Journal of Intelligent & Fuzzy Systems - Volume 45, issue 2

Authors: Han, Puyu | Yao, Wei | Jiang, Xian

Article Type: Research Article

Abstract: A λ-subset, or a [0,λ]-valued fuzzy subset, is a mapping from a nonempty set to the interval [0,λ]. In this paper, we use the notion of hemimetrics, a kind of distance functions, as the basic structure to define and study fuzzy rough set model of λ-subsets by using the usual addition and subtraction of real numbers. We define a pair of fuzzy upper/lower approximation operators and investigate their properties and interrelations. These two operators have nice logical descriptions by using the related Lukasiewicz logical systems. We show that upper definable sets, lower definable sets and definable sets are equivalent, and …they form an Alexandrov fuzzy topology. A processing of a λ-subset via fuzzy upper/lower approximation operators can actually considered as a processing of the related image, and thus has potential applications in image processing. Show more

Keywords: Hemimetric, λ-subset, fuzzy rough set, fuzzy upper/lower approximation operator, definable set

DOI: 10.3233/JIFS-213049

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1883-1889, 2023

Authors: Saidi Sief, Ali

Article Type: Research Article

Abstract: Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error variation . The initial limits of the fuzzy logic membership functions are mostly depend on experiments and previous knowledge of the dynamic process behaviors. Generally, the membership function parameters have a significant impact on control signal amplitude and, consequently on the convergence and stability of the controller-plant system. The proposed technique determines the limits of the antecedent membership functions online using the k th …and k  - 1th outputs of the controlled plant and reference model, respectively. Meanwhile, the limits of the consequent membership functions are calculated using error and error variation. This approach ensures: (i) that the input/output variables have the required fuzzy space, (ii) the controlled plant follows the desired reference model, and (iii) the control signal amplitude is within acceptable limits. Additionally, (iiii ) it takes into account the dynamic variability of the process and the existence of an overshoot. The membership function parameters are updated continuously through a self-adapting procedure, ensuring improved control performance. Ultimately, the proposed approach is improved using two nonlinear systems. Show more

Keywords: Self-adapting fuzzy controller, dynamic membership functions, nonlinear systems, fuzzy experts systems, FLC

DOI: 10.3233/JIFS-222142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1891-1908, 2023

Authors: Shi, Chenchen | Zhang, Changlun | Deng, Lulu | He, Qiang | Wang, Hengyou | Huo, Lianzhi

Article Type: Research Article

Abstract: Data driven deep learning methods have become the mainstream method of building extraction from remote sensing images. In this paper, deep learning algorithm is used to classify and extract buildings from remote sensing images of rural areas around the Great Wall in the suburbs of Beijing captured by unmanned aerial vehicles. Aiming at the shortcomings of the current mainstream instance segmentation algorithm Mask R-CNN in feature fusion and poor prediction of instance mask boundaries, this paper proposes a boundary optimization algorithm for building instance segmentation based on discrete wavelet transform. Firstly, the discrete wavelet transform is introduced into the segmentation …task branch of Mask R-CNN algorithm to extract the low-frequency and high-frequency information of the real mask, in which the high-frequency information includes the boundary information. Secondly, the pixel by pixel prediction of the mask turns into the learning of the low-frequency and high-frequency information of the real mask. The learning of the high-frequency information helps the segmentation network to learn the boundary features better. Finally, using the reversibility of discrete wavelet transform, the low-frequency and high-frequency information of the learned mask is inversely transformed to reconstruct the final mask. The improved algorithm is evaluated on the dataset COCO, and applied to the automatic extraction of buildings. The DWT Mask R-CNN algorithm model achieved 70.2% segmentation accuracy and 71.4% detection accuracy, which were improved by 1% and 0.7% respectively compared with the Mask R-CNN and Cascade Mask R-CNN models. The experimental results show that the instance segmentation edge optimization algorithm combined with wavelet transform has achieved better results on the segmentation boundary, improved the poor effect of mask edge detection and achieved higher detection accuracy, and can accurately extract village buildings. Show more

Keywords: Instance segmentation, Mask R-CNN, wavelet transform, building

DOI: 10.3233/JIFS-222312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1909-1922, 2023

Authors: Li, Ruisong | Hu, Yanrong | Liu, Hongjiu

Article Type: Research Article

Abstract: We studied China’s Common Prosperity process by assessing and comparing the level of Common Prosperity in different regions of China and made some beneficial recommendations to government departments. The research data comes from the China Statistical Yearbook , which includes data from 31 provinces and cities from 2015 to 2020. According to the relevant research, eleven evaluation indicators were selected. We combined GRA with the TOPSIS method for scoring and the K-means clustering algorithm for dividing the GRA-TOPSIS scoring results into three evaluation levels. Then, the convolutional neural network model was used to predict and simulate the level of common …prosperity. Taking 2020 as an example, the results show: (1) From 2015 to 2020, China’s Common Prosperity level reached its highest point in 2020. Due to the impact of COVID-19 in 2019, the scores of 31 regions are generally lower than in the previous four years. The situation changed in 2020; (2) In terms of regional distribution, the economic development of Beijing, Shanghai, and other eastern regions is relatively good, with a higher degree of Common Prosperity than that of other regions; (3) The average prediction accuracy is high in our model. It can be close to 100%, indicating that the model has a good prediction effect. In addition, we made recommendations based on the research results, which have good references for actively promoting common prosperity. Show more

Keywords: The level of China’s common prosperity, gray relational analysis method, cluster analysis, convolutional neural network

DOI: 10.3233/JIFS-222442

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1923-1937, 2023

Authors: Huang, Yuling | Song, Yunlin

Article Type: Research Article

Abstract: Recently, the algorithmic trading of financial assets is rapidly developing with the rise of deep learning. In particular, deep reinforcement learning, as a combination of deep learning and reinforcement learning, stands out among many approaches in the field of decision-making because of its high performance, strong generalization, and high fitting ability. In this paper, we attempt to propose a hybrid method of recurrent reinforcement learning (RRL) and deep learning to figure out the algorithmic trading problem of determining the optimal trading position in the daily trading activities of the stock market. We adopt deep neural network (DNN), long short-term memory …neural network (LSTM), and bidirectional long short-term memory neural network (BiLSTM) to automatically extract higher-level abstract feature information from sequential trading data, respectively, and then generate optimal trading strategies by interacting with the environment in a reinforcement learning framework. In particular, the BiLSTM consisting of two LSTM models with opposite directions is able to make full use of the information from both directions in attempting to capture more effective information. In experiments, the daily data of Dow Jones, S&P500, and NASDAQ (from Jan-01, 2005 to Dec-31, 2020) are applied to verify the performance of the newly proposed DNN-RL, LSTM-RL, and BiLSTM-RL trading systems. Experimental results show that the proposed methods significantly outperform the benchmark methods, such as RRL and Buy and Hold, with higher scalability and better robustness. Especially, BiLSTM-RL performs better than other methods. Show more

Keywords: Reinforcement learning, deep learning, trading strategy, Sharpe ratio

DOI: 10.3233/JIFS-223101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1939-1951, 2023

