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

Authors: Tang, Fei

Article Type: Research Article

Abstract: To improve the optimization efficiency of the intelligent bionic optimization algorithm, this paper proposes intelligent bionic optimization algorithm based on the growth characteristics of tree branches. Firstly, the growth organ of the tree is mapped into the coding of the tree growth algorithm (intelligent bionic optimization algorithm). Secondly, the entire tree, that is the growing tree, is formed by selecting the individual that grows fast to generate the next level of shoot population. Lastly, if the growing tree reaches a certain level, the individual coding of the shoots is added to enhance the searching ability of the individuals of current …generation in the growth tree growth space, so that the algorithm approaches the optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function and showed that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm and the ant colony algorithm. Show more

Keywords: Individual coding, branch population, genetic algorithm, tree growth algorithm

DOI: 10.3233/JIFS-190487

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3821-3829, 2021

Authors: Lin, Jiang | Jianjun, Zhu

Article Type: Research Article

Abstract: As a new business form of innovation and development, new R&D institutions are characterized by their focus on regional and industrial major technical needs, diversified investment subjects, diversified construction models, and marketization of operation mechanism. Their performance evaluation faces new problems and challenges. This paper proposes a new dynamic grey target evaluation model of R&D institutions’ performance in regard to four evaluation indexes, three reference points, and four stages. Aiming at resolving the multi-attribute dynamic decision problem with the attribute value being an interval grey number and the decision-maker’s weight information unknown, we propose the use of the close degree …of grey incidence method to determine the index weight. Our approach revolves around three reference points: peers, development, and expectations. Value matrices of the three reference points are expressed according to the Cumulative Prospect Theory, which also determines the distance from the center of the grey target. Based on the Orness measure, we establish a multi-stage weight optimization model to calculate the stage weight and the comprehensive cumulative prospect value of each agency. Finally, we verify the validity and practicability of our method with the use of parameter sensitivity analysis, a comparison with other methods, and a case study. Show more

Keywords: Three reference points, new R&D institutions, performance, dynamic grey target, evaluation

DOI: 10.3233/JIFS-190602

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3831-3847, 2021

Authors: Yousaf, Waqas | Umar, Arif | Shirazi, Syed Hamad | Khan, Zakir | Razzak, Imran | Zaka, Mubina

Article Type: Research Article

Abstract: Automatic logo detection and recognition is significantly growing due to the increasing requirements of intelligent documents analysis and retrieval. The main problem to logo detection is intra-class variation, which is generated by the variation in image quality and degradation. The problem of misclassification also occurs while having tiny logo in large image with other objects. To address this problem, Patch-CNN is proposed for logo recognition which uses small patches of logos for training to solve the problem of misclassification. The classification is accomplished by dividing the logo images into small patches and threshold is applied to drop no logo area …according to ground truth. The architectures of AlexNet and ResNet are also used for logo detection. We propose a segmentation free architecture for the logo detection and recognition. In literature, the concept of region proposal generation is used to solve logo detection, but these techniques suffer in case of tiny logos. Proposed CNN is especially designed for extracting the detailed features from logo patches. So far, the technique has attained accuracy equals to 0.9901 with acceptable training and testing loss on the dataset used in this work. Show more

Keywords: Logo detection, logo recognition, deep learning, AlexNet, ResNet, CNN

DOI: 10.3233/JIFS-190660

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3849-3862, 2021

Authors: Dabighi, Korosh | Nazari, Akbar | Saryazdi, Saeid

Article Type: Research Article

Abstract: Nowadays, Canny edge detector is considered to be one of the best edge detection approaches for the images with step form. Various overgeneralized versions of these edge detectors have been offered up to now, e.g. Saryazdi edge detector. This paper proposes a new discrete version of edge detection which is obtained from Shen-Castan and Saryazdi filters by using bilinear transformation. Different experimentations are conducted to decide the suitable parameters of the proposed edge detector and to examine its validity. To evaluate the strength of the proposed model, the results are compared to Canny, Sobel, Prewitt, LOG and Saryazdi methods. Finally, …by calculation of mean square error (MSE) and peak signal-to-noise ratio (PSNR), the value of PSNR is always equal to or greater than the PSNR value of suggested methods. Moreover, by calculation of Baddeley’s error metric (BEM) on ten test images from the Berkeley Segmentation DataSet (BSDS), we show that the proposed method outperforms the other methods. Therefore, visual and quantitative comparison shows the efficiency and strength of proposed method. Show more

Keywords: Edge detection, Laplace operator, impulse response invariance, bilinear transformation

DOI: 10.3233/JIFS-191229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3863-3874, 2021

Authors: Jian, Xianzhong | Wang, Xutao

Article Type: Research Article

Abstract: The existing methods for classification of power quality disturbance signals (PQDs) have the problems that the process of signal feature selection is tedious and imprecise, the accuracy of classification has no guiding significance for feature extraction, and lack of adequate labelled training data. To solve these problems, this paper proposes a new semi-supervised method for classification of PQDs based on generative adversarial network (GAN). Firstly, a GAN model is designed which we call it PQDGAN. After the unsupervised pre-training with unlabeled training data, the trained discriminator is extracted alone and conduct supervised training with a small amount of labelled training …data. Finally, the discriminator became a classifier with high accuracy. This model can achieve the step of feature extraction and selection efficiently. In addition, only a small amount of labelled training data is used, which greatly reduces the dependence of classification model on labelled data. Experiments show that this method has high classification accuracy, less computations and strong robustness. It is a new semi-supervised method for classification of PQDs. Show more

Keywords: Deep learning, generative adversarial network, power quality, semi-supervised learning, signal classification.

DOI: 10.3233/JIFS-191274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3875-3885, 2021

Authors: Zhang, Ya | Xiong, Qiang

Article Type: Research Article

Abstract: The traditional method of Guangdong embroidery image color perception recognition has poor stereoscopic color reduction. Therefore, this paper introduces discrete mathematical model to design a new method of Guangdong embroidery image color perception recognition. Through histogram equalization, the input image with relatively concentrated gray distribution is transformed into the histogram output image with approximately uniform distribution to enhance the dynamic range of pixel gray value. The image of Yuexiu is smoothed and filtered by median filtering method to remove the noise in the image of Yuexiu. The RGB spatial model and HSI spatial model of image color are constructed by …normalizing the coordinates and color attributes of pixels. The RGB color space and HSI color space are transformed, and the image color perception recognition model is established to realize the color perception recognition of Guangdong embroidery image. The experimental results show that the pixels of each color in the color pixel image curve of the proposed method are as high as 800, the color pixel image curve distribution is the most intensive, and the color restoration is high. Show more

Keywords: Discrete mathematical model, Guangdong embroidery image, color perception and recognition, histogram coefficient, probability density function

DOI: 10.3233/JIFS-191484

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3887-3897, 2021

Authors: Chen, Yanhao | Yu, Suihuai | Chu, Jianjie | Yu, Mingjiu | Chen, Dengkai

Article Type: Research Article

Abstract: Numerous human factors need to be considered in the analysis and design of aircraft cockpits. The investigation of cockpit color patterns and the design of matched color schemes are particularly important. In this paper, we propose a fuzzy-logic-based emotional evaluation scheme for color pattern design in aircraft cockpits. Color pattern samples were collected and analyzed to construct a color library for cockpit color matching. Color scheme groups were accordingly redesigned and visualized. Based on fuzzy-set theory, the Kansei engineering model was thus used to evaluate the emotional image of the color schemes, and rank them in terms of priority. A …support vector machine was trained to construct a comprehensive intelligent color evaluation system. After training and validating the evaluation model, accurate emotional evaluation of color matching schemes could be achieved. Thus, the proposed system enables the extraction, mapping and evaluation of cockpit color matching schemes, and can be used in color scheme design for other cabins. Show more

