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    Comparative analysis of neural networks in the diagnosis of emerging diseases based on COVID-19
    (Konuralp Journal of Mathematics, 2021) Kirişci, Murat; Demir, İbrahim; Şimşek, Necip
    Dermatological diseases are frequently encountered in children and adults for various reasons. There are many factors that cause the onset of these diseases and different symptoms are generally seen in each age group. Artificial Neural Networks can provide expert level accuracy in the diagnosis of dermatological findings of patients with COVID-19 disease. Therefore, the use of neural network classification methods can give the best estimation method in dermatology. In this study, the prediction of cutaneous diseases caused by COVID-19 was analyzed by Scaled Conjugate Gradient, Levenberg Marquardt, Bayesian Regularization neural networks. At some points, Bayesian Regularization and Levenberg Marquardt were almost equally effective, but Bayesian Regularization performed better than Levenberg Marquard and called Conjugate Gradient in performance. It is seen that neural network model predictions achieve the highest ac-curacy. For this reason, Artificial Neural Networks are able to classify these diseases as accurately as human experts in an experimental setting.
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    Decision making method related to Pythagorean Fuzzy Soft Sets with infectious diseases application
    (King Saud bin Abdulaziz University, 2022) Kirişci, Murat; Şimşek, Necip
    This study presents a new algorithm for group decision-making solutions using Pythagorean Fuzzy Soft Matrices (PFSMs) and confident weight is given by experts. Pythagorean Fuzzy Set (PFS) is a generalization of the intuitionistic fuzzy set (IFS). Therefore, in real-life problems for uncertainty, the decision-making mechanism in PFSs outcomes better than IFS decision-making. Pythagorean Fuzzy Soft Set (PFSS) is deriving from the combination of PFS and Soft Set. PFSM is also the matrix representation of PFSSs. Based on the cardinalities of the PFSS, experts have been given a new method that assigns confident weight. Confident weight is given according to the experience and knowledge of each expert. For this process, the choice matrix and the combined choice matrix are created first. PFSMs and choice matrices given for each expert are multiplied and the matrices obtained are summed. Pythagorean distance measurements were used to check the accuracy of the results obtained by applying the algorithm. A medical case was studied to see if the proposed method for group decision-making is feasible. In the section of medical case, infectious diseases that were common before COVID-19 were selected. The newly given algorithm was applied to the opinions of physicians about these diseases. According to the Hamming Distance values, the results of three out of four physicians are the same; In the values obtained with Euclidean distance, it was seen that the opinions of all physicians were the same. It has been revealed that the newly proposed algorithm has increased the reliability of the results from the group decision analysis.
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    Fermatean fuzzy ELECTRE multi-criteria group decision-making and most suitable biomedical material selection
    (Elsevier B.V., 2022) Kirişci, Murat; Demir, Ibrahim; Şimşek, Necip
    ELECTRE is a family of multi-criteria decision analysis techniques, which has the ability to provide as much as possible precise and suitable set of actions or alternatives to the underlying problem by eliminating the alternatives, which are outranked by others. Group decision-making is an effective process to provide the most appropriate solution to real-world decision-making scenarios by considering and merging the expert opinions of multiple individuals on the problem. The aim of this study is to present an extended version of the ELECTRE I model called the Fermatean fuzzy ELECTRE I method for of multi-criteria group decision-making with Fermatean fuzzy human assessments. The method proposed in this study has the possibility to solve multi-criteria group decision-making problems by using the Fermatean fuzzy decision matrix obtained in Fermatean fuzzy number form in the evaluations made with the available alternatives based on expert opinions. First, the mathematical description of the multi-criteria group decision-making problem with Fermatean fuzzy information has been given. Then, the proposed Fermatean fuzzy ELECTRE I method to deal with the problem has been presented. After the determination of the relative importance degree of experts, the Fermatean fuzzy aggregated averaging operator is employed to merge the individual Fermatean fuzzy decision matrices produced by the experts into the aggregated Fermatean fuzzy decision matrix. Next, for pairwise comparison of available alternatives with respect to considered criteria, the concepts of Fermatean fuzzy strong, midrange, and weak concordance and discordance sets are based on the approach of score function and accuracy function defined for Fermatean fuzzy numbers. Afterward, Fermatean fuzzy concordance and discordance matrices are defined, constructed by concordance and discordance indices. Finally, Fermatean fuzzy effective concordance and discordance matrices are computed to obtain Fermatean fuzzy aggregated outranking matrix, indicating abstract information on dominations of suitable alternatives to the others. The proposed method will be used in material selection in distinct implementations, exclusively in biomedical applications where the prosthesis materials should have similar characteristics to human tissues. Since biomedical materials are used in various parts of the human body for many different purposes, in this study, material selection will be made using the method presented for the femoral component of the hip joint prosthesis for orthopedists and practitioners who will choose biomaterials.
