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

Authors: Zhu, Wenhua | Peng, Hu | Leng, Chaohui | Deng, Changshou | Wu, Zhijian

Article Type: Research Article

Abstract: Breast cancer is a severe disease for women health, however, with expensive diagnostic cost or obsolete medical technique, many patients are hard to obtain prompt medical treatment. Thus, efficient detection result of breast cancer while lower medical cost may be a promising way to protect women health. Breast cancer detection using all features will take a lot of time and computational resources. Thus, in this paper, we proposed a novel framework with surrogate-assisted firefly algorithm (FA) for breast cancer detection (SFA-BCD). As an advanced evolutionary algorithm (EA), FA is adopted to make feature selection, and the machine learning as classifier …identify the breast cancer. Moreover, the surrogate model is utilized to decrease computation cost and expensive computation, which is the approximation function built by offline data to the real object function. The comprehensive experiments have been conducted under several breast cancer dataset derived from UCI. Experimental results verified that the proposed framework with surrogate-assisted FA significantly reduced the computation cost. Show more

Keywords: Breast cancer detection, firefly algorithm, machine learning, surrogate model, feature selection

DOI: 10.3233/JIFS-201124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8915-8926, 2021

Authors: Iranmanesh, Seyed Mehdi | Nasrabadi, Nasser M.

Article Type: Research Article

Abstract: In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelihood framework in a joint training manner which diversify the …estimated density in order to cover different modes. We propose to use an adversarial network to transfer knowledge from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines. Show more

Keywords: Generative adversarial network, adversarial training, mode collapse, network distillation, autoregressive model

DOI: 10.3233/JIFS-201202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8927-8938, 2021

Authors: Liu, Peide | Hendalianpour, Ayad | Hamzehlou, Mohammad

Article Type: Research Article

Abstract: The present study investigates a two-echelon supply chain including a usual retailer and two competing manufacturers. The objective function of our model is the maximization of the whole profit of the supply chain, which consists of the stochastic demand, shortage cost, and holding costs. This paper aims to analyze a single period with two products to define the optimum retail prices and wholesales under different game theory approaches (e.g., Bertrand, cooperation, and Stackelberg competitions) based on Double Interval Grey Numbers (DIGN). The other aim of this paper is to specify the price using the manufacturers and the common retailer and …considering the stochastic different channel power structures and demand function. In this paper, it is considered that different power structures of channel members may affect the optimal pricing decisions. In this paper, two pricing policies of manufacturers, eight pricing models and various structures of distribution channel members are utilized. In these pricing models, the impacts of retail substitutability are evaluated on the decisions of the chain members and the equilibrium profits. In this paper, the products are substitutable and the demand is stochastic. In this model, the demand is not certain then, we may have shortages or unsold products. Finally, sensitivity analysis is provided for illustrating the theoretical outcomes established in each case. Show more

Keywords: Pricing, stochastic demand, supply chain, game theory, double interval grey numbers

DOI: 10.3233/JIFS-201206

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8939-8961, 2021

Authors: Rababa, Salahaldeen | Al-Badarneh, Amer

Article Type: Research Article

Abstract: Large-scale datasets collected from heterogeneous sources often require a join operation to extract valuable information. MapReduce is an efficient programming model for processing large-scale data. However, it has some limitations in processing heterogeneous datasets. This is because of the large amount of redundant intermediate records that are transferred through the network. Several filtering techniques have been developed to improve the join performance, but they require multiple MapReduce jobs to process the input datasets. To address this issue, the adaptive filter-based join algorithms are presented in this paper. Specifically, three join algorithms are introduced to perform the processes of filters creation …and redundant records elimination within a single MapReduce job. A cost analysis of the introduced join algorithms shows that the I/O cost is reduced compared to the state-of-the-art filter-based join algorithms. The performance of the join algorithms was evaluated in terms of the total execution time and the total amount of I/O data transferred. The experimental results show that the adaptive Bloom join, semi-adaptive intersection Bloom join, and adaptive intersection Bloom join decrease the total execution time by 30%, 25%, and 35%, respectively; and reduce the total amount of I/O data transferred by 18%, 25%, and 50%, respectively. Show more

Keywords: Join algorithms, big data management, query optimization, MapReduce

DOI: 10.3233/JIFS-201220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8963-8980, 2021

Authors: Lu, Ting | Xiang, Yan | Liang, Junge | Zhang, Li | Zhang, Mingfang

Article Type: Research Article

Abstract: The grand challenge of cross-domain sentiment analysis is that classifiers trained in a specific domain are very sensitive to the discrepancy between domains. A sentiment classifier trained in the source domain usually have a poor performance in the target domain. One of the main strategies to solve this problem is the pivot-based strategy, which regards the feature representation as an important component. However, part-of-speech information was not considered to guide the learning of feature representation and feature mapping in previous pivot-based models. Therefore, we present a fused part-of-speech vectors and attention-based model (FAM) . In our model, we fuse part-of-speech …vectors and feature word embeddings as the representation of features, giving deep semantics to mapping features. And we adopt Multi-Head attention mechanism to train the cross-domain sentiment classifier to obtain the connection between different features. The results of 12 groups comparative experiments on the Amazon dataset demonstrate that our model outperforms all baseline models in this paper. Show more

Keywords: Part-of-speech vectors, Multi-Head attention mechanism, cross-domain sentiment analysis

DOI: 10.3233/JIFS-201295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8981-8989, 2021

Authors: Jamil, Faisal | Kim, DoHyeun

Article Type: Research Article

Abstract: In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. …The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach. Show more

Keywords: ANN, FIS based Kalman Filter, navigation system, inertial measurement unit, indoor navigation, sensors fusion

DOI: 10.3233/JIFS-201352

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8991-9005, 2021

Authors: Subudhi, Jyotirmayee | Indumathi, P.