Authors: Lu, MeiLi | Lin, Chin-Tsai

Article Type: Research Article

Abstract: Chinese firms are actively investing in movie ticketing Apps, but there is no unified model for how to evaluate such investments, which can hinder investment decision-making into such ticketing Apps. Currently, there is limited research on the criteria for investors to select the best Chinese movie ticketing Apps. To fill this gap, the present study proposes a three-stage evaluation model for investments in these specific Apps. First, it constructs a new hierarchy for movie ticketing App networks via the Modified Delphi Method (MDM), which consists of 5 criteria and 20 sub-criteria. Second, we apply the Analytic Network Process (ANP) to …calculate the weight of the criteria and sub-criteria, finding that information is the most important, followed by system, trust, service, and word-of-mouth (WOM) in that order. Finally, three movie ticketing Apps are utilized as alternatives, and the best alternative is selected by Techniques for Order Preference Similarity Ideal Solution (TOPSIS) to verify the model’s feasibility. The results herein offer theoretical and practical insights for the development and promotion of movie ticketing Apps and provide a reference for investors to formulate relevant financing strategies. Show more

Keywords: Investment selection, movie ticketing App, ANP, TOPSIS

DOI: 10.3233/JIFS-223566

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1953-1969, 2023

Authors: Fan, Yuwei | Shi, Lei | Yuan, Lu

Article Type: Research Article

Abstract: In the present day, online users are incentivized to engage in short text-based communication. These short texts harbor a significant amount of implicit information, including opinions, topics, and emotions, which are of notable value for both exploration and analysis. By alleviating the sparsity in short texts, topic models can be used to discover topics from large collections of short texts. While there is a large body of surveys focused on topic modeling, but only a few of them have focused on the short texts. This paper presents a comprehensive overview of topic modeling methods for short texts from a novel …perspective. Firstly, it discusses short text probabilistic topic models and outlines the directions in which they can be improved. Secondly, it explores short text neural topic models, which can be categorized into three groups based on their underlying structures. In addition, this paper provides a detailed investigation of embedding methods in topic modeling. Moreover, various applications and corresponding works are surveyed, with a focus on short texts. The commonly used public corpora and evaluation indicators for topic modeling are also summarized. Finally, the advantages and disadvantages of short text topic modeling are discussed in detail, and future research directions are proposed. Show more

Keywords: Short text, probabilistic topic model, neural topic model, word embeddings, deep learning

DOI: 10.3233/JIFS-223834

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1971-1990, 2023

Authors: Wang, Renjie | Tan, Fei | Yang, Kunlong | Hao, Yuwen | Li, Fengguo | Yu, Xiaoyuan

Article Type: Research Article

Abstract: With the development of convolutional neural networks, many improved algorithms have been successively proposed to promote the accuracy of dense crowd counting. However, these algorithms are deployed with expensive computing resources, which is unbearable for small devices such as embedded systems with limited computing resources. To realize the real-time counting on the small devices, it is of great significance how to trade off the computation cost and processing accuracy of the dense crowd-counting algorithm. Thus, we propose a lightweight dense crowd-counting algorithm (LCNNet) to improve this issue. Specifically, the proposed LCNNet consists of two subnetworks, a feature extraction subnetwork, and …a regression subnetwork, with a bottleneck depth-separable convolution with a residuals module as the basic module. The LCNNet effectively improves computational efficiency and reduces the computational cost, which can be performed on small devices. Extensive evaluations on four benchmark datasets well demonstrate the effectiveness of the proposed LCNNet for dense crowd-counting models. Meanwhile, the proposed LCNNet can maintain a comparable level of computational accuracy and computational cost on Vehicle counting datasets. Show more

Keywords: Crowd counting, lightweight, depth-separable convolution, highly congested scenes

DOI: 10.3233/JIFS-224081

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1991-2004, 2023

Authors: Mashak, Neda Pirzad | Akbarizadeh, Gholamreza | Farshidi, Ebrahim

Article Type: Research Article

Abstract: Since prostate cancer is one of the most important causes of death in today’s society, the investigation of why and how to diagnose and predict it has received much attention from researchers. The cooperation of computer and medical experts provides a new solution in analyzing these data and obtaining useful and practical models, which is deep learning. In fact, deep learning as one of the most important tools for analyzing data and discovering relationships between them and predicting the occurrence of events is one of the practical tools of researchers in this way. This study segments and classifies prostate cancer …using a deep learning approach and architectures tested in the ImageNet dataset and based on a method to identify factors affecting this disease. In the proposed method, after increasing the number of data based on removing dominant noises in MRI images, image segmentation using a network based on deep learning called faster R-CNN, and then feature extraction and classification with architecture Various deep learning networks have reached the appropriate accuracy and speed in detection and classification. The aim of this study is to reduce unnecessary biopsies and to choose and plan treatment to help the doctor and the patient. Achieving the minimum error in the diagnosis of malignant lesion with a criterion called Sensitivity of 93.54% and AUC equal to 95% with the ResNet50 architecture has achieved the goal of this research. Show more

Keywords: Magnetic Resonance Imaging (MRI), prostate cancer prediction, morphological properties, deep learning architecture, transfer learning, Receiver Operating Characteristic (ROC) Curve

DOI: 10.3233/JIFS-224274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2005-2017, 2023

Authors: Xiu, Zhen-Yu | Jiang, Yu-Xiu

Article Type: Research Article

Abstract: In this article, we present new methods for constructing uninorms on bounded lattices under the additional constraints and prove that some of these constraints are sufficient and necessary for the uninorms. Moreover, some illustrative examples for the construction of uninorms are provided. At last, we show that the additional constraints on t -norms (t -conorms) and t -subnorms (t -subconorms) of some uninorms in the literature are exactly sufficient and necessary.

Keywords: Bounded lattices, t-norms, t-subnorms, uninorms

DOI: 10.3233/JIFS-224537

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2019-2030, 2023

Authors: Zhao, Jie | Wan, Renxia | Miao, Duoqian

Article Type: Research Article

Abstract: Multi-granulation decision-theoretic rough set effectively combines Bayesian decision approaches with multi-granulation rough set theory, and provides an important theoretical framework for studying rough set. In this paper, we explore several extensional models of multi-granulation decision-theoretic rough sets under the normal distribution of the decision loss function. Using the 3σ rule of normal distribution, we transform the decision loss of the multi-granulation decision-theoretic rough set into a set of interval values. We construct the upper and lower approximations from the optimistic, weakly optimistic, pessimistic, weakly pessimistic, optimistic-pessimistic, weakly optimistic-pessimistic, pessimistic-optimistic, and weakly pessimistic-optimistic viewpoints, and provide the decision rules of the …proposed rough set models. The work in this paper brings the decision behavior based on a multi-granulation decision-theoretic rough set closer to the actual situation. Show more

Keywords: Loss function, normal distribution, interval value, multi-granulation decision-theoretic rough set