Keywords: Emotional evaluation, aircraft cockpit design, color matching, fuzzy TOPSIS, intelligent design

DOI: 10.3233/JIFS-191960

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3899-3917, 2021

Authors: Hei, Hongzhong | Jian, Xianzhong | Xiao, Erliang

Article Type: Research Article

Abstract: The widespread application of infrared human action recognition in intelligent surveillance has attracted significant attention. However, the infrared action recognition dataset is limited, which limits the development of infrared action recognition. Existing methods for infrared action recognition are based on features in the same sample, without paying attention to within-class differences. Motivated by the idea of weighting video information, this paper proposes a novel infrared action recognition framework to reweight the samples of training sets named REWS to solve the problems of limited infrared action data and the large within-class differences in the infrared action recognition dataset. In the proposed …framework, we first map infrared action video data to a low-dimensional feature space, and use the cosine similarity between the feature data of the training set and the testing set to determine the weight of the training set samples. Each training set sample has an independent weight. Then, a support vector machine (SVM) is trained by the training sets with weights to recognize the infrared actions. Experimental results demonstrate that our approach can achieve state-of-the-art performance compared with hand-crafted features based methods on the benchmark InfAR dataset. Show more

Keywords: Infrared, action recognition, within-class differences, samples reweighting, cosine similarity

DOI: 10.3233/JIFS-192068

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3919-3930, 2021

Authors: Li, Yundong | Liu, Yi | Dong, Han | Hu, Wei | Lin, Chen

Article Type: Research Article

Abstract: The intrusion detection of railway clearance is crucial for avoiding railway accidents caused by the invasion of abnormal objects, such as pedestrians, falling rocks, and animals. However, detecting intrusions using deep learning methods from infrared images captured at night remains a challenging task because of the lack of sufficient training samples. To address this issue, a transfer strategy that migrates daytime RGB images to the nighttime style of infrared images is proposed in this study. The proposed method consists of two stages. In the first stage, a data generation model is trained on the basis of generative adversarial networks using …RGB images and a small number of infrared images, and then, synthetic samples are generated using a well-trained model. In the second stage, a single shot multibox detector (SSD) model is trained using synthetic data and utilized to detect abnormal objects from infrared images at nighttime. To validate the effectiveness of the proposed method, two groups of experiments, namely, railway and non-railway scenes, are conducted. Experimental results demonstrate the effectiveness of the proposed method, and an improvement of 17.8% is achieved for object detection at nighttime. Show more

Keywords: Railway clearance, infrared image detection, CycleGAN, SSD

DOI: 10.3233/JIFS-192141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3931-3943, 2021

Authors: Liao, Yu-Hsien

Article Type: Research Article

Abstract: In real situations, players might represent administrative areas of different scales; players might have different activity abilities. Thus, we propose an extension of the Banzhaf-Owen index in the framework of fuzzy transferable-utility games by considering supreme-utilities and weights simultaneously, which we name the weighted fuzzy Banzhaf-Owen index. Here we adopt three existing notions from traditional game theory and reinterpret them in the framework of fuzzy transferable-utility games. The first one is that this weighted index could be represented as an alternative formulation in terms of excess functions. The second is that, based on an reduced game and related consistency, we …offer an axiomatic result to present the rationality of this weighted index. Finally, we introduce two dynamic processes to illustrate that this weighted index could be reached by players who start from an arbitrary efficient payoff vector and make successive adjustments. Show more

Keywords: Weight, the weighted fuzzy Banzhaf-Owen index, excess function, dynamic process, 91A, 91B

DOI: 10.3233/JIFS-192165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 3945-3956, 2021

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

Authors: Xu, Junxiang | Guo, Jingni | Zhang, Jin | Liu, Weihua | Ma, Hui

Article Type: Research Article

Abstract: In order to study the influence of travelers’ self-adaptive adjustment behavior on transportation network under the assumption of bounded rationality, using cellular automaton to discretize the selection model under the analytic paradigm in the existing research, abstract each cell into a traveler, and describe the traveler characteristics with finite rationality characteristics through the travel risk attitude and travel generalized cost budget. Cellular automata and cumulative prospect theory is used to establish the travel route choice model, giving the dynamic evolution process of different reference points for travelers and taking the actual regional transportation network of Sichuan Tibet region in China …as the study object, analyzes the impact of bounded rational travel behavior on route choice. The model and algorithm proposed in our study can not only guide the transportation organization of Sichuan Tibet region, but also provide theoretical support for the implementation of regional transportation planning and traffic control scheme in the future. Show more

Keywords: Bounded rationality, cellular automaton, cumulative prospect theory, dynamic reference points, travel route choice

DOI: 10.3233/JIFS-201578

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4721-4735, 2021

Authors: Bakhthemmat, Ali | Izadi, Mohammad

Article Type: Research Article

Abstract: Many scientists apply fully dynamic bin packing problem solving for resource allocation of virtual machines in cloud environments. The goal of problem-solving is to reduce the number of allocated hosts (bins) and virtual machines (items) migration rates for reducing energy consumption. This study demonstrates a greedy futuristic algorithm (proposed algorithm) for fully dynamic bin packaging with an average asymptotic approximation ratio of 1.231, better than other existing algorithms. The proposed algorithm identifies inappropriate local selections using special futuristic conditions to prevent them as much as possible. Eventually, suitable choices determine and discard the improper ones. The proposed algorithm illustrates an …asymptotic approximation ratio of (t/ (t-1)) OPT, where the value of t depends on the distribution of the arrived and departed items. Also, OPT is the number of bins by an optimal solution. Finally, in experiments of datasets using a maximum utilization of 80% of each bin, the average migration rate is 0.338. Using the proposed method for allocating resources in the cloud environment can allocate hosts to a virtual machine using almost optimal use. This allocation can reduce the cost of maintaining and purchasing hosts. Also, this method can reduce the migration rate of virtual machines. As a result, decreasing migration improves the energy consumption cost in the cloud environment. Show more

Keywords: Fully dynamic bin packing, special futuristic conditions, futuristic greedy, migration reducing

DOI: 10.3233/JIFS-201581

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4737-4760, 2021

Authors: Ahmadini, Abdullah Ali H. | Ahmad, Firoz

Article Type: Research Article

Abstract: This paper investigates novel intuitionistic fuzzy preferences relations to determine the imprecise linguistic terms with fuzzy goals. The proposed intuitionistic fuzzy goal programming (IFGP) considers the degree of vagueness and hesitations simultaneously. Different sorts of membership functions such as linear, exponential, parabolic, and hyperbolic have been introduced to depict the linguistic importance term. The overall satisfaction level is achieved by maximizing the convex combination of each fuzzy goals and the preference relations simultaneously. To verify and validate the proposed IFGP model, a numerical example is presented with the comparative study. Further, it is also applied to a banking financial statement …management system problem. The proposed IFGP approach outperforms over others. At last, the conclusion and future research direction are suggested based on the performed study. Show more

Keywords: Intuitionistic fuzzy set, membership and non-membership function, score functions, imprecise goal hierarchy

DOI: 10.3233/JIFS-201588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4761-4777, 2021

