Journal of Intelligent & Fuzzy Systems - Volume 39, issue 5

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 5977-5977, 2020

Authors: Kahraman, Cengiz

Article Type: Other

DOI: 10.3233/JIFS-189071

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 5979-5979, 2020

Authors: Atanassov, Krassimir T.

Article Type: Research Article

Abstract: In this paper an extension of the Intuitionistic Fuzzy Set called Circular Intuitionistic Fuzzy Set is introduced and studied. Some of the relations and operations over C-IFS are established.

Keywords: Intutionistic fuzzy sets, Circular IFS, extension of intuitionistic fuzzy sets

DOI: 10.3233/JIFS-189072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 5981-5986, 2020

Authors: Onar, Sezi Cevik | Kahraman, Cengiz | Oztaysi, Basar

Article Type: Research Article

Abstract: The catastrophes due to widespread outbreaks create a long-standing distraction and have an accelerating transmission. The uncontrolled outbreaks cause not only health-related problems but also supply chain related problems. The outbreak caused by the coronavirus (COVID-19) shows how vulnerable the Healthcare systems and the supporting systems such as supply chains of the countries to such type of disasters. Keeping high levels of inventory, especially for healthcare products, can be beneficial to overcome such shortage problems. Nevertheless, keeping a high level of inventory can be costly, and the durability of the products creates a limit. The decision-makers have to carefully decide …the inventory levels by considering many factors such as the criticality of the product and the easiness of producing the product. In this study, we try to develop a decision model for defining the inventory levels in Healthcare systems by considering multiple scenarios such as outbreaks. A novel spherical regret based multi-criteria decision-making approach is developed and used for evaluating the total regret of not keeping stock of the healthcare equipment. Show more

Keywords: Spherical fuzzy sets, multi-criteria decision making, AHP, regret

DOI: 10.3233/JIFS-189073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 5987-5997, 2020

Authors: Torra, Vicenç

Article Type: Research Article

Abstract: Microaggregation is an effective data-driven protection method that permits us to achieve a good trade-off between disclosure risk and information loss. In this work we propose a method for microaggregation based on fuzzy c -means, that is appropriate when there are constraints (linear constraints) on the variables that describe the data. Our method leads to results that satisfy these constraints even when the data to be masked do not satisfy them.

Keywords: Microaggregation, edit constraints, k-Anonymity, clustering statistical disclosure control, data privacy

DOI: 10.3233/JIFS-189074

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 5999-6008, 2020

Authors: Ashibani, Yosef | Mahmoud, Qusay H.

Article Type: Research Article

Abstract: Smartphones have now become ubiquitous for accessing and controlling home appliances in smart homes, a popular application of the Internet of Things. User authentication on smartphones is mostly achieved at initial access. However, without applying a continuous authentication process, the network will be susceptible to unauthorized users. This issue emphasizes the importance of offering a continuous authentication scheme to identify the current user of the device. This can be achieved by extracting information during smartphone usage, including application access patterns. In this paper, we present a flexible machine learning user authentication scheme for smart home networks based on smartphone usage. …Considering that users may run their smartphone applications differently during different day time intervals as well as different days of the week, new features are extracted by considering this information. The scheme is evaluated on a real-world dataset for continuous user authentication. The results show that the presented scheme authenticates users with high accuracy. Show more

Keywords: App interactions, user authentication, smart home network, classification, foreground apps

DOI: 10.3233/JIFS-189075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6009-6020, 2020

Authors: Sezgin, Anıl | Çetin, Ömer

Article Type: Research Article

Abstract: The autonomous navigation problem is currently a popular research topic encountered in many different areas, from smart cars to automated warehouse operations. Autonomous navigation of robotic systems is examined in two basic areas, with and without external supports. Even if the robot uses external supports (like GPS, road signs, ground surface lines, barcodes etc.) or positioning itself with internal sensors (like gyro, IMU, etc.), it independently needs a control method to follow the desired route. When the widely used control approaches in the literature are examined, PID and fuzzy-based approaches are frequently encountered. They both offer advantages and disadvantages. Within …the scope of this study, the positive aspects of both of control approaches are utilized and the hybrid control approach for a robot that can autonomously follow the ground line presented as an external reference is mentioned. The success of the approach was tested comparatively on an exemplary mobile platform. The applied results obtained experimentally showed that a more efficient fuzzy logic-based hybrid control method could be developed. In addition, the effect of the number of conditions in the rule table on the success of the hybrid control technique is shown in the study. It has also been experimentally demonstrated that the success of hybrid control approach with a limited low power consuming ARM based processor that is suitable to control most of lightweight mobile platforms. Show more

Keywords: Autonomous line follower robot, fuzzy control, PD control, hybrid control

DOI: 10.3233/JIFS-189076

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6021-6031, 2020

Authors: Kalathian, Shriram | Ramalingam, Sujatha | Raman, Sundareswaran | Srinivasan, Narasimman

Article Type: Research Article

Abstract: A fuzzy graph is one of the versatile application tools in the field of mathematics, which allows the user to easily describe the fuzzy relation between any objects. The nature of fuzziness is favorable for any environment, which supports to predict the problem and solving it. Fuzzy graphs are beneficial to give more precision and flexibility to the system as compared to the classical model (i.e.,) crisp theory. A topological index is a numerical quantity for the structural graph of the molecule and it can be represented through Graph theory. Moreover, its application not only in the field of …chemistry can also be applied in areas including computer science, networking, etc. A lot of topological indices are available in chemical-graph theory and H. Wiener proposed the first index to estimate the boiling point of alkanes called ‘Wiener index ’. Many topological indices exist only in the crisp but it’s new to the fuzzy graph environment. The main aim of this paper is to define the topological indices in fuzzy graphs. Here, indices defined in fuzzy graphs are Modified Wiener index, Hyper Wiener index, Schultz index, Gutman index, Zagreb indices, Harmonic index, and Randić index with illustrations. Bounds for some of the indices are proved. The algorithms for distance matrix and MWI are shown. Finally, the application of these indices is discussed. Show more

Keywords: Fuzzy graphs, distance matrix, topological indices, operations on topological indices

DOI: 10.3233/JIFS-189077

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6033-6046, 2020

Authors: Cinar, Ulas | Cebi, Selcuk

Article Type: Research Article

Abstract: Conventional risk assessment methods are widely used for industrial safety applications. However, there are serious obstacles to their usage as; (i) all of the potential hazards are considered as an independent event, (ii) various risks are identified based on these hazards, (iii) risk magnitudes of these risks are obtained without considering interdependencies among the hazards, and then (iv) the protective measures against the defined risks are taken based on these risk magnitudes. Therefore, conventional methods do not provide any assessment for overall risks in the working environment. Furthermore, although an accident may cause different severity such as loss of working …days, loss of limbs, occupational disease, and death, the conventional methods do not consider all potential consequences of any accident, simultaneously. The main objective of this paper is to propose an effective risk assessment approach by using the fuzzy set theory, Analytical Hierarchy Process (AHP), Fuzzy Inference System (FIS), and Quality Function Deployment (QFD) methods to quantify the risk of any hazard considering interdependencies among all potential hazards and consequences in working environment. Within the scope of this research, an application in the mining sector has been presented to illustrate the validation and the effectiveness of the proposed approach** . Show more

Keywords: Risk assessment, mining sector, QFD, fuzzy logic, AHP

DOI: 10.3233/JIFS-189078

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6047-6058, 2020

Authors: Traneva, Velichka | Tranev, Stoyan

Article Type: Research Article

Abstract: The intuitionistic fuzzy InterCriteria analysis (ICrA) is a new method for correlation analysis, which is based on the concepts of index matrices (IMs) and intuitionistic fuzzy sets (IFSs), aiming at detecting of the dependencies between pairs of rating criteria in both clear and uncertain environments. In the present paper, which is an extension of [39 ], our aim is to extend ICrA to multidimensional ICrA (n -D ICrA) under intuitionistic fuzzy environment for situations where the evaluations of the objects against multidimensional criteria are completely unknown and to show its efficiency through an application in identifying correlations between pairs of …criteria when referred to actual data gathered through estimates of a restaurant’s kitchen staff over a three-year period in Bulgaria. We also present a comparative analysis of the correlations between the evaluated criteria of the kitchen staff, on the basis the application of the correlation methods of ICrA, Pearson (PCA), Spearman (SCA) and Kendall (KCA). The four-correlation analysis yielded very similar correlation coefficients, but only the ICrA can be applied to intuitionistic fuzzy evaluations. It is observed that considerable divergence of the ICrA results from those obtained by the other classical correlation analyzes, is only found when the input data contains mistakes. Show more

Keywords: Decision making, index matrix, InterCriteria analysis, intuitionistic fuzzy logic, rating criteria

DOI: 10.3233/JIFS-189079

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6059-6071, 2020

Authors: Yontar, Meltem | Namli, Özge Hüsniye | Yanik, Seda

Article Type: Research Article

Abstract: Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a …well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased. Show more

Keywords: Credit card, machine learning, classification, parameter optimization, ANN, SVM, CART

DOI: 10.3233/JIFS-189080

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6073-6087, 2020

Authors: Castillo, Oscar | Cortés-Antonio, Prometeo | Melin, Patricia | Valdez, Fevrier

Article Type: Research Article

Abstract: This work presents a comparative analysis of Type-1 and Type-2 fuzzy controllers to drive an omnidirectional mobile robot in line-following tasks using line detection images. Image processing uses a Prewitt filter for edge detection and determines the error from the line location. The control systems are tested using four different paths from the Robotino® SIM simulator. Also, two different strategies in the design and implementation of the controllers are presented. In the first one, a PD controller scheme is extended by using a fuzzy system to have adaptive parameters P and D, additionally, Type-2 Fuzzy sets are used to …give robustness to the controller. In the second case, a Fuzzy controller is designed to compute in a direct way the control variables and it is extended to Type-2 Fuzzy controller. Finally, experimental results and comparative analysis are presented for the five control schemes by comparing the running time and the standard deviation to measure the robustness of the control systems. Show more

Keywords: Type-2 fuzzy controller, prewitt filter, line follower

DOI: 10.3233/JIFS-189081

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6089-6097, 2020

Authors: Unver, Mustafa | Erginel, Nihal

Article Type: Research Article

Abstract: Density Based Spatial Clustering of Application with Noise (DBSCAN) is one of the mostly preferred algorithm among density based clustering approaches in unsupervised machine learning, which uses epsilon neighborhood construction strategy in order to discover arbitrary shaped clusters. DBSCAN separates dense regions from low density regions and simultaneously assigns points that lie alone as outliers to unearth the hidden cluster patterns in the datasets. DBSCAN identifies dense regions by means of core point definition, detection of which are strictly dependent on input parameter definitions: ε is distance of the neighborhood or radius of hypersphere and MinPts is minimum density …constraint inside ε radius hypersphere. Contrarily to classical DBSCAN’s crisp core point definition, intuitionistic fuzzy core point definition is proposed in our preliminary work to make DBSCAN algorithm capable of detecting different patterns of density by two different combinations of input parameters, particularly is a necessity for the density varying large datasets in multidimensional feature space. In this study, preliminarily proposed DBSCAN extension is studied: IFDBSCAN. The proposed extension is tested by computational experiments on several machine learning repository real-time datasets. Results show that, IFDBSCAN is superior to classical DBSCAN with respect to external & internal performance indices such as purity index, adjusted rand index, Fowlkes-Mallows score, silhouette coefficient, Calinski-Harabasz index and with respect to clustering structure results without increasing computational time so much, along with the possibility of trying two different density patterns on the same run and trying intermediary density values for the users by manipulating α margin. Show more

Keywords: Unsupervised machine learning, clustering, DBSCAN, IFDBSCAN, clustering validation indices, intuitionistic fuzzy sets

DOI: 10.3233/JIFS-189082

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6099-6108, 2020

Authors: Abdullah, Lazim | Rahim, Siti Nuraini

Article Type: Research Article

Abstract: Recently, researchers have shown an increased interest in integrating the neutrosophic sets with multi-criteria decision making (MCDM) methods. Previous literature have suggested the integration of neutrosophic set with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, in which the three memberships of neutrosophic set are utilized to solve problems that are characterized by non-deterministic information. Differently from the neutrosophic DEMATEL, which directly used three independent memberships, this paper proposes bipolar neutrosophic DEMATEL (Bipolar NS-DEMATEL) of which the positive and negative of truth, indeterminate and false memberships of bipolar neutrosophic set are introduced to enhance decision in urban sustainable development. Three …criteria and fifteen sub-criteria of urban sustainable development are the main MCDM structures that need to be solved using the proposed method. A group of experts were invited to provide rating of performance values of sub-criteria of urban sustainable development problem using a bipolar neutrosophic linguistic scale. The proposed Bipolar NS-DEMATEL is applied to segregate the sub-criteria of urban sustainable development into cause and effect groups. In addition, the network relation map is drawn to observe the interaction among the sub-criteria. The Bipolar NS-DEMATEL suggests that air quality (SC12 ) is the most important sub-criteria in managing urban sustainable development. The result also unveils that the sub-criterion safety (SC21 ) is impacted and influenced by other sub-criteria. The identification of sub-criteria in accordance with their net causer and net receiver would help the authority in prioritizing sub-criteria that may directly be affected by urban development. Show more

Keywords: Neutrosophic sets, bipolar neutrosophic sets, DEMATEL method, decision making, urban sustainable development

DOI: 10.3233/JIFS-189083

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6109-6119, 2020

Authors: Senvar, Ozlem | Akburak, Dilek | Yel, Necla

Article Type: Research Article

Abstract: Firms need to integrate multiple business functions in order to acquire, analyze, model, and evaluate information necessary for better understanding customer behaviors and making data-driven decisions to enhance the customer experience journey. This study proposes a customer oriented intelligent decision support system (IDSS) to ultimately improve the customer experience journey. Besides, a real application study is handled for a multinational company located in Turkey, considering its abrasives product sales for years of 2017 and 2018. For the data utilized in application study, the proposed methodology is constructed for customer segmentation to develop appropriate data-driven marketing strategies for customers with similar …values, preferences and other factors for creating customer-centric organizations. In this regard; firstly two-phased clustering process, which involves the hierarchical multivariate average linkage clustering algorithm and partitional k-means clustering algorithm, is used to present the number of clusters on the basis of three variables (expenditure, transaction and unit cost) and then to assign the customers to the related clusters (VIP, Platinum, Gold and Bronze), respectively. Secondly, the performances of company’s departments are ranked according to the preferences of customers from each segment considering 4Ps marketing mix concept via integrated methodology of interval type-2 Fuzzy AHP and hesitant fuzzy TOPSIS. Show more

