Journal of Intelligent & Fuzzy Systems - Volume 40, issue 3

Authors: Wang, Zhaocai | Wang, Dangwei | Bao, Xiaoguang | Wu, Tunhua

Article Type: Research Article

Abstract: The vertex coloring problem is a well-known combinatorial problem that requires each vertex to be assigned a corresponding color so that the colors on adjacent vertices are different, and the total number of colors used is minimized. It is a famous NP-hard problem in graph theory. As of now, there is no effective algorithm to solve it. As a kind of intelligent computing algorithm, DNA computing has the advantages of high parallelism and high storage density, so it is widely used in solving classical combinatorial optimization problems. In this paper, we propose a new DNA algorithm that uses DNA molecular …operations to solve the vertex coloring problem. For a simple n -vertex graph and k different kinds of color, we appropriately use DNA strands to indicate edges and vertices. Through basic biochemical reaction operations, the solution to the problem is obtained in the O  (kn 2 ) time complexity. Our proposed DNA algorithm is a new attempt and application for solving Nondeterministic Polynomial (NP) problem, and it provides clear evidence for the ability of DNA calculations to perform such difficult computational problems in the future. Show more

Keywords: NP-hard problem, the vertex coloring problem, Adleman-Lipton model, DNA computating

DOI: 10.3233/JIFS-200025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3957-3967, 2021

Authors: Li, Liping | Tian, Zean | Li, Kenli | Chen, Cen

Article Type: Research Article

Abstract: Anomaly detection based on time series data is of great importance in many fields. Time series data produced by man-made systems usually include two parts: monitored and exogenous data, which respectively are the detected object and the control/feedback information. In this paper, a so-called G-CNN architecture that combined the gated recurrent units (GRU) with a convolutional neural network (CNN) is proposed, which respectively focus on the monitored and exogenous data. The most important is the introduction of a complementary double-referenced thresholding approach that processes prediction errors and calculates threshold, achieving balance between the minimization of false positives and the false …negatives. The outstanding performance and extensive applicability of our model is demonstrated by experiments on two public datasets from aerospace and a new server machine dataset from an Internet company. It is also found that the monitored data is close associated with the exogenous data if any, and the interpretability of the G-CNN is discussed by visualizing the intermediate output of neural networks. Show more

Keywords: Anomaly detection, CNN, GRU, time series, deep learning

DOI: 10.3233/JIFS-200175

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3969-3980, 2021

Authors: Gulistan, Muhammad | Yaqoob, Naveed | Elmoasry, Ahmed | Alebraheem, Jawdat

Article Type: Research Article

Abstract: Zadeh’s fuzzy sets are very useful tool to handle imprecision and uncertainty, but they are unable to characterize the negative characteristics in a certain problem. This problem was solved by Zhang et al. as they introduced the concept of bipolar fuzzy sets. Thus, fuzzy set generalizes the classical set and bipolar fuzzy set generalize the fuzzy set. These theories are based on the set of real numbers. On the other hand, the set of complex numbers is the generalization of the set of real numbers so, complex fuzzy sets generalize the fuzzy sets, with wide range of values to handle the …imprecision and uncertainty. So, in this article, we study complex bipolar fuzzy sets which is the generalization of bipolar fuzzy set and complex fuzzy set with wide range of values by adding positive membership function and negative membership function to unit circle in the complex plane, where one can handle vagueness in a much better way as compared to bipolar fuzzy sets. Thus this paper leads us towards a new direction of research, which has many applications in different directions. We develop the notions of union, intersection, complement, Cartesian product and De-Morgan’s laws of complex bipolar fuzzy sets. Furthermore, we develop the complex bipolar fuzzy relations, fundamental operations on complex bipolar fuzzy matrices and some operators of complex bipolar fuzzy matrices. We also discuss the distance measure on complex bipolar fuzzy sets and complex bipolar fuzzy aggregation operators. Finally, we apply the developed approach to a numerical problem with the algorithm. Show more

Keywords: Complex fuzzy sets, complex bipolar fuzzy relations, complex bipolar fuzzy matrices, distance measure

DOI: 10.3233/JIFS-200234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3981-3997, 2021

Authors: Zhang, Hengshan | Chen, Chunru | Chen, Tianhua | Wang, Zhongmin | Chen, Yanping

Article Type: Research Article

Abstract: A scenario that often encounters in the event of aggregating options of different experts for the acquisition of a robust overall consensus is the possible existence of extremely large or small values termed as outliers in this paper, which easily lead to counter-intuitive results in decision aggregation. This paper attempts to devise a novel approach to tackle the consensus outliers especially for non-uniform data, filling the gap in the existing literature. In particular, the concentrate region for a set of non-uniform data is first computed with the proposed searching algorithm such that the domain of aggregation function is partitioned into …sub-regions. The aggregation will then operate adaptively with respect to the corresponding sub-regions previously partitioned. Finally, the overall aggregation is operated with a proposed novel consensus measure. To demonstrate the working and efficacy of the proposed approach, several illustrative examples are given in comparison to a number of alternative aggregation functions, with the results achieved being more intuitive and of higher consensus. Show more

Keywords: Aggregation function, concentrate region, t-norm, t-conorm, consensus measure

DOI: 10.3233/JIFS-200278

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3999-4012, 2021

Authors: Li, Longjie | Wang, Lu | Luo, Hongsheng | Chen, Xiaoyun

Article Type: Research Article

Abstract: Link prediction is an important research direction in complex network analysis and has drawn increasing attention from researchers in various fields. So far, a plethora of structural similarity-based methods have been proposed to solve the link prediction problem. To achieve stable performance on different networks, this paper proposes a hybrid similarity model to conduct link prediction. In the proposed model, the Grey Relation Analysis (GRA) approach is employed to integrate four carefully selected similarity indexes, which are designed according to different structural features. In addition, to adaptively estimate the weight for each index based on the observed network structures, a …new weight calculation method is presented by considering the distribution of similarity scores. Due to taking separate similarity indexes into account, the proposed method is applicable to multiple different types of network. Experimental results show that the proposed method outperforms other prediction methods in terms of accuracy and stableness on 10 benchmark networks. Show more

Keywords: Complex networks, link prediction, node similarity, hybrid model, Grey Relation Analysis

DOI: 10.3233/JIFS-200344

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4013-4026, 2021

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

Article Type: Research Article

Abstract: The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only …balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem. Show more

Keywords: Whale optimization algorithm, lateral inhibition, image matching, AUV docking

DOI: 10.3233/JIFS-200365

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4027-4038, 2021

Authors: He, Tingting | Wei, Guiwu | Wu, Jiang | Wei, Cun

Article Type: Research Article

Abstract: The overall quality evaluation of operation personnel helps contain site safety accidents, in this study, we proposed a combination of the Pythagorean 2-tuple linguistic fuzzy set and qualitative flexible multiple criteria (QUALIFLEX) method to evaluate comprehensive quality of operation personnel in engineering projects, Pythagorean 2-tuple linguistic fuzzy numbers to express decision makers’ evaluation on each scheme with original QUALIFLEX approach to decision making process. In the end, an example of the performance evaluation of operation personnel in the engineering project is provided to test the applicability and practicability of the method, comparison analysis for further elaboration.

