Journal of Intelligent & Fuzzy Systems - Volume 40, issue 5

Authors: Emeç, Şeyma | Akkaya, Gökay

Article Type: Research Article

Abstract: Energy consumption increases due to technological developments, urbanization, industrialization and population. The fact that the constantly increasing energy demand is not exactly known is an important issue for countries. In addition, due to changing climate conditions, the amount of emission emitted and energy produced from energy sources are also not quite known. Therefore, determining the energy demand, protecting the environment, and minimizing the energy cost by using resources effectively has become one of the most important problems of countries. In this context, the present study developed a fuzzy optimal renewable energy model (F-OREM) to solve the energy problem involving fuzzy …parameters. Fuzzy linear programming (FLP) models provide the best decision by producing faster and more flexible solutions compared to classical linear programming (CLP) models in situations where there are uncertainties and a lack of information. The purpose of the developed model was to minimize the cost of generating electrical energy from different energy sources in an uncertain environment under potential, demand, emission and efficiency constraints. The developed F-OREM was operated using CPLEX decoder in the GAMS 24.2.3 package program and using the particle swarm optimization (PSO) for ∝ different values between 0-1. The results showed that the results of the metaheuristic method and the results of the GAMS package program were the same, and the results were consistent. According to the results obtained, the emission level at which the objective function was minimum (when ∝=1) was at the lowest level. In this case, the total emitted amount was 1,06125E+14 g-CO2/kWh. In this context, the developed model can be applied using metaheuristic or heuristic methods for larger test cases with thousands of variables. This study contributed to the practicality of FLP by offering decision-makers a wider solution area than the CLP approach. Show more

Keywords: Energy economics, energy policy, fuzzy programming, mathematical model, optimization

DOI: 10.3233/JIFS-201994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9529-9542, 2021

Authors: Liu, Peide | Wang, Dongyang | Zhang, Hui | Yan, Liang | Li, Ying | Rong, Lili

Article Type: Research Article

Abstract: T-spherical fuzzy numbers (FNs), which add an abstinence degree based on membership and non-membership degrees, can express neutral information conveniently and have a considerable large range of information expression. The normal FNs (NFNs) are very available to characterize normal distribution phenomenon widely existing in social life. In this paper, we first define the normal T-SFNs (NT-SFNs) which can combine the advantages of T-SFNs and NFNs. Then, we define their operational laws, score value, and accuracy value. By considering the interrelationship among multi-input parameters, we propose the Maclaurin symmetric mean operator with NT-SFNs (NT-SFMSM) and its weighted form (NT-SFWMSM). Furthermore, we …study some characteristics and special cases of them. Based on the NT-SFWMSM operator, we put forward a novel multi-attribute decision-making (MADM) approach. Finally, some numerical examples are conducted to prove that the proposed approach is valid and superior to some other existing methods. Show more

Keywords: MADM, normal T-spherical fuzzy numbers, normal distribution

DOI: 10.3233/JIFS-202000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9543-9565, 2021

Authors: Tak, Nihat | Egrioglu, Erol | Bas, Eren | Yolcu, Ufuk

Article Type: Research Article

Abstract: Intuitionistic meta fuzzy forecast combination functions are introduced in the paper. There are two challenges in the forecast combination literature, determining the optimum weights and the methods to combine. Although there are a few studies on determining the methods, there are numerous studies on determining the optimum weights of the forecasting methods. In this sense, the questions like “What methods should we choose in the combination?” and “What combination function or the weights should we choose for the methods” are handled in the proposed method. Thus, the first two contributions that the paper aims to propose are to obtain the …optimum weights and the proper forecasting methods in combination functions by employing meta fuzzy functions (MFFs). MFFs are recently introduced for aggregating different methods on a specific topic. Although meta-analysis aims to combine the findings of different primary studies, MFFs aim to aggregate different methods based on their performances on a specific topic. Thus, forecasting is selected as the specific topic to propose a novel forecast combination approach inspired by MFFs in this study. Another contribution of the paper is to improve the performance of MFFs by employing intuitionistic fuzzy c-means. 14 meteorological datasets are used to evaluate the performance of the proposed method. Results showed that the proposed method can be a handy tool for dealing with forecasting problems. The outstanding performance of the proposed method is verified in terms of RMSE and MAPE. Show more

Keywords: Forecast combination, meta-analysis, intuitionistic fuzzy c-means, meta fuzzy functions, meteorology

DOI: 10.3233/JIFS-202021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9567-9581, 2021