Keywords: Intelligent decision support system (IDSS), customer experience journey, clustering, fuzzy multi criteria decision making (MCDM), interval type-2 fuzzy AHP, hesitant fuzzy TOPSIS

DOI: 10.3233/JIFS-189084

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6121-6143, 2020

Authors: Vatankhah, Ramin | Ghanatian, Mohammad

Article Type: Research Article

Abstract: There would always be some unknown geometric, inertial or any other kinds of parameters in governing differential equations of dynamic systems. These parameters are needed to be numerically specified in order to make these dynamic equations usable for dynamic and control analysis. In this study, two powerful techniques in the field of Artificial Intelligence (AI), namely Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to explain how unknown parameters in differential equations of dynamic systems can be identified. The data required for training and testing the ANN and the ANFIS are obtained by solving the direct …problem i.e. solving the dynamic equations with different known parameters and input stimulations. The governing ordinary differential equations of the system is numerically solved and the output values in different time steps are obtained. The output values of the system and their derivatives, the time and the inputs are given to the ANN and the ANFIS as their inputs and the unknown parameters in the dynamic equations are estimated as the outputs. Finally, the performances of the ANN and the ANFIS for identifying parameters of the system are compared based on the test data Percent Root Mean Square Error (% RMSE) values. Show more

Keywords: System identification, parameter identification, artificial neural network, ANFIS

DOI: 10.3233/JIFS-189085

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6145-6155, 2020

Authors: Öztürk, Melike | Alabaş-Uslu, Çiğdem

Article Type: Research Article

Abstract: Metaheuristics gained world-wide popularity and researchers have been studying them vigorously in the last two decades. A relatively less explored approach in the improvement of metaheuristics is to design new neighbor generation mechanisms. Neighbor generation mechanisms are very important in the success of any single solution-based heuristic since they directly guide the search. In this study, a neighbor generation mechanism called cantor-set based (CB) method for single solution-based heuristics which use permutation solution representation is described. The inspiration for CB method stems from the recursive algorithm that constructs a cantor set which is a fractal set. Three variations of CB …method are discussed (CB-1, CB-2, CB-3) considering the presented design possibilities. The computational experiments are conducted by embedding the mechanisms into the classical local search and simulated annealing algorithms, separately, to test their efficiency and effectiveness by comparing them to classical swap and insertion mechanisms. The traveling salesman problem (TSP) and quadratic assignment problem (QAP) which are very different problems that have incompatible characteristics have been chosen to test the mechanisms and sets of benchmark instances with varying sizes are chosen for the comparisons. The computational tests show that CB-2 gives very favorable results for TSP and CB-1 gives favorable results for QAP which means that CB-2 is suitable for problems that have steep landscapes and CB-1 is suitable for the problems that have flat landscapes. It is observed that CB-3 is a more generalized mechanism because it gives consistently good results for both TSP and QAP instances. The best mechanism for a given instance of the both problem types outperforms the classical neighbor generation of swap and insertion in terms of effectiveness. Show more

Keywords: Neighbor generation, local search, simulated annealing, cantor set, traveling salesman problem, quadratic assignment problem

DOI: 10.3233/JIFS-189086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6157-6168, 2020

Authors: Valdez, Fevrier | Castillo, Oscar | Cortes-Antonio, Prometeo | Melin, Patricia

Article Type: Research Article

Abstract: In this paper, we are presenting a survey of research works dealing with Type-2 fuzzy logic controllers designed using optimization algorithms inspired on natural phenomena. Also, in this review, we analyze the most popular optimization methods used to find the important parameters on Type-1 and Type-2 fuzzy logic controllers to improve on previously obtained results. To this end have included a summary of the results obtained from the web of science database to observe the recent trend of using optimization methods in the area of optimal type-2 fuzzy logic control design. Also, we have made a comparison among countries of …the network of researchers using optimization methods to analyze the distribution and impact of the papers. Show more

Keywords: Nature inspired optimization, Type-2 fuzzy logic, fuzzy control

DOI: 10.3233/JIFS-189087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6169-6179, 2020

Authors: Tüysüz, Nurdan | Kahraman, Cengiz

Article Type: Research Article

Abstract: This study presents a multi-experts multiple criteria decision making approach for quantitatively evaluating social sustainable development factors. The proposed model which integrates Z-fuzzy numbers and fuzzy AHP enables to weight and rank social sustainable development factors, which may give guidance to many sustainable development researches. In addition to the first usage of the Z-fuzzy numbers for the weighting decision of social sustainable development factors, another contribution of the study is presenting the Z-fuzzy numbers integrated AHP method with multi-experts which can be useful in many problems and applications containing uncertainty. The most important advantage of the Z-fuzzy numbers integrated AHP …method is that it allows the degree of confidence of decision makers to be included to the calculations. An application of the proposed approach is also presented for prioritizing the social sustainable development factors based on the experts’ evaluations together with a sensitivity analysis. Show more

Keywords: AHP, group decision making, z-number, sustainability, multicriteria

DOI: 10.3233/JIFS-189088

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6181-6192, 2020

Authors: Otay, Irem | Jaller, Miguel

Article Type: Research Article

Abstract: This paper proposes an Integrated Fuzzy Analytic Hierarchy Process (AHP) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method using Pythagorean fuzzy sets for the wind farm location selection problem. The method combines the advantages of the two methodologies to consider uncertainties and lack of information in the decision-making process (human expert capacities) and enables better representation of membership and non-membership functions, and at the same time, allows for the evaluation of large numbers of alternatives and criteria. The authors implement the method to evaluate four potential sites near cities located in the west and north-west …regions of Turkey, using seven main criteria and twenty-five sub-criteria. The analyses are based on the judgements of three experts/decision-makers. Moreover, the authors compare the results (site ranking) of the methodology, with those from a Pythagorean Fuzzy AHP and an interval-valued Type-2 fuzzy AHP method. With the additional consideration of the sub-criteria, the proposed method generates slight differences in the ranking compared to the previously evaluated methods. Show more

Keywords: Wind energy, location selection, Pythagorean fuzzy AHP, Pythagorean fuzzy TOPSIS, interval-valued Pythagorean fuzzy sets

DOI: 10.3233/JIFS-189089

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6193-6204, 2020

Authors: Algin, Ramazan | Alkaya, Ali Fuat | Agaoglu, Mustafa

Article Type: Research Article

Abstract: Feature selection (FS) has become an essential task in overcoming high dimensional and complex machine learning problems. FS is a process used for reducing the size of the dataset by separating or extracting unnecessary and unrelated properties from it. This process improves the performance of classification algorithms and reduces the evaluation time by enabling the use of small sized datasets with useful features during the classification process. FS aims to gain a minimal feature subset in a problem domain while retaining the accuracy of the original data. In this study, four computational intelligence techniques, namely, migrating birds optimization (MBO), simulated …annealing (SA), differential evolution (DE) and particle swarm optimization (PSO) are implemented for the FS problem as search algorithms and compared on the 17 well-known datasets taken from UCI machine learning repository where the dimension of the tackled datasets vary from 4 to 500. This is the first time that MBO is applied for solving the FS problem. In order to judge the quality of the subsets generated by the search algorithms, two different subset evaluation methods are implemented in this study. These methods are probabilistic consistency-based FS (PCFS) and correlation-based FS (CFS). Performance comparison of the algorithms is done by using three well-known classifiers; k -nearest neighbor, naive bayes and decision tree (C4.5). As a benchmark, the accuracy values found by classifiers using the datasets with all features are used. Results of the experiments show that our MBO-based filter approach outperforms the other three approaches in terms of accuracy values. In the experiments, it is also observed that as a subset evaluator CFS outperforms PCFS and as a classifier C4.5 gets better results when compared to k -nearest neighbor and naive bayes. Show more

Keywords: Feature selection, computational intelligence, dimensionality reduction, meta-heuristics, classification algorithms, subset evaluators

DOI: 10.3233/JIFS-189090

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6205-6216, 2020

Authors: Gutiérrez, Inmaculada | Gómez, Daniel | Castro, Javier | Espínola, Rosa

Article Type: Research Article

Abstract: In this work we introduce the notion of the weighted graph associated with a fuzzy measure. Having a finite set of elements between which there exists an affinity fuzzy relation, we propose the definition of a group based on that affinity fuzzy relation between the individuals. Then, we propose an algorithm based on the Louvain’s method to deal with community detection problems with additional information independent of the graph. We also provide a particular method to solve community detection problems over extended fuzzy graphs. Finally, we test the performance of our proposal by means of some detailed computational tests calculated …in several benchmark models. Show more

Keywords: Fuzzy clustering, networks, community detection, fuzzy graph

DOI: 10.3233/JIFS-189091

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6217-6230, 2020

Authors: Ucal Sari, Irem | Sergi, Duygu | Aytore, Can

Article Type: Research Article

Abstract: Fundraising is one of the most critical issues for non-governmental organizations (NGOs) to carry out their projects. In this paper, a search engine project which aims to find additional financial sources and increase donations for NGOs is proposed. The proposed search engine project is analyzed using fuzzy cognitive maps (FCMs) to define and manage factor influences on the success of the project. FCMs are useful tools to define long term effects of important factors for a system. First casual relations of the factors are determined and then using sigmoid function for learning algorithm, the equilibrium state for the system is …obtained. It is found that the factors generating monetary values are the most important ones for the project to be successful in long term. Show more

Keywords: Fuzzy cognitive maps, fundraising, non-governmental organizations

DOI: 10.3233/JIFS-189092

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6231-6243, 2020

Authors: Labella, Álvaro | Rodríguez, Rosa M. | Martínez, Luis

Article Type: Research Article

Abstract: Uncertainty is so common in real decision situations that it has given rise to a new decision making approach so-called linguistic decision making, in which such uncertainty is modeled by using linguistic information. Many contributions have been proposed in order to solve LDM problems by following a Computing with Words (CW) approach to obtain linguistic outputs from linguistic premises by emulating the human beings’ reasoning process. Nowadays, there are several LDM models that, together with the complexity of LDM problems, make almost impossible to find a proper solution for these problems without a support tool. FLINTSTONES is a fuzzy decision …support system that facilitates the decision process in LDM problems. This software aimed to solve LDM problems by means of the 2-tuple linguistic model, whose main advantages are high interpretability and precision of the results. However, it has other drawbacks such as the modeling of linguistic information by using solely single linguistic terms or the impossibility to model the experts’ hesitancy. Recently, the Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) linguistic representation model was introduced in order to overcome existing drawbacks in terms of interpretability and accuracy in CW processes. This model allows to model experts’ hesitancy and, at the same time, carry out precise linguistic computations and provide interpretable results, overcoming the limitations of previous LDM models. Therefore, this contribution presents an updated version of FLINTSTONES able to manage ELICIT information in LDM problems and which integrates the ELICIT CW approach. Show more

Keywords: Linguistic decision making, ELICIT information, FLINTSTONES

DOI: 10.3233/JIFS-189093

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6245-6258, 2020

Authors: Aktas, Ahmet | Aydin, Serhat | Kabak, Mehmet

Article Type: Research Article

Abstract: To cut several types of hard materials in manufacturing, Computer Numeric Control (CNC) router machines are commonly used. The tasks to be done by different machines can be performed by a single CNC router machine. Production of parts with better quality is possible at lower costs by production with CNC router machines, and these machines improve the productivity of manufacturing system. For these reasons, determination of the appropriate CNC router machine for manufacturing systems is a crucial decision. Different factors related to properties of machines are effective on the decision. Therefore, decision makers must include different effective aspects into decision …process. Under this consideration, an analytic selection procedure for CNC router machines by taking uncertain expressions of experts on the selection criteria and variable values occur over time is proposed in this study. In order to handle the modelling difficulty of uncertainty of the statements and the value changes by time, dynamic intuitionistic multi attribute decision making is used to select the best CNC router. Applicability of the proposed selection procedure is demonstrated on an application, and a comparative analysis with dynamic neutrosophic multi attribute decision making is presented. Show more

Keywords: Dynamic decision making, intuitionistic fuzzy sets, neutrosophic sets, multi attribute decision making, CNC router selection

DOI: 10.3233/JIFS-189094

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6259-6269, 2020

Authors: Atalik, Gultekin | Senturk, Sevil

Article Type: Research Article

Abstract: Since proposed by Zadeh in 1965, ordinary fuzzy sets help us to model uncertainty and developed many types such as type 2 fuzzy, intuitionistic fuzzy, hesitant fuzzy etc. Intuitionistic fuzzy sets include both membership and non-membership functions for their each element. Ranking of a number is to identify a relationship of scalar quantity between these numbers. Ranking of fuzzy numbers play an important role in modeling problems such as fuzzy decision making, fuzzy linear programming problems. In this study, a new ranking method for triangular intuitionistic fuzzy numbers is proposed. The method based on the incircle of the membership function …and non-membership function of TIFN uses lexicographical order to rank intuitionistic fuzzy numbers. Two examples are provided to illustrate the applicability of the method. Also, a comparative study is performed to demonstrate the validity of the proposed method. The results indicate that proposed method is consistent with other methods in the literature. Also, the method overcomes the problems such as numbers being very small or close to each other. Show more

Keywords: Intuitionistic fuzzy sets, triangular intuitionistic fuzzy numbers, ranking of fuzzy numbers, incircle, inradius