Article Type: Research Article

Abstract: Non-Orthogonal Multiple Access (NOMA) provides a positive solution for multiple access issues and meets the criteria of fifth-generation (5G) networks by improving service quality that includes vast convergence and energy efficiency. The problem is formulated for maximizing the sum rate of MIMO-NOMA by assigning power to multiple layers of users. In order to overcome these problems, two distinct evolutionary algorithms are applied. In particular, the recently implemented Salp Swarm Algorithm (SSA) and the prominent Optimization of Particle Swarm (PSO) are utilized in this process. The MIMO-NOMA model optimizes the power allocation by layered transmission using the proposed Joint User Clustering …and Salp Particle Swarm Optimization (PPSO) power allocation algorithm. Also, the closed-form expression is extracted from the current Channel State Information (CSI) on the transmitter side for the achievable sum rate. The efficiency of the proposed optimal power allocation algorithm is evaluated by the spectral efficiency, achievable rate, and energy efficiency of 120.8134bits/s/Hz, 98Mbps, and 22.35bits/Joule/Hz respectively. Numerical results have shown that the proposed PSO algorithm has improved performance than the state of art techniques in optimization. The outcomes on the numeric values indicate that the proposed PSO algorithm is capable of accurately improving the initial random solutions and converging to the optimum. Show more

Keywords: Energy efficiency, MIMO-NOMA, Non-orthogonal multiple access, PSO optimization, power allocation, layered transmission, user clustering

DOI: 10.3233/JIFS-201412

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9007-9019, 2021

Authors: He, Peng | Wang, Xue-ping

Article Type: Research Article

Abstract: This paper first describes a characterization of a lattice L which can be represented as the collection of all up-sets of a poset. It then obtains a representation of a complete distributive lattice L 0 which can be embedded into the lattice L such that all infima, suprema, the top and bottom elements are preserved under the embedding by defining a monotonic operator on a poset. This paper finally studies the algebraic characterization of a finite distributive.

Keywords: 03E72, 06D05, L-fuzzy set, cut set, complete distributive lattice, embedding, monotonic operator

DOI: 10.3233/JIFS-201430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9021-9030, 2021

Authors: Xiao, Hui-Min | Wang, Mei-Qi | Cao, Yan-Li | Guo, Yu-Jie

Article Type: Research Article

Abstract: In this paper, to improve the situation of singleness of selecting results in hesitant fuzzy set decision-making and expand the range of choices for decision makers, we construct a hesitant fuzzy set clustering algorithm combined with fuzzy matroid operation. The algorithm synthesizes the r-cut set, fuzzy shrinking matroids in the fuzzy matroids and the operational properties of the fuzzy derived matroids, the r value also is used to connect the two types of fuzzy matroids to form a clustering algorithm. Finally, we apply the algorithm to the hesitant fuzzy set decision-making of job seekers choosing recruitment websites, each recruitment website …as an optional scheme is divided into three categories of excellent to inferior schemes to provide job seekers with ideas and methods for favorably selecting recruitment websites. Show more

Keywords: Hesitant fuzzy set decision-making, fuzzy matroid, contraction matroid, derived matroid, clustering algorithm

DOI: 10.3233/JIFS-201476

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9031-9039, 2021

Authors: Sahoo, Arun Kumar | Panigrahi, Tapas Kumar | Dhiman, Gaurav | Singh, Krishna Kant | Singh, Akansha

Article Type: Research Article

Abstract: In this paper, an enhanced version of the emperor penguin optimization algorithm is proposed for solving dynamic economic dispatch (DED) problem incorporating renewable energy sources and microgrid. Dynamic economic load dispatch optimally shares the power on an hourly basis for a day among the committed generating units to satisfy the feasible load demand. Emission of pollutants from the combustion fossil fuel and gradual depletion of fossil fuel encourages the usage of renewable energy sources. Implementation of renewable energy sources with the reinforcement of green energy transforms the fossil fuel-based plant into a hybrid generating plant. The increase in power production …with the increase in electricity demand implicates challenges for economical operation. The proposed algorithm is applied to the DED problem for fossil fuel based and renewable energy system to find economic schedule of generated power among the committed generating units. The proposed optimization algorithm is inspired by the huddling behavior of the emperor penguin. The exploration strategy is enhanced by adapting oppositional based learning. Chaotic mapping is used to maintain a proper balance between exploration and exploitation in the entire search space, which minimizes the cost of generation in the power system. Show more

Keywords: Dynamic economic dispatch (DED), emperor penguin optimization (EPO), chaotic oppositional learning-based emperor penguin optimization (COLEPO), constraints, wind energy, micro grid

DOI: 10.3233/JIFS-201483

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9041-9058, 2021

Authors: Priambodo, Bagus | Ahmad, Azlina | Kadir, Rabiah Abdul

Article Type: Research Article

Abstract: Traffic congestion on a road results in a ripple effect to other neighbouring roads. Previous research revealed existence of spatial correlation on neighbouring roads. Similar traffic patterns with regards to day and time can be seen amongst roads in a neighbouring area. Presently, nonlinear models of neural network are applied on historical data to predict traffic congestion. Even though neural network has successfully modelled complex relationships, more time is needed to train the network. A non-parametric approach, the k-nearest neighbour (K-NN) is another method for forecasting traffic condition which can capture the nonlinear characteristics of traffic flow. An earlier study …has been done to predict traffic flow using K-NN based on connected roads (both downstream and upstream). However, impact of road congestion is not only to connected roads, but also to roads surrounding it. Surrounding roads that are impacted by road congestion are those having ‘high relationship’ with neighbouring roads. Thus, this study aims to predict traffic state using K-NN by determining high relationship roads within neighbouring roads. We determine the highest relationship neighbouring roads by clustering the surrounding roads by combining grey level co-occurrence matrix (GLCM) with k-means. Our experiments showed that prediction of traffic state using K-NN based on high relationship roads using both GLCM and k-means produced better accuracy than using k-means only. Show more

