Journal of Intelligent & Fuzzy Systems - Volume 42, issue 1

Authors: Cagri Tolga, A. | Basar, Murat

Article Type: Research Article

Abstract: By 2050, the global population is estimated to rise to over 9 billion people, and the global food need is expected to ascend 50%. Moreover, by cause of climate change, agricultural production may decrease by 10%. Since cultivable land is constant, multi-layered farms are feasible alternatives to yield extra food from the unit land. Smart systems are logical options to assist production in these factory-like farms. When the amount of food grown per season is assessed, a single indoor hectare of a vertical farm could deliver yield equal to more than 30 hectares of land consuming 70% less water with …nearly zero usage of pesticides. In this study, we evaluated technology selection for three vertical farm alternatives via MCDM methods. Even though commercial vertical farms are set up in several countries, area is still fresh and acquiring precise data is difficult. Therefore, we employed fuzzy logic as much as possible to overcome related uncertainties. WEDBA (Weighted Euclidean Distance Based Approximation) and MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) methods are employed to evaluate alternatives. Show more

Keywords: Smart farming, WEDBA method, MACBETH method, urban agriculture

DOI: 10.3233/JIFS-219170

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 1-12, 2022

Authors: Radaev, Alexander | Korobov, Alexander | Yatsalo, Boris

Article Type: Research Article

Abstract: Assessing functions of fuzzy arguments and ranking of fuzzy quantities are two key steps in fuzzy modeling and Fuzzy Multicriteria Decision Analysis (FMCDA). Approximate calculations along with the use of centroid index as a defuzzification based ranking methods are a generally accepted approach to applications in the fuzzy environment. This paper presents a novel fuzzy system, F-CalcRank , which is integration of two coupled fuzzy systems: F-Calc (Fuzzy Calculator) and F-Ranking (Fuzzy Ranking). F-Calc allows assessing functions of fuzzy numbers with the use of different approaches: approximate calculations, standard fuzzy arithmetic, and transformation methods. …The input values to F-Calc are fuzzy numbers with the following membership functions: triangular and trapezoidal, Gaussian, bell shape, sigmoid, and piece-wise linear continuous or upper semicontinuous membership functions of any complexity, as well as fuzzy linguistic terms of a given term set. F-Ranking system is intended for ranking of a given set of fuzzy numbers, including those, which are inputs and/or outputs of the F-Calc system. F-Ranking includes six ranking methods: three defuzzification based and three pairwise comparison ones. The structure of F-CalcRank as well as input and output information and the user interfaces of both F-Calc and F-Ranking systems, which can also be used independently, are presented. Examples of computing functions of fuzzy arguments and ranking of fuzzy numbers using implemented methods as well as exploring a real case study in agro-ecology with the use of a math model in fuzzy environment are considered. These examples stress the features and novelty of F-CalcRank system as well as presented applied research. The computer modules created within F-CalcRank are a basis for different FMCDA models developed by the authors. F-CalcRank system is intended for university education, research and various applications in engineering and technology. Show more

Keywords: Fuzzy numbers, approximate computations, standard fuzzy arithmetic, transformation methods, fuzzy ranking methods, fuzzy systems, fuzzy calculator

DOI: 10.3233/JIFS-219171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 13-28, 2022

Authors: Kahraman, Cengiz | Onar, Sezi Cevik | Öztayşi, Başar

Article Type: Research Article

Abstract: Linguistic terms are quite suitable to make evaluations in multiple criteria decision making problems since humans prefer them rather than sharp evaluations. When linguistic evaluations are used in the decision matrix instead of exact numerical values, fuzzy set theory can capture the vagueness in the linguistic evaluations. Ordinary fuzzy sets have been extended to many new types of fuzzy sets such as intuitionistic fuzzy sets, neutrosophic sets, spherical fuzzy sets and picture fuzzy sets. Spherical fuzzy sets are an extension of picture fuzzy sets whose squared sum of their parameters is at most equal to one. This paper develops a …novel spherical fuzzy CRiteria Importance Through Intercriteria Correlation (CRITIC) method and applies it for prioritizing supplier selection criteria. Supplier selection is one of the most critical aspects of any organization since any mistake in this process may cause poor supplier performance and inefficiencies in the business processes. Supplier selection is a multi-criteria decision making problem involving several conflicting criteria and alternatives. A numerical illustration of the proposed method is also given for this problem. Show more

Keywords: Spherical fuzzy sets, CRITIC, supplier selection, aggregation, decision making

DOI: 10.3233/JIFS-219172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 29-36, 2022

Authors: Boltürk, Eda

Article Type: Research Article

Abstract: Engineering economics is an essential issue in investments and can be quietly difficult to make decisions especially in indefinite, vague and incomplete environments because of human thought. Usage of fuzzy sets gives better solutions in vagueness. Fuzzy sets could be an agreeable tool when no probabilities are accessible for states of nature and decisions are given under incompleteness. In this study, fuzzy engineering economics studies are summarized for showing fuzzy sets usage in engineering economics applications and finding gaps for future studies. The possible suggested are given in conclusion.