DOI: 10.3233/JIFS-189095

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6271-6278, 2020

Authors: Maden, Ayça | Alptekin, Emre

Article Type: Research Article

Abstract: Blockchain practices have been attracting attention in industries other than financial services, since blockchain is not only an information technology, but also an institutional technology owing to its new currency economics and distributed structures. Today, supply chains, power, and food/agriculture have emerged as promising areas in terms of their potential to incorporate blockchain technology for improving processes and reducing costs. Logistics corporations, especially, have been concentrating on developing efficiency in integrated data, fleet management, and communication issues, to achieve cost advantages. Experts from a well-known logistics company in Turkey contributed to our study by helping to assess critical factors for …successful blockchain technology implementation. Our research topic included determining whether blockchain technology is suitable for this company. Fuzzy decision-making trial and evaluation laboratory (DEMATEL) was used to determine and evaluate the critical factors to encourage blockchain technology adoption, based on the company’s requirements. For the company experts, the factors affecting the decision to adopt blockchain technology were, in order of priority: cryptocurrency, instant money transfer, privacy, real time processing, smart contract, security, authentication, transparency, immutability, traceability, distributed ledger, reduced delays, and peer-to-peer networks. Show more

Keywords: Blockchain, Fuzzy DEMATEL, supply chain, logistics

DOI: 10.3233/JIFS-189096

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6279-6291, 2020

Authors: Oztaysi, Basar | Onar, Sezi Cevik | Kahraman, Cengiz

Article Type: Research Article

Abstract: Location-Based Systems enable novel business models that can locate a person and send an action or message to him/her. One of the most commonly adopted location-based business models is location-based advertisements. Companies can send customized messages to target customers by using location-based ads. The model is promising since the conversion rate of the customers is high. On the other side, since the customers can be targeted based on their locations and interests, the price of the advertisement should be modeled a dynamic pricing model. In this study, we propose a dynamic pricing model for location-based ads by using the Spherical …Fuzzy AHP method. Show more

Keywords: Location-based advertisements, AHP scoring, spherical fuzzy sets

DOI: 10.3233/JIFS-189097

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6293-6302, 2020

Authors: Yigit, Ahmet Talha | Samak, Baris | Kaya, Tolga

Article Type: Research Article

Abstract: Sports analytics is a field that is growing in popularity and application throughout the world. One of the open problems in this field is the valuation of football players. The aim of this study is to establish a football player value assessment model using machine learning techniques to support the transfer decisions of football clubs. The proposed model is mainly based on the intrinsic features of the individual players which are provided in Football Manager simulation game. To do this, based on the individual statistics of 5316 players who are active in 11 different major leagues from Europe and South …America, different value assessment models are conducted using advanced supervised learning techniques which include ridge and lasso regressions, random forests and extreme gradient boosting. All the models have been built in R programming language. The performances of the models are compared based on their mean squared errors and their fit to the real world examples. An ensemble model with inflation is proposed as the output. Show more

Keywords: Football analytics, machine learning, ensemble learning, extreme gradient boosting, lasso regression

DOI: 10.3233/JIFS-189098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6303-6314, 2020

Authors: Ates, Çagatay | Özdel, Süleyman | Anarim, Emin

Article Type: Research Article

Abstract: While internet technologies have been evolving day by day, threats against them have been increasing with the same pace. One of the most serious and commonly executed attack type is Distributed Denial of Service (DDoS) attacks. Despite there are many security mechanisms against this type of attack, there is still need for new solutions due to the occurred DDoS attacks worldwide. In this work, a DDoS attack detection approach based on fuzzy logic and entropy is proposed. Network is modelled as a graph and graph-based features are used for discriminating attack traffic from attack-free traffic. Fuzzy-c-means clustering is applied based …on these features in order to show the tendencies of IP addresses or port numbers to be in a same cluster or not. Based on this uncertainty, attack and attack-free traffic are modelled. In detection phase, fuzzy membership function is used. This algorithm is tested on the real data collected from Bogaziçi University network. Show more

Keywords: Graph theory, fuzzy logic, DDoS attacks, entropy, intrusion detection

DOI: 10.3233/JIFS-189099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6315-6324, 2020

Authors: Tolga, A. Cagri | Basar, Murat

Article Type: Research Article

Abstract: Increasing population in the world drives people to find a different type of feeding regime. Even if there is an immense augmentation in crowd brilliant innovators are looking for new ways of farming more efficiently. Hydroponics is one of the novel paths that is a planting system without soil. The system reduces water usage by 95% and with the same rate provides efficiency in the crop, furthermore, sustainability is highly supplied. Traditional smart farming applied in the rural area strains immense transportation and brokership costs. In these days innovators make smart agriculture in vessel containers. Especially vertical and smart farming …made in the suburban area of the cities offers new opportunities on vegetables’ abundance. In this paper, the efficiency of this offered system is examined with minimizing the investment cost data. The system itself and the investment area have abounded with myriad uncertainties. Fuzzy logic tackles with those vaguenesses and fuzzy Evaluation Based on Distance from Average Solution (EDAS) method supplies assistance in the decision-making process of system evaluation. In addition, TODIM (a risk sensitive iterative multi-criteria decision making method based on Prospect Theory) is employed to check the evaluation of those three alternatives and to monitor how risk perception affects decision processes. A micro-based application is performed and attractive results are achieved. Show more

Keywords: Vertical urban agriculture, fuzzy EDAS method, fuzzy TODIM, investment cost, smart farming

DOI: 10.3233/JIFS-189100

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6325-6337, 2020

Authors: Çakır, Esra | Ulukan, Ziya

Article Type: Research Article

Abstract: Due to the increase in energy demand, many countries suffer from energy poverty because of insufficient and expensive energy supply. Plans to use alternative power like nuclear power for electricity generation are being revived among developing countries. Decisions for installation of power plants need to be based on careful assessment of future energy supply and demand, economic and financial implications and requirements for technology transfer. Since the problem involves many vague parameters, a fuzzy model should be an appropriate approach for dealing with this problem. This study develops a Fuzzy Multi-Objective Linear Programming (FMOLP) model for solving the nuclear power …plant installation problem in fuzzy environment. FMOLP approach is recommended for cases where the objective functions are imprecise and can only be stated within a certain threshold level. The proposed model attempts to minimize total duration time, total cost and maximize the total crash time of the installation project. By using FMOLP, the weighted additive technique can also be applied in order to transform the model into Fuzzy Multiple Weighted-Objective Linear Programming (FMWOLP) to control the objective values such that all decision makers target on each criterion can be met. The optimum solution with the achievement level for both of the models (FMOLP and FMWOLP) are compared with each other. FMWOLP results in better performance as the overall degree of satisfaction depends on the weight given to the objective functions. A numerical example demonstrates the feasibility of applying the proposed models to nuclear power plant installation problem. Show more

Keywords: Project management, nearest interval approximation method, goal programming, fuzzy multi-objective linear programming, nuclear power plant

DOI: 10.3233/JIFS-189101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6339-6350, 2020

Authors: Alcalde, Cristina | Burusco, Ana

Article Type: Research Article

Abstract: Information extracted from L-fuzzy contexts is substantially improved by taking into account different points of view, which can roughly be represented by criteria. This work addresses the general study of L-fuzzy contexts were a set of criteria is introduced, analyzing situations in which their evolution over time is known. The relationship among criteria is also an important point in the study. In this sense, the treatment will vary depending on whether they are independent criteria or there exists dependency among them. Of special importance will be those elements that stand out for presenting a positive temporal evolution. Four algorithms are …proposed in order to analyze the different situations. Finally, the applicability of the results is shown thought an example where the opinion of the clients of several hotels is analyzed taking into account both the type of traveler considered and the different aspects of the establishments on which a score is given. Show more

Keywords: L-fuzzy concept analysis, L-fuzzy context sequences, L-fuzzy contexts associated with criteria, WOWA operators, Choquet integrals

DOI: 10.3233/JIFS-189102

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6351-6362, 2020

Authors: Büyüközkan, Gülçin | Mukul, Esin

Article Type: Research Article

Abstract: Smart health applications are raising a growing interest around the world thanks to its potential to act proactively and solve health related problems with smart technologies. Smart health technologies can provide effective healthcare services such as personalization of treatments through big data, robotics in cure and care, artificial intelligence support to doctors, etc. The mixed structure of the evaluation of smart health technologies involves various contradictory criteria. However, when information is of uncertain nature, it is difficult to decide on how to treat. A hesitant fuzzy linguistic term set (HFLTS) approach is applied to overcome such uncertainties related to this …multi-criteria decision-making (MCDM) problem. This approach can be used to facilitate experts’ decision-making processes in complex and uncertain situations. In this study, an integrated hesitant fuzzy linguistic (HFL) MCDM approach is proposed to evaluate smart health technologies. The criteria are weighted with HFL Analytic Hierarchy Process (AHP), and then, smart health technologies are evaluated with the HFL Combinative Distance-based Assessment (CODAS) method. A comparative analysis with HFL COPRAS and HFL TOPSIS is applied. Lastly, the potential of this approach is presented through a case study. Show more

Keywords: Hesitant fuzzy linguistic term set, multi-criteria decision making, smart health, smart health technologies

DOI: 10.3233/JIFS-189103

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6363-6375, 2020

Authors: Barbara, Gładysz | Dorota, Kuchta

Article Type: Research Article

Abstract: The paper is based on a survey analyzing the success of IT projects in Poland as function of the cooperation with different stakeholders. The project’s participants expressed their subjective opinions on the effectiveness of the collective cooperation with various stakeholder groups. The impact of cooperation with different stakeholder groups: project team, management of the project implementation unit, suppliers and end users of the final product on the success of the project is examined. To this end, intuitionistic fuzzy sets, a correlation coefficient of intuitionistic fuzzy sets and an original method of intuitionistic fuzzy regression are applied. The conclusions point to …the most important stakeholder groups for the complete success and for the avoidance of a complete failure of IT projects. Some possibilities of the extension of the proposed method are indicated, so that the decision maker can adopt it to his or her preferences in searching for project success or failure factors. Show more

Keywords: IT Project Management, IT project success, project stakeholder, intuitionistic fuzzy set, intuitionistic correlation, intuitionistic regression

DOI: 10.3233/JIFS-189104

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6377-6389, 2020

Authors: Kalender, Zeynep Tugce | Kilic, Huseyin Selcuk | Tuzkaya, Gulfem | Dascioglu, Busra Gulnihan

Article Type: Research Article

Abstract: The prevalence of environmental studies in the academy has increased in recent years, depending on the adverse effects of global warming on natural resources. Besides various environmentally benign applications, one of the most important instruments on eliminating the negative environmental effects of an increasing population is electric vehicles. There are various topics within the concept of electric vehicles, including the determination of electric vehicle type, routing, network design, and so on. However, in this study, determining the locations of electric charging stations is the main focus. The problem is handled as a multi-criteria decision-making problem with the consideration of the …uncertainties in the decision-making environment. Specifically, the judgments of decision-makers play a critical role in the success of decisions, but for a decision-maker, it is usually difficult to express his/her preferences by using only one linguistic term due to the structure of some criteria type. Hence, with the proposed methodology, in this study, criteria are firstly classified as fuzzy and crisp according to their objective or subjective characteristics. Afterwards, besides the utilization of classic techniques for crisp type criteria, probabilistic linguistic terms sets are utilized for fuzzy type criteria with an extended version of TOPSIS. The proposed methodology is used for the comparison of 39 alternative electric charging locations in Istanbul, which is one of the most crowded cities in Europe. Show more

Keywords: Electric charging stations, plug-in electric vehicles, parking-lot-based charging location, TOPSIS, multi-criteria decision-making, probabilistic linguistic term sets

DOI: 10.3233/JIFS-189105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6391-6406, 2020

Authors: Ilbahar, Esra | Cebi, Selcuk | Kahraman, Cengiz

Article Type: Research Article

Abstract: Effective utilization of renewable energy sources is an essential component of countries’ sustainable development strategies. A thorough evaluation of renewable energy alternatives is required to assure maximum exploitation of resources. The evaluation of renewable energy sources is a complicated problem since many criteria, even some of them are conflicting, must be taken into account simultaneously. Pythagorean fuzzy sets are better able to reflect uncertainty and vagueness in an assessment process by providing a greater domain for decision makers to describe their opinions. Therefore, this study aims at prioritizing renewable energy alternatives by employing interval-valued Pythagorean fuzzy WASPAS method. The obtained …results are compared to the results of intuitionistic type-2 fuzzy WASPAS, interval-valued intuitionistic fuzzy WASPAS and crisp WASPAS methods. Biomass is selected to be the best renewable energy alternative for Central Anatolia Region of Turkey. Show more

Keywords: Renewable energy evaluation, Pythagorean fuzzy sets, WASPAS

DOI: 10.3233/JIFS-189106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6407-6417, 2020

Authors: Marcek, Dusan

Article Type: Research Article

Abstract: To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the …optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers. Show more

Keywords: ARIMA models, neural networks, learning algorithms, time series forecasting

DOI: 10.3233/JIFS-189107

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6419-6430, 2020

Authors: Haktanır, Elif

Article Type: Research Article

Abstract: Malcolm Baldrige National Quality Award (MBNQA) is a quality assessment and rewarding system that aims to increase the awareness of quality management. Although the award is launched in the USA in 1989 and only given to the U.S based companies, it is recognized internationally. There are 7 types of categories in the award system (Leadership, Strategic planning, Customer focus, Measurement, analysis, and knowledge management, Workforce focus, Process management, and Results) where the evaluation is made over 1000 points and each category has its own weight. Since almost all the publications in the literature are based on crisp measurements and evaluations …of the system performances, we proposed a multi attribute decision making (MADM) method using interval valued Pythagorean fuzzy weighted averaging (IVPFWA) and interval valued Pythagorean fuzzy weighted geometric (IVPFWG) aggregation operators for MBNQA assessment to represent the decision makers’ subjective evaluations better. A comparison of the results with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and an illustrative example are presented in the study. Show more

Keywords: Malcolm Baldrige National Quality Award, interval-valued Pythagorean fuzzy sets, multi attribute decision making, interval-valued Pythagorean fuzzy aggregation operators

DOI: 10.3233/JIFS-189108

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6431-6441, 2020

Authors: Piltan, Farzin | Prosvirin, Alexander E. | Kim, Jong-Myon

Article Type: Research Article

Abstract: Robotic manipulators represent a class of nonlinear and multiple-degrees-of-freedom robots that have pronounced coupling effects and can be used in various applications. The challenge of understanding complexity in a system’s dynamic behavior, coupling effects, and sources of uncertainty presents substantial challenges regarding fault estimation, detection, identification, and tolerant-control (FEDIT) in a robot manipulator. Thus, a proposed active fault-tolerant control algorithm, based on an adaptive modern sliding mode observer, is represented. Due to the effect of the system’s complexities and uncertainties for fault estimation, detection, and identification (FEDI), a sliding mode observer (SMO) is proposed. To address the sliding mode observer …drawbacks for FEDI such as high-frequency oscillation (chattering) and fault estimation accuracy, the modern (T-S fuzzy higher order) technique is represented. In addition, the adaptive technique is applied to the modern sliding mode observer (MSMO) to self-tune the coefficients of the fault estimation observer to increase the reliability and robustness of decision-making for diagnosis of the fault. Next, the residual delivered by the adaptive MSMO (AMSMO) is split into windows, and each window is characterized by a numerical parameter. Finally, the machine learning technique known as a decision tree adaptively derives the threshold values that are used for problems of fault detection and fault identification in this work. Due to control of the effective fault, a surface automated new sliding mode controller (SANSMC) is presented in this work. To address the challenge of chattering and unlimited uncertainties (faults), the AMSMO is applied to the sliding mode controller (SMC). In addition, the surface-automated technique is used to fine-tune the surface coefficient to reduce the chattering and faults in the robot manipulator. The results show that the machine learning-based automated robust hybrid observer significantly improves the robustness, reliability, and accuracy of FEDIT in unknown conditions. Show more

Keywords: Robot manipulator, sliding mode algorithm, observation technique, fuzzy logic technique, high-order sliding mode observer, adaptive technique, fault estimation, fault detection, fault identification, fault-tolerant control.