DOI: 10.3233/JIFS-224538

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2031-2046, 2023

Authors: Ma, Mingxi | Wang, Jinliang

Article Type: Research Article

Abstract: The point features of low-texture images are insufficient and unreliable, so it is difficult to achieve good alignment and easy to damage the image structure. To solve these problems, in this paper, we propose a new image stitching method by using the sigmoid function to create perception mask. Firstly, the point features and line features are used to improve the accuracy of image registration and the naturalness of distortion. Secondly, an energy function is used to optimize the alignment model. Finally, we propose to use sigmoid function to create perception mask image to reduce artifacts and retain image structure. The …gradient domain fusion algorithm is combined to achieve image fusion. Experimental results are provided to demonstrate that the proposed method is superior to some previous methods in reducing artifacts and maintaining image structure. Show more

Keywords: Image stitching, sigmoid function, perception mask, gradient domain fusion algorithm, double features

DOI: 10.3233/JIFS-230006

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2047-2061, 2023

Authors: Qu, Liangdong | Li, Xiaoqin | Tan, Mindong | Jia, Yingjuan

Article Type: Research Article

Abstract:  Reducing the dimensions of the original data set while preserving the information as much as possible is conducive to improving the accuracy and efficiency of the model. To achieve this, this paper presents a multi-strategy African vulture optimization algorithm that is the chaotic and elite opposition-based African vulture optimization with the simplex method and differential evolution strategy(CESDAVO). Three main improvements are introduced into African vultures optimization(AVO) to improve its capabilities in this study. Firstly, the chaotic elite opposition-based learning strategy is used to initialize and diversify individual positions of vultures. Secondly, the simplex method is used to optimize those poor …individuals so as to further improve the local exploitation ability of the algorithm. Thirdly, the differential evolution strategy is used to make the algorithm escape from the local optimum and improve the global optimization capability of the algorithm. The results of the ablation experiments show that mixing the three strategies greatly improves the optimization performance of the algorithm. In addition, Nine algorithms are compared with CESDAVO on 15 benchmark functions, and this experimental result shows that its optimization capability is superior to the others. Then, the proposed CESDAVO is employed for feature selection, and 12 standard datasets are used for experiments. According to the experimental results, CESDAVO obtained the highest average classification accuracy on 11 datasets and the highest feature selection rate on 8 datasets, which is significantly better than other algorithms. Finally, CESDAVO is also applied to feature reduction for essays, removing 24 features and significantly improving the classification accuracy on multiple classifiers. Show more

Keywords: Multi-strategy African vulture optimization algorithm, feature selection, essay scoring

DOI: 10.3233/JIFS-230421

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2063-2082, 2023

Authors: Zhao, Hua | Li, Xiaoqian | Wang, Fengling | Zeng, Qingtian | Diao, Xiuli

Article Type: Research Article

Abstract:  As one of the fundamental tasks in natural language processing, Multi-Label Text Classification (MLTC) is used to mark one or more relevant labels for a given text from a large set of labels. Existing MLTC methods have increasingly focused on improving classification effectiveness by fusing the correlations of labels. Still, the research suffers from difficulties in comprehensively extracting text features and distinguishing similar labels. This paper proposed a multi-label text classification model based on keyword extraction and attention mechanism. The model proposed using keywords to represent labels, adopting both self-attention and interactive attention mechanisms (between labels and text) to extract …text features and create text vectors. Finally, fusing text vectors as the classifier’s input. Experiments were conducted on two public datasets and a self-built dataset of illegal advertisements. The experimental results showed that the keyword-based label representation approach proposed in this paper can better obtain label semantics, avoid noise and improve the performance of the multi-label text classification. Show more

Keywords: Multi-label text classification, keyword extraction, attention mechanism, label indicates, natural language processing

DOI: 10.3233/JIFS-230506

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2083-2093, 2023

Authors: Ma, Mingxi | Yang, Junci

Article Type: Research Article

Abstract: Many recent studies have shown that Euler’s elastica regularization performs better than the famous total variation (TV) regularization on keeping image features in smooth regions during the process of denoising. In addition, an adaptive weighted matrix combined with total variation is a key technique for well restoring local features of image. Considering these two factors, in this paper, we propose an adaptive Euler’s elastica model for Poisson image restoration so as to well preserve both image features in smooth regions and local features of image. To solve this non-smooth and non-convex model efficiently, we design an alternating direction method of …multipliers. Experiments on both natural and synthetic images illustrate the effectiveness and efficiency of the proposed method over the state-of-the-art methods in Poisson restoration and denoising, respectively. In particular, for Poisson restoration, our proposed method improves the TV method up to 2.46 about PSNR for dealing with the Peppers image with Gaussian blur and noise level σ = 1. In addition, the proposed method for Poisson denoising gets higher PSNR and SSIM values than the TAC method, while costing less CPU time. Show more

Keywords: Poisson restoration, Euler’s elastica regularization, adaptive weighted matrix, alternating direction method of multipliers (ADMM)

DOI: 10.3233/JIFS-230562

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2095-2110, 2023

Authors: Khamis, Adela | Ahmad, Abd Ghafur

Article Type: Research Article

Abstract: This paper presents the concepts of a complex intuitionistic fuzzy subfield (CIFSF) and the direct product of a complex intuitionistic fuzzy subfield which is generalized from the concept of a complex fuzzy subfield by adding the notion of intuitionistic fuzzy into a complex fuzzy subfield. The main contribution and originality of this research are adding the non-membership term to the definition of a complex fuzzy subfield that assigns for any element a complex-valued grade. We expand the complex fuzzy subfield and obtain a new structure called CIFSF. This new concept is innovative in that it may attain a wider range …of values for both membership and non-membership functions where these functions are expanded to the unit disc in the complex plane. Furthermore, we discuss that the direct product of two CIFSFs is CIFSF, and some related properties are investigated. In addition, we present the definition of necessity and possibility operators on the direct product of CIFSF, and some associated theorems are given. Finally, we propose the level subsets of the direct product of two complex intuitionistic fuzzy subsets of a field and prove that the level subset of the direct product of two CIFSFs is a subfield and discuss some related results. Show more

Keywords: Fuzzy sets, intuitionistic fuzzy sets, direct product of complex intuitionistic fuzzy sets, complex fuzzy subfield, complex intuitionistic fuzzy subfield, direct product of complex intuitionistic fuzzy subfield

DOI: 10.3233/JIFS-230597

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2111-2132, 2023

Authors: Zhao, Jiangtao | Sheng, Yuhong

Article Type: Research Article

Abstract: Support vector machine (SVM) is a supervised binary classifier with good generalization ability and excellent computational properties. It has been widely used in many fields such as image recognition, bioinformatics and so on. However, the traditional SVM requires the input data to be clear and knowable, while in the actual application process, there will be many cases that the input data is uncertain. In order to solve this problem, a new SVM model is proposed in this paper by combining the uncertainty theory with the SVM theory. The uncertainty theory was proposed by Liu in 2007, and it is often …used to describe the uncertainty of things. The uncertain set in uncertainty theory is often used to model unsharp concepts. Therefore, this paper regards each uncertain data as an uncertain set and establishes a SVM model with uncertain chance constraints. However, the uncertain chance constraints are non-convex. Therefore, this paper gives the equivalent transformation process of constraint conditions when the input data are triangular uncertain sets. Finally, the non-convex constraint conditions are transformed into the linear constraint conditions, so that the model is transformed into a nonlinear programming model. In the numerical experiment, the Particle Swarm Optimization (PSO) algorithm is used to solve the problem, which proves the feasibility of the model. Show more