Keywords: Multiple attribute group decision making (MAGDM), Pythagorean 2-tuple linguistic numbers, QUALIFLEX method, construction projects

DOI: 10.3233/JIFS-200379

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4039-4050, 2021

Authors: Yang, Zhi | Gan, Haitao | Li, Xuan | Wu, Cong

Article Type: Research Article

Abstract: Since label noise can hurt the performance of supervised learning (SL), how to train a good classifier to deal with label noise is an emerging and meaningful topic in machine learning field. Although many related methods have been proposed and achieved promising performance, they have the following drawbacks: (1) They can lead to data waste and even performance degradation if the mislabeled instances are removed; and (2) the negative effect of the extremely mislabeled instances cannot be completely eliminated. To address these problems, we propose a novel method based on the capped ℓ1 norm and a graph-based regularizer to …deal with label noise. In the proposed algorithm, we utilize the capped ℓ1 norm instead of the ℓ1 norm. The used norm can inherit the advantage of the ℓ1 norm, which is robust to label noise to some extent. Moreover, the capped ℓ1 norm can adaptively find extremely mislabeled instances and eliminate the corresponding negative influence. Additionally, the proposed algorithm makes full use of the mislabeled instances under the graph-based framework. It can avoid wasting collected instance information. The solution of our algorithm can be achieved through an iterative optimization approach. We report the experimental results on several UCI datasets that include both binary and multi-class problems. The results verified the effectiveness of the proposed algorithm in comparison to existing state-of-the-art classification methods. Show more

Keywords: Artificial intelligence, classification algorithm, graph-based learning, label noise, ℓ1 norm

DOI: 10.3233/JIFS-200432

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4051-4063, 2021

Authors: Humaira, | Sarwar, Muhammad | Abdeljawad, Thabet

Article Type: Research Article

Abstract: The purpose of this article is to investigate the existence of unique solution for the following mixed nonlinear Volterra Fredholm-Hammerstein integral equation considered in complex plane; (0.1) ξ ( τ ) = g ( t ) + ρ ∫ 0 τ K 1 ( τ , ℘ ) ϝ 1 ( ℘ , ξ ( ℘ ) ) d ℘ + ϱ ∫ 0 1 K 2 ( τ , ℘ ) ϝ 2 ( ℘ , ξ ( ℘ ) ) d ℘ , such …that ξ = ξ 1 + ξ 2 , ξ 1 , ξ 2 ∈ ( C ( [ 0 , 1 ] ) , R ) g = g 1 + g 2 , g l : [ 0 , 1 ] → R , l = 1 , 2 , ϝ l ( ℘ , ξ ( ℘ ) ) = ϝ l 1 * ( ℘ , ξ 1 * ) + i ϝ l 2 * ( ℘ , ξ 2 * ) , ϝ lj * : [ 0 , 1 ] × R → R for l , j = 1 , 2 , and ξ 1 * , ξ 2 * ∈ ( C ( [ 0 , 1 ] ) , R ) K l ( t , ℘ ) = K l 1 * ( t , ℘ ) + iK l 2 * ( t , ℘ ) , for l , j = 1 , 2 and K lj * : [ 0 , 1 ] 2 → R , where ρ and ϱ are constants, g (t ), the kernels K l  (τ , ℘) and the nonlinear functions ϝ1  (℘, ξ  (℘)), ϝ 2  (℘, ξ  (℘)) are continuous functions on the interval 0 ≤ τ  ≤ 1. In this direction we apply fixed point results for self mappings with the concept of (ψ , ϕ ) contractive condition in the setting of complex-valued fuzzy metric spaces. This study will be useful in the development of the theory of fuzzy fractional differential equations in a more general setting. Show more

Keywords: (ψ, ϕ) contraction, mixed Volterra Fredholm-Hammerstein integral equation, complex valued fuzzy metric space, Primary 47H10, secondary 54H25

DOI: 10.3233/JIFS-200459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4065-4074, 2021

Authors: Shi, Meihui | Shen, Derong | Kou, Yue | Nie, Tiezheng | Yu, Ge

Article Type: Research Article

Abstract: With the widespread of location-based social networks (LBSNs), the amount of check-in data grows rapidly, which helps to recommend the next point-of-interest (POI). Extracting sequential patterns from check-in data has become a meaningful way for next POI recommendation, since human movement exhibits sequential patterns in LBSNs. However, due to the check-ins’ sparsity problem, exploiting sequential patterns in next POI recommendation is a challenging issue, which makes the learned sequential patterns unreliable. Inspired by the fact that auxiliary information can be incorporated to alleviate this situation, in this paper, we model sequential transition based on both item-wise check-in sequences and region-wise …spatial information. Besides, we propose an attention-aware recurrent neural network (ATTRNN) to learn the contribution of different time steps. Furthermore, considering users’ decision-making is influenced by public’s common preference to some extent, we design a novel framework, namely HSP (short for “H ybrid model based on S equential feature mining and P ublic preference awareness”), to recommend POIs for a given user. We conduct a comprehensive performance evaluation for HSP on two real-world datasets. Experimental results demonstrate that compared to other state-of-the-art techniques, the proposed HSP achieves significantly improvements. Show more

Keywords: Point-of-interest, recommendation, sequential pattern, public preference

DOI: 10.3233/JIFS-200465

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4075-4090, 2021

Authors: Zhao, Mengwei | Wei, Guiwu | Wei, Cun | Wu, Jiang | Wei, Yu

Article Type: Research Article

Abstract: The urban ecological risk assessment is a new research field, which has been rising and developing with the change of environment management objectives and environment conception. The urban ecological risk assessment could be regarded as a classical multi-attribute group decision making (MAGDM) issue. The interval-valued intuitionistic fuzzy set (IVIFS) can fully describe the uncertain information for the urban ecological risk assessment. Furthermore, the classical TODIM (an acronym in Portuguese for Interactive Multi-Criteria Decision Making) is built on cumulative prospect theory (CPT), which is a selectable method in reflecting the DMs’ psychological behavior. Thus, in this paper, the TODIM method based …on the CPT is proposed for MAGDM issue under IVIFS. At the same time, it is enhancing rationality to get the weight information of attributes by using the interval-valued intuitionistic fuzzy entropy weight method. And focusing on hot issues in contemporary society, this article applies the discussed method to urban ecological risk assessment, and demonstrates urban ecological risk assessment model based on the proposed method. Finally, through comparing the outcome of comparative analysis, we conclude that this improved approach is acceptable. Show more

Keywords: Multi-attribute group decision making (MAGDM), interval-valued intuitionistic fuzzy sets, TODIM, urban ecological risk assessment

DOI: 10.3233/JIFS-200534

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4091-4106, 2021

Authors: Raza, Zahid | Bataineh, Mohammad Saleh | Sukaiti, Mark Essa

Article Type: Research Article

Abstract: Regular plane tessellations can easily be constructed by repeating regular polygons. This design is of extreme importance for direct interconnection networks as it yields high overall performance. The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks. The first and second Zagreb indices are among the most studied topological indices. We now consider analogous graph invariants, based on the second degrees of vertices, called Zagreb connection indices. The main objective of this paper is to compute these connection indices for the Hex, Hex derived and some honeycomb networks.