DOI: 10.3233/JIFS-189109

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6443-6463, 2020

Authors: Ercan-Teksen, Hatice | Anagün, Ahmet Sermet

Article Type: Research Article

Abstract: Control chart is one of the statistical methods to analyze the process. The use of fuzzy sets in control charts, which are divided into qualitative and quantitative data, has been applied in many studies recently. Especially for qualitative control charts, data collection is more difficult and more subjective. Therefore, fuzzy sets are used to reduce losses in data. There are many control chart studies created by type-1 fuzzy sets available in the literature. In recent years, examples of fuzzy control charts with extensions of fuzzy sets have been found. The aim of this study is to obtain c-control chart for …intuitionistic fuzzy sets. For this purpose, defuzzification and likelihood methods are used. In particular, with the application of the likelihood method to intuitionistic fuzzy control charts, this will be considered as a pioneering study in the literature. In addition, a novel likelihood method was developed for intuitionistic fuzzy sets and used here to provide flexibility. Show more

Keywords: Intuitionistic fuzzy sets, fuzzy control charts, intuitionistic fuzzy comparison methods

DOI: 10.3233/JIFS-189110

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6465-6473, 2020

Authors: Karadayi-Usta, Saliha | Bozdag, Cafer Erhan

Article Type: Research Article

Abstract: Medical tourism service offers a professional healthcare opportunity by travelling abroad with the chance of touristic and cultural activities at the destination country. Medical travelers prefer a foreign country for treatment due to long waiting periods, high costs, excessive number of patients, inadequate number of healthcare professionals and inadequate cutting-edge technological equipment at their country of residence. An assistance company (AC) is a legal requirement to support medical tourists in Turkey during the treatment period, and offers alternative healthcare service providers (HSPs) that are public hospitals, private hospitals and private clinics at the first phase of the medical tourism service. …Moreover, there are specific HSPs certificated by the government, and a few number of public hospitals authenticated for medical tourism. By taking the whole above statements into consideration, HSP selection is a key decision-making point differentiating from a traditional hospital selection of a patient. Medical tourists must evaluate various criteria in order to select a proper HSP. Additionally, these decision criteria are often vague, complex, indeterminate and inconsistent information in the HSP type decision. Hence, in this study, a decision making model based on neutrosophic fuzzy sets considering HSP selection in every aspect (truthiness, indeterminacy and falsity) is suggested. Show more

Keywords: Neutrosophic fuzzy sets, decision making, medical tourists, healthcare service provider type selection

DOI: 10.3233/JIFS-189111

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6475-6485, 2020

Authors: Kalaycı, Tolga Ahmet | Asan, Umut

Article Type: Research Article

Abstract: A frequently encountered case in developing a classification model is the presence of embedded clusters, formed by data used for training. A good example for this case may be the differences in purchasing styles of e-commerce customers in a purchase propensity modelling problem. While some customers prefer a detailed research about prices, functionalities and comments, some others may need a shorter examination to make a purchase decision. Although feeding such cluster information into the classification model has been shown by recent studies to improve the prediction performance, this valuable information has been largely ignored in classical modeling techniques in general …and neural networks in particular. This paper proposes a feedforward neural network regularization method which incorporates cluster information into networks’hidden nodes. Within the forward propagation and backpropagation calculations of the network, a non-randomized matrix is used to assign hidden nodes to different observation clusters. This matrix manipulates the activation value of a hidden node for each observation in line with the observation’s membership degree to the cluster that the node is assigned to. Also, through the alternating use of randomized binary and non-randomized matrices within iterations, the proposed method successfully fulfills the regularization task. Experiments were performed for different settings and network architectures. Empirical results demonstrate that the proposed method works well in practice and performs statistically significantly better than existing alternatives. Show more

Keywords: Neural networks, fuzzy clustering, classification, regularization, machine learning

DOI: 10.3233/JIFS-189112

Citation: Journal of Intelligent &Fuzzy Systems, vol. 39, no. 5, pp. 6487-6496, 2020

Authors: Goker, Nazli | Dursun, Mehtap | Albayrak, Esra

Article Type: Research Article

Abstract: Supply chain agility is an indispensable way for the companies to quickly response to the demands of the customers. For this reason, agility of supply chain is indispensable in dynamic markets that have high scale of diversity and subjective needs. Supply chain agility needs a systemic procedure that gives priority to feedbacks of customers and follows the changes of competitors in the sector. An efficient supplier evaluation procedure is indispensable for reaching supply chain agility. Agile supplier selection needs to take into account various criteria that incorporate vagueness and uncertainty, obtaining general a multiple level hierarchical system that allows conducting …a more efficient decision analysis. Thus, in this paper an integrated fuzzy multi-criteria group decision making procedure based on quantifier-guided ordered weighted average (OWA) method and fuzzy integral, which allows incorporating uncertain data expressed as linguistic terms into the analysis, is proposed for identifying the most suitable agile supplier alternative. In group decision making issues, aggregating experts’opinions is vital to achieve more robust results. As quantifier-guided OWA method is appropriate for decision making problems under uncertain environments, it is employed for the aggregation of experts’evaluations. The developed decision procedure is illustrated via a case study performed in a dye producer in Turkish dye sector. Show more

Keywords: Imprecise data, agile supplier selection, fuzzy measure, quantifier-guided OWA, fuzzy integral

DOI: 10.3233/JIFS-189113

Citation: Journal of Intelligent &Fuzzy Systems, vol. 39, no. 5, pp. 6497-6505, 2020

Authors: Kahraman, Cengiz | Boltürk, Eda | Onar, Sezi Cevik | Oztaysi, Basar

Article Type: Research Article

Abstract: Pythagorean fuzzy sets (PFS) are an extension of intuitionistic fuzzy sets introduced by Atanassov [1 ]. PFSs have the advantage of providing larger domains for assigning membership and non-membership degrees satisfying that their squared sum is at most equal to one. PFS have been often used in modeling the problems under vagueness and impreciseness in order to better define the problems together with the hesitancy of decision makers. Different human emotions and behaviors can be modeled in humanoid robots (HR) by fuzzy sets. In this paper, facial expressions of a humanoid robot are modeled depending on the degrees of the …emotions. Larger degree of emotion causes a stronger indicator of the facial mimic. Show more

Keywords: Fuzzy sets, extensions, intuitionistic fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, q-rung orthopair fuzzy sets, spherical fuzzy sets

DOI: 10.3233/JIFS-189114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6507-6515, 2020

Authors: Caglayan, Nadide | Satoglu, Sule Itir | Kapukaya, E. Nisa

Article Type: Research Article

Abstract: Sales forecasting with high accuracy is crucial in many industries. Especially, in fast-moving consumer goods, retail and apparel industries, the products are not tailor-made and must be produced and made available in chain stores to the customers, in advance. Therefore, for sales and operations planning, forecast information is required. However, traditionally, time series based forecasting techniques are used that merely consider the seasonality, trend, auto-regressive and cyclic factors. This type of forecasting is not suitable especially in cases where many other factors involved and affect the product sales. In apparel retail industry, special factors such as promotions, special days, weather …(temperature), and location of the store may affect the product demands of the chain stores. The unique aspect of this study is that the sales of a product family of the fashion retail chain stores were estimated by means of artificial neural networks, for the first time in the literature. Besides, in this study, new significant factors in forecasting were explored that influence the demand of the chain stores. So, in this study, artificial neural networks are developed and used for sales forecasting of a product family of a real chain store, in Turkey. The stores exist in many cities, and some of the cities have much more stores than the other cities. The city with the highest number of stores was selected and some of the stores in this city chosen among them. The past sales, sales price and promotion data of selected stores are used. In addition, store information, number of customers visiting the store, and weather temperature data are included in the model. Sales are estimated by artificial neural networks. Besides, Regression Analysis was used for forecasting and the results of both techniques were compared. As a result of the study, the most appropriate network structure has been obtained, and a high sales forecasting performance has been reached. Show more

Keywords: Artificial neural networks, data analysis, demand forecasting, retail sectors

DOI: 10.3233/JIFS-189115

Citation: Journal of Intelligent &Fuzzy Systems, vol. 39, no. 5, pp. 6517-6528, 2020

Authors: Dogan, Onur | Oztaysi, Basar

Article Type: Research Article

Abstract: Customer-based practices enable benefits to organizations in a contentious business. Offering individualized proposals increase customer loyalty to be able to afloat. Understanding customers is a vital difficulty to perform personalized recommendations. As a demographic feature, gender information essentially cannot be captured by human tracking technologies. Hence, several procedures are improved to predict undiscovered gender information. In the research, the followed indoor paths in a shopping mall are used to predict customer genders using fuzzy c-medoids, one of the soft clustering techniques. A Levenshtein-based fuzzy classification methodology is proposed the followed paths as string data. Although some studies focused on gender …prediction, no research has centered on path-oriented. The novelty of the investigation is to analyze customer path data for the gender classes. Show more

Keywords: Gender prediction, string classification, soft clustering, path classification, levenshtein, fuzzy c-medoids

DOI: 10.3233/JIFS-189116

Citation: Journal of Intelligent &Fuzzy Systems, vol. 39, no. 5, pp. 6529-6538, 2020

Authors: Ömerali, Mete | Kaya, Tolga

Article Type: Research Article

Abstract: Since decades managers and scientists have been investigating the vertical boundaries of the firms to understand when to buy goods and services or make them internally. Since there are number of pros and cons on making or buying, the decision is very complex. Firms not only focus on the tangible terms like transaction costs and economies of scale but also consider other factors like information asymmetry, know-how protection and data source quality to keep or gain competitive advantage. Yet this isn’t simple enough, with the rapid growth of technology, the fourth industry revolution and digitalization challenge firms even further. Deciding …on digitalization strategy isn’t anywhere different than the existing make buy decision that firms have faced in the past, however this time with an increased complexity. In this article, we are aiming to understand what strategies firms should apply during their journey in industry 4.0 and a verification with an industrial case study. The purpose of this study is to suggest a Type-2 Fuzzy COPRAS methodology to aid the buy or implement decisions of firms in IOT domain. Show more

Keywords: Internet of Things, Type-2 Fuzzy COPRAS, Digitalization, Industry 4.0, Make or Buy

DOI: 10.3233/JIFS-189117

Citation: Journal of Intelligent &Fuzzy Systems, vol. 39, no. 5, pp. 6539-6552, 2020

Authors: Bolturk, Eda | Gülbay, Murat | Kahraman, Cengiz

Article Type: Research Article

Abstract: Sustainable energy selection has been a very popular problem among the researchers and various models including deterministic, probabilistic and fuzzy approaches have been developed for the solution of this problem. Fuzzy approaches to sustainable energy selection problems have been often handled in the literature. Aggregation operators for multi-expert decision making problems are an alternative solution technique for multi criteria decision making problems. Since neutrosophic and intuitionistic fuzzy aggregation operators are comparable extensions of ordinary fuzzy sets, they have been employed to aggregate multi-expert judgments. An illustrative energy selection problem is presented, solved by two approaches, and results are compared. The …same linguistic data have been used for the comparison purpose. Show more

Keywords: Fuzzy aggregation operator, multi-attribute decision making, intuitionistic fuzzy set, neutrosophic fuzzy sets, sustainable energy selection

DOI: 10.3233/JIFS-189118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6553-6563, 2020

Authors: Jahanandish, Roya | Khosravifard, Amir | Vatankhah, Ramin

Article Type: Research Article

Abstract: This paper proposes a new method to improve fuzzy control performance accuracy in the stabilization of the two-axis gimbal system. To this end, due to the fact that the knowledge of the accurate behavior of the system plays a substantial role in fuzzy control performance, all the uncertain parameters of the dynamic model such as friction, mass imbalance and moments of inertia are estimated prior to the controller design and without imposing any computational burden on the control scheme. To estimate the uncertainties and disturbances which exist in the dynamic equations, an identification process formulated as an inverse problem is …utilized, and the Gauss– Newton method is adopted for the optimization process. Regarding the severe sensitivity of inverse problems to measurement errors, this undesirable effect is reduced by using a proper smoothing technique. In order to increase the accuracy of the final results, a novel procedure for calculation of the sensitivity coefficients of the inverse problem is proposed. This procedure is based on the direct differentiation of the governing equations with respect to the unknown parameters. At the end, simulation results are presented to confirm the effectiveness of the proposed scheme. Show more

