart University, Mathematics, Undergraduate

TUBITAK, Priority Area of Artificial Intelligence and Machine Learning, Fellowship

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Samet Memiş was born in Turkey in 1991. He received the BSc, MSc, and PhD degrees from the Department of Mathematics, Çanakkale Onsekiz Mart University in 2014, 2016, and 2021, respectively. In his master's thesis, he studied soft decision-making algorithms and their C# applications under the advisory of Dr Serdar Enginoğlu. In his doctoral dissertation, he applied fuzzy parameterized fuzzy soft matrices (fpfs-matrices) to machine learning under the supervisory of Dr Serdar Enginoğlu and Dr Uğur Erkan.

His current research interests are Fuzzy Sets, Soft Sets, Soft Matrices, fpfs-matrices, Similarity Measure, Distance Measure, Decision-Making, Image Denoising, and Machine Learning.
Supervisors: Assoc. Prof. Dr. Serdar Enginoğlu and Asst. Prof. Dr. Uğur Erkan

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Independent Researcher

Pune University, MIT Academy of Engineering

Dr. Somen Debnath

Tripura University

University of Nova Gorica

Florentin Smarandache

University of New Mexico

Michael Voskoglou

Mind Reader Publications

Sandia National Laboratories

Wojciech Sałabun

West Pomeranian University of Technology, Szczecin

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Papers by Samet Memiş

Picture Fuzzy Soft Matrices and Application of Their Distance Measures to Supervised Learning: Picture Fuzzy Soft k-Nearest Neighbor (PFS-kNN)

Electronics

This paper redefines picture fuzzy soft matrices (pfs-matrices) because of some of their inconsis... more This paper redefines picture fuzzy soft matrices (pfs-matrices) because of some of their inconsistencies resulting from Cuong’s definition of picture fuzzy sets. Then, it introduces several distance measures of pfs-matrices. Afterward, this paper proposes a new kNN-based classifier, namely the Picture Fuzzy Soft k-Nearest Neighbor (PFS-kNN) classifier. The proposed classifier utilizes the Minkowski’s metric of pfs-matrices to find the k-nearest neighbor. Thereafter, it performs an experimental study utilizing four UCI medical datasets and compares to the suggested approach using the state-of-the-art kNN-based classifiers. To evaluate the performance of the classification, it conducts ten iterations of five-fold cross-validation on all the classifiers. The findings indicate that PFS-kNN surpasses the state-of-the-art kNN-based algorithms in 72 out of 128 performance results based on accuracy, precision, recall, and F1-score. More specifically, the proposed method achieves higher accu...

Operability-Oriented Configurations of the Soft Decision-Making Methods Proposed between 2013 and 2016 and Their Comparisons

Journal of new theory, Mar 30, 2021

Distance and Similarity Measures of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices and Their Applications to Data Classification in Supervised Learning

Axioms, May 10, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more

Publisher Correction: Picture Fuzzy Parameterized Picture Fuzzy Soft Sets and Their Application in a Performance-Based Value Assignment Problem to Salt-and-Pepper Noise Removal Filters

International Journal of Fuzzy Systems

In the original publication of the article [1], formulas in Definition 22, Definition 23, Definit... more In the original publication of the article [1], formulas in Definition 22, Definition 23, Definition 27, Example 5 and Example 6 were incorrectly formatted. In addition, due to typesetting errors, the abbreviations list was incorrectly published as Table 1. These errors have now been corrected in the original version of the paper. The publisher apologizes for the inconvenience caused. The original article was updated.

A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2021

Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method ... more Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy parameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM) and classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity measures of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and a generalised eigenvalue-based similarity measure. Then, it applies an SDM method based on the aggregation operator of fpfs-matrices to a decision matrix and classifies the given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success of our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN, EigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time. Moreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that FPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics.