Keywords: Honeycomb network, hexagonal network, hex-derived networks, connection number, Zagreb connection indices

DOI: 10.3233/JIFS-200659

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4107-4114, 2021

Authors: Abulaish, Muhammad | Fazil, Mohd

Article Type: Research Article

Abstract: In online social networks (OSNs), socialbots are responsible for various malicious activities, and they are mainly programmed to imitate human-behavior to bypass the existing detection systems. The socialbots are generally successful in their malicious intent due to the existence of OSN users who follow them and thereby increase their reputation in the network. Analysis of the socialbot networks and their users is vital to comprehend the socialbot problem from target users’ perspective. In this paper, we present a machine learning-based approach for characterizing and detecting socialbot targets , i.e., users who are susceptible to be trapped by the socialbots. We …model OSN users based on their identity and behavior information, representing the static and dynamic components of their personality. The proposed approach classifies socialbot targets into three categories viz. active , reactive , and inactive users. We evaluate the proposed approach using three classifiers over a dataset collected from a live socialbot injection experiment conducted on Twitter. We also present a comparative evaluation of the proposed approach with a state-of-the-art method and show that it performs significantly better. On feature ablation analysis , we found that network structure and user intention and personality related dynamic features are most discriminative, whereas static features show the least impact on the classification. Additionally, following rate , multimedia ratio , and follower rate are most relevant to segregate different categories of the socialbot targets . We also perform a detailed topical and behavioral analysis of socialbot targets and found active users to be suspicious. Further, joy and agreeableness are the most dominating personality traits among the three categories of the users. Show more

Keywords: Machine learning, social network analysis, social network security, user profiling, socialbots

DOI: 10.3233/JIFS-200682

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4115-4133, 2021

Authors: Pei, Lidan | Jin, Feifei | Langari, Reza | Garg, Harish

Article Type: Research Article

Abstract: Unlike other linguistic modellings, probabilistic linguistic term sets can express clearly the importance of different linguistic variables. The notion of Probabilistic Linguistic Preference Relations (PLPRs) constitutes an extension of linguistic preference relations, and as such has received increasing attention in recent years. In group decision-making (GDM) problems with PLPRs, the processes of consistency adjustment, consensus-achieving and desirable alternative selection play a key role in deriving the reliable GDM results. Therefore, this paper focuses on the construction of a GDM method for PLPRs with local adjustment strategy. First, we redefine the concepts of multiplicative consistency and consistency index for PLPRs, and …some properties for multiplicative consistent PLPRs are studied. Then, in order to obtain the acceptable multiplicative consistent PLPRs, we propose a convergent consistency adjustment algorithm. Subsequently, a consensus-achieving method with PLPRs is constructed for reaching the consensus goal of experts. In both consistency adjustment process and consensus-achieving method, the local adjustment strategy is utilized to retain the original evaluation information of experts as much as possible. Finally, a GDM method with PLPRs is investigated to determine the reliable ranking order of alternatives. In order to show the advantages of the developed GDM method with PLPRs, an illustration for determining the ranking of fog-haze influence factors is given, which is followed by the comparative analysis to clarify its validity and merits. Show more

Keywords: Group decision making, consistency-improving algorithm, consensus-achieving algorithm, local adjustment strategy, probabilistic linguistic preference relations

DOI: 10.3233/JIFS-200724

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4135-4154, 2021

Authors: Zong, Yi | Xing, Hao

Article Type: Research Article

Abstract: Scientific customer stratification method can help enterprises identify valuable customers, thus effectively improving the operating profit of enterprises. However, current customer stratification methods have not considered the impact of cost to service (CTS) on customer value (such as the RFM model). In this paper, K-mean clustering method is adopted to classify customers into four categories, namely 1) the most valuable customers, 2) valuable customers, 3) general customers and 4) customers with low contribution. By adding a new evaluation dimension of CTS, the original RFM model is improved. In this way, the RFMC model is built and can provide more comprehensive …evaluation on customer value. Finally, the results show that the addition of CTS index significantly changes the clustering results of the original RFM model and the overall consideration of consumption amount and CTS truly reflect the customer value. Thus, the improved RFMC model optimizes the results of customer stratification and it can effectively sort out the valuable customers for enterprises. Enterprises will be more dedicated to serving the valuable customers so as to maximize profits and reduce service costs of customers with lower value to make up for profit losses. Show more

Keywords: Cost-to-serve (CTS), RFM model, RFMC model, customer stratification

DOI: 10.3233/JIFS-200737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4155-4167, 2021

Authors: Yang, Dongqi | Zhang, Wenyu | Wu, Xin | Ablanedo-Rosas, Jose H. | Yang, Lingxiao | Yu, Wangzhi

Article Type: Research Article

Abstract: With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based …on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency. Show more

Keywords: Bankruptcy prediction, ensemble learning, fuzzy mini-batch clustering, heterogeneous model construction, genetic algorithm

DOI: 10.3233/JIFS-200741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4169-4185, 2021

Authors: De (Maity), Ritu Rani | Mudi, Rajani K. | Dey, Chanchal

Article Type: Research Article

Abstract: This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic …control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller. Show more

Keywords: Type-2 fuzzy control, Lyapunov stability, self-tuning, servo position control and real time experimentation

DOI: 10.3233/JIFS-200802

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4187-4205, 2021

Authors: Badr, Majdah M.

Article Type: Research Article

Abstract: Lifetime data collected from reliability tests are among data that often exhibit significant heterogeneity caused by variations in manufacturing which make standard lifetime models inadequate. In this paper we introduce a new lifetime distribution derived from T-X family technique called exponentiated exponential Burr XII (EE-BXII) distribution. We establish various mathematical properties. The maximum likelihood estimates (MLE) for the EE-BXII parameters are derived. We estimate the precision of the maximum likelihood estimators via simulation study. Some numerical illustrations are performed to study the behavior of the obtained estimators. Finally the model is applied to a real dataset. We apply goodness of …fit statistics and graphical tools to examine the adequacy of the EE-BXII distribution. The importance of this research lies in deriving a new distribution under the name EE-BXII, which is considered the best distributions in analyzing data of life times at present if compared to many distributions in analysis real data. Show more

Keywords: EE-BXII distribution, the maximum likelihood method, Monte Carlo simulation, variance covariance matrix

DOI: 10.3233/JIFS-200819

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4207-4221, 2021

Authors: Khan, Muhammad Sajjad Ali | Khan, Amir Sultan | Khan, Israr Ali | Mashwani, Wali Khan | Hussain, Fawad

Article Type: Research Article

Abstract: The aim of this paper is to introduce the notion of linguistic interval-valued q-rung orthopair fuzzy set (LIVq-ROFS) as a generalization of linguistic q-rung orthopair fuzzy set. We develop some basic operations, score and accuracy functions to compare the LIVq-ROF values (LIVq-ROFVs). Based on the proposed operations a series of aggregation techniques to aggregate the LIVq-ROFVs and some of their desirable properties are discussed in detail. Moreover, a TOPSIS method is developed to solve a multi-criteria decision making (MCDM) problem under LIVq-ROFS setting. Furthermore, a MCDM approach is proposed based on the developed operators and TOPSIS method, then a practical …decision making example is given in order to explain the proposed method. To illustrate to effectiveness and application of the proposed method a comparative study is also conducted. Show more

Keywords: Linguistic interval-valued q-rung orthopair fuzzy set (LIVq-ROFS), LIVq-ROF aggregation operators, TOPSIS method, MCDM problem

DOI: 10.3233/JIFS-200845

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4223-4235, 2021

Authors: Muthamil Sudar, K. | Deepalakshmi, P.