Authors: Akram, Muhammad | Shahzadi, Gulfam | Shahzadi, Sundas

Article Type: Research Article

Abstract: An q -rung orthopair fuzzy set is a generalized structure that covers the modern extensions of fuzzy set, including intuitionistic fuzzy set and Pythagorean fuzzy set, with an adjustable parameter q that makes it flexible and adaptable to describe the inexact information in decision making. The condition of q -rung orthopair fuzzy set, i.e., sum of q th power of membership degree and nonmembership degree is bounded by one, makes it highly competent and adequate to get over the limitations of existing models. The basic purpose of this study is to establish some aggregation operators under the …q -rung orthopair fuzzy environment with Einstein norm operations. Motivated by innovative features of Einstein operators and dominant behavior of q -rung orthopair fuzzy set, some new aggregation operators, namely, q -rung orthopair fuzzy Einstein weighted averaging, q -rung orthopair fuzzy Einstein ordered weighted averaging, generalized q -rung orthopair fuzzy Einstein weighted averaging and generalized q -rung orthopair fuzzy Einstein ordered weighted averaging operators are defined. Furthermore, some properties related to proposed operators are presented. Moreover, multi-attribute decision making problems related to career selection, agriculture land selection and residential place selection are presented under these operators to show the capability and proficiency of this new idea. The comparison analysis with existing theories shows the superiorities of proposed model. Show more

Keywords: Einstein operators, q-rung orthopair fuzzy numbers, averaging operators, generalized weighted averaging operators

DOI: 10.3233/JIFS-201611

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4779-4798, 2021

Authors: Singh, Harjeet | Sharma, R.K. | Malarvel, Muthukumaran

Article Type: Research Article

Abstract: Formation of Gurmukhi character/akshara from the recognized strokes in online handwriting recognition systems is a challenging task. In this paper, the task of character and akshara formation in an unconstrained environment have been addressed. After the recognition of online handwritten strokes the Gurmukhi akshara is formed using a hybrid approach. Two classifiers, namely, Support Vector Machine (SVM) and Recurrent Neural Network (RNN) have been experimented in this study. The classifier, yielded the maximum cross-validation accuracy has been utilized for stroke recognition. A total of 52,500 word samples have been collected from 175 writers in order to train the classifiers. Three …post processing algorithms have been proposed in this article for improving the character and akshara recognition accuracy. The proposed methodology when tested on a dataset of 21,500 aksharas, written by 50 new writers, achieved average the accuracy rate of 97.1% and 87.1% for base character and akshara recognition, respectively. Show more

Keywords: Online handwritten Gurmukhi script recognition, post processing, SVM, RNN, association of strokes

DOI: 10.3233/JIFS-201613

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4799-4809, 2021

Authors: Liu, Zhibing | Zhou, Chi | Wang, Guoli

Article Type: Research Article

Abstract: We consider an order problem for two channels consisting of one common retailer and two competing suppliers that are subject to supply uncertainty. A new concept of supply risk level (SRL) of a channel is proposed to quantitatively characterize the supply risk of the channel due to supply uncertainty. Under different SRLs, we study the order strategies for the two channels in integrated and decentralized supply chains. Regardless of whether the game is integrated or decentralized, we find that the different SRLs give rise to a difference between the belief-degree costs of the two channels that directly influences the optimal …order strategy, market supply and profit of each channel. This implies that the decision maker of the supply chain can take different risk attitudes toward the supply uncertainty of the two channels to adjust the potential market supply and profit of each channel because the decision maker generally replaces the SRL with his or her risk preference. Under given SRLs, we find that integration is a better strategy than decentralization. However, the channel profit with a higher belief-degree cost under decentralization is greater than that under integration in some cases. Finally, proper risk preferences for both channels are suggested to strike a balance between supply reliability and supply risk. Show more

Keywords: Supply uncertainty, supply risk level, supply chain competition, belief-degree cost

DOI: 10.3233/JIFS-201663

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4811-4833, 2021

Authors: Bouteraa, Yassine | Abdallah, Ismail Ben | Ibrahim, Atef | Ahanger, Tariq Ahamed

Article Type: Research Article

Abstract: In this paper, a robotic system dedicated to remote wrist rehabilitation is proposed as an Internet of Things (IoT) application. The system offers patients home rehabilitation. Since the physiotherapist and the patient are on different sites, the system guarantees that the physiotherapist controls and supervises the rehabilitation process and that the patient repeats the same gestures made by the physiotherapist. A human-machine interface (HMI) has been developed to allow the physiotherapist to remotely control the robot and supervise the rehabilitation process. Based on a computer vision system, physiotherapist gestures are sent to the robot in the form of control instructions. …Wrist range of motion (RoM), EMG signal, sensor current measurement, and streaming from the patient’s environment are returned to the control station. The various acquired data are displayed in the HMI and recorded in its database, which allows later monitoring of the patient’s progress. During the rehabilitation process, the developed system makes it possible to follow the muscle contraction thanks to an extraction of the Electromyography (EMG) signal as well as the patient’s resistance thanks to a feedback from a current sensor. Feature extraction algorithms are implemented to transform the EMG raw signal into a relevant data reflecting the muscle contraction. The solution incorporates a cascade fuzzy-based decision system to indicate the patient’s pain. As measurement safety, when the pain exceeds a certain threshold, the robot should stop the action even if the desired angle is not yet reached. Information on the patient, the evolution of his state of health and the activities followed, are all recorded, which makes it possible to provide an electronic health record. Experiments on 3 different subjects showed the effectiveness of the developed robotic solution. Show more

Keywords: Gesture control, human robot interaction, internet of things, rehabilitation robotics

DOI: 10.3233/JIFS-201671

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4835-4850, 2021

Authors: Tao, Yuwen | Jiang, Yizhang | Xia, Kaijian | Xue, Jing | Zhou, Leyuan | Qian, Pengjiang

Article Type: Research Article

Abstract: The use of machine learning technology to recognize electrical signals of the brain is becoming increasingly popular. Compared with doctors’ manual judgment, machine learning methods are faster. However, only when its recognition accuracy reaches a high level can it be used in practice. Due to the difference in the data distributions of the training dataset and the test dataset and the lack of training samples, the classification accuracies of general machine learning algorithms are not satisfactory. In fact, among the many machine learning methods used to process epilepsy electroencephalogram (EEG) signals, most are black box methods; however, in medicine, methods …with explanatory power are needed. In response to these three challenges, this paper proposes a novel technique based on domain adaptation learning, semi-supervised learning and a fuzzy system. In detail, we use domain adaptation learning to reduce deviation from the data distribution, semi-supervised learning to compensate for the lack of training samples, and the Takagi-Sugen-Kang (TSK) fuzzy system model to improve interpretability. Our experimental results show that the performance of the new method is better than those of most advanced epilepsy classification methods. Show more

Keywords: EEG signal recognition, epilepsy classification, integrated learning mechanism, domain adaptation learning, semi-supervised learning, TSK fuzzy system

DOI: 10.3233/JIFS-201673

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4851-4866, 2021

Authors: Yang, Yanli | Li, Chenxia

Article Type: Research Article

Abstract: Generalization ability is known as an important performance index of artificial neural networks (ANNs). The generalization ability of an ANN usually refers to its ability to recognize untrained samples, but it lacks quantitative analysis. A method is designed by using frequency-domain signals to observe the generalization ability of deep feedforward neural networks (DFFNNs) which are popular ANN models. This method allows us to observe that the generalization ability of DFFNNs is limited to a small neighborhood around the trained samples. Then, the relationship between sample similarity and the DFFNN’s generalization performance is further analyzed. The analysis results show that the …correlation coefficient between samples has a certain positive correlation with the DFFNN’s generalization performance. Based on this new understanding, an algorithm in which shadows of the trained samples are added into the training set is proposed to improve the generalization ability of DFFNNs. The proposed algorithm is tested with some simulated signals and some real-world data. The tests show that the proposed method can indeed improve the DFFNN’s generalization ability by only changing the training sample set. Show more