Keywords: Classification algorithm, clustering algorithm, machine learning algorithm, nearest neighbour search, intelligent transportation system

DOI: 10.3233/JIFS-201493

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9059-9072, 2021

Authors: Zhang, Mo | Zhang, Qinghua | Gao, Man

Article Type: Research Article

Abstract: As a new extended model of fuzzy sets, hesitant fuzzy set theory is a useful tool to process uncertain information in decision making problems. The traditional hesitant fuzzy multi-attribute decision making (MADM) can only choose an optimal strategy, which is not suitable for all of the complex scenarios. Typically, in practical application, decision making problems may be more complicated involving three options of acceptance, non-commitment and rejection decisions. Three-way decisions, which divide universe into three disjoint regions by a pair of thresholds, are more efficient to deal with these problems. Therefore, how to utilize three-way decision theory to process hesitant …fuzzy information is an essential issue to be studied. In this paper, from the perspective of hesitant fuzzy distance, a hesitant fuzzy three-way decision model is proposed. First, because hesitant fuzzy element (HFE) is a set of several possible membership degrees, it cannot be compared with thresholds directly. Hence, this paper converts it into the comparison between the distance and the thresholds. Then, to calculate thresholds more reasonably, shadowed set theory is introduced to avoid the subjectivity of threshold acquisition. Furthermore, sequential strategy is adopted to solve the multi-attribute decision making problems. Finally, an example of medical diagnosis and simulation experiments are given to prove the accuracy and efficiency of the proposed hesitant fuzzy three-way decision model. Show more

Keywords: Hesitant fuzzy sets, three-way decisions, shadowed sets, sequential strategy

DOI: 10.3233/JIFS-201524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9073-9084, 2021

Authors: Liu, Peide | Pan, Qian | Xu, Hongxue

Article Type: Research Article

Abstract: The normal intuitionistic fuzzy number (NIFN), which membership function and non-membership function are expressed by normal fuzzy numbers (NFNs), can better describe the normal distribution phenomenon in the real world, but it cannot deal with the situation where the sum of membership function and non-membership function is greater than 1. In order to make up for this defect, based on the idea of q-rung orthopair fuzzy numbers (q-ROFNs), we put forward the concept of normal q-rung orthopair fuzzy numbers (q-RONFNs), and its remarkable characteristic is that the sum of the qth power of membership function and the qth …power of non-membership function is less than or equal to 1, so it can increase the width of expressing uncertain information for decision makers (DMs). In this paper, firstly, we give the basic definition and operational laws of q-RONFNs, propose two related operators to aggregate evaluation information from DMs, and develop an extended indifference threshold-based attribute ratio analysis (ITARA) method to calculate attribute weights. Then considering the multi-attributive border approximation area comparison (MABAC) method has strong stability, we combine MABAC with q-RONFNs, put forward the q-RONFNs-MABAC method, and give the concrete decision steps. Finally, we apply the q-RONFNs-MABAC method to solve two examples, and prove the effectiveness and practicability of our proposed method through comparative analysis. Show more

Keywords: Normal q-rung orthopair fuzzy numbers, multi-attributive border approximation area comparison, the q-RONFNs-MABAC method, indifference threshold-based attribute ratio analysis

DOI: 10.3233/JIFS-201526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9085-9111, 2021

Authors: Xu, Tingting | Zhang, Hui | Li, Boquan

Article Type: Research Article

Abstract: In this paper, the concept of 2-tuple probability weight is presented, and on this basis, the technique for order preference by similarity to ideal solution (TOPSIS) method in Pythagorean fuzzy environment is given. First, the definition of 2-tuple probability weight is put forward, and two examples are provided to illustrate that 2-tuple probability weight can effectively prevent the loss of information. Second, the notion of real-value 2-tuple is defined for any two real numbers, and some basic operations, operation properties, and sorting functions are introduced. Finally, a 2-tuple probability weight Euclidean distance is provided, a new Pythagorean fuzzy TOPSIS method …is further proposed, and the flexibility and effectiveness of the proposed methods are illustrated by an example and two comparative analyses. Show more

Keywords: Pythagorean fuzzy set, 2-tuple probability weight, real-value 2-tuple, TOPSIS method

DOI: 10.3233/JIFS-201533

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9113-9126, 2021

Authors: Yan, Zheping | Zhang, Jinzhong | Zeng, Jia | Tang, Jialing

Article Type: Research Article

Abstract: In this paper, a water wave optimization (WWO) algorithm is proposed to solve the autonomous underwater vehicle (AUV) path planning problem to obtain an optimal or near-optimal path in the marine environment. Path planning is a prerequisite for the realization of submarine reconnaissance, surveillance, combat and other underwater tasks. The WWO algorithm based on shallow wave theory is a novel evolutionary algorithm that mimics wave motions containing propagation, refraction and breaking to obtain the global optimization solution. The WWO algorithm not only avoids jumps out of the local optimum and premature convergence but also has a faster convergence speed and …higher calculation accuracy. To verify the effectiveness and feasibility, the WWO algorithm is applied to solve the randomly generated threat areas and generated fixed threat areas. Compared with other algorithms, the WWO algorithm can effectively balance exploration and exploitation to avoid threat areas and reach the intended target with minimum fuel costs. The experimental results demonstrate that the WWO algorithm has better optimization performance and is robust. Show more

Keywords: Water wave optimization (WWO), autonomous underwater vehicle (AUV), path planning, randomly generated threat areas, generated fixed threat areas

DOI: 10.3233/JIFS-201544

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9127-9141, 2021

Authors: Shakeri Aski, Baharak | Toroghi Haghighat, Abolfazl | Mohsenzadeh, Mehran