Article Type: Research Article

Abstract: Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new …incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane. Show more

Keywords: Software-defined networking (SDN), machine learning (ML), intrusion detection system (IDS), feature selection, flow-based IDS

DOI: 10.3233/JIFS-200850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4237-4256, 2021

Authors: Li, Bing | Xiao, Binqing | Yang, Yang

Article Type: Research Article

Abstract: This study identifies credit risk sources, credit scoring index classification modes and modelling methods, and constructs a credit scoring system for small and micro businesses (SMBs) with soft information. Through the analysis and comparison of neural network models, this study demonstrates the superiority of the back-propagation neural network (BPNN) models for loan classification prediction. There are three contributions and innovations as follows. (1) Based on the actual demands and default characteristics of SMBs, this study adds the behavioural variables of loan managers to strengthen the role of soft information in credit scoring model. (2) It develops a hybrid analysis and …comparison framework based on the BPNN model. It identifies that the BPNN model is more suitable for approving SMB loans, as it can precisely identify the second type of error. (3) Using the precious ledger data of SMB loans from a rural commercial bank in Jiangsu province, China, this study compares the prediction accuracy of the credit scoring model based on BPNN using two-level and five-level loan classifications. Further, it illustrates the applicability of the BPNN model. By connecting the practical operations of credit scoring and quantitative models, this paper supports commercial bank examination and approval work of SMB loans. Show more

Keywords: Credit scoring, small and micro businesses, soft information, back-propagation neural network, comparative analysis

DOI: 10.3233/JIFS-200866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4257-4274, 2021

Authors: Khoh, Wee How | Pang, Ying Han | Ooi, Shih Yin | Yap, Hui Yen

Article Type: Research Article

Abstract: Dynamic signature recognition emerges to perfectly solve the hygiene concern due to its no-contact characteristic. Nevertheless, the recognition of dynamic texture is challenging compared with the static signature image due to their unknown spatial and temporal nature. In this work, we present a multi-view spatiotemporal approach based on spectral histogramming for hand gesture signature recognition. A Microsoft Kinect sensor is adopted to capture the motion of signing in a sequence of depth frames. The depth frame sequence is viewed from three directional sights to retrieve rich information, such as temporal changes at each spatial location, the signing motion flow of …each vertical and horizontal spatial space in a temporal manner. Furthermore, the proposed approach performs feature description on different levels of locality. This function enables a multi-resolution analysis on this dynamic signature. The robustness of the proposed approach is reflected with the promising result by striking the state-of-the-art performance, as substantiated in the empirical results. Show more

Keywords: Hand gesture signature, dynamic signature, biometrics, spatiotemporal, gesture recognition

DOI: 10.3233/JIFS-200908

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4275-4286, 2021

Authors: Pei, Cong | Jiang, Feng | Li, Mao

Article Type: Research Article

Abstract: With the advent of cost-efficient depth cameras, many effective feature descriptors have been proposed for action recognition from depth sequences. However, most of them are based on single feature and thus unable to extract the action information comprehensively, e.g., some kinds of feature descriptors can represent the area where the motion occurs while they lack the ability of describing the order in which the action is performed. In this paper, a new feature representation scheme combining different feature descriptors is proposed to capture various aspects of action cues simultaneously. First of all, a depth sequence is divided into a series …of sub-sequences using motion energy based spatial-temporal pyramid. For each sub-sequence, on the one hand, the depth motion maps (DMMs) based completed local binary pattern (CLBP) descriptors are calculated through a patch-based strategy. On the other hand, each sub-sequence is partitioned into spatial grids and the polynormals descriptors are obtained for each of the grid sequences. Then, the sparse representation vectors of the DMMs based CLBP and the polynormals are calculated separately. After pooling, the ultimate representation vector of the sample is generated as the input of the classifier. Finally, two different fusion strategies are applied to conduct fusion. Through extensive experiments on two benchmark datasets, the performance of the proposed method is proved better than that of each single feature based recognition method. Show more

Keywords: Action recognition, feature fusion, depth motion maps, completed local binary pattern, polynormal

DOI: 10.3233/JIFS-200954

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4287-4299, 2021

Authors: Zhang, Zhaojun | Li, Xuanyu | Luan, Shengyang | Xu, Zhaoxiong

Article Type: Research Article

Abstract: Particle swarm optimization (PSO) as a successful optimization algorithm is widely used in many practical applications due to its advantages in fast convergence speed and convenient implementation. As a population optimization algorithm, the quality of initial population plays an important role in the performance of PSO. However, random initialization is used in population initialization for PSO. Using the solution of the solved problem as prior knowledge will help to improve the quality of the initial population solution. In this paper, we use homotopy analysis method (HAM) to build a bridge between the solved problems and the problems to be solved. …Therefore, an improved PSO framework based on HAM, called HAM-PSO, is proposed. The framework of HAM-PSO includes four main processes. It contains obtaining the prior knowledge, constructing homotopy function, generating initial solution and solving the to be solved by PSO. In fact, the framework does not change the PSO, but replaces the random population initialization. The basic PSO algorithm and three others typical PSO algorithms are used to verify the feasibility and effectiveness of this framework. The experimental results show that the four PSO using this framework are better than those without this framework. Show more

Keywords: Particle swarm optimization, homotopy analysis method, initial population, t-test

DOI: 10.3233/JIFS-200979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4301-4315, 2021

Authors: Mao, Hua | Cheng, Yilin

Article Type: Research Article

Abstract: Three-way decisions, as a better way than two-way decisions, has played an important role in many fields. As an extension of formal concept, rough semiconcept constitutes a new approach for data analysis. By now, three-way concept, which combines three-way decisions with formal concept, has been an efficient tool for knowledge representation problems. Hence, we want to further apply three-way decisions to rough semiconcept. In this work, we introduce three-way rough semiconcept by an example, which combines rough semiconcept with the assistant of three-way decisions. After that, we attain the structure of all three-way rough semiconcepts from an algebraic perspective. Furthermore, …we give two kinds of approximation operators, which can characterize three-way rough semiconcepts. Finally, we present algorithms for searching three-way rough semiconcepts. An example is to demonstrate the correct and effective of algorithms in this paper. Show more

Keywords: Three-way decisions, semiconcept, rough set, lattice theory

DOI: 10.3233/JIFS-200981

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4317-4330, 2021

Authors: Gao, Weiqi | Huang, Hao

Article Type: Research Article

Abstract: Graph convolutional networks (GCNs), which are capable of effectively processing graph-structural data, have been successfully applied in text classification task. Existing studies on GCN based text classification model largely concerns with the utilization of word co-occurrence and Term Frequency-Inverse Document Frequency (TF–IDF) information for graph construction, which to some extent ignore the context information of the texts. To solve this problem, we propose a gating context-aware text classification model with Bidirectional Encoder Representations from Transformers (BERT) and graph convolutional network, named as Gating Context GCN (GC-GCN). More specifically, we integrate the graph embedding with BERT embedding by using a GCN …with gating mechanism to enable the acquisition of context coding. We carry out text classification experiments to show the effectiveness of the proposed model. Experimental results shown our model has respectively obtained 0.19%, 0.57%, 1.05% and 1.17% improvements over the Text-GCN baseline on the 20NG, R8, R52, and Ohsumed benchmark datasets. Furthermore, to overcome the problem that word co-occurrence and TF–IDF are not suitable for graph construction for short texts, Euclidean distance is used to combine with word co-occurrence and TF–IDF information. We obtain an improvement by 1.38% on the MR dataset compared to Text-GCN baseline. Show more

Keywords: Text classification, graph convolutional network, BERT, gating mechanism, Euclidean distance