Keywords: Deep feedforward neural network, deep learning, artificial neural network, generalization ability, correlation coefficient

DOI: 10.3233/JIFS-201679

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4867-4876, 2021

Authors: Shavaki, Fahimeh Hosseinnia | Jolai, Fariborz

Article Type: Research Article

Abstract: Today with the outbreak of the COVID-19 many people prefer to stay home and buy their required products from online sellers and receive them in their home or office at their desired times. This change has increased the workload of online retailers. In an online retailing system, lots of orders containing different products arrive dynamically and must be delivered in the due dates requested by customers, so there is a limited time to retrieve products from their storage locations, pack them, load them on trucks, and deliver to their destinations. In this study, we deal with the integrated order batching …and delivery planning of an online retailer that stores a variety of products in a warehouse and sells them online. A mixed-integer nonlinear programming model is proposed that decides on order batching, scheduling of batches, assigning orders to trucks, and scheduling and routing of trucks simultaneously in an offline setting. This model clarifies the domain of the problem and its complexity. Two rule-based heuristic algorithms are developed to solve the problem in the online setting. The first algorithm deals with two sub-problems of order batching and delivery planning separately and sequentially, while the second algorithm considers the relationship between two sub-problems. An extensive numerical experiment is carried out to evaluate the performance of algorithms in different problem sizes, demonstrating that the second algorithm by integrating two sub-problems leads to a minimum of 14% reduction in cost per delivered order, as the main finding of this study. Finally, the effect of several parameters on the performance of algorithms is analyzed through a sensitivity analysis, and some managerial insights are provided to help the retail managers with their decision-making that are the other findings of this paper. Show more

Keywords: Delivery planning, online retailing, order batching, rule-based heuristic, specific due dates

DOI: 10.3233/JIFS-201690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4877-4903, 2021

Authors: Chandrasekaran, Gokul | Karthikeyan, P.R. | Kumar, Neelam Sanjeev | Kumarasamy, Vanchinathan

Article Type: Research Article

Abstract: Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony …Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms. Show more

Keywords: System-on-chip, test scheduling, Dragonfly algorithm, Ant Lion optimization

DOI: 10.3233/JIFS-201691

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4905-4917, 2021

Authors: Kumar, Deepika | Batra, Usha

Article Type: Research Article

Abstract: Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. However, machine learning algorithms have given rise to progress across different domains. There are various diagnostic methods available for cancer detection. However, cancer detection through histopathological images is considered to be more accurate. In this research, we have proposed the Stacked Generalized Ensemble (SGE) approach for breast cancer classification into Invasive Ductal Carcinoma+ and Invasive Ductal Carcinoma-. SGE is inspired by the stacking model which utilizes output predictions. …Here, SGE uses six deep learning models as level-0 learner models or sub-models and Logistic regression is used as Level – 1 learner or meta – learner model. Invasive Ductal Carcinoma dataset for histopathology images is used for experimentation. The results of the proposed methodology have been compared and analyzed with existing machine learning and deep learning methods. The results demonstrate that the proposed methodology performed exponentially good in image classification in terms of accuracy, precision, recall, and F1 measure. Show more

Keywords: Breast cancer, histopathology images, SGE, classification, machine learning, deep learning

DOI: 10.3233/JIFS-201702

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4919-4934, 2021

Authors: Nazari, Mohammad Hassan | Bagheri Sanjareh, Mehrdad | Moradi, Mohammad Bagher | Hosseinian, Seyed Hossein

Article Type: Research Article

Abstract: This paper presents an economical approach for reliability improvement, harmonic mitigation and loss reduction in microgrids and active distribution networks that include of the distributed generations (DGs) considering technical constraints. The proposed method is a stochastic approach based on the calculation of the locational marginal price (LMP) in each DG bus. The problem is as a game-theoretic that each DG is taken as a single player considering its contributions on the aforementioned objectives. In this regard, each player gets a financial incentive as incremental price, based on a fair method using cooperative game-theoretic sharing strategy. In other words, each DG …that aligns its generation with the aforementioned objectives will increase the price of selling energy. This increase in prices will lead to higher profits. Therefore, DGs are interested in volunteering to accomplish network goals. As a tool for system management, the proposed method can control the impact of the pricing in the form of incentives to satisfy each objective depending on its decision in the incentive allocation procedure. To obtain a more realistic framework, demands are considered as the uncertainty parameters. To validate the proposed method, it is evaluated on the real Taiwan Power Company (TPC) network. The promising results indicate that the total loss is decreased by 54.5%, harmonics are mitigated by 12.3% and the reliability is improved by 12.6%. Show more

Keywords: Reliability, loss, pricing, harmonic, microgrid, active distribution network, game theory

DOI: 10.3233/JIFS-201703

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4935-4955, 2021

Authors: Meng, Lv | Shaohong, Feng

Article Type: Research Article

Abstract: To cope with the smooth implementation of apron control transfer at Chinese airports, two new departments were established, namely apron tower and airport operation command center. Therefore, based on interview texts of controllers and commanders of these two departments, this paper uses text mining and Decision-making Trial and Evaluation Laboratory-Interpretative Structural Modeling Method methods to determine key influence factors and factor hierarchy that affect communication and collaboration in their daily work. The results show that for controllers, key influence factors are mainly personnel development and professional abilities. These factors are located at the bottom of the factor hierarchy and are …the basis for ensuring smooth communication and collaboration. For commanders, key influence factors are mainly personnel professional abilities and flight status. These factors are at the top of the factor hierarchy and are focus points that affect communication and collaboration. Hence, the case analysis results show the application potential of these three methods in the field of civil aviation. The combined use of these three methods can enable airport managers to clearly understand the degree of influence between factors. Show more

Keywords: Decision-making Trial and Evaluation Laboratory, interpretative structural modeling method, text mining, communication and collaboration

DOI: 10.3233/JIFS-201704

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4957-4966, 2021

Authors: Zhou, Ke | Ma, Gang | Wang, Yafei | Zheng, Junjun | Wang, Shilei | Tang, Yunying

Article Type: Research Article

Abstract: With the development of “Internet+”, online auction platforms of used cars have emerged a lot. As a typical representative of the continuous purchase environment, online sequential auction of used cars faces many uncertainties, including uncertain revenue and risk. To describe them, adopting fuzzy theory to create mean-variance model to estimate the revenue and risk is showed in this paper. Moreover, three types of sellers, aggressive, conservative and rational sellers are analyzed respectively, and strategy models are built, where the multi-criteria optimal function for the latter one is adapted Cobb-Douglas production function. Then, a genetic algorithm based on fuzzy simulation is …proposed through integrating the fuzzy simulation and 0-1 genetic algorithm, which can solve the models validly. Lastly, the practical example from Guazi website shows the optimal strategies derived by models can meet sellers’ demands, especially goals of both higher revenue and lower risk for rational sellers, which proves practicability of the model and validity of algorithm. Show more

Keywords: Online sequential auction, fuzzy theory, optimal strategy, genetic algorithm

DOI: 10.3233/JIFS-201719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4967-4977, 2021