Article Type: Research Article

Abstract: Using Web services to assess data in a distributed configuration, apart from different hardware and software platforms for employing standard criteria, is practical because of development in the Internet and network infrastructure. Distributed applications can transfer data using web services. Trust is the main criterion to select the appropriate web service. Neuro-fuzzy systems including clustering are applied to assess the trust of single web services. This paper considers nine criteria including quality of service, subjective perspectives, user preference, credibility of raters, objective perspectives, dynamic computing, bootstrapping, independency and security. To obtain a neuro-fuzzy system with high prediction accuracy, the paper …considers eight neuro-fuzzy membership functions (i.e., trapmf, gbellmf, trimf, gaussmf, dsigmf, psigmf, gauss2mf, pimf) using the k-means clustering. Also, to increase the speed and reduce the fuzzy rules, a three-level neuro-fuzzy system (13 neuro-fuzzy) is investigated. The main target of this paper is evaluating the trust of single web services using the nine aforementioned criteria, as web services selection is a main issue which is still absorbing researchers to conduct research works on this field and analyze it. Ultimately, the results show reasonable root mean square error (RMSE) amount, precision value, recall value, and F-score value. In comparison to previous research works, this study obtained the lower amounts of errors and presents the more accurate trust of single web services. Show more

Keywords: Web service, internet service, trust, neuro-fuzzy system, k-means

DOI: 10.3233/JIFS-201560

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9143-9157, 2021

Authors: Ye, Jun | Du, Shigui | Yong, Rui | Zhang, Fangwei

Article Type: Research Article

Abstract: In indeterminate and inconsistent setting, existing simplified neutrosophic indeterminate set (SNIS) can be depicted by the neutrosophic number (NN) functions of the truth, falsity and indeterminacy. Then, the three NN functions in SNIS lack their refined expressions and then the simplified neutrosophic indeterminate decision making (DM) method cannot carry out the multicriteria DM problems with both criteria and sub-criteria in the setting of SNISs. To overcome the flaws, this study first proposes a new notion of a refined simplified neutrosophic indeterminate set (RSNIS), which is described by the refined truth, falsity and indeterminate NN information regarding both elements and sub-elements …in a universe set, as the extension of SNIS. Next, we propose the arccosine and arctangent similarity measures of RSNISs and their multicriteria DM method with various indeterminate risk ranges so as to carry out multicriteria DM problems with weight values of both criteria and sub-criteria in RSNIS setting. Lastly, the proposed DM method is applied to a multicriteria DM example of slope design schemes for an open pit mine to illustrate its application in the indeterminate DM problem with RSNISs. The decision results and comparative analysis indicate the rationality and efficiency of the proposed DM method with different indeterminate risk ranges. Show more

Keywords: Refined simplified neutrosophic indeterminate set, arccosine similarity measure, arctangent similarity measure, multicriteria decision making, Slope design scheme

DOI: 10.3233/JIFS-201571

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9159-9171, 2021

Authors: Gao, Yuxuan | Liang, Haiming | Sun, Bingzhen

Article Type: Research Article

Abstract: With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers …for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform. Show more

Keywords: DIHR, recommendation algorithm, network intelligent recommendation system, online shopping platform

DOI: 10.3233/JIFS-201579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9173-9185, 2021

Authors: Jothikumar, C. | Venkataraman, Revathi | Sai Raj, T. | Selvin Paul Peter, J. | Nagamalleswari, T.Y.J.

Article Type: Research Article

Abstract: Wireless sensor network is a wide network that works as a cutting edge model in industrial applications. The sensor application is mostly used for high security systems that provide safety support to the environment. The sensor system senses the physical phenomenon, processes the input signal and communicates with the base station through its neighbors. Energy is the most important criterion to support a live network for long hours. In the proposed system, the EUCOR (Efficient Unequal Clustering and Optimized Routing) protocol uses the objective function to identify the efficient cluster head with variable cluster size. The computation of the objective …function deals with the ant colony approach for minimum energy consumption and the varying size of the cluster in each cycle is calculated based on the competition radius. The system prolongs the lifespan of the nodes by minimizing the utilization of energy in the transmission of packets in the networks when compared with the existing system. Show more

Keywords: Energy optimization, routing, cluster head, wireless sensor network

DOI: 10.3233/JIFS-201607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9187-9195, 2021

Authors: Rajeswari, A.R. | Kulothungan, K. | Ganapathy, Sannasi | Kannan, Arputharaj

Article Type: Research Article

Abstract: WSN plays a major role in the design of IoT system. In today’s internet era IoT integrates the digital devices, sensing equipment and computing devices for data sensing, gathering and communicate the data to the Base station via the optimal path. WSN, owing to the characteristics such as energy constrained and untrustworthy environment makes them to face many challenges which may affect the performance and QoS of the network. Thus, in WSN based IoT both security and energy efficiency are considered as herculean design challenges and requires important concern for the enhancement of network life time. Hence, to address these …problems in this paper a novel secure energy aware cluster based routing algorithm named Trusted Energy Efficient Fuzzy logic based clustering Algorithm (TEEFCA) has been proposed. This algorithm consists of two major objectives. Firstly, the trustworthy nodes are identified, which may act as candidate nodes for cluster based routing. Secondly, the fuzzy inference system is employed under the two circumstances namely selection of optimal Cluster Leader (CL) and cluster formation process by considering the following three parameters such as (i) node’s Residual Energy level (ii) Cluster Density (iii) Distance Node BS. From, the experiment outcomes implemented using MATLAB it have been proved that TEEFCA shows significant improvement in terms of power conservation, network stability and lifetime when compared to the existing cluster aware routing approaches. Show more

Keywords: Internet of Things (IoT), WSN, energy, trust, clustering and routing

DOI: 10.3233/JIFS-201633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9197-9211, 2021