DOI: 10.3233/JIFS-201051

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4331-4343, 2021

Authors: Zhou, Jun | Peng, Jinghong | Liang, Guangchuan | Chen, Chuan | Zhou, Xuan | Qin, Yixiong

Article Type: Research Article

Abstract: Natural gas transmission network is the major facility connecting the upstream gas sources and downstream consumers. In this paper, a multi-objective optimization model is built to find the optimum operation scheme of the natural gas transmission network. This model aims to balance two conflicting optimization objective named maximum a specified node delivery flow rate and minimum compressor station power consumption cost. The decision variables involve continuous and discrete variables, including node delivery flow rate, number of running compressors and their rotational speed. Besides, a series of equality and inequality constraints for nodes, pipelines and compressor stations are introduced to control …the optimization results. Then, the developed optimization model is applied to a practical large tree-topology gas transmission network, which is 2,229 km in length with 7 compressor stations, 2 gas injection nodes and 20 gas delivery nodes. The ɛ -constraint method and GAMS/DICOPT solver are adopted to solve the bi-objective optimization model. The optimization result obtained is a set of Pareto optimal solutions. To verify the validity of the proposed method, the optimization results are compared with the actual operation scheme. Through the comparison of different Pareto optimal solutions, the variation law of objective functions and decision variables between different optimal solutions are clarified. Finally, sensitivity analyses are also performed to determine the influence of operating parameter changes on the optimization results. Show more

Keywords: Natural gas transmission network, operation optimization, compressor station, multi-objective optimization, ɛ-constraint method

DOI: 10.3233/JIFS-201072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4345-4366, 2021

Authors: Sha, Xiuyan | Yin, Chuancun | Xu, Zeshui | Zhang, Shen

Article Type: Research Article

Abstract: In order to fully consider the decision-maker’s limited rationality and attitude to risk, this paper constructs the probabilistic hesitant fuzzy TOPSIS emergency decision-making model based on the cumulative prospect theory under the probabilistic hesitant fuzzy environment. Aiming at the problem of missing probabilistic information in the probabilistic hesitant fuzzy element, a new complement scheme is proposed. In this scheme, the weighted average result of the original data information is used to complement, and the original data information is retained to a large extent. Then this paper proposes several probabilistic hesitant fuzzy distance measures based on Lance distance. The decision reference …point is constructed by the probabilistic hesitant fuzzy Lance distance, which overcomes the influence of the extreme value on the decision-making result, and defines the value function based on the probabilistic hesitant fuzzy Lance distance. In view of the fact that the attribute weights are completely unknown, the probabilistic hesitant fuzzy exponential entropy is constructed by using the actual data, and the attribute weights of different prospect states are obtained. Aiming at the problem that attribute weights of different prospect states have different effects on the cumulative prospect value, the expression of the cumulative prospect value is improved. The improved closeness coefficient of the TOPSIS method is used to order the emergency schemes. Finally, the new method is applied to the emergency decision-making case of a sudden outbreak of epidemic respiratory disease. The results show that the contrast of the new method is obvious, which is conducive to distinguish different schemes. The new method is more suitable for the complex and changeable emergency decision-making field. Show more

Keywords: Probabilistic hesitant fuzzy Lance distance, probabilistic hesitant fuzzy exponential entropy, cumulative prospect theory, TOPSIS

DOI: 10.3233/JIFS-201119

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4367-4383, 2021

Authors: Elaskily, Mohamed A. | Alkinani, Monagi H. | Sedik, Ahmed | Dessouky, Mohamed M.

Article Type: Research Article

Abstract: Protecting information from manipulation is important challenge in current days. Digital images are one of the most popular information representation. Images could be used in several fields such as military, social media, security purposes, intelligence fields, evidences in courts, and newspapers. Digital image forgeries mean adding unusual patterns to the original images that cause a heterogeneity manner in form of image properties. Copy move forgery is one of the hardest types of image forgeries to be detected. It is happened by duplicating part or section of the image then adding again in the image itself but in another location. Forgery …detection algorithms are used in image security when the original content is not available. This paper illustrates a new approach for Copy Move Forgery Detection (CMFD) built basically on deep learning. The proposed model is depending on applying (Convolution Neural Network) CNN in addition to Convolutional Long Short-Term Memory (CovLSTM) networks. This method extracts image features by a sequence number of Convolutions (CNVs) layers, ConvLSTM layers, and pooling layers then matching features and detecting copy move forgery. This model had been applied to four aboveboard available databases: MICC-F220, MICC-F2000, MICC-F600, and SATs-130. Moreover, datasets have been combined to build new datasets for all purposes of generalization testing and coping with an over-fitting problem. In addition, the results of applying ConvLSTM model only have been added to show the differences in performance between using hybrid ConvLSTM and CNN compared with using CNN only. The proposed algorithm, when using number of epoch’s equal 100, gives high accuracy reached to 100% for some datasets with lowest Testing Time (TT) time nearly 1 second for some datasets when compared with the different previous algorithms. Show more

Keywords: Convolutional Long Short-Term Memory (CovLSTM), copy-move forgery detection, image authentication, tampered images, deep learning, and convolutional neural networks

DOI: 10.3233/JIFS-201192

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4385-4405, 2021

Authors: Chen, Chuanming | Zhang, Shuanggui | Yu, Qingying | Ye, Zitong | Ye, Zhen | Hu, Fan

Article Type: Research Article

Abstract: The analysis of user trajectory information and social relationships in social media, combined with the personalization of travel needs, allows users to better plan their travel routes. However, existing methods take only local factors into account, which results in a lack of pertinence and accuracy for the recommended route. In this study, we propose a method by which user clustering, improved genetic, and rectangular region path planning algorithms are combined to design personalized travel routes for users. First, the social relationships of users are analyzed, and close friends are clustered into categories to obtain several friend clusters. Next, the historical …trajectory data of users in the cluster are analyzed to obtain joint points in the trajectory map, these are matched according to the keywords entered by users. Finally, the search area is narrowed and the recommended travel route is obtained through improved genetic and rectangular region path planning algorithms. Theoretical analyses and experimental evaluations show that the proposed method is more accurate at path prediction and regional coverage than other methods. In particular, the average area coverage rate of the proposed method is better than that of the existing algorithm, with a maximum increasement ratio of 31.80%. Show more

Keywords: Tourism route, genetic algorithm, personalized recommendation, route planning

DOI: 10.3233/JIFS-201218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4407-4423, 2021

Authors: Imtiaz, Aneeza | Shuaib, Umer | Razaq, Abdul | Gulistan, Muhammad

Article Type: Research Article

Abstract: The study of complex fuzzy sets defined over the meet operator (ξ – CFS) is a useful mathematical tool in which range of degrees is extended from [0, 1] to complex plane with unit disk. These particular complex fuzzy sets plays a significant role in solving various decision making problems as these particular sets are powerful extensions of classical fuzzy sets. In this paper, we define ξ – CFS and propose the notion of complex fuzzy subgroups defined over ξ – CFS (ξ – CFSG) along with their various fundamental algebraic characteristics. We extend the study …of this idea by defining the concepts of ξ – complex fuzzy homomorphism and ξ – complex fuzzy isomorphism between any two ξ – complex fuzzy subgroups and establish fundamental theorems of ξ – complex fuzzy morphisms. In addition, we effectively apply the idea of ξ – complex fuzzy homomorphism to refine the corrupted homomorphic image by eliminating its distortions in order to obtain its original form. Moreover, to view the true advantage of ξ – complex fuzzy homomorphism, we present a comparative analysis with the existing knowledge of complex fuzzy homomorphism which enables us to choose this particular approach to solve many decision-making problems. Show more