Distance and Similarity Measures of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices and Their Applications to Data Classification in Supervised Learning

Axioms

Intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices), proposed... more Intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices), proposed by Enginoğlu and Arslan in 2020, are worth utilizing in data classification in supervised learning due to coming into prominence with their ability to model decision-making problems. This study aims to define the concepts metrics, quasi-, semi-, and pseudo-metrics and similarities, quasi-, semi-, and pseudo-similarities over ifpifs-matrices; develop a new classifier by using them; and apply it to data classification. To this end, it develops a new classifier, i.e., Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Classifier (IFPIFSC), based on six pseudo-similarities proposed herein. Moreover, this study performs IFPIFSC’s simulations using 20 datasets provided in the UCI Machine Learning Repository and obtains its performance results via five performance metrics, accuracy (Acc), precision (Pre), recall (Rec), macro F-score (MacF), and micro F-score (MicF). It also compares ...

$Research paper thumbnail of Picture fuzzy soft $$\sigma$$-algebra and picture fuzzy soft measure and their applications to multi-criteria decision-making$

Picture fuzzy soft $$\sigma$$-algebra and picture fuzzy soft measure and their applications to multi-criteria decision-making

Granular computing, Jul 29, 2022

Operability-Oriented Configurations of the Soft Decision-Making Methods Proposed between 2013 and 2016 and Their Comparisons

Journal of new theory, Mar 30, 2021

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

Journal of New Theory

In the fight against the COVID-19 pandemic, it is vital to rapidly diagnose possible contagions, ... more 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 m...

Another View on Picture Fuzzy Soft Sets and Their Product Operations with Soft Decision-Making

Journal of New Theory, 2022

A classification method based on Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices

journal of new results in science, Aug 31, 2021

In this study, firstly, Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuition... more In this study, firstly, Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices) have been defined. Afterwards, a classifier based on Hamming pseudo-similarity of ifpifs-matrices (IFPIFS-HC) has been developed. The classifier&#39;s simulations have been performed using datasets provided in the UCI Machine Learning Database, and its performance results via the performance metrics accuracy, precision, recall, macro F-score, and micro F-score have been obtained. Thereafter, the results have been compared with those of the well-known methods. Then, the statistical evaluations of the performance results have been conducted using Friedman and Nemenyi post-hoc tests, and the critical diagrams of the Nemenyi post-hoc test are presented. The results and the statistical evaluations show that the proposed classifier has performed better than the others in 12 of 21 datasets in terms of the five performance metrics, in 4 of 21 in terms of the four performance metrics, and 17 of 21 in terms of accuracy performance metric. Moreover, the mean accuracy, precision, recall, precision, macro F-score, and micro F-score results of Fuzzy kNN, FSSC, FussCyier, HDFSSC, and FPFS-EC for the 21 datasets are 84.90, 71.96, 67.95, 71.91, and 75.28; 78.12, 68.01, 68.05, 66.53, and 67.68; 80.76, 68.63, 69.07, 68.36, and 70.65; 81.93, 69.43, 69.95, 70.25, and 72.36; and 89.59, 80.27, 78.40, 81.20, and 83.60, while those of IFPIFS-HC are 90.59, 82.88, 80.75, 82.89, and 85.48, respectively. Finally, the applications of ifpifs-matrices to machine learning have been discussed for further research.

SDM Methods’ Configurations (2017-2019) and Their Application to a Performance-Based Value Assignment Problem A Follow up Study

Being a follow-up study, this paper configures soft decision-making (SDM) methods (2017-2019), ha... more Being a follow-up study, this paper configures soft decision-making (SDM) methods (2017-2019), having been constructed with soft sets, soft matrices, and their fuzzy hybrid versions, to operate them in fuzzy parameterized fuzzy soft (fpfs) matrices space faithfully to the original. It then analyses the decision-making performances of the configured methods herein by using five test cases. Afterwards, it applies the methods, producing valid ranking order according to all the test cases, to the ranking of seven known noise-removal filters. This paper completes the configurations that allow the available methods (1999-2019) to operate in the fpfs-matrices space. Finally, the need for further research studies is discussed.