Keywords: Fuzzy control, parameter estimation, two-axis gimbal

DOI: 10.3233/JIFS-189119

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6565-6577, 2020

Authors: Çağlıyor, Sandy | Öztayşi, Başar | Sezgin, Selime

Article Type: Research Article

Abstract: The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to …estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature. Show more

Keywords: Machine learning algorithms, motion picture industry, forecasting

DOI: 10.3233/JIFS-189120

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6579-6590, 2020

Authors: Haktanır, Elif | Kahraman, Cengiz

Article Type: Research Article

Abstract: Failure mode and effects analysis (FMEA) is a structured approach for discovering possible failures that may occur in the design of a product or process. Since classical FMEA is not sufficient to represent the vagueness and impreciseness in human decisions and evaluations, many extensions of ordinary fuzzy sets such as hesitant fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, spherical fuzzy sets, and picture fuzzy sets. Classical FMEA has been handled to capture the uncertainty through these extensions. Neutrosophic sets is a different extension from the others handling the uncertainty parameters independently. A novel interval-valued neutrosophic FMEA method is developed …in this study. The proposed method is presented in several steps with its application to an automotive company in order to prioritize the potential causes of failures during the design process by considering multi-experts’ evaluations. Show more

Keywords: Failure mode and effect analysis, interval valued neutrosophic sets, risk priority number

DOI: 10.3233/JIFS-189121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6591-6601, 2020

Authors: Yildiz, Didem | Temur, Gul T. | Beskese, Ahmet | Bozbura, F. Tunc

Article Type: Research Article

Abstract: In contemporary business life, retention of talented employees is crucial for organizations to preserve created value. Considering their attitudes, behaviors and personality, millenials are different from former generations, and retaining them requires a distinct management approach. This study aims to provide the decision makers with a more effective and efficient tool for evaluating career management activity types leading to employee retention of millenials. A novel method, Spherical Fuzzy Analytic Hierarchy Process (SFAHP) is used in the study to; (i) define the importance levels of the criteria having impact on employee retention, and (ii) assess various career management activity types for …employee retention. To ensure the practical use of the model, a numerical example from real world is presented. The results indicate that “leadership and management” is the most important factor, and “development-oriented career management activities” is the highest impact activity type in increasing the employee retention. Show more

Keywords: Employee retention, millenials, multi criteria decision making, spherical fuzzy sets, spherical fuzzy analytic hierarchy process, talent management

DOI: 10.3233/JIFS-189122

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6603-6618, 2020

Authors: Tekin, Ahmet Tezcan | Çebi, Ferhan

Article Type: Research Article

Abstract: Within the most productive route, online travel agencies (OTAs) intend to use advanced digital media ads to expand their piece of the industry as a whole. The metasearch engine platforms are among the most consistently used digital media environments by OTAs. Most OTAs offer day by day deals in metasearch engine platforms that are paying per click for each hotel to get reservations. The administration of offering methodologies is critical along these lines to reduce costs and increase revenue for online travel agencies. In this study, we tried to predict both the number of impressions and the regular Click-Through-Rate (CTR) …level of hotel advertising for each hotel and the daily sales amount. A significant commitment of our research is to use an extended dataset generated by integrating the most informative features implemented in various related studies as the rolling average for a different amount of day and shifted values for use in the proposed test stage for CTR, impression and sales prediction. The data is created in this study by one of Turkey’s largest OTA, and we are giving OTA’s a genuine application. The results at each prediction stage show that enriching the training data with the OTA-specific additional features, which are the most insightful and sliding window techniques, improves the prediction models ’ generalization capability, and tree-based boosting algorithms carry out the greatest results on this problem. Clustering the dataset according to its specifications also improves the results of the predictions. Show more

Keywords: CTR prediction, impression prediction, sales prediction, data enrichment, clustering, fuzzy clustering

DOI: 10.3233/JIFS-189123

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6619-6627, 2020

Authors: Kılıç, Hakan | Kabak, Özgür

Article Type: Research Article

Abstract: Human development and competitiveness have a causal relation. However, the literature is not clear on which one affects the other. This study investigates the bilateral relation between human development and competitiveness. For this purpose, initially, Fuzzy Analytic Network Process (FANP) is utilized to develop a composite index based on the relative importance weights of respective human development and competitiveness drivers. By FANP, the effects of key dimensions of human development and indexes of competitiveness on each other are taken into account. Subsequently, countries’ efficiencies on converting their human development to competitiveness and inversely, competitiveness to human development is measured by …Data Envelopment Analysis (DEA). Two different DEA models are developed to consider the bilateral relations. 45 countries are evaluated using both FANP and DEA models. Finally, the results are synthesized to reveal the direction of the relationship. It is found that the effect of competitiveness on human development is more significant than the effect of human development on competitiveness. Show more

Keywords: Human development, competitiveness of nations, fuzzy analytic network process, data envelopment analysis

DOI: 10.3233/JIFS-189124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6629-6643, 2020

Authors: Dursun, Mehtap | Goker, Nazli | Mutlu, Hakan

Article Type: Research Article

Abstract: Organizations make use of project management methodologies, which provide an effective manner to achieve managerial goals, maintain the strength of the companies in increasing competition. Efficiency in planning, budgeting, and scheduling are provided so that high quality outputs are obtained through these processes. Agile project management methodology, which has been emerged from unpredictability of customer requirements and changeable business environment, is apt to cope with the failures of traditional project management tools. Besides, lean six-sigma project management methodology has become a combination of lean and six-sigma, which were opponent methodologies previously. This paper aims to determine the most suitable outsourcing …provider alternative by presenting a novel cognitive maps-based intuitionistic fuzzy decision making procedure. Interrelationships among evaluation criteria are weighted employing intuitionistic fuzzy cognitive map technique because of the causal links among evaluation criteria, vagueness, fuzziness, and hesitation in data. Moreover, the most appropriate provider alternative for both agile and lean six-sigma project management methodologies is identified by utilizing intuitionistic fuzzy TOPSIS method, which aims for minimizing the closeness to the ideal solution while maximizing the distance from the anti-ideal solution in hesitative environment. The case study is carried out in a bank that performs in Turkish banking sector. Show more

Keywords: Intuitionistic fuzzy sets, intuitionistic fuzzy cognitive map, IFTOPSIS, outsourcing provider selection, project management, agile, lean six-sigma

DOI: 10.3233/JIFS-189125

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6645-6655, 2020

Authors: Oner, Mahir | Ustundag, Alp

Article Type: Research Article

Abstract: Since information science and communication technologies had improved significantly, data volumes had expanded. As a result of that situation, advanced pre-processing and analysis of collected data became a crucial topic for extracting meaningful patterns hidden in the data. Therefore, traditional machine learning algorithms generally fail to gather satisfactory results when analyzing complex data. The main reason of this situation is the difficulty of capturing multiple characteristics of the high dimensional data. Within this scope, ensemble learning enables the integration of diversified single models to produce weak predictive results. The final combination is generally achieved by various voting schemes. On the …other hand, if a large amount of single models are utilized, voting mechanism cannot be able to combine these results. At this point, Deep Learning (DL) provides the combination of the ensemble results in a considerable time. Apart from previous studies, we determine various predictive models in order to forecast the outcome of two different case studies. Consequently, data cleaning and feature selection are conducted in advance and three predictive models are defined to be combined. DL based integration is applied substituted for voting mechanism. The weak predictive results are fused based on Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) using different parameters and datasets and best predictors are extracted. After that, different experimental combinations are evaluated for gathering better prediction results. For comparison, grouped individual results (clusters) with proper parameters are compared with DL based ensemble results. Show more

Keywords: Ensemble learning, deep neural networks, LSTM, deep ensemble learning

DOI: 10.3233/JIFS-189126

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6657-6668, 2020

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6669-6669, 2020

Authors: Barrón-Romero, César | Hernández-Zavala, Antonio

Article Type: Research Article

Abstract: Fuzzy processors are used for control actions in nonlinear mechatronic systems where high processing speed is required. The Field Programmable Gate Arrays (FPGA) are a good option to implement low cost fuzzy hardware in a short development time. A very important block in fuzzy hardware is the fuzzifier, since it affects directly in the accuracy of the result and in the processing time for obtaining a fuzzy number. There have been many design methodologies intended for enhancing the performance of this block. This paper presents a parallel fuzzifier circuit called α -BSSF. Its main design characteristics are the use of …α -levels for membership representation, usage of integer numbers, and avoiding time-consuming operations. As result, we obtained a fuzzifier that shows advantages in the reduction of the response time and computational resources against the existing sequential fuzzification methods. This proposal is targeted not only for T1FS, but also for T2FS, since the membership calculation through fuzzifier is applied in the same way but twice. Show more

Keywords: Digital Circuit design, Fuzzy hardware, Fuzzifier, FPGA, α - levels

DOI: 10.3233/JIFS-190291

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6671-6685, 2020

Authors: Gao, Fei | Zhang, An | Bi, Wenhao

Article Type: Research Article

Abstract: Weapon system operational effectiveness evaluation is of significant importance to weapon system development, and it can be viewed as a multiple criteria decision-making problem with qualitative information, precise data, interval data, and even missing information. Furthermore, due to the complexity of weapon systems and military operations, using prior knowledge such as experiment data, simulation data, and experts’ knowledge could enhance the accuracy and reliability of the evaluation result. To this end, by introducing interval-valued evidential reasoning (ER) approach into belief rule-based system (BRBS), this paper proposed an interval-valued BRB inference method for weapon system operational effectiveness evaluation Firstly, the operational …effectiveness evaluation hierarchy is established based on the analysis of the weapon system. Then, the belief rule base (BRB) is constructed to capture prior knowledge of the weapon system. Next, different kinds of information are transformed into belief distribution, and the proposed interval-valued BRB inference method is applied to relay the input to the BRB and obtain the evaluation result. Finally, three numerical examples of missile system operational effectiveness evaluation with interval data, precise data, and missing information are conducted to illustrate the process of the proposed method and demonstrate its feasibility. Show more

Keywords: Weapon system, operational effectiveness evaluation, belief rule-based system, interval data, evidential reasoning approach

DOI: 10.3233/JIFS-190651

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6687-6701, 2020

Authors: Riaz, Muhammad | Naeem, Khalid | Aslam, Muhammad | Afzal, Deeba | Almahdi, Fuad Ali Ahmed | Jamal, Sajjad Shaukat

Article Type: Research Article

Abstract: Pythagorean fuzzy set (PFS) introduced by Yager (2013) is the extension of intuitionistic fuzzy set (IFS) introduced by Atanassov (1983). PFS is also known as IFS of type-2. Pythagorean fuzzy soft set (PFSS), introduced by Peng et al. (2015) and later studied by Guleria and Bajaj (2019) and Naeem et al. (2019), are very helpful in representing vague information that occurs in real world circumstances. In this article, we introduce the notion of Pythagorean fuzzy soft topology (PFS-topology) defined on Pythagorean fuzzy soft set (PFSS). We define PFS-basis, PFS-subspace, PFS-interior, PFS-closure and boundary of PFSS. We introduce Pythagorean fuzzy soft …separation axioms, Pythagorean fuzzy soft regular and normal spaces. Furthermore, we present an application of PFSSs to multiple criteria group decision making (MCGDM) using choice value method in the real world problems which yields the optimum results for investment in the stock exchange. We also render an application of PFS-topology in medical diagnosis using TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). The applications are accompanied by Algorithms, flow charts and statistical diagrams. Show more

Keywords: PFS-topology, stock exchange investment, choice value method, medical diagnosis, TOPSIS

DOI: 10.3233/JIFS-190854

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6703-6720, 2020

Authors: Wu, Nannan | Xu, Yejun | Xu, Lizhong | Wang, Huimin

Article Type: Research Article

Abstract: Conflict of environmental sustainable development as a common phenomenon can be seen everywhere in life. To capture consensus problems of decision makers (DMs) in conflict, a consensus and non-consensus fuzzy preference relation (FPR) matrix is proposed to the framework of the Graph Model for Conflict Resolution (GMCR). Concentrating on the case of two DMs within GMCR paradigm, four standard fuzzy solution concepts are developed into eight fuzzy stability definitions which can fully represent DMs’ behavior characteristics of win-win and self-interested. To demonstrate how the novel GMCR methodology proposed in this paper can be conveniently utilized in practice, it is then …applied to an environmental sustainable development conflict with two DMs. The results show that the general fuzzy equilibrium solutions are the intersection of consensus fuzzy equilibrium and non-consensus fuzzy equilibrium. Therefore, the GMCR technique considering DMs’ consensus can effectively predict the various possible solutions of conflict development under different DMs’ behavior preferences and provide new insights for analysts into a conflict. Show more

Keywords: Graph model for conflict resolution, consensus, fuzzy preferences, sustainable development

DOI: 10.3233/JIFS-190990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6721-6731, 2020

Authors: Zhang, Zeliang

Article Type: Research Article

Abstract: Artificial intelligence technology has been applied very well in big data analysis such as data classification. In this paper, the application of the support vector machine (SVM) method from machine learning in the problem of multi-classification was analyzed. In order to improve the classification performance, an improved one-to-one SVM multi-classification method was creatively designed by combining SVM with the K-nearest neighbor (KNN) method. Then the method was tested using UCI public data set, Statlog statistical data set and actual data. The results showed that the overall classification accuracy of the one-to-many SVM, one-to-one SVM and improved one-to-one SVM were 72.5%, …77.25% and 91.5% respectively in the classification of UCI publication data set and Statlog statistical data set, and the total classification accuracy of the neural network, decision tree, basic one-to-one SVM, directed acyclic graph improved one-to-one SVM and fuzzy decision method improved one-to-one SVM and improved one-to-one SVM proposed in this study was 83.98%, 84.55%, 74.07%, 81.5%, 82.68% and 92.9% respectively in the classification of fault data of transformer, which demonstrated the improved one-to-one SVM had good reliability. This study provides some theoretical bases for the application of methods such as machine learning in big data analysis. Show more

Keywords: Machine learning, big data, artificial intelligence, support vector machine, data classification

DOI: 10.3233/JIFS-191265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6733-6740, 2020