Keywords: ξ –complex fuzzy sets (ξ – CFS), ξ –complex fuzzy subgroups (ξ – CFSG), ξ –complex fuzzy normal subgroups (ξ – CFNSG), ξ –complex fuzzy homomorphism, ξ –complex fuzzy isomorphism, 08A72, 20N25, 03E72

DOI: 10.3233/JIFS-201261

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4425-4437, 2021

Authors: Mo, Hongming

Article Type: Research Article

Abstract: Wind power is a typical clean and renewable energy, which has been widely regarded as one of the replaceable energies in many countries. Wind turbine is the key equipment to generate wind power. It is necessary to evaluate the risks of each stage of the wind turbine with regard to occupational health and safety. In this study, the stage of production of life cycle of wind turbine is considered. The aim of this study is to propose a new method to identify and evaluate the risk factors based on strengths-weaknesses-opportunities-threats (SWOT) analysis and D number theory, named D-SWOT method. A …wind turbine firm is used to demonstrate the detailed steps of the proposed method. SWOT is conducted to identify the risk factors of production stage of the wind turbine company. Experts are invited to perform the risk assessment, and D number theory is carried out to do the processes of information representation and integration. After that, some suggestions are provided to the company to lower the risks. The D-SWOT method obtains the same results as the previous method of hesitant fuzzy linguistic term set (HFLTS). Compared with HFLTS method, D-SWOT method simplifies the process of information processing, and D-SWOT method is more intuitional and concise. Besides, a property of pignistic probability transformation of D number theory (DPPT) is proposed in the manuscript, which extends D number theory and has been used in the process of decision making of D-SWOT. Show more

Keywords: Belief function, evidence theory, D number theory, strengths-weaknesses-opportunities-threats, risk evaluation, wind turbine

DOI: 10.3233/JIFS-201277

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4439-4452, 2021

Authors: Gao, Xin Wen | Li, ShuaiQing | Jin, Bang Yang | Hu, Min | Ding, Wei

Article Type: Research Article

Abstract: With the large-scale construction of urban subways, the detection of tunnel cracks becomes particularly important. Due to the complexity of the tunnel environment, it is difficult for traditional tunnel crack detection algorithms to detect and segment such cracks quickly and accurately. The article presents an optimal adaptive selection model (RetinaNet-AOS) based on deep learning RetinaNet for semantic segmentation on tunnel crack images quickly and accurately. The algorithm uses the ROI merge mask to obtain a minimum detection area of the crack in the field of view. A scorer is designed to measure the effect of ROI region segmentation to achieve …optimal results, and further optimized with a multi-dimensional classifier. The algorithm is compared with the standard detection based on RetinaNet algorithm with an optimal adaptive selection based on RetinaNet algorithm for different crack types. The results show that our crack detection algorithm not only addresses interference due to mash cracks, slender cracks, and water stains but also the false detection rate decreases from 25.5–35.5% to about 3.6%. Meanwhile, the experimental results focus on the execution time to be calculated on the algorithm, FCN, PSPNet, UNet. The algorithm gives better performance in terms of time complexity. Show more

Keywords: Crack detection, deep learning, retinanet, optimal adaptive selection, ROI merge

DOI: 10.3233/JIFS-201296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4453-4469, 2021

Authors: Ghorani, Maryam | Garhwal, Sunita

Article Type: Research Article

Abstract: In this paper, we study fuzzy top-down tree automata over lattices ( LTA s , for short). The purpose of this contribution is to investigate the minimization problem for LTA s . We first define the concept of statewise equivalence between two LTA s . Thereafter, we show the existence of the statewise minimal form for an LTA . To this end, we find a statewise irreducible LTA which is equivalent to a given LTA …. Then, we provide an algorithm to find the statewise minimal LTA and by a theorem, we show that the output statewise minimal LTA is statewise equivalent to the given input. Moreover, we compute the time complexity of the given algorithm. The proposed algorithm can be applied to any given LTA and, unlike some minimization algorithms given in the literature, the input doesn’t need to be a complete, deterministic, or reduced lattice-valued tree automaton. Finally, we provide some examples to show the efficiency of the presented algorithm. Show more

Keywords: Fuzzy tree automata, minimization problem, lattice-valued logic, statewise minimal

DOI: 10.3233/JIFS-201298

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4471-4480, 2021

Authors: Chen, Lei | Xia, Meimei

Article Type: Research Article

Abstract: Recommender systems can recommend products by analyzing the interests and habits of users. To make more efficient recommendation, the contextual information should be collected in recommendation algorithms. In the restaurant recommendation, the location and the current time of customers should also be considered to facilitate restaurants to find potential customers and give accurate and timely recommendations. However, the existing recommendation approaches often lack the consideration of the influence of time and location. Besides, the data sparsity is an inherent problem in the collaborative filtering algorithm. To address these problems, this paper proposes a recommendation approach which combines the contextual information …including time, price and location. Instead of constructing the user-restaurant scoring matrix, the proposed approach clusters price tags and generates the user-price scoring matrix to alleviate the sparsity of data. The experiment on Foursquare dataset shows that the proposed approach has a better performance than traditional ones. Show more

Keywords: Recommender system, collaborative filtering, contextual information, restaurant recommendation, data sparsity

DOI: 10.3233/JIFS-201299

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4481-4489, 2021

Authors: Alfaqih, Waleed M. | Ali, Based | Imdad, Mohammad | Sessa, Salvatore

Article Type: Research Article

Abstract: In this manuscript, we provide a new and novel generalization of the concept of fuzzy contractive mappings due to Gregori and Sapena [Fuzzy Sets and Systems 125 (2002) 245–252] in the setting of relational fuzzy metric spaces. Our findings possibly pave the way for another direction of relation-theoretic as well as fuzzy fixed point theory. We illustrate several examples to show the usefulness of our proven results. Moreover, we define cyclic fuzzy contractive mappings and utilize our main results to prove a fixed point result for such mappings. Finally, we deduce several results including fuzzy metric, order-theoretic and α -admissible …results. Show more

Keywords: 47H10, 54H25, Fuzzy metric space, fixed point, binary relation, α-admissible

DOI: 10.3233/JIFS-201319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4491-4501, 2021

Authors: Zhou, Xiao-Wu | Shi, Fu-Gui

Article Type: Research Article

Abstract: Considering L be a completely distributive lattice, the notion of the sum of L -convex spaces is introduced and its elementary properties is studied. Firstly, the connections between the sum of L -convex spaces and its factor spaces are established. Secondly, the additivity of separability (S -1 , sub-S 0 , S 0 , S 1 , S 2 , S 3 and S 4 ) are investigated. Finally, the additivity of five types special L -convex spaces are examined.