A classification method in machine learning based on soft decision-making via fuzzy parameterized fuzzy soft matrices

Soft Comput., 2022

Some Soft Decision Making Algorithms and Computer Program of Them (In Turkish)

Comment (2) on Soft Set Theory and uni-int Decision-Making [European Journal of Operational Research, (2010) 207, 848-855]

The uni-int decision-making method, which selects a set of optimum elements from the alternatives... more The uni-int decision-making method, which selects a set of optimum elements from the alternatives, was defined by Cagman and Enginoglu via soft sets and their soft products. Lately, this method constructed by and-product/or-product has been configured by Enginoglu and Memis via fuzzy parameterized fuzzy soft matrices (fpfs-matrices), faithfully to the original, because a more general form is needed for the method in the event that the parameters or objects have uncertainties. In this study, we configure the method via fpfs-matrices and andnot-product/ornot-product, faithfully to the original. However, in the case that a large amount of data is processed, the method still has a disadvantage regarding time and complexity. To deal with this problem and to be able to use this configured method effectively denoted by CE10, we suggest two new algorithms in this paper, i.e. EMA18an and EMA18on, and prove that CE10 constructed by andnot-product (CE10an) and constructed by ornot-product (CE1...

Tuz ve Biber Gürültü Kaldırma için Uyarlamalı Cesáro Ortalama Filtresi

El-Cezeri Fen ve Mühendislik Dergisi, 2020

In this study, we propound a salt-and-pepper noise (SPN) removal method, i.e. Adaptive Cesáro Mea... more In this study, we propound a salt-and-pepper noise (SPN) removal method, i.e. Adaptive Cesáro Mean Filter (ACmF), and provide some of its basic notions. We then apply ACmF to several test images whose noise densities range from 10% to 90%: 15 traditional test images (Baboon, Boat, Bridge, Cameraman, Elaine, Flintstones, Hill, House, Lake, Lena, Living Room, Parrot, Peppers, Pirate, and Plane) and 40 test images, provided in the TESTIMAGES Database. Afterwards, we compare ACmF with the state-of-art methods, such as Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF). The results by The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) show that ACmF performs better than the methods mentioned above. Moreover, we also compare the running time data of these algorithms. These results show that ACmF outperforms the methods except for DAMF. We finally discuss the need for further research.

A Data Classification Method in Machine Learning via Fuzzy Parameterized Fuzzy Soft Matrices

Bilge International Journal of Science and Technology Research, 2019

In this study, we propose a classification method based on normalised Hamming pseudosimilarity of... more In this study, we propose a classification method based on normalised Hamming pseudosimilarity of fuzzy parameterized fuzzy soft matrices (fpfs-matrices). We then compare the proposed method with Fuzzy Soft Set Classifier (FSSC), FussCyier, Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy k-Nearest Neighbor (Fuzzy kNN) in terms of the performance criterions (accuracy, precision, recall, and F-measure) and running time by using four medical data sets in the UCI machine learning repository. The results show that the proposed method performs better than FSSC, FussCyier,

Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal

Multimedia Tools and Applications, 2019

Allopregnanolone (ALLO) is a neurosteroid that acts as a positive allosteric modulator on GABA A ... more Allopregnanolone (ALLO) is a neurosteroid that acts as a positive allosteric modulator on GABA A receptors (GABA A R) in the brain. Such modulations appear to be responsible for its antidepressant effects and for modifying the expression of different GABA A R subunits. The d subunit, for instance, was found to be upregulated in the hippocampus after ALLO infusions in the prefrontal cortex and nucleus accumbens. Moreover, intra-hippocampal ALLO infusion produces antidepressant-like effects in rats, but its effects on the hippocampal d GABA A R subunit expression are still unknown. This study aimed to verify the sub-acute effect of the intra-hippocampal infusion of ALLO (1.25; 2.5; and 5 μg/rat) on the d GABA A R subunit mRNA expression in each hippocampal hemisphere of male rats. An ANOVA detected an interaction between treatment and hemisphere regarding the expression of the d subunit (p = 0.010), and post-hoc analyses showed that the infusion of 5 μg/ rat of ALLO induced a higher d GABA A R subunit mRNA expression in the right hemisphere in comparison to the left (P< 0.001). Additionally, the mRNA expression in the right hemisphere was higher in the 5 μg/rat dose when compared to the 1.25 μg/rat dose (p = 0.028). Thus, intra-hippocampal ALLO infusion asymmetrically increased the mRNA expression of the d GABA A receptor subunit in the same brain region of rats. These results support the importance of the d GABA A R subunit on the antidepressant effects of ALLO, as well as the relevance of exploring inter-hemispheric analysis.