Keywords: Fuzzy sets, present worth analysis, cash flow, internal rate of return, capital budgeting

DOI: 10.3233/JIFS-219173

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 37-46, 2022

Authors: Çakır, Esra | Ulukan, Ziya | Kahraman, Cengiz | Sağlam, Canan Ölçer | Kuleli Pak, Burcu | Pekcan, Bahadır

Article Type: Research Article

Abstract: Milk-run is a delivery method allowing to move small quantities of a large number of different items with predictable lead times from various suppliers to a customer. The main goal is to minimize the transportation cost by minimizing the travel distance and by maximizing vehicle capacities. The effects of uncertainties in arrival times of vehicles and loading times of shipments should also be considered in modeling the milk-run problems. In this paper, a multi-objective linear programming model, an ordinary fuzzy multi-objective linear programming model and an intuitionistic fuzzy multi-objective linear programming model are proposed for the milk-run modeling under time …window constraints. The proposed approaches are applied on the real-life data of Borusan Logistics which is one of the largest logistics firms in Turkey and the results are presented. Show more

Keywords: Ordinary fuzzy set, intuitionistic fuzzy set, fuzzy multi-objective programming, milk-run, time window constraint, logistic network

DOI: 10.3233/JIFS-219174

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 47-62, 2022

Authors: Ozdemir, Cagatay | Onar, Sezi Cevik | Bagriyanik, Selami | Kahraman, Cengiz | Akalin, Burak Zafer | Öztayşi, Başar

Article Type: Research Article

Abstract: Companies started to determine their strategies based on intelligent data analysis due to stagey enhance data production. Literature reviews show that the number of resources where demand estimation, location analysis, and decision-making technique applied together with the machine learning method is low in all sectors and almost none in the shopping mall domain. Within this study’s scope, a new hybrid fuzzy prediction method has been developed that will estimate the customer numbers for shopping malls. This new methodology is applied to predict the number of visitors of three shopping malls on the Anatolian side of Istanbul. The forecasting study for …corresponding shopping malls is made by using the daily signaling data from indoor base stations of large-scale technology and telecommunications services provider and the features to be used in machine learning models is determined by fuzzy multi criteria decision making method. Output revealed by the application of the fuzzy multi criteria decision making method enables the prioritization of features. Show more

Keywords: Shopping malls, customer strategy, machine learning, location analysis, hybrid fuzzy prediction method, multi-criteria decision making

DOI: 10.3233/JIFS-219175

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 63-76, 2022

Authors: Yurttakal, Ahmet Haşim | Erbay, Hasan | İkizceli, Türkan | Karaçavuş, Seyhan | Biçer, Cenker

Article Type: Research Article

Abstract: Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its …excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545. Show more

Keywords: Stacked ensemble, radiomics, deep learning, gradient boosting, breast cancer

DOI: 10.3233/JIFS-219176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 77-85, 2022

Authors: Tekin, Ahmet Tezcan | Kaya, Tolga | Cebi, Ferhan

Article Type: Research Article

Abstract: The use of fuzzy logic in machine learning is becoming widespread. In machine learning problems, the data, which have different characteristics, are trained and predicted together. Training the model consisting of data with different characteristics can increase the rate of error in prediction. In this study, we suggest a new approach to assembling prediction with fuzzy clustering. Our approach aims to cluster the data according to their fuzzy membership value and model it with similar characteristics. This approach allows for efficient clustering of objects with more than one cluster characteristic. On the other hand, our approach will enable us to …combine boosting type ensemble algorithms, which are various forms of assemblies that are widely used in machine learning due to their excellent success in the literature. We used a mobile game’s customers’ marketing and gameplay data for predicting their customer lifetime value for testing our approach. Customer lifetime value prediction for users is crucial for determining the marketing cost cap for companies. The findings reveal that using a fuzzy method to ensemble the algorithms outperforms implementing the algorithms individually. Show more

Keywords: CLTV prediction, fuzzy model selection, fuzzy regression, ensemble learning, the gaming industry

DOI: 10.3233/JIFS-219177

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 87-96, 2022

Authors: Buyuk, Aysu Melis | Temur, Gul T.

Article Type: Research Article

Abstract: In line with the increase in consciousness on sustainability in today’s global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was …used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions. Show more

Keywords: Food waste, fuzzy sets, multi criteria decision making, spherical fuzzy sets, spherical fuzzy analytic hierarchy process

DOI: 10.3233/JIFS-219178

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 97-107, 2022

Authors: Onar, Sezi Çevik | Kahraman, Cengiz | Öztayşi, Başar

Article Type: Research Article

Abstract: Autonomous vehicles are one of the emergent advances of the new technology era that has the prospective to redesign transportation structures. Understanding and measuring the limitations of adopting autonomous vehicles and selecting the best autonomous vehicle based on different aspects is crucial for enhancing the adoption process. Defining the criteria and the appropriate evaluation methodology is very important for selecting the best autonomous vehicles. However, this selection process is a human judgment-based process where both benefit and cost criteria with imprecise linguistic assessments should be considered. The KEmeny Median Indicator Ranks Accordance (KEMIRA) method is a method that enables ranking …the benefit and cost criteria independently. In this paper, a new KEMIRA method based on hesitant fuzzy linguistic term sets is defined. Hesitant Fuzzy Linguistic Term Sets (HFLTS) are newly utilized to represent the hesitancy of the decision-makers. The proposed new KEMIRA is approach the first study that defines the alternative scores and weights of the criteria via HFLTS. The computational steps of the new model are applied to autonomous vehicle selection. A real application is employed to show the applicability of the new KEMIRA method. Show more

Keywords: Autonomous vehicle adoption, KEMIRA, hesitant fuzzy sets, HFLTS

DOI: 10.3233/JIFS-219179

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 109-120, 2022