Authors: Yu, Dejian | Chen, Yitong

Article Type: Research Article

Abstract: Green supply chain (GSC) practice can help enterprises expand the market share, enhance competitive advantage, achieve the sustainable development and maintain the balance among economic, social and environmental benefits. Based on these advantages, the amount of literatures in this field is gradually expanding especially in recent years. This paper combines the bibliometric and main path analysis (MPA) method to introduce the current status and development trend, and explore the dynamic evolution of knowledge and main research topics of this domain. The main results are as follows: (1) Sarkis J is the most prolific author and Hong Kong Polytechnic University is …the most productive institution of this field. (2) Articles on main path mainly focus on the application of GSC in various industries and can be divided into two categories based on the research content, including the evaluation and selection of green practices and green supplier, as well as the identification and evaluation of obstacles and drivers in green supply chain management (GSCM) practices. Moreover, the topics of theoretical innovation of evaluation method, evaluation of entire supply chain performance and circular economy (CE) based on the triple bottom line maybe the possible research direction for scholars. In general, this article not only provides a comprehensive and systematic longitudinal bibliometric overview but also presents the trajectory of knowledge diffusion of GSC domain. Show more

Keywords: Green and supply chain (GSC), bibliometrics, main path analysis

DOI: 10.3233/JIFS-201720

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4979-4991, 2021

Authors: Fan, Jianping | Wang, Jing | Wu, Meiqin

Article Type: Research Article

Abstract: The two-dimensional belief function (TDBF  = (m A , m B )) uses a pair of ordered basic probability distribution functions to describe and process uncertain information. Among them, m B includes support degree, non-support degree and reliability unmeasured degree of m A . So it is more abundant and reasonable than the traditional discount coefficient and expresses the evaluation value of experts. However, only considering that the expert’s assessment is single and one-sided, we also need to consider the influence between the belief function itself. The difference in belief function can measure the difference between two belief functions, based …on which the supporting degree, non-supporting degree and unmeasured degree of reliability of the evidence are calculated. Based on the divergence measure of belief function, this paper proposes an extended two-dimensional belief function, which can solve some evidence conflict problems and is more objective and better solve a class of problems that TDBF cannot handle. Finally, numerical examples illustrate its effectiveness and rationality. Show more

Keywords: Two-dimensional belief function; divergence, Dempster-Shafer evidence theory, evidence conflict

DOI: 10.3233/JIFS-201727

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4993-5000, 2021

Authors: Zhu, Nan | Yin, Yuting

Article Type: Research Article

Abstract: With the great development of image display technologies and the widespread use of various image acquisition device, recapturing high-quality images from high-fidelity LCD (liquid crystal display) screens becomes relatively convenient. These recaptured images pose serious threats on image forensic technologies and bio-authentication systems. In order to prevent the security loophole of image recapture attack, we propose a recaptured image detection method based on multi-resolution residual-based correlation coefficients. Specifically, we first classify the divided image blocks into three categories according to their content complexity. Then, for each classified block, sharpness degree is used as metric to select the local representative block. …Finally, pixel-wise correlation coefficients in the residual of the local representative blocks are adopted as features for training and testing. Single database experiments demonstrate that our proposed method not only performs very close to the state-of-the-art methods on relative low-quality NTU-ROSE and BJTU-IIS databases, but also improves the performance on the most difficult-to-detect ICL-COMMSP database obviously, which verifies the effectiveness of the proposed multi-resolution strategy and the used residual-based correlation coefficients. Besides, mixed database experiments verify the superiority of the generalization ability of our proposed method. Moreover, it is robust to JPEG compression. Show more

Keywords: Image forensics, recaptured image detection, image credibility, bio-authentication, correlation coefficients

DOI: 10.3233/JIFS-201746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5001-5013, 2021

Authors: Yu, Dawei | Yang, Jie | Zhang, Yun | Yu, Shujuan

Article Type: Research Article

Abstract: The Densely Connected Network (DenseNet) has been widely recognized as a highly competitive architecture in Deep Neural Networks. And its most outstanding property is called Dense Connections, which represent each layer’s input by concatenating all the preceding layers’ outputs and thus improve the performance by encouraging feature reuse to the extreme. However, it is Dense Connections that cause the challenge of dimension-enlarging, making DenseNet very resource-intensive and low efficiency. In the light of this, inspired by the Residual Network (ResNet), we propose an improved DenseNet named Additive DenseNet, which features replacing concatenation operations (used in Dense Connections) with addition operations …(used in ResNet), and in terms of feature reuse, it upgrades addition operations to accumulating operations (namely ∑ (·)), thus enables each layer’s input to be the summation of all the preceding layers’ outputs. Consequently, Additive DenseNet can not only preserve the dimension of input from enlarging, but also retain the effect of Dense Connections. In this paper, Additive DenseNet is applied to text classification task. The experimental results reveal that compared to DenseNet, our Additive DenseNet can reduce the model complexity by a large margin, such as GPU memory usage and quantity of parameters. And despite its high resource economy, Additive DenseNet can still outperform DenseNet on 6 text classification datasets in terms of accuracy and show competitive performance for model training. Show more

Keywords: DenseNet, ResNet, deep learning, text classification

DOI: 10.3233/JIFS-201758

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5015-5025, 2021

Authors: Pirozmand, Poria | Ebrahimnejad, Ali | Alrezaamiri, Hamidreza | Motameni, Homayun

Article Type: Research Article

Abstract: In software incremental development methodology, the product develops in several releases. In each release, one set of the requirements is suggested for development. The development team must select a subset of the proposed requirements for development in the next release such that by consideration the limitation of the problem provides the highest satisfaction to the customers and the lowest cost to the company. This problem is known as the next release problem. In complex projects where the number of requirements is high, development teams cannot choose an optimized subset of the requirements by traditional methods, so an intelligent algorithm is …required to help in the decision-making process. The main contributions of this study are fivefold: (1) The customer satisfaction and the cost of every requirement are determined by use of fuzzy numbers because of the possible changing of the customers’ priorities during the product development period; (2) An improved approximate approach is suggested for summing fuzzy numbers of different kinds, (3) A new metaheuristic algorithm namely the Binary Artificial Algae Algorithm is used for choosing an optimized subset of requirements, (4) Experiments performed on two fuzzy datasets confirm that the resulted subsets from the suggested algorithm are free of human mistake and can be a great guidance to development teams in making decisions. Show more

Keywords: Next release problem, software requirements, fuzzy numbers, binary artificial algae algorithm

DOI: 10.3233/JIFS-201759

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5027-5041, 2021

Authors: Gulfam, Muhammad | Mahmood, Muhammad Khalid | Smarandache, Florentin | Ali, Shahbaz

Article Type: Research Article

Abstract: In this paper, we investigate two new Dombi aggregation operators on bipolar neutrosophic set namely bipolar neutrosophic Dombi prioritized weighted geometric aggregation (BNDPWGA) and bipolar neutrosophic Dombi prioritized ordered weighted geometric aggregation (BNDPOWGA) by means of Dombi t-norm (TN) and Dombi t-conorm (TCN). We discuss their properties along with proofs and multi-attribute decision making (MADM) methods in detail. New algorithms based on proposed models are presented to solve multi-attribute decision-making (MADM) problems. In contrast, with existing techniques a comparison analysis of proposed methods are also demonstrated to test their validity, accuracy and significance.

Keywords: Bipolar neutrosophic set, bipolar neutrosophic Dombi prioritized aggregation operators, decision-making environment

DOI: 10.3233/JIFS-201762

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5043-5060, 2021

Authors: Basher, M.