Keywords: Support vector machine, uncertainty theory, uncertain set, uncertain support vector machine

DOI: 10.3233/JIFS-230935

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2133-2144, 2023

Authors: Zhang, Qian | Wang, Jianguo

Article Type: Research Article

Abstract: Association rule algorithm has always been a research hotspot in the field of data mining, in the context of today’s big data era, in order to efficiently obtain association rules and effectively update them, based on the original fast update pruning (FUP) algorithm, an association rule incremental update algorithm (FBSCM) based on sorting compression matrix is proposed to solve the shortcomings of frequent scanning of transaction datasets. Firstly, The algorithm maps the transaction dataset as a Boolean matrix, and changes the storage mode of the matrix(that is, adding two columns and a row vector); Secondly, the matrix is compressed many …times during the generation of frequent k- itemset; After that, the items in the matrix are sorted incrementally according to the support degree of the itemset; Finally, the original string comparison operation is replaced by the vector product of each column of the matrix. Experimental results and analysis show that the FBSCM algorithm has higher temporal performance than the traditional FUP algorithm in different incremental dataset sizes, different minimum support thresholds and different feature datasets, especially when the incremental transaction volume is large or the minimum support degree is small. Show more

Keywords: FUP algorithm, boolean matrix, matrix compression, incremental association rule mining

DOI: 10.3233/JIFS-231252

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2145-2156, 2023

Authors: Zhang, Hao | Sheng, Yuhong

Article Type: Research Article

Abstract: Regression analysis is a potent tool to explore the relationship of variables and widely used in many areas. Classical statistics assume that the residual of regression model should follow the Gauss-Markov hypothesis. However, in many cases, the data is not obeying this hypothesis particularly real-life data. Therefore, this paper explores the Von Bertalanffy regression model under the framework of the uncertainty theory, and employs the uncertain maximum likelihood estimation (MLE) to estimate the unknown parameters. Furthermore, the uncertain hypothesis test and an algorithm for data modification which aimed to find outliers and modify data are studied, then the forecast value …and confidence interval be formulated. Finally, a real-life numerical example of applying the above theories be given, this example shows that the uncertain MLE has better performance compare with the uncertain least squares and the least absolute deviations methods. Consequently, the uncertain MLE is a better way to deal with the real-life data. Show more

Keywords: Regression analysis, uncertain Von Bertalanffy regression model, uncertain maximum likelihood estimation, real-life data

DOI: 10.3233/JIFS-231512

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2157-2165, 2023

Authors: Li, Rui

Article Type: Research Article

Abstract: In the current era of economic and cultural globalization, the demand for packaging design is increasing, and the packaging design requirements for brands and products in the entire consumer market are becoming increasingly strict and refined. Designers are facing one greater challenge after another. Packaging design, as a comprehensive discipline that integrates commerce, culture, and art, diverse cultural forms and elements will run through the entire process of brand packaging design. Good products need to be sold through good channels in order to achieve greater value. As a bridge connecting products and the market, the role of packaging design is …even more evident. It can not only improve the recognition of products, but also help enterprises stand out among many similar products, thereby increasing product value. The brand packaging design effect evaluation is a classical multiple attribute group decision making (MAGDM) problem. This paper extends the EDAS method to the 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). On the basis of the original EDAS method, 2-tuple linguistic Pythagorean fuzzy EDAS (2TLPF-EDAS) method is built for MAGDM. Finally, a case study for brand packaging design effect evaluation show that the new method proposed in this paper is reasonable. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs), EDAS method, brand packaging design effect evaluation

DOI: 10.3233/JIFS-232054

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2167-2177, 2023

Authors: Sinika, S. | Ramesh, G.

Article Type: Research Article

Abstract: Due to present condition, the expenses of allocating a job to a specific person or scheduling transport with a precise value may result in ambiguity. To deal with this, neutrosophic sets which is an extended form of fuzzy sets, appear alongside membership, non-membership and indeterminacy. The assignment problem in a trapezoidal neutrosophic environment with cost as the trapezoidal neutrosophic numbers is discussed in this work. The paper’s motive is to convert the trapezoidal neutrosophic numbers into new interval arithmetic form and the efficiency of the same is shown by comparing the existing method with other assignment models using examples.

Keywords: Assignment problem, Hungarian method, Interval Arithmetic, One’s assignment method, Trapezoidal neutrosophic number

DOI: 10.3233/JIFS-222796

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2179-2191, 2023

Authors: Shah, Sayed Kifayat | Tang, Zhongjun | Yuan, Jingbo | Popp, József | Acevedo-Duque, Ángel

Article Type: Research Article

Abstract: Despite the studies probing the factors associated with the adoption of 5G technology products, the current state of knowledge about this new technology products is still fragmented. Previous research has mainly concentrated on the “cumulative impact” of factors rather than the complexities of behavior. Based on complexity theory, this article aims to explain the behavioral intention of using 5G technology products by evaluating the combination of functional (i.e. price and quality), social (i.e. environmental awareness and knowledge) and consumer personality (i.e. openness and consciousness) factors as causal configuration. A sample of 150 Chinese consumers was examined using the fuzzy set …qualitative comparative analysis (fsQCA) technique. The fsQCA outcomes illustrate that five different and effective configurations of functionality, social, and consumer personality factors exist to attain apex level intention of using 5G technology products. This article extends the existing literature by espousing a new procedural attitude to divulge the intricacy of 5G products adoption. It also provides valuable suggestions for 5G technology service managers and manufacturers to enhance the technology, social, and consumer personality features combination to implement 5G technology products successfully. Show more

Keywords: 5G technology, fsQCA, Complexity, China, Decision-making.

DOI: 10.3233/JIFS-223129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2193-2207, 2023

Authors: Ganesan, Balaraman | Raman, Sundareswaran | Marayanagaraj, Shanmugapriya | Broumi, Said

Article Type: Research Article

Abstract:  Let N  = (V , E ) be a simple graph and let X be a subset of V  (N ). If every node not in X lies on a geodesic path between two nodes from X then it is called a geodetic set. The geodetic number g  (N ) is the minimum cardinality of such set X . The subset X is called a dominating set if every node not in X has at least one neighbour in X . The minimum number of nodes of a dominating set is known as domination number γ (N …). If the subset X is a geodetic set as well as a dominating set then it is called a geodetic dominating set. The minimum cardinality of a geodetic dominating set is known as geodetic domination number γg  (N ). The geodetic domination integrity of N is defined to be DI g  (N ) = min {|X | + m  (N  - X ) : X is a geodetic dominating set of N }, where m  (N  - X ) denotes the order of the largest component of N  - X . Uncertain networks can be modelled using fuzzy graphs. In a graph, each vertex and each edge are equally significant. However, in fuzzy graphs, each vertex and each edge is important in terms of fuzziness in their own right. In this study, the concepts of geodetic dominating sets in fuzzy graphs and geodetic domination number are defined and bounds are obtained. Moreover, the vulnerability parameter Geodetic domination integrity is introduced in fuzzy graphs. Further, the geodetic domination integrity for complete fuzzy graphs, complete bipartite fuzzy graphs, Cartesian product of two strong fuzzy graphs and bounds are also discussed. The applications of this parameter are applied to a telecommunication network system model to identify the key persons in the system and applied in a fuzzy social network to find the most influential group within the network. Show more

Keywords: Geodetic set, dominating set, geodetic dominating set, geodetic domination integrity set, fuzzy graphs, Complete fuzzy graphs

DOI: 10.3233/JIFS-223249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2209-2222, 2023

Authors: Srilakshmi, A. | Geetha, K.