Authors: Gu, Tianlong | Liang, Haohong | Bin, Chenzhong | Chang, Liang

Article Type: Research Article

Abstract: How to accurately model user preferences based on historical user behaviour and auxiliary information is of great importance in personalized recommendation tasks. Among all types of auxiliary information, knowledge graphs (KGs) are an emerging type of auxiliary information with nodes and edges that contain rich structural information and semantic information. Many studies prove that incorporating KG into personalized recommendation tasks can effectively improve the performance, rationality and interpretability of recommendations. However, existing methods either explore the independent meta-paths for user-item pairs in KGs or use a graph convolution network on all KGs to obtain embeddings for users and items separately. …Although both types of methods have respective effects, the former cannot fully capture the structural information of user-item pairs in KGs, while the latter ignores the mutual effect between the target user and item during the embedding learning process. To alleviate the shortcomings of these methods, we design a graph convolution-based recommendation model called Combining User-end and Item-end Knowledge Graph Learning (CUIKG) , which aims to capture the relevance between users’ personalized preferences and items by jointly mining the associated attribute information in their respective KG. Specifically, we describe user embedding from a user KG and then introduce user embedding, which contains the user profile into the item KG, to describe item embedding with the method of Graph Convolution Network. Finally, we predict user preference probability for a given item via multilayer perception. CUIKG describes the connection between user-end KG and item-end KG, and mines the structural and semantic information present in KG. Experimental results with two real-world datasets demonstrate the superiority of the proposed method over existing methods. Show more

Keywords: Personalized recommendation, property knowledge graph, graph convolution network

DOI: 10.3233/JIFS-201635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9213-9225, 2021

Authors: Liu, Shulin

Article Type: Research Article

Abstract: Under the background of the national fitness craze, the demand space for social sports professionals is constantly expanding. However, according to the author’s investigation, the overall situation shows that the number of high-quality social sports professionals in Chinese colleges and universities is relatively small. Among them, the unsound teaching quality evaluation system of social sports major is one of the important reasons affecting the cultivation of high-quality talents, so it is imperative to construct a sound teaching quality evaluation system of social sports major. At the same time, the perfect social physical education teaching quality evaluation system is an important …basis for teachers’ teaching job evaluation and strengthening teachers’ management. And it is frequently considered as a multi-attribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is needed to tackle it. Depending on the conventional TOPSIS method and intuitionistic fuzzy sets (IFSs), this essay designs a novel intuitive distance based IF-TOPSIS method for teaching quality evaluation of physical education. First of all, a related literature review is conducted. What’s more, some necessary theories related to IFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria are decided objectively by utilizing CRITIC method. Afterwards, relying on novel distance measures between IFNs, the conventional TOPSIS method is extended to the intuitionistic fuzzy environment to calculate assessment score of each alternative. Eventually, an application about teaching quality evaluation of physical education and some comparative analysis have been given. The results think that the designed method is useful for teaching quality evaluation of physical education. Show more

Keywords: Multi-attribute group decision-making (MAGDM), intuitionistic fuzzy sets (IFSs), TOPSIS method, CRITIC method, teaching quality evaluation, physical education

DOI: 10.3233/JIFS-201672

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9227-9236, 2021

Authors: Liu, Peide | Khan, Qaisar | Mahmood, Tahir | Khan, Rashid Ali | Khan, Hidayat Ullah

Article Type: Research Article

Abstract: Pythagorean fuzzy set (PyFS) is an extension of various fuzzy concepts, such as fuzzy set (FS), intuitionistic FS, and it is enhanced mathematical gizmo to pact with uncertain and vague information. In this article, some drawbacks in the Dombi operational rules for Pythagorean fuzzy numbers (PyFNs) are examined and some improved Dombi operational laws for PyFNs are developed. We also find out that the value aggregated using the existing Dombi aggregation operators (DAOs) is not a PyFN. Furthermore, we developed two new aggregations, improved existing aggregation operators (AOs) for aggregating Pythagorean fuzzy information (PyFI) and are applied to multiple-attribute decision …making (MADM). To acquire full advantage of power average (PA) operators proposed by Yager, the Pythagorean fuzzy Dombi power average (PyFDPA) operator, the Pythagorean fuzzy Dombi weighted power average (PyFDWPA) operator, Pythagorean fuzzy Dombi power geometric (PyFDPG) operator, Pythagorean fuzzy Dombi weighted geometric (PyFDPWG) operator, improved the existing AOs and their desirable properties are discussed. The foremost qualities of these developed Dombi power aggregation operators is that they purge the cause of discomfited data and are more supple due to general parameter. Additionally, based on these Dombi power AOs, a novel MADM approach is instituted. Finally, a numerical example is given to show the realism and efficacy of the proposed approach and judgment with the existing approaches is also specified. Show more

Keywords: Pythagorean fuzzy set, PA operator, Dombi t-norm and Dombi t-conorm, MADM

DOI: 10.3233/JIFS-201723

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9237-9257, 2021

Authors: Liu, Man | Zhang, Hongjun | Hao, Wenning | Qi, Xiuli | Cheng, Kai | Jin, Dawei | Feng, Xinliang

Article Type: Research Article

Abstract: It is a challenge for existing artificial intelligence algorithms to deal with incomplete information of computer tactical wargames in military research, and one effective method is to take advantage of game replays based on data mining or supervised learning. However, the open source datasets of wargame replays are extremely rare, which obstruct the development of research on computer wargames. In this paper, a data set of wargame replays is opened for predicting algorithm on the condition of incomplete information, to be specific, we propose the dataset processing method for deep learning and an network model for enemy locations predicting. We …first introduce the criteria and methods of data preprocessing, parsing and feature extraction, then the training set and test set for deep learning are predefined. Furthermore, we have designed a newly specific network model for enemy locations predicting, including multi-head input, multi-head output, CNN and GRU layers to deal with the multi-agent and long-term memory problems. The experimental results demonstrate that our method achieves good performance of 84.9% on top-50 accuracy. Finally, we open source the data set and methods on https://github.com/daman043/AAGWS-Wargame-master. Show more