Authors: Liu, Zhimin | Qu, Shaojian | Wu, Zhong | Ji, Ying

Article Type: Research Article

Abstract: The problem of the optimal three-level location allocation of transfer center, processing factory and distribution center for supply chain network under uncertain transportation cost and customer demand are studied. We establish a two-stage fuzzy 0-1 mixed integer optimization model, by considering the uncertainty of the supply chain. Given the complexity of the model, this paper proposes a modified hybrid second order particle swarm optimization algorithm (MHSO-PSO) to solve the resulting model, yielding the optimal location and maximal expected return of supply chain simultaneously. A case study of clothing supply chain in Shanghai of China is then presented to investigate the …specific influence of uncertainties on the transfer center, clothing factory and distribution center three-level location. Moreover, we compare the MHSO-PSO with hybrid particle swarm optimization algorithm and hybrid genetic algorithm, to validate the proposed algorithm based on the computational time and the convergence rate. Show more

Keywords: Two-stage fuzzy 0-1 mixed integer optimization, three-level location allocation, uncertainty, algorithm

DOI: 10.3233/JIFS-191453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6741-6756, 2020

Authors: Mu, Yashuang | Wang, Lidong | Liu, Xiaodong

Article Type: Research Article

Abstract: Fuzzy decision trees are one of the most popular extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Among the majority of fuzzy decision trees learning methods, the number of fuzzy partitions is given in advance, that is, there are the same amount of fuzzy items utilized in each condition attribute. In this study, a dynamic programming-based partition criterion for fuzzy items is designed in the framework of fuzzy decision tree induction. The proposed criterion applies an improved dynamic programming algorithm used in scheduling problems to establish an optimal number of fuzzy items for each condition attribute. Then, …based on these fuzzy partitions, a fuzzy decision tree is constructed in a top-down recursive way. A comparative analysis using several traditional decision trees verify the feasibility of the proposed dynamic programming based fuzzy partition criterion. Furthermore, under the same framework of fuzzy decision trees, the proposed fuzzy partition solution can obtain a higher classification accuracy than some cases with the same amount of fuzzy items. Show more

Keywords: Fuzzy decision trees, Fuzzy partition, Dynamic programming, Fuzzy items

DOI: 10.3233/JIFS-191497

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6757-6772, 2020

Authors: Thakran, Snekha

Article Type: Research Article

Abstract: The Electrocardiogram (ECG) signal records the electrical activity of the heart. It is very difficult for physicians to analyze the ECG signal if noise is embedded during acquisition to inspect the heart’s condition. The denoising of electrocardiogram signals based on the genetic particle filter algorithm(GPFA) using fuzzy thresholding and ensemble empirical mode decomposition (EEMD) is proposed in this paper, which efficiently removes noise from the ECG signal. This paper proposes a two-phase scheme for eliminating noise from the ECG signal. In the first phase, the noisy signal is decomposed into a true intrinsic mode function (IMFs) with the help of …EEMD. EEMD is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise is obtained by using spectral flatness of each IMF and fuzzy thresholding. The corrupted IMFs are filtered using a GPF method to remove the noise. Then, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for a different local hospital database, and it gives better root mean square error and signal to noise ratio than other existing techniques (Wavelet transform (WT), EMD, Particle filter(PF) based method, extreme-point symmetric mode decomposition with Nonlocal Means(ESMD-NLM), and discrete wavelet with Savitzky-Golay(DW-SG) filter). Show more

Keywords: Genetic particle filter algorithm, ensemble empirical mode decomposition, fuzzy thresholding, ECG denoising

DOI: 10.3233/JIFS-191518

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6773-6782, 2020

Authors: Subbiah, Siva Sankari | Chinnappan, Jayakumar

Article Type: Research Article

Abstract: The load forecasting is the significant task carried out by the electricity providing utility companies for estimating the future electricity load. The proper planning, scheduling, functioning, and maintenance of the power system rely on the accurate forecasting of the electricity load. In this paper, the clustering-based filter feature selection is proposed for assisting the forecasting models in improving the short term load forecasting performance. The Recurrent Neural Network based Long Short Term Memory (LSTM) is developed for forecasting the short term load and compared against Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Regression (SVR) and Random Forest (RF). …The performance of the forecasting model is improved by reducing the curse of dimensionality using filter feature selection such as Fast Correlation Based Filter (FCBF), Mutual Information (MI), and RReliefF. The clustering is utilized to group the similar load patterns and eliminate the outliers. The feature selection identifies the relevant features related to the load by taking samples from each cluster. To show the generality, the proposed model is experimented by using two different datasets from European countries. The result shows that the forecasting models with selected features produce better performance especially the LSTM with RReliefF outperformed other models. Show more

Keywords: Load forecasting, feature selection, clustering, deep learning, long short term memory

DOI: 10.3233/JIFS-191568

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6783-6800, 2020

Authors: Kejia, Shen | Parvin, Hamid | Qasem, Sultan Noman | Tuan, Bui Anh | Pho, Kim-Hung

Article Type: Research Article

Abstract: Intrusion Detection Systems (IDS) are designed to provide security into computer networks. Different classification models such as Support Vector Machine (SVM) has been successfully applied on the network data. Meanwhile, the extension or improvement of the current models using prototype selection simultaneous with their training phase is crucial due to the serious inefficacies during training (i.e. learning overhead). This paper introduces an improved model for prototype selection. Applying proposed prototype selection along with SVM classification model increases attack discovery rate. In this article, we use fuzzy rough sets theory (FRST) for prototype selection to enhance SVM in intrusion detection. Testing …and evaluation of the proposed IDS have been mainly performed on NSL-KDD dataset as a refined version of KDD-CUP99. Experimentations indicate that the proposed IDS outperforms the basic and simple IDSs and modern IDSs in terms of precision, recall, and accuracy rate. Show more

Keywords: SVM, data selection, feature selection, fuzzy rough set theory, ids

DOI: 10.3233/JIFS-191621

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6801-6817, 2020

Authors: Lei, Fan | Wei, Guiwu | Wu, Jiang | Wei, Cun | Guo, Yanfeng

Article Type: Research Article

Abstract: Probabilistic uncertain linguistic sets (PULTSs) have extensively been employed in multiple attribute group decision making (MAGDM)problem. The QUALIFLEX method, which is relatively a novel MAGDM technique, aims to obtain the optimal alternative. This paper proposes the probabilistic uncertain linguistic QUALIFLEX (PUL-QUALIFLEX) method with CRITIC method. To show the effectiveness of the designed method, an application is given for green supplier selection and the derived results are compared with some existing methods. Thus, the advantage of this proposed method is that it is simple to understand and easy to compute. The proposed method can also contribute to the selection of suitable …alternative successfully in other selection issues. Show more

Keywords: Multiple attribute group decision making (MAGDM), probabilistic uncertain linguistic term sets (PULTSs), CRITIC method, QUALIFLEX method, green supplier selection

DOI: 10.3233/JIFS-191737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6819-6831, 2020

Authors: Ye, Fei-Fei | Wang, Suhui | Yang, Long-Hao | Wang, Ying-Ming

Article Type: Research Article

Abstract: Air pollution management is becoming a major topic of political concern, and many studies have devoted to the efficiency measurement of air pollution management. However, several drawbacks must be overcome for better applying efficiency measurement to improve air pollution management, including neglect of the importance of different indicators, non-integrity of indicator information for efficiency measurement, and lack of analyzing regional factors in the efficiency of air pollution management. Accordingly, by utilizing the evidential reasoning (ER) approach with entropy weighting method to propose an ER-based indicator integration and introducing the slacks-based measure (SBM) model with consideration of undesirable outputs and the …regression model to propose an SBM-based efficiency analysis, a new air pollution management method, called integrated ER-SBM method, is developed in the present study. In the case study of Chinese 29 provinces, the application procedure and results are provided to illustrate how to apply the integrated ER-SBM method to integrate various air pollution indicators with different importance and further analyze the influence of regional factors, such as technological innovation, regional population density, import-export values, number of industries, and energy resources, on the efficiency of air pollution management. In addition, the policy recommendations targeting the results are concluded as well. Show more

Keywords: Air pollution, indicator integration, efficiency analysis, ER approach, SBM model

DOI: 10.3233/JIFS-191816

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6833-6848, 2020

Authors: Atmaca, S.

Article Type: Research Article

Abstract: In this manuscript, it is aimed to convert the topology on a set X which is on a nearness approximation space to new set families via indiscernibility relation. Then, if the open sets of the present topology are defined as the set of related elements, the set families, which have weakly related elements, will be obtained. Finally, the topological properties and concepts that these new families hold will be examined.

Keywords: Near set, near topology, topology

DOI: 10.3233/JIFS-191922

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6849-6855, 2020

Authors: Adhikary, Krishnendu | Roy, Jagannath | Kar, Samarjit

Article Type: Research Article

Abstract: Due to increasing difficulty and challenging issues of newsboy problem under uncertainty, managers seek newer and appropriate approaches to apprehend more accurately the demand for perishable products and or the products having a short shelf life. This paper investigates a newsboy problem with fuzzy random demand in a single product business scenario. The classical newsboy model is extended to a fuzzy random newsboy problem to determine the optimal order quantity and expected profit under hybrid uncertainty. To solve the proposed model, a new solution approach based on chance constraint programming is proposed to formulate the crisp equivalent form of the …fuzzy random newsboy model. Numerical examples and a real-life case study are presented to show the utility of the projected model. From the outcomes, decision makers can make comprehensive recommendations for the optimal order quantity and expected profit obtained by our proposed model under two-folded uncertainty. Also, a sensitivity analysis suggests that the profit and order quantity will increase (or decrease) with the increase (or decrease) of the mean demand. Show more

Keywords: Newsboy problem, uncertain demand, fuzzy random variable, expected value model

DOI: 10.3233/JIFS-192057

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6857-6868, 2020

Authors: Alsulami, S. H. | Ibedou, Ismail | Abbas, S. E.

Article Type: Research Article

Abstract: In this paper, we join the notion of fuzzy ideal to the notion of fuzzy approximation space to define the notion of fuzzy ideal approximation spaces. We introduce the fuzzy ideal approximation interior operator int Φ λ and the fuzzy ideal approximation closure operator cl Φ λ , and moreover, we define the fuzzy ideal approximation preinterior operator p int Φ λ and the fuzzy ideal approximation preclosure operator p cl Φ λ with respect …to that fuzzy ideal defined on the fuzzy approximation space (X , R ) associated with some fuzzy set λ  ∈ I X . Also, we define fuzzy separation axioms, fuzzy connectedness and fuzzy compactness in fuzzy approximation spaces and in fuzzy ideal approximation spaces as well, and prove the implications in between. Show more

Keywords: Fuzzy rough set, Fuzzy ideal approximation space, Fuzzy separation axioms, Fuzzy connectedness, Fuzzy compactness, 03E72, 03E02, 54C10, 03E20, 54D05, 54D10, 54D30)

DOI: 10.3233/JIFS-192072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6869-6880, 2020

Authors: Wang, Jie | Yan, Linhuang | Tian, Jiayi | Yuan, Minmin

Article Type: Research Article

Abstract: In this paper, a bilateral spectrogram filtering (BSF)-based optimally modified log-spectral amplitude (OMLSA) estimator for single-channel speech enhancement is proposed, which can significantly improve the performance of OMLSA, especially in highly non-stationary noise environments, by taking advantage of bilateral filtering (BF), a widely used technology in image and visual processing, to preprocess the spectrogram of the noisy speech. BSF is capable of not only sharpening details, removing unwanted textures or background noise from the noisy speech spectrogram, but also preserving edges when considering a speech spectrogram as an image. The a posteriori signal-to-noise ratio (SNR) of OMLSA algorithm is …estimated after applying BSF to the noisy speech. Besides, in order to reduce computing costs, a fast and accurate BF is adopted to reduce the algorithm complexity O (1) for each time-frequency bin. Finally, the proposed algorithm is compared with the original OMLSA and other classic denoising methods using various types of noise with different signal-to-noise ratios in terms of objective evaluation metrics such as segmental signal-to-noise ratio improvement and perceptual evaluation of speech quality. The results show the validity of the improved BSF-based OMLSA algorithm. Show more

Keywords: Speech enhancement, bilateral filtering, optimally modified log-spectral amplitude, bilateral spectrogram filtering, spectrogram

DOI: 10.3233/JIFS-192088

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6881-6889, 2020

Authors: Oketch, Godrick | Karaman, Filiz

Article Type: Research Article

Abstract: Count data models are based on definite counts of events as dependent variables. But there are practical situations in which these counts may fail to be specific and are seen as imprecise. In this paper, an assumption that heaped data points are fuzzy is used as a way of identifying counts that are not definite since heaping can result from imprecisely reported counts. Because it is practically unlikely to report all counts in an entire dataset as imprecise, this paper proposes a likelihood function that not only considers both precise and imprecisely reported counts but also incorporates α  - cuts …of fuzzy numbers with the aim of varying impreciseness of fuzzy reported counts. The proposed model is then illustrated through a smoking cessation study data that attempts to identify factors associated with the number of cigarettes smoked in a month. Through the real data illustration and a simulation study, it is shown that the proposed model performs better in predicting the outcome counts especially when the imprecision of the fuzzy points in a dataset are increased. The results also show that inclusion of α  - cuts makes it possible to identify better models, a feature that was not previously possible. Show more

Keywords: Fuzzy α - cuts, fuzzy count data, fuzzy likelihood function, fuzzy probability, heaped data, poisson regression

DOI: 10.3233/JIFS-192094

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6891-6901, 2020

Authors: Riaz, Muhammad | Hamid, Muhammad Tahir | Athar Farid, Hafiz Muhammad | Afzal, Deeba

Article Type: Research Article

Abstract: In this article, we study some concepts related to q-rung orthopair fuzzy soft sets (q-ROFSSs) together with their algebraic structure. We present operations on q-ROFSSs and their specific properties and elaborate them with real-life examples and tabular representations to develop an influx of linguistic variables based on q-rung orthopair fuzzy soft (q-ROFS) information. We present an application of q-ROFSSs to multi-criteria group decision-making (MCGDM) process related to the university choice, accompanied by algorithm and flowchart. We develop q-ROFS TOPSIS method and q-ROFS VIKOR method as extensions of TOPSIS (a technique for ordering preference through the ideal solution) and VIKOR (Vlse …Kriterijumska Optimizacija Kompromisno Resenje), respectively. Finally, we tackle a problem of construction utilizing q-ROFS TOPSIS and q-ROFS VIKOR methods. Show more