Keywords: L-convex space, sum of L-convex space, separability, additivity

DOI: 10.3233/JIFS-201335

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4503-4515, 2021

Authors: Al-Andoli, Mohammed | Cheah, Wooi Ping | Tan, Shing Chiang

Article Type: Research Article

Abstract: Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid …the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms. Show more

Keywords: Complex networks, community detection, autoencoder, particle swarm optimization, continuation method

DOI: 10.3233/JIFS-201342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4517-4533, 2021

Authors: Jin, LeSheng | Yager, Ronald R. | Špirková, Jana | Mesiar, Radko | Paternain, Daniel | Bustince, Humberto

Article Type: Research Article

Abstract: Basic Uncertain Information (BUI) as a newly introduced concept generalized a wide range of uncertain information. The well-known Ordered Weighted Averaging (OWA) operators can flexibly and effectively model bipolar preferences of decision makers over given real valued input vector. However, there are no extant methods for OWA operators to be carried out over given BUI vectors. Against this background, this study firstly discusses the interval transformation for BUI and elaborately explains the reasonability within it. Then, we propose the corresponding preference aggregations for BUI in two different decisional scenarios, the aggregation for BUI vector without original information influencing and the …aggregation for BUI vector with original information influencing after interval transformation. For each decisional scenario, we also discuss two different orderings of preference aggregation, namely, interval-vector and vector-interval orderings, respectively. Hence, we will propose four different aggregation procedures of preference aggregation for BUI vector. Some illustrative examples are provided immediately after the corresponding aggregation procedures. Show more

Keywords: Aggregation function, basic uncertain information (BUI), decision-making, interval information, ordered weighted averaging (OWA) operator

DOI: 10.3233/JIFS-201374

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4535-4544, 2021

Authors: Li, Ming | Su, Bin | Lei, Deming

Article Type: Research Article

Abstract: Assembly flow shop scheduling problem with DPm  → 1 layout has important applications in various manufacturing systems and has been extensively considered in single factory; however, this problem with fuzzy processing time is seldom studied in multiple factories. In this paper, fuzzy distributed assembly flow shop scheduling problem (FDAFSP) is considered, in which each factory has DPm  → 1 layout, and an imperialist competitive algorithm with empire cooperation (ECICA) is developed to minimize fuzzy makespan. In ECICA, an adaptive empire cooperation between the strongest empire and the weakest empire is implemented by exchanging computing resources and search ability, historical evolution data are …used and a new imperialist competition is adopted. Numerical experiments are conducted on various instances and ECICA is compared with the existing methods to test its performance. Computational results demonstrate that ECICA has promising advantages on solving FDAFSP. Show more

Keywords: Assembly flow shop scheduling, distributed scheduling, imperialist competitive algorithm, fuzzy makespan

DOI: 10.3233/JIFS-201391

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4545-4561, 2021

Authors: Malarvizhi, K. | Amshakala, K.

Article Type: Research Article

Abstract: In this paper, a novel Feature-Reduction Fuzzy C-means (FRFCM) with Feature Linkage Weight (FRFCM-FLW) algorithm is introduced. By the combination of FRFCM and feature linkage weight, a new feature selection model is developed, called a Feature Linkage Weight Based FRFCM using fuzzy clustering. The larger amounts of features are superior to the complication of the problem, and the larger the time that is exhausted in creating the outcome of the classifier or the model. Feature selection has been established as a high-quality method for preferring features that best describes the data under certain criteria or measure. The proposed method …presents three stages namely, 1) Data Formation: The process of data collection and data cleaning; 2) FRFCM-FLW. The proposed method can decrease feature elements routinely, and also construct excellent clustering results. The proposed method calculates a novel weight for every feature by combining modified Mahalanobis distance with feature δm variance in FRFCM algorithm; 3) Fuzzy C-means (FCM) cluster. The proposed FRFCM-FLW method proves high Accuracy Rate (AR), Rand Index (RI) and Jaccard Index (JI) ratio when compared to other feature reduction algorithms like WFCM, EWKM, WKM, FCM and FRFCM algorithms. Show more

Keywords: Data mining, fuzzy logic, feature selection, FCM

DOI: 10.3233/JIFS-201395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4563-4572, 2021

Authors: Dhaiban, Ali Khaleel | Jabbar, Baydaa Khalaf

Article Type: Research Article

Abstract: Many studies have attempted to understand the true nature of COVID-19 and the factors influencing the spread of the virus. This paper investigates the possible effect the COVID-19 pandemic spreading in Iraq considering certain factors, that include isolation and weather. A mathematical model of cases representing inpatients, recovery, and mortality was used in formulating the control variable in this study to describe the spread of COVID-19 through changing weather conditions between 17th March and 15th May, 2020. Two models having deterministic and an uncertain number of daily cases were used in which the solution for the model using the Pontryagin …maximum principle (PMP) was derived. Additionally, an optimal control model for isolation and each factor of the weather factors was also achieved. The results simulated the reality of such an event in that the cases increased by 118%, with an increase in the number of people staying outside of their house by 25%. Further, the wind speed and temperature had an inverse effect on the spread of COVID-19 by 1.28% and 0.23%, respectively. The possible effect of the weather factors with the uncertain number of cases was higher than the deterministic number of cases. Accordingly, the model developed in this study could be applied in other countries using the same factors or by introducing other factors. Show more

Keywords: COVID-19 pandemic, optimal control, pontryagin maximum principle, chance-constrained, isolation, weather factors

DOI: 10.3233/JIFS-201419

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4573-4587, 2021

Authors: Lu, Ziqiang | Zhu, Yuanguo | Shen, Jiayu

Article Type: Research Article

Abstract: Uncertain fractional differential equation driven by Liu process plays an important role in describing uncertain dynamic systems. This paper investigates the continuous dependence of solution on the parameters and initial values, respectively, for uncertain fractional differential equations involving the Caputo fractional derivative in measure sense. Several continuous dependence theorems are obtained based on uncertainty theory by employing the generalized Gronwall inequality, in which the coefficients of uncertain fractional differential equation are required to satisfy the Lipschitz conditions. Several illustrative examples are provided to verify the validity of the obtained results.

Keywords: Uncertainty theory, fractional differential equation, Caputo derivative, continuous dependence

DOI: 10.3233/JIFS-201428

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4589-4598, 2021

Authors: Poongodi, K. | Kumar, Dhananjay

Article Type: Research Article

Abstract: The Frequent Episode Mining (FEM) is a challenging framework to identify frequent episodes from a sequence database. In a sequence, an ordered collection of events defines an episode, and frequent episodes are only considered by the earlier studies. Also, it doesn’t support for the serial based episode rule mining. In this work, the episode rules are mined with precise and serial based rule mining considering the temporal factor, so that, the occurrence time of the consequent is specified in contrast to the traditional episode rule mining. The proposed work has a larger number of candidates and specific time constraints to …generate the fixed-gap episodes, and mining such episodes from whole sequence where the time span between any two events is a constant which is utilized to improve the proposed framework’s performance. In order to improve the efficiency, an Optimal Fixed-gap Episode Occurrence (OFEO) is performed using the Natural Exponent Inertia Weight based Swallow Swarm Optimization (NEIWSSO) algorithm. The temporal constraints significantly evaluate the effectiveness of episode mining, and a noticeable advantage of the present work is to generate optimal fixed-gap episodes for better prediction. The effective use of memory consumption and performance enhancement is achieved by developing new trie-based data structure for Mining Serial Positioning Episode Rules (MSPER) using a pruning method. The position of frequent events is updated in the precise-positioning episode rule trie instead of frequent events to reduce the memory space. The benchmark datasets Retail, Kosarak, and MSNBC is used to evaluate the proposed algorithm’s efficiency. Eventually, it is found that it outperforms the existing techniques with respect to memory consumption and execution time. On an average, the proposed algorithm achieves 28 times lesser execution time and consumes 45.5% less memory space for the highest minimum support value on the Retail dataset compared to existing methods. Show more

Keywords: Frequent episode mining, fixed-gap episode occurrence, natural exponent inertia weight, support of fixed-gap episode

DOI: 10.3233/JIFS-201438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4599-4615, 2021

Authors: Kudłacik, Przemysław | Łęski, Jacek M.