Configurations of Soft Decision-Making Methods Provided in fpfs-Matrices Space to Render Them Operable in ifpifs-Matrices Spaces and Their Application to Performance Ranking of the Noise Removal Filters

Journal of New Theory, 2021

− Recently, the concept of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices ... more − Recently, the concept of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices) has allowed to mathematically model some problems in which the parameters and alternatives exhibit intuitionistic fuzzy uncertainties. To this end, the present study aims to generalise 24 soft decision-making (SDM) methods operating in the fuzzy parameterized fuzzy soft matrices space with a single fpfs-matrix to the ifpifs-matrices spaces. Afterwards, we propose five test scenarios to analyse whether the SDM methods constructed by ifpifs-matrices consistently work. Moreover, we make performance comparisons of the generalised SDM methods successful in the five test scenarios by applying them to the performance-based value assignment (PVA) problem of the wellknown noise-removal filters. Finally, we discuss the need for further research.

A New Approach to Group Decision-Making Method Based on TOPSIS Under Fuzzy Soft Environment

journal of new results in science, 2019

TOPSIS, developed in 1981 by Hwang and Yoon, is one of the known multi-criteria decision-making (... more TOPSIS, developed in 1981 by Hwang and Yoon, is one of the known multi-criteria decision-making (MCDM) methods. In 2015, the group decision-making method based on TOPSIS under fuzzy soft environment was defined and applied to a decision-making problem by Eraslan and Karaaslan. Recently, this method has been configured by Enginoglu and Memis via fuzzy parameterized fuzzy soft matrices (fpfs-matrices), faithfully to the original, because a more general form is needed for the method in the event that the parameters have uncertainties. However, the configured method has two drawbacks which affect its running time and the ranking order negatively. We, in this study, improve this method by removing the disadvantages. We then compare the running time of these algorithms. The results show that the new method outperforms it, in particular, a large number of data come into question. For example, the proposed method offers up to 97.7672% of time advantage for ten objects and 9000 parameters. A...

Picture Fuzzy Soft Matrices and Application of Their Distance Measures to Supervised Learning: Picture Fuzzy Soft k-Nearest Neighbor (PFS-kNN)

Electronics

This paper redefines picture fuzzy soft matrices (pfs-matrices) because of some of their inconsis... more This paper redefines picture fuzzy soft matrices (pfs-matrices) because of some of their inconsistencies resulting from Cuong’s definition of picture fuzzy sets. Then, it introduces several distance measures of pfs-matrices. Afterward, this paper proposes a new kNN-based classifier, namely the Picture Fuzzy Soft k-Nearest Neighbor (PFS-kNN) classifier. The proposed classifier utilizes the Minkowski’s metric of pfs-matrices to find the k-nearest neighbor. Thereafter, it performs an experimental study utilizing four UCI medical datasets and compares to the suggested approach using the state-of-the-art kNN-based classifiers. To evaluate the performance of the classification, it conducts ten iterations of five-fold cross-validation on all the classifiers. The findings indicate that PFS-kNN surpasses the state-of-the-art kNN-based algorithms in 72 out of 128 performance results based on accuracy, precision, recall, and F1-score. More specifically, the proposed method achieves higher accu...