Keywords: q-ROFNs, TOPSIS, aggregation operators, VIKOR, soft sets

DOI: 10.3233/JIFS-192175

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6903-6917, 2020

Authors: Guo, Xingru | Liu, Aijun | Li, Xia | Liu, Taoning

Article Type: Research Article

Abstract: Rh-negative rare blood inventory protection plays an important role in emergency blood protection. Normally, hospitals typically hold a fixed amount of daily reserve in response to emergency needs, but the measure can increase the unnecessary cost of repeated freezing and thawing. In order to save manpower, protect blood resources and reduce costs, a two-stage stochastic model is proposed to determine the optimal daily reserve of Rh-negative red blood cells, taking into account the uncertainty of demand. First, the model focuses on minimizing operational cost, shortage cost and damage caused by blood substitution. Then, the proposed model generates a series of …discrete scenarios to solve the uncertainty of demand and predict the demand. In addition, a case study is presented to prove the validity of the proposed model with real data. Sensitivity analysis is also established to observe the effect of parameter changes on the results. Finally, the results show that the proposed model can effectively reduce the cost and current waste. Show more

Keywords: Rh-negative, red blood cells, inventory management, stochastic demand, two-stage stochastic model

DOI: 10.3233/JIFS-192182

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6919-6933, 2020

Authors: Lee, Chang-Yong

Article Type: Research Article

Abstract: Under a flexible mass-production system, a manufacturer may need to provide highly customized products to meet customer satisfaction. It is likely that components in a customized product are correlated in such a way that the demands of some components depend on those of others. In order to cope with dependence in the demands, we proposed a continuous review multi-item inventory (Q , r ) model that included a general form of correlation and dependence in demands among components. We represented the proposed model by using a probabilistic graphical model under the assumption that the demands of all components and their …correlations were represented by a multivariate Gaussian probability distribution. By taking an advantage of a directed acyclic graph and its topological order, we demonstrated that the correlated demands among components in the proposed model could be solved without any approximation and assumption. As an illustration of the proposed method, we solved an inventory (Q , r ) model of eight correlated components and discussed the experimental results in terms of correlation and dependence in demand. Show more

Keywords: Inventory, continuous review multi-item inventory, directed acyclic graph, topological order, correlation and dependence

DOI: 10.3233/JIFS-200014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6935-6947, 2020

Authors: Barlak, Damla

Article Type: Research Article

Abstract: In this study, we introduce the concepts of φ λ ,μ -double statistically convergence of order β in fuzzy sequences and strongly λ - double Cesaro summable of order β for sequences of fuzzy numbers. Also we give some inclusion theorems.

Keywords: Statistical convergence, Cesàro summability, Modulus function, 40A05, 40A25, 40A30, 40C05, 03E72

DOI: 10.3233/JIFS-200039

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6949-6954, 2020

Authors: Zhang, Nian | Han, Yunpeng | Si, Quanshen | Wei, Guiwu

Article Type: Research Article

Abstract: To consider the decision makers’ regret behavior and describe the hybrid evolution information in the risk decision-making problem, a new approach is proposed based on regret theory in this paper. Firstly, the probable value of different states are calculated by Pignistic probability transformation method. Secondly, the relative closeness formula of hybrid information are established and the utility values of alternatives are computed. Then, decision makers’ utility values are obtained according to the regret theory. Moreover, the overall perceived utility values of alternatives are obtained by weighted arithmetic mean and got the optimal one by the ranking order. Finally, an numerical …example is illustrated the method and comparative analysis are offered between the proposed approach and other existed methods to show that is feasible and usable. Show more

Keywords: Regret theory, hybrid information, multi-attribute risk decision-making

DOI: 10.3233/JIFS-200081

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6955-6964, 2020

Authors: Zhang, Xianquan | Yang, Ju | Dong, Yu | Yu, Chunqiang | Tang, Zhenjun

Article Type: Research Article

Abstract: Most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, a robust data hiding with multiple backups and optimized reference matrix is proposed in this paper. Specifically, secret data is divided into a set of groups and multiple backups of each group data are generated according to the number of backups. The cover image is divided into several blocks. A reference matrix is constructed by four constraints to assist data hiding and data extraction. The proposed method aims to extract exactly at least one backup of each group data so that the correct backups …can construct the secret data well if the stego-image is corrupted. Experimental results show that the proposed algorithm is robust to cropping and noise attacks. Show more

Keywords: Data hiding, anti-cropping, anti-noise, multi-backup data, data security

DOI: 10.3233/JIFS-200089

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6965-6977, 2020

Authors: El Atik, Abd El Fattah A. | wahba, Ashgan S.

Article Type: Research Article

Abstract: Rough set theory is used in simple directed graphs to study nano topology. Adjacent vertices was used in digraphs only to define their neighborhoods. Four types of neighborhood systems for vertices are introduced in this article which depend on both adjacent vertices and associated edges. Additionally, the generalization of some notions presented by Pawlak and Lellis Thivagar and some of their properties are investigated. Finally, we present a new model of a blood circulation system of the human heart based on blood paths. Also, different kinds of topological separation axioms are presented and studied between vertices and edges of the …heart blood circulation model. Show more

Keywords: Graph theory, Rough sets, Nano topology, Human heart, Separation axioms

DOI: 10.3233/JIFS-200126

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6979-6992, 2020

Authors: Han, Lu | Su, Zhi | Lin, Jing

Article Type: Research Article

Abstract: Ever increasing ordinal variables are being collected by the Personal Credit Reference System in China, however this system suffers from analysis of this kind of data, which cannot be calculated by Euclidean distance. In this study, we put forward a hybrid KNN algorithm based on Sugeno measure, and we prove that the error of this algorithm is smaller than that of Euclidean distance, furthermore, we use real data obtained from the Personal Credit Reference System to perform experiments and get the user’s initial portrait. Through the comparisons with Kmeans algorithm and other different distance measures in KNN algorithm, we find …that the hybrid KNN algorithm is more suitable for clustering personal credit data. Show more

Keywords: Hybrid KNN clustering, personal credit reference system, Sugeno measure, user’s portrait

DOI: 10.3233/JIFS-200191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6993-7004, 2020

Authors: Zhu, Zhanlong | Liu, Yongjun | Wang, Yuan

Article Type: Research Article

Abstract: Adding spatial penalty to fuzzy C-means (FCM) model is an important way to reduce the influence of noise in image segmentation. However, these improved algorithms easily cause segmentation failures when the image has the characteristics of unequal cluster sizes. Besides, they often fall into local optimal solutions if the initial cluster centers are improper. This paper presents a noise robust hybrid algorithm for segmenting image with unequal cluster sizes based on chaotic crow search algorithm and improved fuzzy c-means to overcome the above defects. Firstly, each size of clusters is integrated into the objective function of noise detecting fuzzy c-means …algorithm (NDFCM), which can reduces the contribution of larger clusters to objective function and then the new membership degree and cluster centers are deduced. Secondly, a new expression called compactness, representing the pixel distribution of each cluster, is introduced into the iteration process of clustering. Thirdly, we use two- paths to seek the optimal solutions in each step of iteration: one path is produced by the chaotic crow search algorithm and the other is originated by gradient method. Furthermore, the better solutions of the two-paths go to next generation until the end of the iteration. Finally, the experiments on the synthetic and non–destructive testing (NDT) images show that the proposed algorithm behaves well in noise robustness and segmentation performance. Show more

Keywords: Image segmentation, fuzzy clustering, chaotic crow search algorithm, unequal cluster sizes

DOI: 10.3233/JIFS-200197

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7005-7020, 2020

Authors: Li, Feng

Article Type: Research Article

Abstract: Mining maximal frequent patterns is significant in many fields, but the mining efficiency is often low. The bottleneck lies in too many candidate subgraphs and extensive subgraph isomorphism tests. In this paper we propose an efficient mining algorithm. There are two key ideas behind the proposed methods. The first is to divide each edge of every certain graph (converted from equivalent uncertain graph) and build search tree, avoiding too many candidate subgraphs. The second is to search the tree built in the first step in order, avoiding extensive subgraph isomorphism tests. The evaluation of our approach demonstrates the significant cost …savings with respect to the state-of-the-art approach not only on the real-world datasets as well as on synthetic uncertain graph databases. Show more

Keywords: Uncertain graph, maximal frequent pattern, data mining

DOI: 10.3233/JIFS-200237

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7021-7033, 2020

Authors: Parsa, Navid | Bahmani-Firouzi, Bahman | Niknam, Taher

Article Type: Research Article

Abstract: Distribution automation is well recognized as an effective solution to enhance the reliability and efficiency of these grids in a timely manner. This paper introduces an effective probabilistic operation framework for the automated distribution networks (ADNs) incorporating the plug-in electric vehicles (PEVs) charging/discharging schemes in the presence of different renewable energy sources (RESs). To this end, this paper pursues four different strategic approaches. Firstly, an effective fuzzy based probabilistic method is proposed to model the forecast error in the wind and solar units well as the load demand through the cloud theory. Secondly, an appropriate framework is devised to model …the PEVs random behaviour considering their essential parameters such as the charging/discharging rate and arrival/departure time to/from the parking lots (PLs), the discharging level at driving mode on the road and the effects of battery degradation. As the third goal, an appropriate objective function which can consider automation indices including the social welfare and reliability is considered. Since the operation problem is a nonlinear continuous non-numerical problem, it requires an applicable and effective optimization algorithm which is regarded as the fourth goal of this paper. In this regard, a new θ -modified bat algorithm is introduced to find the optimal solution of the problem. The proposed model is simulated and examined on the IEEE 69-bus standard test system wherein results reveal the effectiveness and applicability of the proposed operation management framework. Show more

Keywords: Automated distribution networks, reliability, electric vehicles, renewable energy sources, optimization and operation management

DOI: 10.3233/JIFS-200246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7035-7051, 2020

Authors: Saini, Jagriti | Dutta, Maitreyee | Marques, Gonçalo

Article Type: Research Article

Abstract: Indoor air pollution (IAP) has become a serious concern for developing countries around the world. As human beings spend most of their time indoors, pollution exposure causes a significant impact on their health and well-being. Long term exposure to particulate matter (PM) leads to the risk of chronic health issues such as respiratory disease, lung cancer, cardiovascular disease. In India, around 200 million people use fuel for cooking and heating needs; out of which 0.4% use biogas; 0.1% electricity; 1.5% lignite, coal or charcoal; 2.9% kerosene; 8.9% cow dung cake; 28.6% liquified petroleum gas and 49% use firewood. Almost 70% …of the Indian population lives in rural areas, and 80% of those households rely on biomass fuels for routine needs. With 1.3 million deaths per year, poor air quality is the second largest killer in India. Forecasting of indoor air quality (IAQ) can guide building occupants to take prompt actions for ventilation and management on useful time. This paper proposes prediction of IAQ using Keras optimizers and compares their prediction performance. The model is trained using real-time data collected from a cafeteria in the Chandigarh city using IoT sensor network. The main contribution of this paper is to provide a comparative study on the implementation of seven Keras Optimizers for IAQ prediction. The results show that SGD optimizer outperforms other optimizers to ensure adequate and reliable predictions with mean square error = 0.19, mean absolute error = 0.34, root mean square error = 0.43, R2 score = 0.999555, mean absolute percentage error = 1.21665%, and accuracy = 98.87%. Show more

Keywords: Indoor air quality, pollutants, prediction system, optimizers

DOI: 10.3233/JIFS-200259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7053-7069, 2020

Authors: Zhao, Ruirui | Luo, Minxia | Li, Shenggang

Article Type: Research Article

Abstract: The theory of single valued neutrosophic sets, which is a generalization of intuitionistic fuzzy sets, is more capable of dealing with inconsistent information in practice. In this paper, we propose reverse triple I method under single valued neutrosophic environment. Firstly, we give the definitions of single valued neutrosophic t-representation t-norms and single valued neutrosophic residual implications. Secondly, we develop a formula for calculating single valued neutrosophic residual implications. Then we propose reverse triple I method based on left-continuous single valued neutrosophic t-representation t-norms and its solutions. Lastly, we discuss the robustness of reverse triple I method based on the proposed …similarity measure. Show more

Keywords: Single valued neutrosophic sets, similarity measure, reverse triple I method

DOI: 10.3233/JIFS-200265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7071-7083, 2020

Authors: Liu, Shuqi | Shao, Mingwen | Liu, Xinping

Article Type: Research Article

Abstract: In recent years, deep neural networks have made significant progress in image classification, object detection and face recognition. However, they still have the problem of misclassification when facing adversarial examples. In order to address security issue and improve the robustness of the neural network, we propose a novel defense network based on generative adversarial network (GAN). The distribution of clean - and adversarial examples are matched to solve the mentioned problem. This guides the network to remove invisible noise accurately, and restore the adversarial example to a clean example to achieve the effect of defense. In addition, in order to …maintain the classification accuracy of clean examples and improve the fidelity of neural network, we input clean examples into proposed network for denoising. Our method can effectively remove the noise of the adversarial examples, so that the denoised adversarial examples can be correctly classified. In this paper, extensive experiments are conducted on five benchmark datasets, namely MNIST, Fashion-MNIST, CIFAR10, CIFAR100 and ImageNet. Moreover, six mainstream attack methods are adopted to test the robustness of our defense method including FGSM, PGD, MIM, JSMA, CW and Deep-Fool. Results show that our method has strong defensive capabilities against the tested attack methods, which confirms the effectiveness of the proposed method. Show more

Keywords: Deep neural network, generative adversarial network, adversarial example, defense

DOI: 10.3233/JIFS-200280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7085-7095, 2020