Article Type: Research Article

Abstract: In this study, a unique generative adversarial network (GAN) architectural variation was suggested, which engages in adversarial game serve by preserving an appropriate distance in the latent dimension of the network. This method overcomes the mode collapse problem with a small dataset. Extensive experiments are conducted using the segmented medical leaf dataset with various classes and the generator network is able to produce all the artificial image classes. This is accomplished by combining a unique training technique with a reasonably simple model design.

Keywords: Mode collapse, image generation, generative adversarial networks, leaf images

DOI: 10.3233/JIFS-230212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2223-2233, 2023

Authors: Nandipati, Bhagya Lakshmi | Devarakonda, Nagaraju

Article Type: Research Article

Abstract: Lung cancer is a dangerous tumor that requires accurate diagnosis for effective treatment. Traditional diagnosis involves invasive and time-consuming histologic examination, and radiologists face challenges in localizing lung tumors. Deep neural convolutional networks are frequently used to locate lung cancer, but this is still difficult when not accounting for surrounding lung tissue. Despite progress in research, healthcare still uses deep learning models to improve the precision and sensitivity of large datasets. CNN (Convolutional Neural Network) accuracy standards are adequate, but image properties such as flips, construction, and other uncommon alignments diminish its efficiency. CNN also does not store the geometric …distribution between scanned picture features. CT (Computed Tomography) and PET (Positron Emission Tomography) scans require a method that takes into consideration the spatial information of picture characteristics, as they are vulnerable to alignment problems during the perusing process. To address these issues, the authors propose MCNet (MobileNetV2 with Capsule Network), a hybrid network that adopts feature extraction and categorization from MobileNetV2, and capsule network is used to overcome the limitations of convolutional neural networks (CNNs) when it comes to processing images with abnormal orientations, such as tilting or rotation. Although CNNs are effective in processing images presented in a standard orientation, they have difficulty handling variations in image orientation. In this work, MobileNetV2 serves as a backbone network for Capsule Networks in lung cancer diagnosis. The lung image collection dataset verifies the effectiveness of MCNet, and experimental results show that MCNet technology performs better than previous state-of-the-art techniques. The proposed hybrid MCNet architecture achieves the clinical goal of lung cancer diagnosis with a lower computational cost, reducing processing time complexity and false positive rates compared to current techniques. Show more

Keywords: MCNet, CNN, CT, PET, LUAD, LUSC, feature extraction

DOI: 10.3233/JIFS-231145

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2235-2252, 2023

Authors: Sivasankar, S. | Bera, Sanchari | Maaz, Syed Ibrahim | Pal, Madhumangal

Article Type: Research Article

Abstract: A new concept of vertices in a fuzzy graph known as defective vertices is introduced here. A vertex in a fuzzy network is called defective if no edges incident with it are strong. Defective vertex cannot be ignored when determining dominance in a fuzzy network because they are a part of the network. Finding defective vertices in a network is not much difficult when the adjacency matrix is given. In this paper, the novel concept of defective vertices of a fuzzy graph is introduced. Based on this idea a stable domination set and a stable domination number are defined. This …also optimised the network by establishing minimal connectivity. We have proposed three algorithms for finding the defective vertices, establishing stable connectivity, and determining the stable domination number for a given graph. An application of stable domination in the diagnosis of chickenpox disease is demonstrated to show the effectiveness of the proposed algorithms. Show more

Keywords: Fuzzy graph, stable fuzzy graph, defective vertex, stable domination set, stable domination number

DOI: 10.3233/JIFS-223545

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2253-2265, 2023

Authors: Qiao, Lin | Fan, Lijun | He, Yao | Zhou, Yan

Article Type: Research Article

Abstract: Aiming at the problem that manufacturing enterprises that rely more on asset projects currently lack effective means of divestiture of non performing assets, starting from incomplete information theory, this paper derives an optimal decision-making model for the divestiture path of non performing assets caused by the quality and safety crisis of manufacturing enterprises in an incomplete information environment, and uses the comprehensive method of SUMDII to establish a comprehensive integrated optimization model that reflects subjective evaluation and objective information. In addition, this study provides specific decision-making methods and implementation steps for optimizing the stripping path of non-performing assets. The empirical …analysis results verify and demonstrate the feasibility, operability, accuracy, and applicability of the established model. The results show that the model designed in the study exhibits strong stability in sensitivity testing. When the parameter vectors are taken as (1,0,0,0), (1,1,0,0), (1,1,0,0), and (1,1,1,1), respectively, the ranking results corresponding to the first three parameter vectors are stable, all of which are A2 > A4 > A1 > A3. At the same time, the applied decision-making result of the model is A4 > A1 > A2 > A3, which is consistent with the best scheme evaluated by experts and superior to most comparative models. At the same time, in the analysis of decision-making characteristics, the research and design model has the most comprehensive review of decision-making elements, which is superior to other comparative models. It can be seen that the model designed by the research can lead to higher quality NPL divestiture schemes, which can help manufacturing enterprises improve asset quality and weaken the negative impact of the quality and safety crisis in manufacturing enterprises. Show more

Keywords: Non-performing asset, stripping path optimization, stochastic and uncertain multi-attribute decision-making with incomplete information, fuzzy rough sets, projection pursuit, optimization decision-making

DOI: 10.3233/JIFS-224162

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2267-2278, 2023

Authors: Lu, Xiaohong | Ma, Chong | Yang, Banghua | Sun, Shixuan | Xu, Kai

Article Type: Research Article

Abstract: Friction stir welding (FSW) is a complex thermo-mechanical coupling process. Tensile strength is an important evaluation index of the mechanical properties of welded joints. How to realize the real-time prediction of tensile strength of the friction stir welded joints to reflect the dynamic change of welding state is a problem in the field. To solve this problem, this paper presents a multi-scale one-dimensional convolutional neural network (Multi-scale 1D CNN) prediction model using time series data of temperature and axial force as inputs to realize the online prediction of tensile strength of welded joints. Firstly, FSW experiments are carried out to …obtain time series data of temperature and axial force. Tensile strength values of the welded joints is obtained by tensile tests. The time series data and tensile strength values are fused as a dataset. Then Multi-scale 1D CNN, traditional 1D CNN and Multi-channel 1D CNN prediction models are established and trained with the dataset, respectively. Finally, by comparing the prediction performance of the three models, Multi-scale 1D CNN is proved to be more suitable for analyzing time series data to feedback the dynamic change of tensile strength of the joints during welding. Show more

Keywords: FSW, axial force, tensile strength, 1D CNN, temperature, online prediction