Article Type: Research Article

Abstract: A k -Zumkeller labeling for the graph G  = (V , E ) is an assignment f of a label to each vertices of G such that each edge uv  ∈ E is assigned the label f  (u ) f  (v ), the resulting edge labels are k distinct Zumkeller numbers. In this paper, we prove that the graph P m  × P n is k -Zumkeller graph for m , n  ≥ 3 while P m  × C n and C m  × C n are k -Zumkeller graphs for n  ≡ 4 (mod2). …Also we show that the graphs P m  ⊗ P n and P m  ⊗ C n for m , n  ≥ 3 admit k -Zumkeller labeling. Further, the graph C m  ⊗ C n where m or n is even admit a k -Zumkeller labeling. Show more

Keywords: Zumkeller number, k-Zumkeller labeling, Cartesian and tensor product of graphs, 05C78

DOI: 10.3233/JIFS-201765

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5061-5070, 2021

Authors: Zhai, Jia | Zheng, Haitao | Bai, Manying | Jiang, Yunyun

Article Type: Research Article

Abstract: This paper explores a multiperiod portfolio optimization problem under uncertain measure involving background risk, liquidity constraints and V-shaped transaction costs. Unlike traditional studies, we establish multiperiod mean-variance portfolio optimization models with multiple criteria in which security returns, background asset returns and turnover rates are assumed to be uncertain variables that can be estimated by experienced experts. When the returns of the securities and background assets follow normal uncertainty distributions, we use the deterministic forms of the multiperiod portfolio optimization model. The uncertain multiperiod portfolio selection models are practical but complicated. Therefore, the models are solved by employing a genetic algorithm. …The uncertain multiperiod model with multiple criteria is compared with an uncertain multiperiod model without background risk and an uncertain multiperiod model without liquidity constraint respectively, we discuss how background risk and liquidity affect optimal terminal wealth. Finally, we give two numerical examples to demonstrate the effectiveness of the proposed approach and models. Show more

Keywords: Uncertainty theory, multiple criteria, uncertain multiperiod mean-variance model, background risk, liquidity constraint

DOI: 10.3233/JIFS-201769

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5071-5086, 2021

Authors: Maghawry, Eman | Ismail, Rasha | Gharib, Tarek F.

Article Type: Research Article

Abstract: Paroxysmal Atrial Fibrillation (PAF) is a special class of Atrial Fibrillation. Predicting PAF events from electrocardiogram (ECG) signal streams plays a vital role in generating real-time alerts for cardiac disorders. These alerts are extremely important to cardiologists in taking precautions to prevent their patients from having a stroke. In this study, an effective predictive approach to PAF events using the Extreme Learning Machine classification technique is proposed. Besides, we propose a feature extraction method that integrates new ECG signal features to its time-domain ones. The new features are based on the construction of sparse vectors for peaks in ECG signals …that provide high overlap between similar ECGs. The proposed prediction approach with the new ECG features representation were evaluated on a real PAF dataset using the five-fold cross-validation method. Experiments show promising results for predicting PAF in terms of accuracy and execution time compared to other existing studies. The proposed approach achieved classification accuracy of 97% for non-streaming ECG signals mode and 94.4% for streaming mode. Show more

Keywords: Paroxysmal atrial fibrillation, feature extraction, extreme learning machine, electrocardiogram (ECG) signals classification, streaming ECG Signals

DOI: 10.3233/JIFS-201832

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5087-5099, 2021

Authors: Yao, Shuaiyu | Yang, Jian-Bo | Xu, Dong-Ling

Article Type: Research Article

Abstract: In this paper, we propose a new probabilistic modeling approach for interpretable inference and classification using the maximum likelihood evidential reasoning (MAKER) framework. This approach integrates statistical analysis, hybrid evidence combination and belief rule-based (BRB) inference, and machine learning. Statistical analysis is used to acquire evidence from data. The BRB inference is applied to analyze the relationship between system inputs and outputs. An interdependence index is used to quantify the interdependence between input variables. An adapted genetic algorithm is applied to train the models. The model established by the approach features a unique strong interpretability, which is reflected in three …aspects: (1) interpretable evidence acquisition, (2) interpretable inference mechanism, and (3) interpretable parameters determination. The MAKER-based model is shown to be a competitive classifier for the Banana , Haberman ’s survival , and Iris data set, and generally performs better than other interpretable classifiers, e.g., complex tree, logistic regression, and naive Bayes. Show more

Keywords: Probabilistic modeling, interpretable inference and classification, maximum likelihood evidential reasoning (MAKER) framework, belief rule-base, machine learning

DOI: 10.3233/JIFS-201833

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5101-5117, 2021

Authors: Zhang, Dongli | Yang, Yanbo | Wang, Weican | You, Xinshang

Article Type: Research Article

Abstract: During the development of regional economy, introducing collaborative innovation is an important policy. Constructing a scientific and effective measurement for evaluating the collaborative innovation degree is essential to determine an optimum collaborative innovation plan. As this problem is complex and has a long-lasting impact, this paper will propose a novel large scale group decision making (LSGDM) method both considering decision makers’ social network and their evaluation quality. Firstly, the decision makers will be detected based on their social connections and aggregated into different subgroups by an optimization algorithm. Secondly, decision makers are weighted according to their important degree and decision …information, where the information is carried by interval valued intuitionistic fuzzy number (IVIFN). During the information processing, IVIFN is put in rectangular coordinate system considering its geometric meaning. And some related novel concept are given based on the barycenter of rectangle region determined by IVIFN. Meanwhile, the criteria’s weights are calculated by the accurate degree and deviation degree. A classical example is used to illustrate the effect of weighting methods. In summary, a large scale group decision making method based on the geometry characteristics of IVIFN (GIVIFN-LSGDM) is proposed. The scientific and practicability of GIVIFN-LSGDM method is illustrated through evaluating four different projects based on the constructed criteria system. Comparisons with the other methods are discussed, followed by conclusions and further research. Show more

Keywords: Keywords: Large scale group decision making, intuitionistic fuzzy number, social network analysis, interval valued intuitionistic fuzzy number, Barycenter

DOI: 10.3233/JIFS-201848

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5119-5138, 2021

Authors: Jenefa, A. | BalaSingh Moses, M.

Article Type: Research Article

Abstract: Application Traffic Identification is an imperative device for sorting out the system as it is the most popular approach to distinguish and characterize the network traffic created from different applications. The classification using conventional Port-based and Payload-based techniques has become counterproductive due to inconsistencies. However, in recent times, approaches with machine learning and statistical techniques have guaranteed higher accuracy. However, learning techniques are inadequate for solving problems with Time and Memory intricacies in vast datasets. Hence, the proposed paper presents a novel scheme of Statistical based traffic classification named Multi-Phased Statistical Based Classification methodology that renders Semi-supervised machines with advanced …K-medoid clustering and C5.0 Classification algorithm. The proposed system displays a classic competence in observing the known and unknown application flows by statistical features utilization scheme that enhances the classification preciseness. Further, the trial results show that the proposed work outperforms previous approaches by achieving a higher granularity of 98–99% and reducing complexities. Ultimately, the new proposed work is evaluated on our campus traffic traces (AU-IDS). It is proven that the proposed approach accomplishes a higher exactness rate and thus encourages its implementation in real-time. Show more

Keywords: Communication networks, machine learning, clustering methods, semi supervised learning, statistical learning

DOI: 10.3233/JIFS-201895

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5139-5157, 2021

Authors: Bai, Haoyue | Zhang, Haofeng | Wang, Qiong

Article Type: Research Article

Abstract: Zero Shot learning (ZSL) aims to use the information of seen classes to recognize unseen classes, which is achieved by transferring knowledge of the seen classes from the semantic embeddings. Since the domains of the seen and unseen classes do not overlap, most ZSL algorithms often suffer from domain shift problem. In this paper, we propose a Dual Discriminative Auto-encoder Network (DDANet), in which visual features and semantic attributes are self-encoded by using the high dimensional latent space instead of the feature space or the low dimensional semantic space. In the embedded latent space, the features are projected to both …preserve their original semantic meanings and have discriminative characteristics, which are realized by applying dual semantic auto-encoder and discriminative feature embedding strategy. Moreover, the cross modal reconstruction is applied to obtain interactive information. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method. Show more

Keywords: Zero shot learning, domain shift, dual auto-encoder, discriminative projection

DOI: 10.3233/JIFS-201920

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5159-5170, 2021

Authors: Ramalingam, S. | Baskaran, K.