Keywords: Incomplete information, dataset, tactical wargame, locations prediction, deep learning, prediction model

DOI: 10.3233/JIFS-201726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9259-9275, 2021

Authors: Jia, Heming | Lang, Chunbo

Article Type: Research Article

Abstract: Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the …proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way. Show more

Keywords: Salp swarm algorithm, crossover scheme, Lévy flight, functions optimization

DOI: 10.3233/JIFS-201737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9277-9288, 2021

Authors: Huang, Jinfang | Jin, Xin | Lee, Shin-Jye | Huang, Shanshan | Jiang, Qian

Article Type: Research Article

Abstract: Since the intuitionistic fuzzy set (IFS) was proposed by Atanassov, many explorations of this particular fuzzy set were conducted. One of the most important areas is the study of similarity and distance between IFSs, which can measure the degree of deviation of objects with uncertain and vague features, and this technique has great value and potential to solve the fuzzy and uncertain problems in the real world. Based on our previous similarity/distance measure model D JJ  (α , β ), a new method is proposed for improving the performance of similarity/distance measure model of IFSs, which is derived from …the sum of the areas of two triangles constructed by the transformed isosceles triangles of two IFSs. A great effort is made to prove the validity of the proposed method by mathematical derivation. In order to further demonstrate the performance of the proposed method, we apply this method to solve some practical problems such as pattern recognition, medical diagnosis, and cluster analysis. In addition, we also list a series of the existing methods which are used to compare with the proposed method to prove the effectiveness and superiority. The experimental results confirm that the performance of the proposed method exceeds most of the existing methods. Show more

Keywords: Intuitionistic fuzzy set, similarity/distance measure, transformed isosceles triangle fuzzy number, decision-making, cluster analysis

DOI: 10.3233/JIFS-201763

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9289-9309, 2021

Authors: Kalsum, Tehmina | Mehmood, Zahid | Kulsoom, Farzana | Chaudhry, Hassan Nazeer | Khan, Amjad Rehman | Rashid, Muhammad | Saba, Tanzila

Article Type: Research Article

Abstract: Facial emotion recognition system (FERS) recognize the person’s emotions based on various image processing stages including feature extraction as one of the major processing steps. In this study, we presented a hybrid approach for recognizing facial expressions by performing the feature level fusion of a local and a global feature descriptor that is classified by a support vector machine (SVM) classifier. Histogram of oriented gradients (HoG) is selected for the extraction of global facial features and local intensity order pattern (LIOP) to extract the local features. As HoG is a shape-based descriptor, with the help of edge information, it can …extract the deformations caused in facial muscles due to changing emotions. On the contrary, LIOP works based on the information of pixels intensity order and is invariant to change in image viewpoint, illumination conditions, JPEG compression, and image blurring as well. Thus both the descriptors proved useful to recognize the emotions effectively in the images captured in both constrained and realistic scenarios. The performance of the proposed model is evaluated based on the lab-constrained datasets including CK+, TFEID, JAFFE as well as on realistic datasets including SFEW, RaF, and FER-2013 dataset. The optimal recognition accuracy of 99.8%, 98.2%, 93.5%, 78.1%, 63.0%, 56.0% achieved respectively for CK+, JAFFE, TFEID, RaF, FER-2013 and SFEW datasets respectively. Show more

Keywords: Facial emotion recognition, histogram-of-oriented-gradients, local intensity order pattern, support vector machine, texture features

DOI: 10.3233/JIFS-201799

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9311-9331, 2021

Authors: Keikha, Abazar

Article Type: Research Article

Abstract: Uncertainty has long been explored as an objective and inalienable reality, and then modeled via different theories such as probability theory, fuzzy sets (FSs) theory, vague sets, etc. Hesitant fuzzy sets (HFSs) as a generalization of FSs, because of their flexibility and capability, extended and applied in many practical problems very soon. However, the above theories cannot meet all the scientific needs of researchers. For example, in some decision-making problems we encounter predetermined definite data, which have inductive uncertainties. In other words, the numbers themselves are crisp in nature, but are associated with varying degrees of satisfaction or fairness from …the perspective of each decision-maker/judge. To this end, in this article, hesitant fuzzy numbers as a generalization of hesitant fuzzy sets will be introduced. Some concepts such as the operation laws, the arithmetic operations, the score function, the variance of hesitant fuzzy numbers, and a way to compare hesitant fuzzy numbers will be proposed. Mean-based aggregation operators of hesitant fuzzy numbers, i.e. hesitant fuzzy weighted arithmetic averaging (HWAA), hesitant fuzzy weighted geometric averaging (HWGA), hesitant fuzzy ordered weighted arithmetic averaging (HOWAA), and hesitant fuzzy ordered weighted geometric averaging (HOWGA) operators have been discussed in this paper, too. These new concepts will be used to model, and solve an uncertain multi-attribute group decision making (MAGDM) problem. The proposed method will be illustrated by a numerical example and the validity of the obtained solution will be checked by test criteria. Show more

Keywords: Hesitant fuzzy numbers, hesitant fuzzy sets, self-assessment, hesitant fuzzy averaging, hesitant fuzzy weighted averaging

DOI: 10.3233/JIFS-201808

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9333-9344, 2021

Authors: Liu, Haiqing | Li, Daoxing | Li, Yuancheng

Article Type: Research Article

Abstract: Reading digits from natural images is a challenging computer vision task central to a variety of emerging applications. However, the increased scalability and complexity of datasets or complex applications bring about inevitable label noise. Because the label noise in the scene digit recognition dataset is sequence-like, most existing methods cannot deal with label noise in scene digit recognition. We propose a novel sequence class-label noise filter called Confident Sequence Learning. Confident Sequence Learning consists of two critical parts: the sequence-like confidence segmentation algorithm and the Confident Learning method. The sequence-like confidence segmentation algorithms slice the sequence-like labels and the sequence-like …predicted probabilities, reorganize them in the form of the independent stochastic process and the white noise process. The Confident Learning method estimates the joint distribution between observed labels and latent labels using the segmented labels and probabilities. The TRDG dataset and SVHN dataset experiments showed that the confident sequence learning could find label errors with high accuracy and significantly improve the VGG-Attn and the TPS-ResNet-Attn model’s performance in the presence of synthetic sequence class-label noise. Show more