DOI: 10.3233/JIFS-230144

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2279-2288, 2023

Authors: Nirupama, V. | Nair, Prabha Shreeraj | Kishore Kumar, ATA | Murthy, Mantripragada Yaswanth Bhanu | Malhotra, Priyanka | Taqui, Syed Noeman | Almoallim, Hesham S. | Alharbi, Sulaiman Ali | Raghavan, S.S.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-231903

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2289-2304, 2023

Authors: Balcı, Sonay Görgülü | Ersöz, Süleyman | Lüy, Murat | Türker, Ahmet Kürşad | Barışçı, Necaattin

Article Type: Research Article

Abstract: It is known that in crowded environments such as educational institutions and workplaces, keeping indoor air quality and climate within certain limits contributes to success and production. For this purpose, a system has been developed to ensure air quality well-being in working environments. In our study, the Arduino processor managed by the fuzzy logic control system (FLC) starts to work with the trigger of the motion sensor HC-SR501. The inputs of the FLC system are defined as LM-35 sensor for temperature, DHT-11 for humidity, MQ-135 for air quality, MQ-9 sensor for CO and explosive gas. The designed system evaluates the …instantaneous data obtained from the fuzzy logic system rule base and decides which of the output air filter, heater and alarm systems will operate at what speed. In order to increase system efficiency, fuzzy logic input membership values are optimized by genetic algorithm. Show more

Keywords: Arduino, fuzzy logic, air quality, genetic optimization

DOI: 10.3233/JIFS-223955

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2305-2317, 2023

Authors: Jin, Feifei | Zhu, Yajun | Zhang, Yixiao | Guo, Shuyan | Liu, Jinpei | Zhou, Ligang

Article Type: Research Article

Abstract: Interval type-2 trapezoidal fuzzy (IT2TrF) number is a powerful tool to depict fuzzy information. Information measures methods have received more and more attention in recent years as they play an important role in decision-making theory. A new multi-attribute decision-making (MADM) method supported by IT2TrF information measures is investigated in this paper under the IT2TrF information environment. Firstly, three axiomatic definitions of IT2TrF information measures are introduced, which include information entropy, similarity measure and cross-entropy. Secondly, with the help of the exponential function, we formulate some information measure formulas, which are followed by the proofs that the exponential entropy, exponential similarity …measure and exponential cross-entropy fit the three axiomatic definitions. Subsequently, a novel IT2TrF MADM method is designed, in which the IT2TrF exponential entropy and cross-entropy are utilized to generate the attribute weights, the IT2TrF exponential similarity measure is employed to obtain the closeness degree of the ideal solution and derive the most satisfying solution. Lastly, we provide a numerical example of corporate investment to demonstrate the applicability and feasibility of the proposed MADM method. The robustness and merits of the developed MADM method are highlighted by the comparative analysis. Show more

Keywords: Multi-attribute decision-making, interval type-2 trapezoidal fuzzy numbers, information measures, exponential function

DOI: 10.3233/JIFS-230310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2319-2330, 2023

Authors: Yuan, Yuxia | Zhang, Yachao

Article Type: Research Article

Abstract: Background: Image semantic segmentation can be understood as the allocation of a predefined category label to each pixel in the image to achieve the region segmentation of the image. Different categories in the image are identified with different colors. While achieving pixel classification, the position information of pixel points of different categories in the image is retained. Purpose: Due to the influence of background and complex environment, the traditional semantic segmentation methods have low accuracy. To alleviate the above problems, this paper proposes a new real-time image semantic segmentation framework based on a lightweight deep convolutional encoder-decoder architecture …for robotic environment sensing. Methodology: This new framework is divided into three stages: encoding stage, decoding stage and dimension reduction stage. In the coding stage, a cross-layer feature map fusion (CLFMF) method is proposed to improve the effect of feature extraction. In the decoding stage, a new lightweight decoder (LD) structure is designed to reduce the number of convolutional layers to speed up model training and prediction. In the dimension reduction stage, the convolution dimension reduction method (CDR) is presented to connect the encoder and decoder layer by layer to enhance the decoder effect. Results: Compared with other state-of-the-art image semantic segmentation methods, we conduct comparison experiments on datasets Cityscapes, SUN RGB-D, CamVid, KITTI. The Category iIoU combined with the proposed method is more than 70%, and the Category IoU is as high as 89.7%. Conclusion: The results reflect that the new method can achieve the better semantic segmentation effect. Show more

Keywords: Image semantic segmentation, lightweight deep convolutional encoder-decoder architecture, cross-layer feature map fusion, convolution dimension reduction method, robotic environment sensing

DOI: 10.3233/JIFS-222221

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2331-2345, 2023

Authors: Hu, Zhexian

Article Type: Research Article

Abstract: The motivation for this paper is to consider that in recent years, the concept of metaverse, as the latest and most popular concept in the world, has been widely applied and studied in various industries, including economic management, art design, education and teaching. However, the academic and scientific circles have not reached a consensus on whether to define the metaverse as a technology or an intelligent scene. We believe that the metaverse should be a key concept and emerging theory for constructing the future wisdom field. Therefore, in this study, our research objective is to focus on the visual art …evaluation in digital works, and propose a visual art quality evaluation method in future metaverse digital works. This method is based on the quality function deployment theory and fuzzy mathematics theory in marketing. The second core point of this study is to build a field framework for the visual art evaluation of future digital works based on the metaverse by combing the current international and domestic understanding of the concept of metaverse. In addition, taking visual art quality evaluation as the research object, we have constructed a visual art quality evaluation index system for digital works under the background of metaverse. The index system is composed of one first-class index, three second-class indexes and nine third-class indexes. At the same time, we proposed a new fuzzy mathematics evaluation method in the research, called G1 entropy method. This algorithm combines subjective weighting method: G1 method and objective weighting method: entropy method as an important method of quality evaluation, and carries out the final rating through the combination weight of G1 entropy method. This study makes up for the concept of the future metaverse, introduces the gaps in the theory of visual art evaluation of future digital works, innovates the analysis of new concepts and the improvement of old methods, builds a new scene of organic combination of new technology and traditional visual art, and provides new ideas for the improvement of art quality at home and abroad in the future. In general, we sorted out the contributions of this research, including the following three aspects: (1) we constructed the metaverse field structure of digital works. By analyzing the current international and domestic research literature on the application of metaverse technology, especially the concept of metaverse in art scenes, we proposed to construct the field structure of online visual art after introducing the concept of metaverse, including blockchain technology, artificial intelligence technology Interaction technology and Internet of things technology as the four characteristics; (2) Method theoretical contribution: we further take the visual art quality evaluation as the research object, construct the index system of visual art quality evaluation of digital works under the background of metaverse, and propose an evaluation method of G1 entropy method, which is actually a method of subjective weighting by experts; (3) We use the method proposed in (2) to complete the calculation and ranking of the importance of 9 indicators in a practical case, and give some countermeasures for the calculation results of the importance of indicators. In conclusion, this study has realized the construction of new application scenarios of concepts and the new improvement of methods, and can provide theoretical and practical case experience support for the quality improvement of international and domestic metaverse visual art. Show more

Keywords: Metaverse, visual art, field architecture, quality function deployment, G1 entropy method

DOI: 10.3233/JIFS-223376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2347-2365, 2023

Authors: Davvaz, Bijan | Chinram, Ronnason | Lekkoksung, Somsak | Lekkoksung, Nareupanat

