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

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