Journal of Intelligent & Fuzzy Systems - Volume 39, issue 3

Authors: Jia, Wei | Yan, Li | Ma, Zongmin | Niu, Weinan

Article Type: Research Article

Abstract: Influence maximization is a fundamental problem, which is aimed to specify a small number of individuals as seed set to influence the largest number of individuals under a certain influence cascade model. Most existing works on influence maximization may have either high effectiveness or good efficiency,which can not balance both the effectiveness and efficiency. One of the reason is that they do not consider the effect of influence overlap on the effectiveness. That is, these works ignore the phenomenon that the same set of nodes may be influenced by a subset of different influential nodes. To tackle the effectiveness of …heuristic algorithm, we propose a three-phase-based heuristic algorithm, called Three-Phase-based Heuristic (TPH), which uses K-shell method to find influential nodes firstly. Moreover, we utilize weighed degree to make up for the coarse-grained of K-shell method. At last, we take advantage of similarity index to reduce the effect of influence overlap by covering the similar neighbor nodes with low influence. Furthermore, exhaustive experiments indicate that the proposed algorithm outperforms the other baseline algorithms in the aspects of influence spread and running time. Show more

Keywords: Influence maximization, similarity index, influence overlap, social networks

DOI: 10.3233/JIFS-200383

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4393-4403, 2020

Authors: Chung, Yao-Liang | Chung, Hung-Yuan | Tsai, Wei-Feng

Article Type: Research Article

Abstract: In the present study, we sought to enable instant tracking of the hand region as a region of interest (ROI) within the image range of a webcam, while also identifying specific hand gestures to facilitate the control of home appliances in smart homes or issuing of commands to human-computer interaction fields. To accomplish this objective, we first applied skin color detection and noise processing to remove unnecessary background information from the captured image, before applying background subtraction for detection of the ROI. Then, to prevent background objects or noise from influencing the ROI, we utilized the kernelized correlation filters (KCF) …algorithm to implement tracking of the detected ROI. Next, the size of the ROI image was resized to 100×120 and input into a deep convolutional neural network (CNN) to enable the identification of various hand gestures. In the present study, two deep CNN architectures modified from the AlexNet CNN and VGGNet CNN, respectively, were developed by substantially reducing the number of network parameters used and appropriately adjusting internal network configuration settings. Then, the tracking and recognition process described above was continuously repeated to achieve immediate effect, with the execution of the system continuing until the hand is removed from the camera range. The results indicated excellent performance by both of the proposed deep CNN architectures. In particular, the modified version of the VGGNet CNN achieved better performance with a recognition rate of 99.90% for the utilized training data set and a recognition rate of 95.61% for the utilized test data set, which indicate the good feasibility of the system for practical applications. Show more

Keywords: Deep CNN, gesture recognition, VGGNet, AlexNet

DOI: 10.3233/JIFS-200385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4405-4418, 2020

Authors: Liu, Shiqin | Liu, Liying | Wang, Na | Zhang, Jianguang

Article Type: Research Article

Abstract: Under the axiom system of uncertainty theory, the paper mainly introduce the new definition of the pth moment exponential stability for uncertain differential equation with jumps. For illustrating the concept, some examples and counterexamples are given. Furthermore, we obtain a necessary and sufficient condition of stability in pth moment exponential for the linear uncertain differential equation with jumps. Also, the conclusion condition is illustrated very clearly by two examples.

Keywords: Stability, uncertain differential equation, uncertainty theory

DOI: 10.3233/JIFS-200409

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4419-4425, 2020

Authors: Xu, Bin

Article Type: Research Article

Abstract: The concept of fuzzy number intuitionistic fuzzy sets (FNIFSs) is designed to effectively depict uncertain information in decision making problems which fundamental characteristic of the FNIFS is that the values of its membership function and non-membership function are depicted with triangular fuzzy numbers (TFNs). The dual Hamy mean (DHM) operator gets good performance in the process of information aggregation due to its ability to capturing the interrelationships among aggregated values. In this paper, we used the dual Hamy mean (DHM) operator and dual weighted Hamy mean (WDHM) operator with fuzzy number intuitionistic fuzzy numbers (FNIFNs) to propose the fuzzy number …intuitionistic fuzzy dual Hamy mean (FNIFDHM) operator and fuzzy number intuitionistic fuzzy weighted dual Hamy mean (FNIFWDHM) operator. Then the MADM methods are proposed along with these operators. In the end, we utilize an applicable example for computer network security evaluation to prove the proposed methods. Show more

Keywords: Multiple attribute decision making (MADM), dual weighted hamy mean (WDHM) operator, fuzzy number intuitionistic fuzzy weighted dual hamy operators (FNIFWDHM), computer network security evaluation

DOI: 10.3233/JIFS-200414

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4427-4441, 2020

Authors: Wang, Xiao | Peng, Zhen

Article Type: Research Article

Abstract: Uncertain pantograph differential equations are an important class of pantograph differential equations driven by uncertain process. This paper investigates two types of stability, namely stability in mean and almost sure stability, for uncertain pantograph differential equations. In detail, the concepts of stability in mean and almost sure stability for uncertain pantograph differential equations are presented. Moreover, we reveal the sufficient conditions for uncertain pantograph differential equations being stable in mean and stable almost surely. Finally, this paper attempts to explore the relationships among stability in mean, almost sure stability as well as stability in measure.