Authors: Li, Chenliang | Yu, Xiaobing

Article Type: Research Article

Abstract: Communities are the fundamental units of society, and community-based disaster management is the foundation of societal disaster management systems. It is important to implement disaster prevention and mobilize all residents in the community to participate in preparedness activities. However, people’s attitudes and understanding of these issues are often ambiguous because meteorological disaster prevention and mitigation (MDPM) is complex. A hybrid model based on probabilistic term sets (PLTSs) and PROMETHEE method is put forward to solve this problem. To solve the problem from the view of big data, the experimental data are from Baidu’s disaster prevention and mitigation questionnaires. The data …of these questionnaires are aggregated through PLTSs. Then, the PROMETHEE method is used to learn about the public’s understanding of community meteorological disaster prevention and mitigation (CMDPM) information and their willingness to participate in activities. The results indicate that communities in East, Northwest, Southwest, and North China have a higher willingness to join volunteer services. The proposed model makes it more convenient for decision-makers (DMs) to describe problems by PLTSs and is more appropriate for individuals’ understanding and communication. Show more

Keywords: Meteorological disaster prevention and mitigation, PROMETHEE method, community-based disaster management

DOI: 10.3233/JIFS-202026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9583-9595, 2021

Authors: Jiang, Kui | Shang, Yujuan | Wang, Lei | Zhang, Zheqing | Zhou, Siwei | Dong, Jiancheng | Wu, Huiqun

Article Type: Research Article

Abstract: This study aims to propose a framework for developing a sharable predictive model of diabetic nephropathy (DN) to improve the clinical efficiency of automatic DN detection in data intensive clinical scenario. Different classifiers have been developed for early detection, while the heterogeneity of data makes meaningful use of such developed models difficult. Decision tree (DT) and random forest (RF) were adopted as training classifiers in de-identified electronic medical record dataset from 6,745 patients with diabetes. After model construction, the obtained classification rules from classifier were coded in a standard PMML file. A total of 39 clinical features from 2159 labeled …patients were included as risk factors in DN prediction after data preprocessing. The mean testing accuracy of the DT classifier was 0.8, which was consistent to that of the RF classifier (0.823). The DT classifier was choose to recode as a set of operable rules in PMML file that could be transferred and shared, which indicates the proposed framework of constructing a sharable prediction model via PMML is feasible and will promote the interoperability of trained classifiers among different institutions, thus achieving meaningful use of clinical decision making. This study will be applied to multiple sites to further verify feasibility. Show more

Keywords: Meaningful use, prediction model, diabetic nephropathy, real world data

DOI: 10.3233/JIFS-202030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9597-9608, 2021

Authors: Chen, Xiangtang | Sun, Bingzhen | Zhang, Xinrui | Qi, Chang | Chu, Xiaoli | Wang, Ting | Huang, Yantai

Article Type: Research Article

Abstract: Linguistic variable is an effective method of representation the preferences of a decision-maker for inaccuracy available information in decision making under uncertainty. This article investigates a multiple attribute ranking decision making problem with linguistic preference by using linguistic value soft rough set. Firstly, we present the definition of linguistic value fuzzy set by introducing the concept of linguistic variable into the original Zadeh’s fuzzy set. We then define the concept of linguistic value soft set and the pseudo linguistic value soft set over the alternative set and parameter set of discourse. Moreover, we investigate the basic operators and the mathematical …properties of the linguistic value soft set. Subsequently, we establish the rough approximation of an uncertainty concept with linguistic value over the object set and parameter set, i.e., the linguistic value soft rough set model. Meanwhile, we discuss several deformations of the linguistic value soft rough lower and upper approximations as well as some fundamental properties of the linguistic value soft approximation operators. With reference on the exploring of the fundamental of linguistic value soft rough set, we construct a new method for handling with the multiple attribute ranking decision making problems with linguistic information by combining the proposed soft rough set and the VIKOR method. Then, we give the detailed decision procedure and steps for the established decision approach. At last, an extensive numerical example is further conducted to illustrate the process of the decision making principle and the results are satisfactory. The main contribution of this paper is twofold. One is to provide a new model of granular computing by infusion the soft set and rough set theory with linguistic valued information. Another is to try making a new way to handle multiple attribute decision making problems based on linguistic value soft rough set and the VIKOR method. Show more

Keywords: Rough set, Soft set, Linguistic variable, Linguistic value soft approximation space, VIKOR method

DOI: 10.3233/JIFS-202085

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9609-9626, 2021

Authors: Ansar, Wazib | Goswami, Saptarsi | Chakrabarti, Amlan | Chakraborty, Basabi