Keywords: Scene digit recognition, label noise, confident learning

DOI: 10.3233/JIFS-201825

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9345-9359, 2021

Authors: Iqbal, Naeem | Ahmad, Rashid | Jamil, Faisal | Kim, Do-Hyeun

Article Type: Research Article

Abstract: Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and …social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc. Show more

Keywords: Movie quality prediction, machine learning, data mining, business intelligence, predictive analytics

DOI: 10.3233/JIFS-201844

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9361-9382, 2021

Authors: Chen, Huiping

Article Type: Research Article

Abstract:  This paper aims to put forward a hesitant fuzzy multi-attribute group decision making (MAGDM) method based on the weighted power aggregation operators in social network. From the point of view of social network analysis, decision makers (DMs) are interconnected in the process of MAGDM. Furthermore, the dimension of the obtained hesitant fuzzy element (HFE) by original power operators will be greater with the increasing number of attributes and alternatives and DMs, which will lead to the problem of “intermediate expression swell". This paper combines the order operation laws with the power operators to redefine two novel hesitant fuzzy power aggregation …operators to simplify the involved calculation and explore new operators’ properties. Meanwhile, when two given elements have different number of values, we use the strength of social ties and social influence to develop an algorithm for extending the HFEs objectively. On the other hand, the PageRank algorithm and the deviation method are used to determine DMs’ combined weights. The feasibility of the proposed hesitant fuzzy MAGDM method based on social network is illustrated by the application to the actual issue of decision making and the comparative analysis with the existing method. Show more

Keywords: Group decision making, hesitant fuzzy set, social network, power operator, PageRank algorithm

DOI: 10.3233/JIFS-201859

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9383-9401, 2021

Authors: Jia, Zhifu | Liu, Xinsheng | Zhang, Yu

Article Type: Research Article

Abstract: Uncertain pantograph differential equation (UPDE for short) is a special unbounded uncertain delay differential equation. Stability in measure, stability almost surely and stability in p -th moment for uncertain pantograph differential equation have been investigated, which are not applicable for all situations, for the sake of completeness, this paper mainly gives the concept of stability in distribution, and proves the sufficient condition for uncertain pantograph differential equation being stable in distribution. In addition, the relationships among stability almost surely, stability in measure, stability in p -th moment, and stability in distribution for the uncertain pantograph differential equation are also discussed.

Keywords: uncertainty theory, uncertain pantograph differential equation, stability in distribution, the relationships among stabilities

DOI: 10.3233/JIFS-201864

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9403-9411, 2021

Authors: Bhullar, Amrit Kaur | Kaur, Ranjit | Sondhi, Swati

Article Type: Research Article

Abstract: Today optimization algorithms are widely used in every application to increase quality, quantity and efficiency of making products as well as to minimize the production cost. Most of the techniques applied on different applications try to satisfy more than one parameter of interest in the design problem. In doing so, an objective function based on weighted aggregation has been designed to fulfill multi-objective optimization (MOO). A lot of computational time and energy is wasted in tuning the value of weighting factor in terms of number of trials each having hundreds of iterations to achieve the optimum solution. To reduce such …tedious practice of adjustment of weighting factor with multiple iterations, Fuzzy technique is proposed for auto-tuning of weighting factor in this paper that will benefit the researchers who are working upon optimization of their designed objectives using artificial intelligence techniques. This paper proposes MOO settlement method that does not require complex mathematical equations in order to simplify the weight finding problem of weighted aggregation objective function (WAOF). The results have been compared in terms of time and space efficiency to show the importance of Fuzzy-WAOF (F-WAOF). Further the results taken on Automatic Voltage Regulator (AVR) system for set point tracking, load disturbance, controller effort and modelling errors, prove the superior performance of the proposed method as compared to state of the art techniques. Show more

Keywords: Fuzzy technique, optimization techniques, weighted aggregation objective function (WAOF), weighting factor, automatic voltage regulator (AVR) system

DOI: 10.3233/JIFS-201911

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9413-9436, 2021

Authors: Durairaj, M. | Asha, J. Hirudhaya Mary

Article Type: Research Article

Abstract: Biometric features are used to verify the people identity in the living places like smart apartments. To increase the chance of classification and recognition rate, the recognizing procedure contains various steps such as detection of silhouette from the gait profile, silhouette segmentation, reading features from the silhouette, classification of features and finally recognition of person using its probability value. Person recognition accuracy will be oscillated and declined due to blockage, radiance and posture variance problems. In the proposed work, the gait profile will be formed by capturing the gait of a targeted person in stipulated time to reach the destination. …From the profile the silhouettes are detected using frame difference and segmented from the background using immediate thresholding and features are extracted from the silhouette using gray-level covariance matrix and optimized feature set is formed using PSO. These optimized features are fused, trained and classified using nearest neighbor support vectors. The fuzzy probability method is used for recognizing the person based on the probability value of the authentic and imposter scores. The relationship between the CMS, TPR, TNR and F-rate are calculated for 1 : 1 matcher from the gallery set. The performance of the classifiers are found to be perfect by plotting the DET graph and ROC curve. The proposed fuzzy probability theory is mingled with GLCMPSO and NSFV method for human recognition purpose. The performance of the proposed is proved to be acceptable for recognition with the optimal parameters (Entropy, SSIM, PSNR, CQM) calculation From the work, it is clear that, the rank probability is proportional to the match score value of the silhouette stored in the gallery. Show more