Article Type: Research Article

Abstract: The article presents a thorough analysis of fuzzy inference introduced by Baldwin and compares this approach to Zaheh’s compositional rule of inference. The comparison is performed in order to analyze the equivalence of the two methods and describe practical aspects of this fact for simple and compound premises, indicating advantages and disadvantages of both approaches. The main aim of the analysis is focus on the computational complexity of the methods. The most important feature of Baldwin’s inference is transfer of the inference process into a truth space, unified for all input variables. Such environment allows to obtain one fuzzy truth …value describing a compound premise in a sequence of low dimensional computations. The article proves equality of such approach with the compositional rule of inference. Therefore, this solution is much more computationally efficient in case of compound cases, for which compositional rule of inference is multidimensional. Show more

Keywords: Fuzzy inference, fuzzy truth value, fuzzy sets

DOI: 10.3233/JIFS-201443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4617-4636, 2021

Authors: Deng, Xue | Chen, Chuangjie

Article Type: Research Article

Abstract: Considering that most studies have taken the investors’ preference for risk into account but ignored the investors’ preference for assets, in this paper, we combine the prospect theory and possibility theory to provide investors with a portfolio strategy that meets investors’ preference for assets. Firstly, a novel reference point is proposed to give investors a comprehensive impression of assets. Secondly, the prospect return rate of assets is quantified as trapezoidal fuzzy number, and its possibilistic mean value and variance are regarded as prospect return and risk and then used to define the fuzzy prospect value. This new definition is presented …to denote the score of an asset in investors’ subjective cognition. And then, a prospect asset filtering frame is proposed to help investors select assets according to their preference. When assets are selected, another new definition called prospect consistency coefficient is proposed to measure the deviation of a portfolio strategy from investors’ preference. Some properties of the definition are presented by rigorous mathematical proof. Based on the definition and its properties, a possibilistic model is constructed, which can not only provide investors optimal strategies to make profit and reduce risk as much as possible, but also ensure that the deviation between the strategies and investors’ preference is tolerable. Finally, a numerical example is given to validate the proposed method, and the sensitivity analysis of parameters in prospect value function and prospect consistency constraint is conducted to help investors choose appropriate values according to their preferences. The results show that compared with the general M-V model, our model can not only better satisfy investors’ preference for assets, but also disperse risk effectively. Show more

Keywords: Possibility theory, prospect theory, portfolio selection, asset altering framework, prospect consistency coefficient

DOI: 10.3233/JIFS-201457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4637-4660, 2021

Authors: Hu, Chengxiang | Zhang, Li | Liu, Shixi

Article Type: Research Article

Abstract: Multigranulation rough set (MGRS) theory provides an effective manner for the problem solving by making use of multiple equivalence relations. As the information systems always dynamically change over time due to the addition or deletion of multiple objects, how to efficiently update the approximations in multigranulation spaces by making fully utilize the previous results becomes a crucial challenge. Incremental learning provides an efficient manner because of the incorporation of both the current information and previously obtained knowledge. In spite of the success of incremental learning, well-studied findings performed to update approximations in multigranulation spaces have relatively been scarce. To address …this issue, in this paper, we propose matrix-based incremental approaches for updating approximations from the perspective of multigranulation when multiple objects vary over time. Based on the matrix characterization of multigranulation approximations, the incremental mechanisms for relevant matrices are systematically investigated while adding or deleting multiple objects. Subsequently, in accordance with the incremental mechanisms, the corresponding incremental algorithms for maintaining multigranulation approximations are developed to reduce the redundant computations. Finally, extensive experiments on eight datasets available from the University of California at Irvine (UCI) are conducted to verify the effectiveness and efficiency of the proposed incremental algorithms in comparison with the existing non-incremental algorithm. Show more

Keywords: Dynamic data, approximations, multigranulation, matrix, knowledge discovery

DOI: 10.3233/JIFS-201472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4661-4682, 2021

Authors: Sedova, Nelly | Sedov, Viktor | Bazhenov, Ruslan | Bogatenkov, Sergey

Article Type: Research Article

Abstract: The authors continued their research on the development of an intelligent automatic ships pilot containing a controller based on fuzzy logic. Its features are determined by the optimizer based on a genetic algorithm. It also contains a modular unit of neural network models of ship navigation paths, as well as a neural network classifier. This paper is devoted to the description of a neural network classifier designed to classify the movement patterns of marine vessels to identify the peculiarities of the ship depending on its type and sailing conditions. The introduction of such classifier to an autopilot allows for more …precise consideration of multivariate and difficult to formalize factors affecting the vessel while operating, such as varying weather conditions, irregular waves, hydrodynamic characteristics of the vessel, draft, water under the keel, rate of the vessel sailing, etc. The article outlines the technique concerning the development of a neural network classifier and the results of its computer modelling on the example of a refrigerated transport vessel type. The authors used such methods for obtaining and processing findings as spectral estimation, machine learning methods, in particular, neural network technology and computer or simulation modelling. Show more

Keywords: Neural network classifier, automatic course-keeping, fuzzy logic, autopilot

DOI: 10.3233/JIFS-201495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4683-4694, 2021

Authors: Jasrotia, Swati | Singh, Uday Pratap | Raj, Kuldip

Article Type: Research Article

Abstract: In this article, we introduce and study some difference sequence spaces of fuzzy numbers by making use of λ -statistical convergence of order (η , δ  + γ ) . With the aid of MATLAB software, it appears that the statistical convergence of order (η , δ  + γ ) is well defined every time when (δ  + γ ) > η and this convergence fails when (δ  + γ ) < η . Moreover, we try to set up relations between (Δv , λ )-statistical convergence of order (η , δ  + γ ) and strongly (Δv , p , λ )-Cesàro summability of order (η …, δ  + γ ) and give some compelling instances to show that the converse of these relations is not valid. In addition to the above results, we also graphically exhibits that if a sequence of fuzzy numbers is bounded and statistically convergent of order (η , δ  + γ ) in (Δv , λ ), then it need not be strongly (Δv , p , λ )-Cesàro summable of order (η , δ  + γ ). Show more

Keywords: Cesàro summability, difference operator, fuzzy numbers, λ-statistical convergence

DOI: 10.3233/JIFS-201539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4695-4703, 2021

Authors: Zhong, Leiguang | Luo, Yiyue | Zhang, Xin | Zhang, Hongyu | Wang, Jianqiang

Article Type: Research Article

Abstract: User rating information on multiple predefined aspects gathered by hotel recommendation systems generally shows a deviation between the overall rating and detailed criteria ratings. In this study, to address this deviation, we proposed a novel hotel recommendation method that clusters users with different preferences into different groups using the K-means algorithm. Moreover, we allocated weights to different criteria and obtained a comprehensive score. A case study on actual data from Tripadvisor.com showed that compared with three other models, our proposed model demonstrated a more impressive performance. This research can offer advantages to hotel service providers and customers in terms of …decision making. Show more

Keywords: Recommender system, hotel recommendation, multi criteria rating, K-means, Tripadvisor.com

DOI: 10.3233/JIFS-201577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4705-4720, 2021