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