Keywords: Gait cycle, silhouette image, feature detection, feature extraction, feature classification, person recognition using fuzzy probability

DOI: 10.3233/JIFS-201913

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9437-9452, 2021

Authors: Kumar, Rajeev | Singh, Laxman | Tiwari, Rajdev

Article Type: Research Article

Abstract: Path planning for robots plays a vital role to seek the most feasible path due to power requirement, environmental factors and other limitations. The path planning for the autonomous robots is tedious task as the robot needs to locate a suitable path to move between the source and destination points with multifaceted nature. In this paper, we introduced a new technique named modified grey wolf optimization (MGWO) algorithm to solve the path planning problem for multi-robots. MGWO is modified version of conventional grey wolf optimization (GWO) that belongs to the category of metaheuristic algorithms. This has gained wide popularity for …an optimization of different parameters in the discrete search space to solve various problems. The prime goal of the proposed methodology is to determine the optimal path while maintaining a sufficient distance from other objects and moving robots. In MGWO method, omega wolves are treated equally as those of delta wolves in exploration process that helps in escalating the convergence speed and minimizing the execution time. The simulation results show that MGWO gives satisfactory performance than other state of art methods for path planning of multiple mobile robots. The performance of the proposed method is compared with the standard evolutionary algorithms viz., Particle Swarm Optimization (PSO), Intelligent BAT Algorithm (IBA), Grey Wolf Optimization (GWO), and Variable Weight Grey Wolf Optimization (VW-GWO) and yielded better results than all of these. Show more

Keywords: Meta-heuristic, particle swarm optimization, intelligent BAT algorithm, grey wolf optimization (GWO), modified grey wolf optimization (MGWO), variable weight grey wolf optimization (VW-GWO)

DOI: 10.3233/JIFS-201926

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9453-9470, 2021

Authors: Jin, Yilun | Liu, Yanan | Zhang, Wenyu | Zhang, Shuai | Lou, Yu

Article Type: Research Article

Abstract: With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by …composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models. Show more

Keywords: Credit scoring, ensemble model, imbalanced learning, K-means, stacking

DOI: 10.3233/JIFS-201954

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9471-9484, 2021

Authors: Tang, Han | Li, Wenfei

Article Type: Research Article

Abstract: Interest rate, stock and option are all important parts of finance. This paper applies uncertain differential equation to the study of the evolution of interest rate and stock price separately. Based on actual observations, we estimate the parameters in uncertain differential equation with the method of moments. Using the introduced interest rate and stock models, we price European options and compare the results with actual observations. Finally, a paradox of the stochastic financial model is stated.

Keywords: Uncertain differential equation, geometric Liu process, uncertain exponential Ornstein-Uhlenbeck process, parameter estimation, European option pricing

DOI: 10.3233/JIFS-201955

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9485-9492, 2021

Authors: Nadeem, Asim | Kashif, Agha | Zafar, Sohail | Zahid, Zohaib

Article Type: Research Article

Abstract: The partition dimension is a variant of metric dimension in graphs. It has arising applications in the fields of network designing, robot navigation, pattern recognition and image processing. Let G  (V  (G ) , E  (G )) be a connected graph and Γ  = {P 1 , P 2 , …, P m } be an ordered m -partition of V  (G ). The partition representation of vertex v with respect to Γ is an m -vector r  (v |Γ ) = (d  (v , P 1 ) , d  (v , P 2 ) , …, d  (v , P m …)), where d  (v , P ) = min  {d  (v , x ) |x  ∈ P } is the distance between v and P . If the m -vectors r  (v |Γ ) differ in at least 2 positions for all v  ∈ V  (G ), then the m -partition is called a 2-partition generator of G . A 2-partition generator of G with minimum cardinality is called a 2-partition basis of G and its cardinality is known as the 2-partition dimension of G . Circulant graphs outperform other network topologies due to their low message delay, high connectivity and survivability, therefore are widely used in telecommunication networks, computer networks, parallel processing systems and social networks. In this paper, we computed partition dimension of circulant graphs C n  (1, 2) for n  ≡ 2 (mod 4), n  ≥ 18 and hence corrected the result given by Salman et al. [Acta Math. Sin. Engl. Ser. 2012, 28, 1851-1864]. We further computed the 2-partition dimension of C n  (1, 2) for n  ≥ 6. Show more

Keywords: Network topology design, Circulant graphs, partition dimension, k-partition dimension

DOI: 10.3233/JIFS-201982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9493-9503, 2021

Authors: Wahid, Khola | Das, Angsuman | Rani, Anam | Amanat, Saira | Imran, Muhammad | Ali, Usman

Article Type: Research Article

Abstract: There are several approaches to lower the complexity of huge networks. One of the key notions is that of twin nodes, exhibiting the same connection pattern to the rest of the network. We extend this idea by defining a twin preserving spanning subgraph (TPS-subgraph) of a simple graph as a tool to compute certain graph related invariants which are preserved by the subgraph. We discuss how these subgraphs preserve some distance based parameters of the simple graph. We introduce a sub-skeleton graph on a vector space and examine its basic properties. The sub-skeleton graph is a TPS-subgraph of the non-zero …component graph defined over a vector space. We prove that some parameters like the metric-dimension are preserved by the sub-skeleton graph. Show more

Keywords: Basis, graph, independent set, maximal clique, metric-dimension, twins, 05C12, 05C35

DOI: 10.3233/JIFS-201989

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9505-9513, 2021

Authors: Zeng, Qingtian | Zhao, Xishi | Hu, Xiaohui | Duan, Hua | Zhao, Zhongying | Li, Chao

Article Type: Research Article

Abstract: Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE ) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, …which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models. Show more

Keywords: Sentiment analysis, word embedding, classification, representation learning

DOI: 10.3233/JIFS-201993

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9515-9527, 2021

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