Article Type: Research Article

Abstract: Wireless Sensor Networks (WSNs) are consistently gathering environmental weather data from sensor nodes on a random basis. The wireless sensor node sends the data via the base station to the cloud server, which frequently consumes immoderate power consumption during transmission. In distribution mode, WSN typically produces imprecise measurable or missing data and redundant data that influence the whole network of WSN. To overcome this complexity, an effective data prediction model was developed for decentralized photovoltaic plants using hybrid Harris Hawk Optimization with Random Forest algorithm (HHO-RF) primarily based on the ensemble learning approach. This work is proposed to predict the …precise data and minimization of error in WSN Node. An efficient model for data reduction is proposed based on the Principal Component Analysis (PCA) for processing data from the sensor network. The datasets were gathered from the Tamil Nadu photovoltaic power plant, India. A low cost portable wireless sensor node was developed for collecting PV plant weather data using Internet of Things (IoT). The experimental outcomes of the proposed hybrid HHO-RF approach were compared with the other four algorithms, namely: Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and Long Short Term Memory (LSTM) algorithm. Results show that the determination coefficient (R2 ), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of the HHO-RF model are 0.9987, 0.0693, 0.2336 and 0.15881, respectively. For the prediction of air temperature, the RMSE of the proposed model is 3.82 %, 3.84% and 6.92% model in the lowest, average and highest weather days. The experimental outcomes of the proposed hybrid HHO-RF model have better performance compared to the existing algorithms. Show more

Keywords: Wireless sensor network, data prediction, internet of things, machine learning, harris hawk optimization, random forest, photovoltaic plant, error minimization

DOI: 10.3233/JIFS-201921

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5171-5195, 2021

Authors: Jin, Ting | Ding, Hui | Li, Bo | Xia, Hongxuan | Xue, Chenxi

Article Type: Research Article

Abstract: As an economic lever in financial market, interest rate option is not only the function of facilitating the bank to adjust the market fund supply and demand relation indirectly, but also provides the guarantee for investors to choose whether to exercise the right at the maturity date, thereby locking in the interest rate risk. This paper mainly studies the price of the interest rate ceiling as well as floor under the uncertain environment. Firstly, from the perspective of expert reliability, rather than relying on a large amount of historical financial data, to consider interest rate trends, and further assume that …the dynamic change of the interest rate conforms to the uncertain process. Secondly, since uncertain fractional-order differential equations (UFDEs) have non-locality features to reflect memory and hereditary characteristics for the asset price changes, thus is more suitable to model the real financial market. We construct the mean-reverting interest rate model based on the UFDE in Caputo type. Then, the pricing formula of the interest rate ceiling and floor are provided separately. Finally, corresponding numerical examples and algorithms are given by using the predictor-corrector method, which support the validity of the proposed model. Show more

Keywords: Fractional differential equation, uncertain theory, interest rate, mean-reverting, predictor-corrector method

DOI: 10.3233/JIFS-201930

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5197-5206, 2021

Authors: Abu-Saleem, M.

Article Type: Research Article

Abstract: The main aim of this article is to present neutrosophic folding and neutrosophic retractions on a single-valued neutrosophic graph Ğ from the viewpoint of geometry and topology. For this reason, we use a sequence of neutrosophic transformations on Ğ to obtain a new single-valued neutrosophic graph G ˇ 1 which contains different parameters under new conditions. We deduce the isometric neutrosophic folding on neutrosophic spheres and neutrosophic torii. Also, we determine the relationship between the limit neutrosophic folding and the limit of neutrosophic retraction on Ğ. Theorems regulating these relations are attained.

Keywords: Single valued neutrosophic graph, neutrosophic folding, neutrosophic retraction, 51H20, 57N10, 57M05, 14F35, 20F34

DOI: 10.3233/JIFS-201957

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5207-5213, 2021

Authors: Feng, Rui | Huang, Cheng-Chen | Luo, Kun | Zheng, Hui-Jun

Article Type: Research Article

Abstract: The West Lake of Hangzhou, a world famous landscape and cultural symbol of China, suffered from severe air quality degradation in January 2015. In this work, Random Forest (RF) and Recurrent Neural Networks (RNN) are used to analyze and predict air pollutants on the central island of the West Lake. We quantitatively demonstrate that the PM2.5 and PM10 were chiefly associated by the ups and downs of the gaseous air pollutants (SO2 , NO2 and CO). Compared with the gaseous air pollutants, meteorological circumstances and regional transport played trivial roles in shaping PM. The predominant meteorological factor …for SO2 , NO2 and surface O3 was dew-point deficit. The proportion of sulfate in PM10 was higher than that in PM2.5 . CO was strongly positively linked with PM. We discover that machine learning can accurately predict daily average wintertime SO2 , NO2 , PM2.5 and PM10 , casting new light on the forecast and early warning of the high episodes of air pollutants in the future. Show more

Keywords: Random forest, recurrent neural network, air pollutants prediction

DOI: 10.3233/JIFS-201964

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5215-5223, 2021

Authors: Elmuogy, Samir | Hikal, Noha A. | Hassan, Esraa

Article Type: Research Article

Abstract: Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID-19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s …life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training, 524 validation, 524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 in terms of accuracy, precision, recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool. Show more

Keywords: Deep learning, CNN, COVID-19 dataset, automatic classification, CT scan

DOI: 10.3233/JIFS-201985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5225-5238, 2021

Authors: Lin, Rongde | Li, Jinjin | Chen, Dongxiao | Huang, Jianxin | Chen, Yingsheng

Article Type: Research Article

Abstract: Fuzzy covering rough set model is a popular and important theoretical tool for computation of uncertainty, and provides an effective approach for attribute reduction. However, attribute reductions derived directly from fuzzy lower or upper approximations actually still occupy large of redundant information, which leads to a lower ratio of attribute-reduced. This paper introduces a kind of parametric observation sets on the approximations, and further proposes so called parametric observational-consistency, which is applied to attribute reduction in fuzzy multi-covering decision systems. Then the related discernibility matrix is developed to provide a way of attribute reduction. In addition, for multiple observational parameters, …this article also introduces a recursive method to gradually construct the multiple discernibility matrix by composing the refined discernibility matrix and incremental discernibility matrix based on previous ones. In such case, an attribute reduction algorithm is proposed. Finally, experiments are used to demonstrate the feasibility and effectiveness of our proposed method. Show more

Keywords: Attribute reduction, fuzzy discernibility matrix, fuzzy multi-covering systems, incremental discernibility matrix, observational consistency, refined discernibility matrix

DOI: 10.3233/JIFS-201998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5239-5253, 2021

