Journal of Intelligent & Fuzzy Systems - Volume 40, issue 1

Authors: Shiri Daryani, Zahra | Tohidi, Ghasem | Daneshian, Behrouz | Razavyan, Shabnam | Hosseinzadeh Lotfi, Farhad

Article Type: Research Article

Abstract: Inputs and outputs of Decision Making Units (DMUs) are estimated by the Inverse Data Envelopment Analysis (InvDEA) models, while their relative efficiency scores remain unchanged. But, in some cases, cost/price information of the inputs and outputs are available. This paper employs the input and output cost/price information, including the generalized InvDEA concept in two-stage structures. To this end, it proposes a four-stage method to deal with the InvDEA concept, for estimating the inputs and outputs of the DMUs with a two-stage network structure method, while the allocative efficiency scores of all the units remain stable. Eventually, an empirical example is …rendered to illustrate the competence of the method which is presented. Show more

Keywords: Inverse DEA, network DEA, two-stage network, cost efficiency, input/output estimation

DOI: 10.3233/JIFS-200386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 591-603, 2021

Authors: Xu, Lei | Liu, Yi | Liu, Haobin

Article Type: Research Article

Abstract: For the sake of better handle the imprecise and uncertain information in decision making problems(DMPs), linguistic interval-valued intuitionistic fuzzy numbers(LIVIFNs) based aggregation operators (AOS) are proposed by combining extended Copulas (ECs), extended Co-copulas (ECCs), power average operator and linguistic interval-valued intuitionistic fuzzy information (LIVIFI). First of all, ECs and ECCs, some specifics of ECs and ECCs, score and accuracy functions of LIVIFNs are gained. Then, based on ECs and ECCs, several aggregation operators are proposed to aggregate LIVIFI, which can offer decision makers (DMs) desirable generality and flexibility. In addition, the desired properties of proposed AOS are discussed. Last but …not least, a MAGDM approach is constructed based on proposed AOs; Consequently, the effectiveness of the proposed approach is verified by a numerical example, and then the advantages are showed by comparing with other approaches. Show more

Keywords: linguistic interval-valued intuitionistic fuzzy set (LIVIFS), Extended Copulas, Extended Co-copulas, PA operator, multi-attribute group decision making(MAGDM)

DOI: 10.3233/JIFS-200387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 605-624, 2021

Authors: Mahmood, Tahir | Ur Rehman, Ubaid | Ali, Zeeshan | Mahmood, Tariq

Article Type: Research Article

Abstract: Fuzzy set (FS) theory is one of the most important tool to deasl with complicated and difficult information in real-world. Now FS has many extensions and hesitant fuzzy set (HFS) is one of them. Further generalization of FS is complex fuzzy set (CFS), which contains only the membership grade, whose range is unit disc instead of [0, 1]. The aim of this paper is to present the idea of complex hesitant fuzzy set (CHFS) and to introduce its basic properties. Basically, CHFS is the combination of CFS and HFS to deal with two dimension information in a single set. Further, …the vector similarity measures (VSMs) such as Jaccard similarity measures (JSMs), Dice similarity measures (DSMs) and Cosine similarity measures (CSMs) for CHFSs are discussed. The special cases of the proposed measures are also discussed. Then, the notion of complex hesitant fuzzy hybrid vector similarity measures are utilized in the environment of pattern recognition and medical diagnosis. Further, based on these distance measures, a decision-making method has been presented for finding the best alternative under the set of the feasible one. Illustrative examples from the field of pattern recognition as well as medical diagnosis have been taken to validate the approach. Finally, the comparison between proposed approaches with existing approaches are also discussed to find the reliability and proficiency of the elaborated measures for complex hesitant fuzzy elements. Show more

Keywords: Complex fuzzy set, complex hesitant fuzzy sets, similarity measures, hybrid vector similarity measures

DOI: 10.3233/JIFS-200418

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 625-646, 2021

Authors: El_Tokhy, Mohamed S.

Article Type: Research Article

Abstract: Development of a robust triple multimodal biometric approach for human authentication using fingerprint, iris and voice biometric is the main objective of this manuscript. Accordingly, three essential algorithms for biometric authentication are presented. The extracted features from these multimodals are combined via feature fusion center (FFC) and feature scores. These features are trained through artificial neural network (ANN) and support vector machine (SVM) classifiers. The first algorithm depends on boundary energy method (BEM) extracted features from fingerprint, normalized combinational features from iris and dimensionality reduction methods (DRM) from voice using sum/average FFC. The second proposed algorithm uses extracted features from …zoning method of fingerprint, SIFT of iris and higher order statistics (HOS) of voice signals. The third proposed algorithm consists of extracted features from zoning method for fingerprint, SIFT from iris and DRM from voice signals. Classification accuracy of implemented algorithms is estimated. Comparison between proposed algorithms is introduced in terms of equal error rate (EER) and ROC curves. The experimental results confirm superiority of second proposed algorithm which achieves a classification rate of 100% using SVM classifier and sum FFC. From computational point of view, the first algorithm consumes the lowest time using SVM classifier. On other hand, the lowest EER is achieved by first proposed algorithm for extracted features from Karhunen-Loeve transform (KLT) method of DRM. Additionally, the lowest ROC curves are accomplished respectively for extracted features from multidimensional scaling (MDS), generated ARMA synthesis and Isomap features. Their accuracy is improved with SVM. Also, the sum FFC introduces efficient results compared to average FFC. These algorithms have the advantages of robustness and the strength of selecting unimodal, double and triple biometric authentication. The obtained results accomplish a remarkable accuracy for authentication and security within multi practical applications. Show more