Keywords: Uncertain pantograph differential equation, stability in mean, almost sure stability, uncertainty theory

DOI: 10.3233/JIFS-200426

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4443-4452, 2020

Authors: Wang, Yuanyuan | Li, Xiang | Jiang, Mingxin | Zhang, Haiyan | Tang, E

Article Type: Research Article

Abstract: At present, supervised person re-identification method achieves high identification performance. However, there are a lot of cross cameras with unlabeled data in the actual application scenarios. The high cost of marking data will greatly reduce the effect of the supervised learning model transferring to other scene domains. Therefore, unsupervised learning of person re-identification becomes more attractive in the real world. In addition, due to changes in camera angle, illumination and posture, the extracted person image representation is generally different in the non-cross camera view, but the existing algorithm ignores the difference among cross camera images under camera parameters and environments. …In order to overcome the above problems, we propose unsupervised person re-identification metric learning method. The model learns a shared space to reduce the discrepancy under different cameras. The graph convolution network is further employed to cluster the cross-view image features extracted from the shared space. Our model improves the scalability of pedestrian re-identification in practical application scenarios. Extensive experiments on four large-scale person re-identification public datasets have been conducted to demonstrate the effectiveness of the proposed model. Show more

Keywords: Person re-identification, unsupervised, clustering, graph convolution network, cross-view

DOI: 10.3233/JIFS-200435

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4453-4462, 2020

Authors: Deli, Irfan | Long, Hoang Viet | Son, Le Hoang | Kumar, Raghvendra | Dey, Arindam

Article Type: Research Article

Abstract: Soft set is the power tool to deal with uncertainty in a parametric manner. In applications of soft set, one of the most important steps is to define mappings on soft sets. In this study, we model theory of game under theory of soft set which is an effective tool for handling uncertainties events and problems that may exist in a game. To this end, we first define some expected impact functions of players in soft games. Then, we propose three new decision making algorithms to solve the 2.2 × p , 2 . n  × p and m  . 2 × p soft matrix games, …which cannot be settled by the relevant soft methods such as saddle points, lover and upper values, dominated strategies and Nash equilibrium. The proposed soft game algorithms are illustrated by examples. Show more

Keywords: Soft sets, soft games, impact functions, soft payoff, probabilistic solution methods

DOI: 10.3233/JIFS-200440

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4463-4472, 2020

Authors: Afzaal, Muhammad Umar | Sajjad, Intisar Ali | Khan, Muhammad Faisal Nadeem | Haroon, Shaikh Saaqib | Amin, Salman | Bo, Rui | ur Rehman, Waqas

Article Type: Research Article

Abstract: The characterization of electrical demand patterns for aggregated customers is considered as an important aspect for system operators or electrical load aggregators to analyze their behavior. The variation in electrical demand among two consecutive time intervals is dependent on various factors such as, lifestyle of customers, weather conditions, type and time of use of appliances and ambient temperature. This paper proposes an improved methodology for probabilistic characterization of aggregate demand while considering different demand aggregation levels and averaging time step durations. At first, a probabilistic model based on Weibull distribution combined with generalized regression neural networks (GRNN) is developed to …extract the inter-temporal behavior of demand variations and, then, this information is used to regenerate aggregate demand patterns. Average Mean Absolute Percentage Error (AMAPE) is used as a statistical indicator to assess the accuracy and effectiveness of proposed probabilistic modeling approach. The results have demonstrated that the performance of proposed approach is better in comparison with an existing Beta distribution-based method to characterize aggregate electrical demand patterns. Show more

Keywords: Electrical demand characterization, generalized regression neural networks, scenario generations, time series, Weibull probability distribution

DOI: 10.3233/JIFS-200462

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4491-4503, 2020

Authors: Dong, Guishan | Mu, Xuewen

Article Type: Research Article

Abstract: The support vector machine is a classification approach in machine learning. The second-order cone optimization formulation for the soft-margin support vector machine can ensure that the misclassification rate of data points do not exceed a given value. In this paper, a novel second-order cone programming formulation is proposed for the soft-margin support vector machine. The novel formulation uses the l 2 -norm and two margin variables associated with each class to maximize the margin. Two regularization parameters α and β are introduced to control the trade-off between the maximization of margin variables. Numerical results illustrate that the proposed …second-order cone programming formulation for the soft-margin support vector machine has a better prediction performance and robustness than other second-order cone programming support vector machine models used in this article for comparision. Show more

Keywords: Support vector machine, second-order cone programming, binary data classification

DOI: 10.3233/JIFS-200467

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4505-4513, 2020

Authors: Atef, Mohammed | Khalil, Ahmed Mostafa | Li, Sheng-Gang | Azzam, A.A. | El Atik, Abd El Fattah

Article Type: Research Article

Abstract: In this paper, we generalize three types of rough set models based on j -neighborhood space (i.e, type 1 j -neighborhood rough set, type 2 j -neighborhood rough set, and type 3 j -neighborhood rough set), and investigate some of their basic properties. Also, we present another three types of rough set models based on j -adhesion neighborhood space (i.e, type 4 j -adhesion neighborhood rough set, type 5 j -adhesion neighborhood rough set, and type 6 j -adhesion neighborhood rough set). The fundamental properties of approximation operators based on j -adhesion neighborhood space are established. The relationship between the …properties of these types is explained. Finally, according to j -adhesion neighborhood space, we give a comparison between the Yao’s approach and our approach. Show more

Keywords: Rough sets, lower and upper approximations, j-neighborhood, j-adhesion neighborhood, accuracy measure

DOI: 10.3233/JIFS-200482

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4515-4531, 2020