Authors: Qi, Ping | Shu, Hong | Zhu, Qiang

Article Type: Research Article

Abstract: Computation offloading is a key computing paradigm used in mobile edge computing. The principle of computation offloading is to leverage powerful infrastructures to augment the computing capability of less powerful devices. However, the most existing computation offloading algorithms assume that the mobile device is not moving, and these algorithms do not take into account the reliability of task execution. In this paper, we firstly present the formalized description of the workflow, the wireless signal, the wisdom medical scenario and the moving path. Then, inspired by the Bayesian cognitive model, a trust evaluation model is presented to reduce the probability of …failure for task execution based on the reliable behaviors of multiply computation resources. According to the location and the velocity of the mobile device, the execution time and the energy consumption model based on the moving path are constructed, task deferred execution and task migration are introduced to guarantee the service continuity. On this basis, considering the whole scheduling process from a global viewpoint, the genetic algorithm is used to solve the energy consumption optimization problem with the constraint of response time. Experimental results show that the proposed algorithm optimizes the workflow under the mobile edge environment by increasing 20.4% of successful execution probability and decreasing 21.5% of energy consumption compared with traditional optimization algorithms. Show more

Keywords: Edge computing, computation offloading, trust evaluation model, energy consumption

DOI: 10.3233/JIFS-202025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5255-5273, 2021

Authors: Lun, Xiangmin | Yu, Zhenglin | Wang, Fang | Chen, Tao | Hou, Yimin

Article Type: Research Article

Abstract: In order to develop an efficient brain-computer interface system, the brain activity measured by electroencephalography needs to be accurately decoded. In this paper, a motor imagery classification approach is proposed, combining virtual electrodes on the cortex layer with a convolutional neural network; this can effectively improve the decoding performance of the brain-computer interface system. A three layer (cortex, skull, and scalp) head volume conduction model was established by using the symmetric boundary element method to map the scalp signal to the cortex area. Nine pairs of virtual electrodes were created on the cortex layer, and the features of the time …and frequency sequence from the virtual electrodes were extracted by performing time-frequency analysis. Finally, the convolutional neural network was used to classify motor imagery tasks. The results show that the proposed approach is convergent in both the training model and the test model. Based on the Physionet motor imagery database, the averaged accuracy can reach 98.32% for a single subject, while the averaged values of accuracy, Kappa, precision, recall, and F1-score on the group-wise are 96.23%, 94.83%, 96.21%, 96.13%, and 96.14%, respectively. Based on the High Gamma database, the averaged accuracy has achieved 96.37% and 91.21% at the subject and group levels, respectively. Moreover, this approach is superior to those of other studies on the same database, which suggests robustness and adaptability to individual variability. Show more

Keywords: Brain-computer interface, Electroencephalography, motor imagery, convolutional neural network

DOI: 10.3233/JIFS-202046

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5275-5288, 2021

Authors: Liu, Jin | Xie, Jinsheng | Ahmadzade, Hamed | Farahikia, Mehran

Article Type: Research Article

Abstract: Entropy is a measure for characterizing indeterminacy of a random variable or an uncertain variable with respect to probability theory and uncertainty theory, respectively. In order to characterize indeterminacy of uncertain variables, the concept of exponential entropy for uncertain variables is proposed. For computing the exponential entropy for uncertain variables, a formula is derived via inverse uncertainty distribution. As an application of exponential entropy, portfolio selection problems for uncertain returns are optimized via exponential entropy-mean models. For better understanding, several examples are provided.

Keywords: Uncertain variable, uncertainty theory, exponential entropy, inverse uncertainty distribution, portfolio selection

DOI: 10.3233/JIFS-202073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5289-5293, 2021

Authors: Li, Yufeng | Jiang, HaiTian | Lu, Jiyong | Li, Xiaozhong | Sun, Zhiwei | Li, Min

Article Type: Research Article

Abstract: Many classical clustering algorithms have been fitted into MapReduce, which provides a novel solution for clustering big data. However, several iterations are required to reach an acceptable result in most of the algorithms. For each iteration, a new MapReduce job must be executed to load the dataset into main memory, which results in high I/O overhead and poor efficiency. BIRCH algorithm stores only the statistical information of objects with CF entries and CF tree to cluster big data, but with the increase of the tree nodes, the main memory will be insufficient to contain more objects. Hence, BIRCH has to …reduce the tree, which will degrade the clustering quality and decelerate the whole execution efficiency. To deal with the problem, BIRCH was fitted into MapReduce called MR-BIRCH in this paper. In contrast to a great number of MapReduce-based algorithms, MR-BIRCH loads dataset only once, and the dataset is processed parallel in several machines. The complexity and scalability were analyzed to evaluate the quality of MR-BIRCH, and MR-BIRCH was compared with Python sklearn BIRCH and Apache Mahout k-means on real-world and synthetic datasets. Experimental results show, most of the time, MR-BIRCH was better or equal to sklearn BIRCH, and it was competitive to Mahout k-means. Show more

Keywords: Clustering, BIRCH, k-means, MapReduce, Hadoop

DOI: 10.3233/JIFS-202079

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5295-5305, 2021

Authors: Hu, Chaofang | Zhang, Yuting

Article Type: Research Article

Abstract:  An interactive α -satisfactory method via relaxed order of desirable α -satisfactory degrees is proposed for multi-objective optimization with fuzzy parameters and linguistic preference in this paper. Fuzzy parameters existing in objectives and constraints of multi-objective optimization are defined as fuzzy numbers and α -level set is used to build the feasible domain of parameters. On the basis, the original problem with fuzzy parameters is transformed into multi-objective optimization with fuzzy goals. Linguistic preference of decision-maker is modelled by the relaxed order of desirable α -satisfactory degrees of all the objectives. In order to achieve a compromise between optimization and …preference, the multi-objective optimization problem is divided into two single-objective sub-problems: the preliminary optimization and the linguistic preference optimization. A preferred solution can be found by parameter adjustment of inner-outer loop. The minimum stable relaxation algorithm of parameter is developed for calculating the relaxation bound of maximum desirable satisfaction difference. The M-α -Pareto optimality of solution is guaranteed by the test model. The effectiveness, flexibility and sensitivity of the proposed method are well demonstrated by numerical example and application example to heat conduction system. Show more

Keywords: Multi-objective optimization, linguistic preference, fuzzy parameter, satisfactory degree

DOI: 10.3233/JIFS-202114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5307-5322, 2021

Authors: Konstantakopoulos, Grigorios D. | Gayialis, Sotiris P. | Kechagias, Evripidis P. | Papadopoulos, Georgios A. | Tatsiopoulos, Ilias P.

Article Type: Research Article

Abstract: Routing of vehicles and scheduling of deliveries play a crucial role in logistics operations as they affect both the distribution cost and customer satisfaction. That is why researchers have intensively studied this problem in conjunction with the multiple variables and constraints involved in the logistics operations. In this paper, the cases of time windows and simultaneous pickups and deliveries, where goods are simultaneously delivered and collected from customers within a predetermined time slot, are studied. The objective of our research is to create efficient routes that minimize both the number of vehicles and the total distance travelled, as both of …them affect the total distribution cost. Considering various plans of routes that are differentiated by the number of routes and the sequence of visitations, can be beneficial for decision-makers, since they have the opportunity to select the plan that better fits their needs. Therefore, in this paper we develop a multiobjective evolutionary algorithm (MOEA) that integrates an improved construction algorithm and a new crossover operator for efficient distribution services. Through the proposed MOEA a set of solutions (route plans), known as Pareto-optimal, is obtained, while single biased solutions are avoided. The proposed algorithm is tested in two well-known datasets in order to evaluate the algorithm’s efficiency. The results indicate that the algorithm’s solutions have small deviation from the best-published and some non-dominated solutions are also obtained. Show more

Keywords: Vehicle routing problem, time windows, simultaneous pickups and deliveries, multiobjective optimization, evolutionary algorithm, logistics

DOI: 10.3233/JIFS-202129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5323-5336, 2021