Keywords: Recognition system, digital signal and image processing, authentication systems

DOI: 10.3233/JIFS-200425

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 647-672, 2021

Authors: Wang, Degang | Song, Wenyan | Pedrycz, Witold | Cai, Lili

Article Type: Research Article

Abstract: In this paper, an integrated model combining interval deep belief network (IDBN) and neural network with nonlinear weights, called IDBN-NN, is proposed for interval-valued data modeling. Firstly, the IDBN with variable learning rate is designed to initialize parameters of each sub-model. Based on a modified contrastive divergence algorithm the least square method is adopted to identify the coefficients of nonlinear weights in the output layer. Then, to improve the modeling accuracy, the Fuzzy C-Means (FCM) method and the Particle Swarm Optimization (PSO) algorithm are applied to tune the weights of sub-models. Though each sub-model can capture the nonlinear feature of …the original system, by intersecting cut sets the synthesizing modeling scheme can further improve the performance of the proposed model. Some numerical examples show that the IDBN-NN with nonlinear output structure can achieve higher accuracy than some interval-valued data modeling methods. Show more

Keywords: Interval data, neural network, integrated model, fuzzy clustering

DOI: 10.3233/JIFS-200500

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 673-683, 2021

Authors: Wang, Huiru | Zhou, Zhijian

Article Type: Research Article

Abstract:  In Rough margin-based ν -Twin Support Vector Machine (Rν -TSVM) algorithm, the rough theory is introduced. Rν -TSVM gives different penalties to the corresponding misclassified samples according to their positions, so it avoids the overfitting problem to some extent. While the input data is a tensor, Rν -TSVM cannot handle it directly and may not utilize the data information effectively. Therefore, we propose a novel classifier based on tensor data, termed as Rough margin-based ν -Twin Support Tensor Machine (Rν -TSTM). Similar to Rν -TSVM, Rν -TSTM constructs rough lower margin, rough upper margin and rough boundary in tensor space. …Rν -TSTM not only retains the superiority of Rν -TSVM, but also has its unique advantages. Firstly, the data topology is retained more efficiently by the direct use of tensor representation. Secondly, it has better classification performance compared to other classification algorithms. Thirdly, it can avoid overfitting problem to a great extent. Lastly, it is more suitable for high dimensional and small sample size problem. To solve the corresponding optimization problem in Rν -TSTM, we adopt the alternating iteration method in which the parameters corresponding to the hyperplanes are estimated by solving a series of Rν -TSVM optimization problem. The efficiency and superiority of the proposed method are demonstrated by computational experiments. Show more

Keywords: Classification problem, ν-Twin support vector machine, rough margin, tensor learning

DOI: 10.3233/JIFS-200573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 685-702, 2021

Authors: Ali, Aqib | Mashwani, Wali Khan | Tahir, Muhammad H. | Belhaouari, Samir Brahim | Alrabaiah, Hussam | Naeem, Samreen | Nasir, Jamal Abdul | Jamal, Farrukh | Chesneau, Christophe

Article Type: Research Article

Abstract: The purpose of this study is the statistical analysis and discrimination of maize seed using a machine vision (MV) approach. The foundation of the digital image dataset holds six maize seed varieties named as Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88. The digital image dataset acquired via a digital imaging laboratory. For preprocessing, we crop the image into a size of 600×600 pixels, and convert it into a gray level image format. After that, line and edge detection are performed by using a Prewitt filter, and five non-overlapping areas of interest (AOIs) size of …(200×200), and (250×250) are drawn. A total of 56 statistical features, containing texture features, histogram features, and spectral features, is extracted from each AOI. The 11 optimized statistical features have been selected by deploying “Correlation-based Feature Selection” (CFS) with the Greedy algorithm. For the discrimination analysis, four MV classifiers named as “Support Vector Machine” (SVM), “Logistic” (Lg), “Bagging” (B), and “LogitBoost” (LB) have been deployed on optimized statistical features dataset. After analysis, the SVM classifier has shown a promising accuracy of 99.93% on AOIs size (250×250). The obtained accuracy by SVM classifier on six maize seed varieties, namely Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88, were 99.9%, 99.8%, 100%, 100%, 99.9%, and 99.8%, respectively. Show more