Operability-Oriented Configurations of the Soft Decision-Making Methods Proposed between 2013 and 2016 and Their Comparisons

Journal of new theory, Mar 30, 2021

Distance and Similarity Measures of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices and Their Applications to Data Classification in Supervised Learning

Axioms, May 10, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more

Publisher Correction: Picture Fuzzy Parameterized Picture Fuzzy Soft Sets and Their Application in a Performance-Based Value Assignment Problem to Salt-and-Pepper Noise Removal Filters

International Journal of Fuzzy Systems

In the original publication of the article [1], formulas in Definition 22, Definition 23, Definit... more In the original publication of the article [1], formulas in Definition 22, Definition 23, Definition 27, Example 5 and Example 6 were incorrectly formatted. In addition, due to typesetting errors, the abbreviations list was incorrectly published as Table 1. These errors have now been corrected in the original version of the paper. The publisher apologizes for the inconvenience caused. The original article was updated.

A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2021

Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method ... more Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy parameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM) and classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity measures of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and a generalised eigenvalue-based similarity measure. Then, it applies an SDM method based on the aggregation operator of fpfs-matrices to a decision matrix and classifies the given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success of our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN, EigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time. Moreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that FPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics.

Distance and Similarity Measures of Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Matrices and Their Applications to Data Classification in Supervised Learning

Axioms

Intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices), proposed... more Intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices), proposed by Enginoğlu and Arslan in 2020, are worth utilizing in data classification in supervised learning due to coming into prominence with their ability to model decision-making problems. This study aims to define the concepts metrics, quasi-, semi-, and pseudo-metrics and similarities, quasi-, semi-, and pseudo-similarities over ifpifs-matrices; develop a new classifier by using them; and apply it to data classification. To this end, it develops a new classifier, i.e., Intuitionistic Fuzzy Parameterized Intuitionistic Fuzzy Soft Classifier (IFPIFSC), based on six pseudo-similarities proposed herein. Moreover, this study performs IFPIFSC’s simulations using 20 datasets provided in the UCI Machine Learning Repository and obtains its performance results via five performance metrics, accuracy (Acc), precision (Pre), recall (Rec), macro F-score (MacF), and micro F-score (MicF). It also compares ...

$Research paper thumbnail of Picture fuzzy soft $$\sigma$$-algebra and picture fuzzy soft measure and their applications to multi-criteria decision-making$

Picture fuzzy soft $$\sigma$$-algebra and picture fuzzy soft measure and their applications to multi-criteria decision-making

Granular computing, Jul 29, 2022

Operability-Oriented Configurations of the Soft Decision-Making Methods Proposed between 2013 and 2016 and Their Comparisons

Journal of new theory, Mar 30, 2021

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

Journal of New Theory

In the fight against the COVID-19 pandemic, it is vital to rapidly diagnose possible contagions, ... more 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 m...

Another View on Picture Fuzzy Soft Sets and Their Product Operations with Soft Decision-Making

Journal of New Theory, 2022

A classification method based on Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices

journal of new results in science, Aug 31, 2021

In this study, firstly, Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuition... more In this study, firstly, Hamming pseudo-similarity of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices) have been defined. Afterwards, a classifier based on Hamming pseudo-similarity of ifpifs-matrices (IFPIFS-HC) has been developed. The classifier&#39;s simulations have been performed using datasets provided in the UCI Machine Learning Database, and its performance results via the performance metrics accuracy, precision, recall, macro F-score, and micro F-score have been obtained. Thereafter, the results have been compared with those of the well-known methods. Then, the statistical evaluations of the performance results have been conducted using Friedman and Nemenyi post-hoc tests, and the critical diagrams of the Nemenyi post-hoc test are presented. The results and the statistical evaluations show that the proposed classifier has performed better than the others in 12 of 21 datasets in terms of the five performance metrics, in 4 of 21 in terms of the four performance metrics, and 17 of 21 in terms of accuracy performance metric. Moreover, the mean accuracy, precision, recall, precision, macro F-score, and micro F-score results of Fuzzy kNN, FSSC, FussCyier, HDFSSC, and FPFS-EC for the 21 datasets are 84.90, 71.96, 67.95, 71.91, and 75.28; 78.12, 68.01, 68.05, 66.53, and 67.68; 80.76, 68.63, 69.07, 68.36, and 70.65; 81.93, 69.43, 69.95, 70.25, and 72.36; and 89.59, 80.27, 78.40, 81.20, and 83.60, while those of IFPIFS-HC are 90.59, 82.88, 80.75, 82.89, and 85.48, respectively. Finally, the applications of ifpifs-matrices to machine learning have been discussed for further research.