Article Type: Research Article

Abstract: Ideals play an essential part in studying ordered semigroups. There are several generalizations of ideals that are used to investigate ordered semigroups. It is known that (m , n )-ideals are an abstraction of bi-ideals, and n -interior ideals are an abstraction of interior ideals. This paper introduces a generality of (m , n )-ideals and n -interior ideals, so-called (α, β)-fuzzy (m , n )-ideals and (α, β)-fuzzy n -interior ideals. Furthermore, we discuss our current notions with those that already exist. We examine connections between (m , n )- (resp., n -interior) ideals and (α, β)-fuzzy (m , …n )- (resp., n -interior) ideals. A characterization of (α, β)-fuzzy (m , n )- (resp., n -interior) ideals, by a particular product, in ordered semigroups is provided. We demonstrate that our results generalize the known results through specific settings. Show more

Keywords: Ordered semigroup, (α, β)-fuzzy (m, n)-ideal, (α, β)-fuzzy n-ideal, bi-ideal, interior-ideal

DOI: 10.3233/JIFS-224255

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2367-2380, 2023

Authors: Zhou, Bin | Chen, Jieshi | Zhang, Yang | Yang, Shanglei | Lu, Hao

Article Type: Research Article

Abstract: In the laser spiral welding (LSW) process, the welding parameters have a significant impact on the weld quality. In this paper, experiments were conducted and experimental data were collected on galvanized steel sheets using the LSW process, and mathematical models were developed using response surface methodology (RSM) and genetic algorithm (GA) to verify the specific effects of each process parameter on the weld and to perform process optimization. Laser power, welding speed, gap and focal length were selected as the influencing factors, and melt depth, melt width and concave as the output results. In the RSM model we found that …the laser power was positively correlated with the weld depth and width, while the welding speed was inversely correlated with the weld depth and width, the gap was positively correlated with the amount of concave, and the focal length had no significant effect on the weld. In the GA model we use a large amount of experimental data for BP neural network training and iterative optimization using a genetic algorithm. Validation experiments were conducted on two models, and the results indicated that the two models had higher accuracy in predicting the welding depth and width compared to predicting the concave. The GA model had an 8% increase in tensile strength and a 25% increase in plasticity of the weld joint obtained from the optimal process compared to the RSM model. The GA model has higher accuracy in optimizing the LSW process. Show more

Keywords: Laser spiral welding, response surface methodology, genetic algorithm, process optimization, mechanical property

DOI: 10.3233/JIFS-224448

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2381-2392, 2023

Authors: Deng, Guannan | Zhang, Mei | Meng, Xiangqi | Yuan, Jiaming

Article Type: Research Article

Abstract: In this paper, we establish the matching relation between implication operator and aggregation operator, which provides a new solution for the design and construction of multi-rule fuzzy inference system. Firstly, according to the definition and monotonicity of implication operator, a new classification method of implication operator is proposed, and then the fuzzy inference process using different implication operators is classified. Then, dynamic maximum aggregation operator and dynamic minimum aggregation operator are proposed. Based on the compositional rule of inference (CRI) method, a matching method and basis of implication operator and aggregation operator for fuzzy inference systems is given and illustrated …with examples. Finally, the applicability of the proposed method in this paper is further illustrated by comparing the method with existing methods in the literature and using the nearness degree as an evaluation index. Show more

Keywords: Multi-rule fuzzy inference systems, classification of fuzzy implication, aggregation operators, nearness degree

DOI: 10.3233/JIFS-230866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2393-2408, 2023

Authors: Shi, Bo | Lv, Bin

Article Type: Research Article

Abstract: To improve the recognition accuracy of badminton players’ swing movements, this study proposes a single inertial sensor based method for badminton swing movement recognition. This article proposes a badminton racket-mounted data gathering system with a single inertial sensor and proposes a real-time motion data flow-based window segmentation technique to capture motion data. On this basis, a two-layer classifier recognition model based on C4.5 Decision Tree (C4.5 T) algorithm and Random Forest (RF) method is constructed to recognize swing technical actions. Using the C4.5 T to identify the swing style of athletes; The RF method is used to recognize the swing …technical action. The final experiment showed that the method studied achieved a recognition accuracy of 95.36% for six common swing movements. The proposed model has good application prospects in the recognition of badminton swing movements. However, due to the limitations of the experimental conditions, the recognition effect of this method on more complex swing movements needs to be further verified. Show more

Keywords: Single inertial sensor, the swinging movement of badminton, action recognition, random forest

DOI: 10.3233/JIFS-231409

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2409-2418, 2023

Authors: Huang, Jingcao | Guo, Bin | Dian, Songyi

Article Type: Research Article

Abstract: Hydropower station is vital for the stable growth of the national economy. How to timely warn the possible faults of hydropower stations has become an increasingly popular research topic. The traditional detection model is difficult to detect the small abnormal changes in the data, and these abnormal changes are often the precursor of faults. To improve the sensitivity of the traditional detection model, this study introduced a weight factor into the traditional LSTM detection model. By using the correction mechanism, the LSTM correction model makes the prediction model never deviate from the normal track following the appearance of abnormal data. …This ensures that the model can generate large residuals after abnormal data occur so that we can detect these abnormal data in time. Finally, this paper puts forward two factors related to equipment health and integrates these two factors to form a health index. The results show that the LSTM correction model based on the health index can not only detect small changes that cannot be detected by traditional detection models but also knows the wear and tear of equipment during operation based on the changes in health indicators. Show more

Keywords: Hydropower station, LSTM, correction mechanism, anomaly detection, health factors

DOI: 10.3233/JIFS-223461

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2419-2436, 2023

Authors: Yu, Ping | Wang, Haotian | Cao, Jie

Article Type: Research Article

Abstract: In order to address the timing problem, invalid data problem and deep feature extraction problem in the current deep learning based aero-engine remaining life prediction, a remaining life prediction method based on time-series residual neural networks is proposed. This method uses a combination of temporal feature extraction layer and deep feature extraction layer to build the network model. First, the temporal feature extraction layer with multi-head structure is used to extract rich temporal features; then, the spatial attention mechanism is applied to improve the weights of important data; finally, the deep feature extraction layer is used to process the deep …features of the data. To verify the effectiveness of the proposed method, experiments are conducted on the C-MAPSS dataset provided by NASA. The experimental results show that the method proposed in this paper can make accurate predictions of the remaining service life under different sub-datasets and has outstanding performance advantages in comparison with other outstanding networks. Show more

Keywords: Time sequential resnet, temporal feature extraction layer, spatial attention module, deep feature extraction layer, remaining useful life Introduction