Authors: Nasr Saleh, Hayel | Imdad, Mohammad | Khan, Idrees | Hasanuzzaman, Md

Article Type: Research Article

Abstract: In the present article, inspired by the work of Jleli et al. [J. Inequal. Appl. 2014, 38 (2014)] and [J. Inequal. Appl. 2014, 439 (2014)] in metric spaces, we proposed a new class of contractive mappings termed as: fuzzy Θ f -contractive mappings by using an auxiliary function Θ f  : (0, 1) → (0, 1) satisfying suitable properties. This class has further been weakened by defining the class of fuzzy Θ f -weak contractive mappings to realize yet another class of contractive mappings. Thereafter, these two newly introduced classes of contractive mappings are utilized to establish some fixed point …theorems in M -complete fuzzy metric spaces (in the sense of George and Veeramani). In support of our newly obtained results, we provide some examples besides furnishing applications to dynamic programming. Show more

Keywords: Fixed point, fuzzy Θf-contractive mappings, fuzzy Θf-weak contractive mappings, fuzzy metric space, dynamic programming

DOI: 10.3233/JIFS-200319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7097-7106, 2020

Authors: Chuanchao, Zhang

Article Type: Research Article

Abstract: In view of the characteristics with big data, high feature dimension, and dynamic for a large-scale intuitionistic fuzzy information systems, this paper integrates intuitionistic fuzzy rough sets and generalized dynamic sampling theory, proposes a generalized attribute reduction algorithm based on similarity relation of intuitionistic fuzzy rough sets and dynamic reduction. It uses dynamic reduction sampling theory to divide a big data set into small data sets and relative positive domain cardinality instead of dependency degree as decision-making condition, and obtains reduction attributes of big intuitionistic fuzzy decision information systems, and achieves the goal of extracting key features and fault diagnosis. …The innovation of this paper is that it integrates generalized dynamic reduction and intuitionistic fuzzy rough set, and solves the problem of big data set which cannot be solved by intuitionistic fuzzy rough set. Taking an actual data as an example, the scientificity, rationality and effectiveness of the algorithm are verified from the aspects of stability, diagnostic accuracy, optimization ability and time complexity. Compared with similar algorithms, the advantages of the proposed algorithm for big data processing are confirmed. Show more

Keywords: Intuitionistic fuzzy rough set, similarity relation, relative positive domain, generalized dynamic reduction, large fuzzy decision information system, attribute reduction

DOI: 10.3233/JIFS-200347

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7107-7122, 2020

Authors: Maryum, Ilsa | Nawaz, Waqas | Ud Din, Amad

Article Type: Research Article

Abstract: Non-uniformity in medical procedures, expensive medical treatments, and the shortage of medicines in different areas are health care problems in our country. This paper aims to resolve that problem by developing a web-based-application called Hospital Management Society (HMS) based on a novel Dynamic Optimized Fuzzy C-mean Clustering and Association Rule Mining (DOFCCARM). The purpose of HMS is to enhance the hospitals (and clinics) by regulating, overseeing and accrediting them to bring uniformity in health care facilities, to make the medical treatment cost effective, to find common diseases in a particular age and area, and to help government in identifying the …areas facing the shortage of licensed medicines. Therefore, HMS creates a single platform for both the doctors of central hospital (CH) and the doctors of member hospitals (MH). The CH provides clinical practice guidelines for various diseases. A team of doctors at CH evaluate the medical treatment provided by MH. If a hospital fails to maintain the standard then HMS blacklists such hospital. In our approach, we take a range of values to distinct successive partitions and generate a parallel membership function to make fuzzy sets of patients report, rather than single partitioning point. We determine the effectiveness of our approach through experiments on a dataset. The results revealed the most common age, symptoms and location for a particular disease and shortage of particular medicine in a specific area. Show more

Keywords: Fuzzy C-mean, association rule mining, hospital management society, intelligent system

DOI: 10.3233/JIFS-200349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7123-7134, 2020

Authors: Li, Shugang | Zhu, Lirong | Zhu, Boyi | Wang, Ru | Zheng, Lingling | Yu, Zhaoxu | Lu, Hanyu

Article Type: Research Article

Abstract: 3D printing is the important part of the emerging industry, and the accurate prediction of technology hot spots (THS) in the 3D printing industry is crucial for the strategic technology planning. The patents of the THS are always in the minority and have outlier characteristics, so the existing single and rigid models cannot accurately and robustly predict the THS. In order to make up for the shortcomings of the existing research, this study proposes a model for robust composite attraction indicator (MRCAI), which avoids the impact of outlier patents on prediction accuracy depending on not only extracting the patent attraction …indicators (AIs) but also constructing the robust composite attraction indicator (CAI) according to the rough consensus of predicted results of CAIs with high generalization. Specifically, firstly, this study selects the patent AIs from the four dimensions of the attraction: technology group attraction, state attraction, enterprise attraction and inventor attraction. Secondly, in order to completely describe the attraction features of patent, AIs are directly and indirectly integrated into CAIs. Thirdly, we reduce the influence of outlier patents on prediction accuracy from two aspects: on the one hand, we initially select the CAIs with good generalization performance based on the prediction error fluctuation range. On the other hand, we build the robust CAIs by calculating the consensus of CAIs with high generalization performance based on the rough set. Fourthly, the 3D printing industry technology attention matrix is constructed to map the effective technology strategic planning based on predicted patent backward citation count by MRCAI in the short, medium and long term. Finally, the experimental results on 3D printing patent data show that MRCAI can effectively improve the efficiency in dealing with samples with outlier patents and has strong flexibility and robustness in predicting the THS in 3D printing industry. Show more

Keywords: Technology hot spots, outlier samples, robust CAI, 3D printing, technology attention matrix

DOI: 10.3233/JIFS-200404

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7135-7149, 2020

Authors: Lio, Waichon | Jia, Lifen

Article Type: Research Article

Abstract: Since the practical production is not continuously available and sometimes suffers unexpected breakdowns, this paper applies uncertainty theory to introducing an uncertain production risk process with breakdowns to handle the production problem with uncertain cycle times (consisting of uncertain on-times and uncertain off-times) and uncertain production amounts. The concept of shortage index of the uncertain production risk process with breakdowns is provided and some formulas for the calculation are given. Furthermore, the shortage time of the uncertain production risk process with breakdowns is proposed and its uncertainty distribution is obtained. Finally, some numerical examples are revealed.

Keywords: Production, risk process, uncertainty theory, uncertain renewal process

DOI: 10.3233/JIFS-200453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7151-7160, 2020

Authors: Alberto Morales-Rosales, Luis | Algredo-Badillo, Ignacio | Lobato-Baez, Mariana | Hernández-Gracidas, Carlos | Rodríguez-Rangel, Héctor

Article Type: Research Article

Abstract: In this research, we implement an intelligent quantitative model to assess a specific qualitative intelligence scale in children between 5 and 8 years old, based on augmented reality and the well known WISC-IV test. The output of the model is a cognitive factor associated with the analogical reasoning level of the child, and the ulterior analysis of the evaluation measure is intended to serve as an aid for the teacher to discover problems related to the child’s ability to solve visual analogies. A quantitative approach to assess analogical reasoning is suitable to avoid ambiguous evaluations of qualitative results. Also, given …that the assessment employs a visual WISC subtest, it constitutes a non-verbal evaluation. Finally, the fact that the model is based on an intelligent approach guarantees that the assessment process is impartial, based on the quantitative scores obtained, instead of an interpretation of the results. The purpose of this work is to give evidence that a computer-aided adaption, employing augmented reality and a Fuzzy Petri Net, for the WISC test, will improve the teaching-learning process in children ranges from 5 to 8 years old. A case study is analyzed, where both the paper-based and the augmented reality versions are applied to five children with Spanish as their native tongue. We show the feasibility and potentiality of implementing the test in a multimedia version to provide teachers with a more reliable resource for the diagnosis and treatment of possible learning deficiencies in the child regarding disambiguation, non-verbality, and impartiality. Show more

Keywords: Intelligent quantitative model, analogical reasoning, WISC-IV test, augmented reality learning environment, computer-aided assessments

DOI: 10.3233/JIFS-200588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7161-7175, 2020

Authors: Vijayabalaji, Srinivasan | Balaji, Parthasarathy | Ramesh, Adhimoolam

Article Type: Research Article

Abstract: The impetus of this paper is to broaden the structure of linguistic soft set (LSS) to a new domain namely sigmoid valued fuzzy soft set (SVFSS). Some operating laws on SVFSS are also provided. Using the complement concept on SVFSS we define maximum rejection. This maximum rejection paves a way for defining a new similarity measure on SVFSS termed as maximum likely ratio (MLR). A new MCGDM algorithm for SVFSS is proposed using MLR. An illustrative example of haze equipment problem on sigmoid valued fuzzy soft set setting is also given. A comparative analysis of our approach with the existing …approaches are also presented to justify our work. Show more

Keywords: Sigmoid valued fuzzy soft set, maximum rejection, maximum likely ratio, generalized maximum likely ratio, weighted maximum likely ratio

DOI: 10.3233/JIFS-200594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7177-7187, 2020

Authors: Al-Zoubi, Ahmad | Tatas, Konstantinos | Kyriacou, Costas

Article Type: Research Article

Abstract: Heterogeneous systems featuring multiple kinds of processors are becoming increasingly attractive due to their high performance and energy savings over their homogeneous counterparts. With the OpenCL as a unified programming language providing program portability across different types of accelerators, finding the best task-to-device mapping will be the key to achieve such a high performance. We introduce in this work the design of a fuzzy logic classifier and the evaluation of its performance in classifying OpenCL workloads in a CPU-GPU-FPGA heterogeneous environment based on carefully analyzed kernel features. The classifier is designed as part of a scheduling scheme. Results demonstrate substantial …improvement in accuracy when compared to other classifiers such as the K-Nearest- Neighbor (KNN), Support-Vector-Machine (SVM), Random-Forest (RF), Naïve-Bayes (NB) and the Bayes-Network (BN) with low computational complexity, facilitating run-time operation. Show more

Keywords: Fuzzy Logic, Heterogeneous, Classification, OpenCL

DOI: 10.3233/JIFS-200616

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7189-7202, 2020

Authors: Ontiveros-Robles, Emanuel | Castillo, Oscar | Melin, Patricia

Article Type: Research Article

Abstract: In recent years, successful applications of singleton fuzzy inference systems have been made in a plethora of different kinds of problems, for example in the areas of control, digital image processing, time series prediction, fault detection and classification. However, there exists another relatively less explored approach, which is the use of non-singleton fuzzy inference systems. This approach offers an interesting way for handling uncertainty in complex problems by considering inputs with uncertainty, while the conventional Fuzzy Systems have their inputs with crisp values (singleton systems). Non-singleton systems have as inputs Type-1 membership functions, and this difference increases the complexity of …the fuzzification, but provides the systems with additional non-linearities and robustness. The main limitations of using a non-singleton fuzzy inference system is that it requires an additional computational overhead and are usually more difficult to apply in some problems. Based on these limitations, we propose in this work an approach for efficiently processing non-singleton fuzzy systems. To verify the advantages of the proposed approach we consider the case of general type-2 fuzzy systems with non-singleton inputs and their application in the classification area. The main contribution of the paper is the implementation of non-singleton General Type-2 Fuzzy Inference Systems for the classification task, aiming at analyzing its potential advantage in classification problems. In the present paper we propose that the use of non-singleton inputs in Type-2 Fuzzy Classifiers can improve the classification rate and based on the realized experiments we can observe that General Type-2 Fuzzy Classifiers, but with non-singleton fuzzification, obtain better results in comparison with respect to their singleton counterparts. Show more

Keywords: Type-2 fuzzy classifiers, Type-2 fuzzy logic, non-singleton

DOI: 10.3233/JIFS-200639

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7203-7215, 2020

Authors: Gao, Wenjing | Zhang, Wenjun | Gao, Haiyan | Zhu, Yonghua

Article Type: Research Article

Abstract: The increasing tendency of people expressing opinions via images online has motivated the development of automatic assessment of sentiment from visual contents. Based on the observation that visual sentiment is conveyed through many visual elements in images, we put forward to tackle visual sentiment analysis under multiple instance learning (MIL) formulation. We propose a deep multiple clustered instance learning formulation, under which a deep multiple clustered instance learning network (DMCILN) is constructed for visual sentiment analysis. Specifically, the input image is converted into a bag of instances through visual instance generation module, which is composed of a pre-trained convolutional neural …network (CNN) and two adaptation layers. Then, a fuzzy c-means routing algorithm is introduced for generating clustered instances as semantic mid-level representation to bridge the instance-to-bag gap. To explore the relationships between clustered instances and bags, we construct an attention based MIL pooling layer for representing bag features. A multi-head mechanism is integrated to form MIL ensembles, which enables to weigh the contribution of each clustered instance in different subspaces for generating more robust bag representation. Finally, we conduct extensive experiments on several datasets, and the experimental results verify the feasibility of our proposed approach for visual sentiment analysis. Show more

Keywords: Visual sentiment analysis, deep multiple clustered instance learning, fuzzy c-means routing, multi-head mechanism

DOI: 10.3233/JIFS-200675

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7217-7231, 2020

Authors: Yoseph, Fahed | Heikkilä, Markku

Article Type: Research Article

Abstract: Market Intelligence is knowledge extracted from numerous data sources, both internal and external, to provide a holistic view of the market and to support decision-making. Association Rules Mining provides powerful data mining techniques for identifying associations and co-occurrences in large databases. Market Basket Analysis (MBA) uses ARM to gain insights from heterogeneous consumer shopping patterns and examines the effects of marketing initiatives. As Artificial Intelligence (AI) more and more finds its way to marketing, it entails fundamental changes in the skills-set required by marketers. For MBA, AI provides important ways to improve both the outcomes of the market basket analysis …and the performance of the analysis process. In this study we demonstrate the effects of AI on MBA by our proposed new MBA model where results of computational intelligence are used in data preprocessing, in market segmentation and in finding market trends. We show with point-of-sale (POS) data of a small, local retailer that our proposed “Åbo algorithm” MBA model increases mining performance/intelligence and extract important marketing insights to assess both demand dynamics and product popularity trends. Additionally, the results show how, as related to the 80/20 percent rule, 78% of revenue is derived 16% of the product assortment. Show more

Keywords: Association rules mining, artificial intelligence, market intelligence, small and medium-sized retailer

DOI: 10.3233/JIFS-200707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7233-7246, 2020