SDM Methods’ Configurations (2017-2019) and Their Application to a Performance-Based Value Assignment Problem A Follow up Study

Being a follow-up study, this paper configures soft decision-making (SDM) methods (2017-2019), ha... more Being a follow-up study, this paper configures soft decision-making (SDM) methods (2017-2019), having been constructed with soft sets, soft matrices, and their fuzzy hybrid versions, to operate them in fuzzy parameterized fuzzy soft (fpfs) matrices space faithfully to the original. It then analyses the decision-making performances of the configured methods herein by using five test cases. Afterwards, it applies the methods, producing valid ranking order according to all the test cases, to the ranking of seven known noise-removal filters. This paper completes the configurations that allow the available methods (1999-2019) to operate in the fpfs-matrices space. Finally, the need for further research studies is discussed.

A classification method in machine learning based on soft decision-making via fuzzy parameterized fuzzy soft matrices

Soft Comput., 2022

Some Soft Decision Making Algorithms and Computer Program of Them (In Turkish)

Comment (2) on Soft Set Theory and uni-int Decision-Making [European Journal of Operational Research, (2010) 207, 848-855]

The uni-int decision-making method, which selects a set of optimum elements from the alternatives... more The uni-int decision-making method, which selects a set of optimum elements from the alternatives, was defined by Cagman and Enginoglu via soft sets and their soft products. Lately, this method constructed by and-product/or-product has been configured by Enginoglu and Memis via fuzzy parameterized fuzzy soft matrices (fpfs-matrices), faithfully to the original, because a more general form is needed for the method in the event that the parameters or objects have uncertainties. In this study, we configure the method via fpfs-matrices and andnot-product/ornot-product, faithfully to the original. However, in the case that a large amount of data is processed, the method still has a disadvantage regarding time and complexity. To deal with this problem and to be able to use this configured method effectively denoted by CE10, we suggest two new algorithms in this paper, i.e. EMA18an and EMA18on, and prove that CE10 constructed by andnot-product (CE10an) and constructed by ornot-product (CE1...

Tuz ve Biber Gürültü Kaldırma için Uyarlamalı Cesáro Ortalama Filtresi

El-Cezeri Fen ve Mühendislik Dergisi, 2020

In this study, we propound a salt-and-pepper noise (SPN) removal method, i.e. Adaptive Cesáro Mea... more In this study, we propound a salt-and-pepper noise (SPN) removal method, i.e. Adaptive Cesáro Mean Filter (ACmF), and provide some of its basic notions. We then apply ACmF to several test images whose noise densities range from 10% to 90%: 15 traditional test images (Baboon, Boat, Bridge, Cameraman, Elaine, Flintstones, Hill, House, Lake, Lena, Living Room, Parrot, Peppers, Pirate, and Plane) and 40 test images, provided in the TESTIMAGES Database. Afterwards, we compare ACmF with the state-of-art methods, such as Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF). The results by The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) show that ACmF performs better than the methods mentioned above. Moreover, we also compare the running time data of these algorithms. These results show that ACmF outperforms the methods except for DAMF. We finally discuss the need for further research.