Keywords: Maize seeds, statistical features, discrimination, machine vision, support vector machine

DOI: 10.3233/JIFS-200635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 703-714, 2021

Authors: Gou, Hongyuan | Zhang, Xianyong

Article Type: Research Article

Abstract: The multi-granulation rough sets serve as important hierarchical models for intelligent systems. However, their mainstream optimistic and pessimistic models are respectively too loose and strict, and this defect becomes especially serious in hierarchical processing on an attribute-expansion sequence. Aiming at the attribute-addition chain, compromised multi-granulation rough set models are proposed to systematically complement and balance the optimistic and pessimistic models. According to the knowledge refinement and measure order induced by the attribute-enlargement sequence, the basic measurement positioning and corresponding pointer labeling based on equilibrium statistics are used, and thus we construct four types of compromised models at three levels of …knowledge, approximation, and accuracy. At the knowledge level, the median positioning of ordered granulations derives Compromised-Model 1; at the approximation level, the average positioning of approximation cardinalities is performed, and thus the separation and integration of dual approximations respectively generate Compromised-Models 2 and 3; at the accuracy level, the average positioning of applied accuracies yields Compromised-Model 4. Compromised-Models 1–4 adopt distinctive cognitive levels and statistical perspectives to improve and perfect the multi-granulation rough sets, and their properties and effectiveness are finally verified by information systems and data experiments. Show more

Keywords: Multi-granulation rough set, statistical compromised modeling, attribute-addition chain, granular computing, tri-level analysis

DOI: 10.3233/JIFS-200708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 715-732, 2021

Authors: Gui, Wangyang | Zhang, Xu | Wang, Ai

Article Type: Research Article

Abstract: The construction of high-speed rails is regarded as a major opportunity for urban development by local governments in China, so various grand development plans are actively formulated to promote urban economic development. In this paper, the development of station space is evaluated empirically based on the calculated node and place values of 24 high-speed rail stations along the Beijing-Shanghai line and Bertolini’s “node-place” model. The results show that: (1) The 24 stations along the Beijing-Shanghai line have different development scale, which mostly act as sub-centers of the city, where the real estate industry, modern service industry and cultural industry are …dominated in station space planning. Moreover, local governments are optimistic about the accelerant effect of high-speed rail stations whose functional configuration along the line is relatively repeated, because all 24 stations are basically set with business centers. (2) The size of cities along the Beijing-Shanghai line is related to the node value, the higher the urban function level, the greater the node value, with great differences among cities. The node value of big cities is far higher than that of small and medium-sized cities, hence there are node-oriented station areas in big cities and place-oriented ones in middle-sized and small cities. However, there is no direct relationship between the urban function level of stations along the line and the value of urban places. In some small and medium-sized cities, the planning and development intensity and scale of station areas even exceed that of big cities. (3) Only Wuxi station and Nanjing station are in a balanced development state in the space planning of railway stations along the Beijing-Shanghai line. Therefore, the risk of long-term development of station area should be considered in the planning, and reasonable measures should be formulated to promote the sustainable development of station area, so as to form the overall development of Station City. Show more

Keywords: High speed railway, station area, Beijing-Shanghai line, node-place model

DOI: 10.3233/JIFS-200712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 733-743, 2021

Authors: Chellamani, Ganesh Kumar | Firdouse Ali Khan, M. | Chandramani, Premanand Venkatesh

Article Type: Research Article

Abstract: Day-ahead electricity tariff prediction is advantageous for both consumers and utilities. This article discusses the home energy management (HEM) scheme consisting of an electricity tariff predictor and appliance scheduler. The random forest (RF) technique predicts a short-term electricity tariff for the next 24 hours using the past three months of electricity tariff information. This predictor provides the tariff information to schedule the appliances at the most preferred time slot of a consumer with minimum electricity tariff, aiming high consumer comfort and low electricity bill for consumers. The proposed approach allows a user to be aware of their demand and their …comfort. The proposed approach makes use of present-day (D) tariff and immediate previous 30 days (D-1, D-2, ...  , D-30) of tariff information for training achieves minimum error values for next day electricity tariff prediction. The simulation results demonstrate the benefits of the RF approach for tariff prediction by comparing it with the support vector machine (SVM) and decision tree (DT) predicted tariffs against the actual tariff, provided by the utility day-ahead. The outcomes indicate that the RF produces the best results compared to SVM and DT predictions for performance metrics and end-user comfort. Show more

Keywords: Day-ahead tariff, decision tree, home energy management, random forest, support vector machine

DOI: 10.3233/JIFS-200722

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 745-757, 2021