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    Incomplete Fermatean fuzzy preference relations and group decision-making
    (De Gruyter Open Ltd, 2023) Şimşek, Necip; Kirişci, Murat
    There may be cases where experts do not have in-depth knowledge of the problem to be solved in decision-making problems. In such cases, experts may fail to express their views on certain aspects of the problem, resulting in incomplete preferences, in which some preference values are not provided or are missing. In this article, we present a new model for group decision-making (GDM) methods in which experts’ preferences can be expressed as incomplete Fermatean fuzzy preference relations. This model is guided by the additive-consistency property and only uses the preference values the expert provides. An additive consistency definition characterized by a Fermatean fuzzy priority vector has been given. The additive consistency property is also used to measure the level of consistency of the information provided by the experts. The proposed additive consistency definition’s property is presented, as well as a model for obtaining missing judgments in incomplete Fermatean fuzzy preference relations. We present a method for adjusting the inconsistency for Fermatean fuzzy preference relations, a model for obtaining the priority vector, and a method for increasing the consensus degrees of Fermatean fuzzy preference relations. In addition, we present a GDM method in environments with incomplete Fermatean fuzzy preference relations. To show that our method outperforms existing GDM methods in incomplete Fermatean fuzzy preference relations environments, we have provided an example and compared it with some methods. It has been seen that our proposed GDM method is beneficial for GDM in deficient Fermatean fuzzy preference relation environments and produces meaningful results for us.
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    The new algorithm involving minimum spanning tree for computer networks in a growing company
    (İstanbul Ticaret Üniversitesi, 2017) Kirişci, Murat; Öncel, Deniz
    The aim of this article is to present a new algorithm based on minimum spanning trees. Minimum Spanning Trees have long been used in data mining, pattern recognition and machine learning. However, it is difficult to apply traditional minimum spanning tree algorithms to a large dataset since the time complexity of the algorithms is quadratic. The given algorithm is designed to reduce this difficulty. This application has reduced the cost.
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    A novel kernel principal component analysis with application disaster preparedness of hospital: interval-valued Fermatean fuzzy set approach
    (Springer, 2023) Kirişci, Murat; Şimşek, Necip
    n an interval-valued Fermatean fuzzy environment, a group decision-making issue relating to data on the disaster preparedness of hospitals is presented in this presentation. Interval-valued Fermatean fuzzy sets have the benefit of being able to accurately reflect the assessment data provided by decision-makers through both qualitative and quantitative elements for the examination of “disaster preparedness of hospitals” challenges. The conventional decision-making techniques will falter, nevertheless, if the dimension and nonlinear connection of the choice data keep expanding. To lower the dimensionality for nonlinear characteristics, we build the interval-valued Fermatean fuzzy linguistic kernel principal component analysis model. In the last part of the study, an illustrative example is given about the method proposed and the assessment of the disaster preparedness of the university hospital according to the hospital management cycle and the detection of its deficiencies. After making a comparison analysis and expressing the advantages of the method, we explained the theoretical, managerial, and political implications of the evaluations to be made with the method we recommend in all hospitals, based on the illustrative example given.
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    Soft set based new decision-making method with cardiovascular disease application
    (Yildiz Technical Univ, 2021) Kirişci, Murat; Demir, İbrahim; Şimşek, Necip
    A Pythagorean fuzzy set is characterized by values satisfying the condition that the square sum of the degree of membership and degree of non-membership is less than or equal to 1. As a generalized set, Pythagorean fuzzy sets have a close relationship with intuitionistic fuzzy sets. In this study, an algorithm is given that can select patients at risk of developing heart disease based on cardiovascular data. This given algorithm is created with Pythagorean fuzzy soft sets. The new algorithm is offered a medical decision-making method to assist in medical diagnosis. A medical case was examined as a real-life application to see if the proposed method is applicable. The real dataset which is called the Cleveland heart disease dataset has been chosen. In the application, the dataset is arranged as PFSS. In addition, the parameter set was determined and calculations were made in accordance with PFSS. A comparison table was created with the values obtained from these calculations. By choosing the maximum of the values obtained with the score function, the patient with the highest risk of developing heart disease was determined.