Article Type: Research Article

Abstract: Aspect-Based Sentiment Analysis (ABSA) has become a trending research domain due to its ability to transform lives as well as the technical challenges involved in it. In this paper, a unique set of rules has been formulated to extract aspect-opinion phrases. It helps to reduce the average sentence length by 84% and the complexity of the text by 50%. A modified rank-based version of Term-Frequency - Inverse-Document-Frequency (TF-IDF) has been proposed to identify significant aspects. An innovative word representation technique has been applied for aspect categorization which identifies both local as well as global context of a word. For sentiment …classification, pre-trained Bidirectional Encoder Representations from Transformers (BERT) has been applied as it helps to capture long-term dependencies and reduce the overhead of training the model from scratch. However, BERT has drawbacks like quadratic drop in efficiency with an increase in sequence length which is limited to 512 tokens. The proposed methodology mitigates these drawbacks of a typical BERT classifier accompanied by a rise in efficiency along with an improvement of 8% in its accuracy. Furthermore, it yields enhanced performance and efficiency compared to other state-of-the-art methods. The assertions have been established through extensive analysis upon movie reviews and Sentihood data-sets. Show more

Keywords: Aspect-based sentiment analysis, aspect extraction, BERT, TF-IDF, word embedding

DOI: 10.3233/JIFS-202140

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9627-9644, 2021

Authors: Pirozmand, Poria | Kalantari, Kimia Rezaei | Ebrahimnejad, Ali | Motameni, Homayun

Article Type: Research Article

Abstract: Many methods have been presented in recent years for identifying the quality of agricultural products using machine vision that due to the huge amount of redundant information and noisy data of images of products, the retrieval accuracy and speed of such methods were not much acceptable. All of them try to provide approaches to extract efficient features and determine optimal methods to measure similarity between images. One of the basic problems of these methods is determination of desirable features of the user as well as using an appropriate similarity measure. This study tries to recognize the importance of each feature …according to user’s opinion in every feedback stage through using weighted feature vector, rough theory and fuzzy logic for identifying important features and finding a higher accuracy in retrieval result. The proposed method is compared with fuzzy color histogram, combined approach and fuzzy neighborhood entropy characterized by color location. The simulation results indicate that the proposed method has higher applicability in image marketing compared to the existing methods. Show more

Keywords: Quality evaluation, machine vision, rough theory, fuzzy logic, image processing

DOI: 10.3233/JIFS-202147

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9645-9654, 2021

Authors: Zhang, Pengdan | Liu, Qing | Kang, Bingyi

Article Type: Research Article

Abstract: Multi-attribute decision-making (MADM) is an important part of modern decision-making science. Fuzzy Analytic Hierarchy Process (Fuzzy AHP) is a popular model to deal with the issue of MADM for its flexible and effective advantages. However, The traditional Fuzzy AHP with some limitations does not consider the preference (attitude) of decision makers (DMs). In addition, some ideas of combining Ordered Weighted Average (OWA) and Fuzzy AHP don’t investigated the MADM well. Some programs are only applicable to a few examples, and more general cases do not result in effective decision making. Considering these shortcomings, an OWA-Fuzzy AHP decision model using OWA …weights and Fuzzy AHP is proposed in this paper. Our contribution is that the proposed method can handle situations where the degree of fuzzy synthesis is not intersected. Moreover, the loss of information can be reduced in the process of applying the proposed method, so that the decision result is more reasonable than the previous methods. Several examples and comparative experimental simulation are given to illustrate the effectiveness and superiority of the proposed model. Show more

Keywords: Fuzzy analytic hierarchy process(Fuzzy AHP), ordered weighted average (OWA), analytic hierarchy process (AHP), uncertain preferences, multi-attribute decision-making (MADM)

DOI: 10.3233/JIFS-202168

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9655-9668, 2021

Authors: Shi, Honghua | Ni, Yaodong

Article Type: Research Article

Abstract: Today’s supply chains have a greater likelihood of disruption risks than ever before. Sometimes, a lengthy recovery period is needed for supply chains to return to regular operation after being disrupted. During the recovery time window, how to increase the performance of supply chains is not sufficiently studied. Furthermore, the works considering parameter uncertainty arising from the lack of historical data are also limited. To address these problems, we formulate the recovery scheduling of supply chains under major disruption as mixed-integer linear programming models. In the presented models, outsourcing strategy and capacity expansion strategy are introduced to increase the service …level of the supply chain after the disruption. The effects of disruption risks on supply chain performance are quantified using uncertainty theory in the absence of historical data. A set of computational examples illustrate that cost may increase markedly when more facilities are disrupted simultaneously. Thus, decision-makers have to pay close attention to supply chain disruption management and plan for disruption in advance. Moreover, the results suggest that outsourcing strategy is more useful to reduce cost when a higher service level is required. Show more

Keywords: Supply chain, facility disruptions, recovery strategies, uncertainty

DOI: 10.3233/JIFS-202176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9669-9686, 2021