A Data Classification Method in Machine Learning via Fuzzy Parameterized Fuzzy Soft Matrices

Bilge International Journal of Science and Technology Research, 2019

In this study, we propose a classification method based on normalised Hamming pseudosimilarity of... more In this study, we propose a classification method based on normalised Hamming pseudosimilarity of fuzzy parameterized fuzzy soft matrices (fpfs-matrices). We then compare the proposed method with Fuzzy Soft Set Classifier (FSSC), FussCyier, Fuzzy Soft Set Classification Using Hamming Distance (HDFSSC), and Fuzzy k-Nearest Neighbor (Fuzzy kNN) in terms of the performance criterions (accuracy, precision, recall, and F-measure) and running time by using four medical data sets in the UCI machine learning repository. The results show that the proposed method performs better than FSSC, FussCyier,

Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal

Multimedia Tools and Applications, 2019

Allopregnanolone (ALLO) is a neurosteroid that acts as a positive allosteric modulator on GABA A ... more Allopregnanolone (ALLO) is a neurosteroid that acts as a positive allosteric modulator on GABA A receptors (GABA A R) in the brain. Such modulations appear to be responsible for its antidepressant effects and for modifying the expression of different GABA A R subunits. The d subunit, for instance, was found to be upregulated in the hippocampus after ALLO infusions in the prefrontal cortex and nucleus accumbens. Moreover, intra-hippocampal ALLO infusion produces antidepressant-like effects in rats, but its effects on the hippocampal d GABA A R subunit expression are still unknown. This study aimed to verify the sub-acute effect of the intra-hippocampal infusion of ALLO (1.25; 2.5; and 5 μg/rat) on the d GABA A R subunit mRNA expression in each hippocampal hemisphere of male rats. An ANOVA detected an interaction between treatment and hemisphere regarding the expression of the d subunit (p = 0.010), and post-hoc analyses showed that the infusion of 5 μg/ rat of ALLO induced a higher d GABA A R subunit mRNA expression in the right hemisphere in comparison to the left (P< 0.001). Additionally, the mRNA expression in the right hemisphere was higher in the 5 μg/rat dose when compared to the 1.25 μg/rat dose (p = 0.028). Thus, intra-hippocampal ALLO infusion asymmetrically increased the mRNA expression of the d GABA A receptor subunit in the same brain region of rats. These results support the importance of the d GABA A R subunit on the antidepressant effects of ALLO, as well as the relevance of exploring inter-hemispheric analysis.

Configurations of Soft Decision-Making Methods Provided in fpfs-Matrices Space to Render Them Operable in ifpifs-Matrices Spaces and Their Application to Performance Ranking of the Noise Removal Filters

Journal of New Theory, 2021

− Recently, the concept of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices ... more − Recently, the concept of intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices) has allowed to mathematically model some problems in which the parameters and alternatives exhibit intuitionistic fuzzy uncertainties. To this end, the present study aims to generalise 24 soft decision-making (SDM) methods operating in the fuzzy parameterized fuzzy soft matrices space with a single fpfs-matrix to the ifpifs-matrices spaces. Afterwards, we propose five test scenarios to analyse whether the SDM methods constructed by ifpifs-matrices consistently work. Moreover, we make performance comparisons of the generalised SDM methods successful in the five test scenarios by applying them to the performance-based value assignment (PVA) problem of the wellknown noise-removal filters. Finally, we discuss the need for further research.

A New Approach to Group Decision-Making Method Based on TOPSIS Under Fuzzy Soft Environment

journal of new results in science, 2019

TOPSIS, developed in 1981 by Hwang and Yoon, is one of the known multi-criteria decision-making (... more TOPSIS, developed in 1981 by Hwang and Yoon, is one of the known multi-criteria decision-making (MCDM) methods. In 2015, the group decision-making method based on TOPSIS under fuzzy soft environment was defined and applied to a decision-making problem by Eraslan and Karaaslan. Recently, this method has been configured by Enginoglu and Memis via fuzzy parameterized fuzzy soft matrices (fpfs-matrices), faithfully to the original, because a more general form is needed for the method in the event that the parameters have uncertainties. However, the configured method has two drawbacks which affect its running time and the ranking order negatively. We, in this study, improve this method by removing the disadvantages. We then compare the running time of these algorithms. The results show that the new method outperforms it, in particular, a large number of data come into question. For example, the proposed method offers up to 97.7672% of time advantage for ten objects and 9000 parameters. A...

A Fast and Simple Soft Decision Making Algorithm EMA18an

by Samet Memiş and Burak Arslan

International Conference on Mathematical Studies and Applications, 2018