DOI: 10.3233/JIFS-223971

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2437-2448, 2023

Authors: Ramasamy, Uma | Santhoshkumar, Sundar

Article Type: Research Article

Abstract: A machine learning model intends to produce a secure model with low bias and variance. Finding the optimal machine learning model for a dataset is a challenging task. A suitable machine learning model is yet to be specified for the Arthritis Profile Data dataset. Autoimmune disease is widely spread all over the world. Some autoimmune arthritis diseases are Rheumatoid Arthritis, Psoriatic Arthritis, Juvenile Arthritis, etc. These diseases come under both categories autoimmune and inflammatory diseases. The proposed work is designed to suggest the best machine learning model with the highest observed accuracy for the Arthritis Profile Data. Many authors do …not compare newly created datasets with previously used datasets. This can lead to inaccurate results due to the lack of reliable comparison. Additionally, it can prevent researchers from detecting potential bias in the data. Comparing datasets can help to identify and address any potential issues and improve the accuracy of the results. It is important to review existing datasets before beginning a new project to ensure the accuracy of the results. This article is the first study on the topic that analysis the accuracy behavior of each machine learning model concerning the Arthritis Profile Data and various benchmark disease datasets with different hold-out and k-fold cross-validation methods. The study concludes with a glimpse of whether dataset and feature size affect model prediction accuracy and proffers a machine learning model for the Arthritis Profile Data. The proposed research explores base learning classification algorithms and ensemble methods such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting from machine learning. Our empirical evidence clearly states XGBoost ensemble technique shows the highest accuracy for the Arthritis Profile Data. Show more

Keywords: Bias, variance, hold-out, cross-validation, autoimmune arthritis disease, machine learning, ensemble method

DOI: 10.3233/JIFS-224115

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2449-2463, 2023

Authors: Guo, Wei | Zhang, Chuchen

Article Type: Research Article

Abstract: The expansive growth of information on the Internet has led to new developments in computer vision technology and image processing techniques. Since stone inscriptions are subject to erosion and polishing by the external environment for years, it is difficult to extract image and text information. In this study, the fuzzy control theory is combined with edge detection technology for image edge detection. Firstly, a suitable fuzzy rule and affiliation function are set, then a fuzzy control system is used to extract and detect the image edge information, and then a fuzzy logic rule-based edge detection algorithm is proposed to detect …the inscription images. To test the performance of the algorithm, the detection effect of the image is first analyzed from a subjective perspective. The experimental results show that the proposed algorithm has better edge detection for both inscription and lena images, with better noise suppression without excessive distortion, and clearer inscription images. The proposed algorithm has the lowest MSE value of 41.26 when the detection object is the lena image b, and the highest PSNR value of 33.84 when the detection object is the lena image a. The proposed algorithm has the lowest MSE value of 41.26 when the detection object is the lena image b, and the highest PSNR value of 41.26 when the detection object is the lena image b. The proposed algorithm has the highest PSNR value of 33.84 when the detection object is the lena image b. In summary, the analysis of both subjective and objective indicators shows that the inscription image processing algorithm used in this paper has better processing effect, and the processed images become clearer with less distortion, which is helpful for both inscription image and text extraction. Show more

Keywords: Fuzzy logic, inscription picture, EA, picture processing technology

DOI: 10.3233/JIFS-230218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2465-2475, 2023

Authors: Yang, Long-Hao | Ye, Fei-Fei | Wang, Ying-Ming | Huang, Yan | Hu, Haibo

Article Type: Research Article

Abstract: Performance evaluation is one of the most important standards to measure the competitiveness and productivity of enterprises. Although existing studies could obtain the specific values of enterprises performance based on historical data, they usually failed to effectively evaluate enterprises performance in the consideration of different indicators. Meanwhile, as the characteristics of existing performance evaluation models are uneven, how to choose a reasonable data envelopment analysis (DEA) model for enterprises performance evaluation must be considered. Therefore, a new ensemble model on the basis of homogeneous, heterogeneous, and hybrid efficiency evaluation together with the evidential reasoning (ER) approach is proposed in this …study for enterprises performance evaluation, so called the ER-based ensemble model. The ER-based ensemble model can overcome the inconsistency results caused by the application of different indicators and different DEA models. In case study, 40 state-own holding enterprises in China are selected and all these enterprises are evaluated and ranked using the integrated efficiency obtained from the ER-based ensemble model. Comparative analysis demonstrates that the ER-based model is better than some traditional efficiency evaluation models in enterprises performance evaluation and performance ranking. Show more

Keywords: Data envelopment analysis, efficiency evaluation, efficiency ensemble, enterprise performance, evidential reasoning

DOI: 10.3233/JIFS-230247

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2477-2495, 2023

Authors: Zhou, Jiaqi | Wu, Tingming | Yu, Xiaobing | Wang, Xuming

Article Type: Research Article

Abstract: Accurate and reliable prediction of PM2.5 concentrations is the basis for appropriate warning measures, and a single prediction model is often ineffective. In this paper, we propose a novel decomposition-and-ensemble model to predict the concentration of PM2.5 . The model utilizes Ensemble Empirical Mode Decomposition (EEMD) to decompose PM2.5 series, Support Vector Regression (SVR) to predict each Intrinsic Mode Function (IMF), and a hybrid algorithm based on Differential Evolution (DE) and Grey Wolf Optimizer (GWO) to optimize SVR parameters. The proposed prediction model EEMD-SVR-DEGWO is employed to forecast the concentration of PM2.5 in Guangzhou, Wuhan, and Chongqing of …China. Compared with six prediction models, the proposed EEMD-SVR-DEGWO is a reliable predictor and has achieved competitive results. Show more

Keywords: PM2.5, prediction, decomposition-and-ensemble, support vector regression

DOI: 10.3233/JIFS-230343

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2497-2512, 2023

Authors: Yi, Weiguo | Ma, Bin | Zhang, Heng | Ma, Siwei

Article Type: Research Article

Abstract:  Compared with other traditional community discovery algorithms, density peak clustering algorithm is more efficient in getting network structures through clustering. However, DPC needs to contain the distance information between all nodes as sources, so it cannot directly processing the complex network represented by the adjacency matrix. DPC introduces truncation distance when calculating the local density of nodes, which is usually set as a fixed value according to experience, and lacks self-adaptability for different network structures. A feasible solution to those problems is to combined rough set theory and kernel fuzzy similarity measures. In this work, we present overlapping community detection …algorithm based on improved rough entropy fusion density peak. The algorithm applied rough set theory to attribute reduction of massive high-dimensional data. Another algorithm defines the similarity of sample points by the inner product between two vectors on the basis of fuzzy partition matrix. Finally, a community detection algorithm based on rough entropy and kernel fuzzy density peaks clustering (CDRKD) has proposed by combining the two algorithms above, we perform an extensive set of experiments to verify the effectiveness and feasibility of the algorithm. Show more

Keywords: Overlapping community detection, rough neighborhood mutual information entropy, density peaks clustering, kernel fuzzy similarity measure

DOI: 10.3233/JIFS-230614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2513-2527, 2023

Authors: Xu, Li | Bai, Jinniu

Article Type: Research Article

Abstract: Brain cancer is one of the most deadly forms of cancer today, and its timely and accurate diagnosis can significantly impact the patient’s quality of life. A computerized tomography scan (CT) and magnetic resonance imaging (MRI) of the brain is required to diagnose this condition. In the past, several methods have been proposed as a means of diagnosing brain tumors through the use of medical images. However, due to the similarity between tumor tissue and other brain tissues, these methods have not proven to be accurate. A novel method for diagnosing brain tumors using MRI and CT scan images is …presented in this paper. An architecture based on deep learning is used to extract the distinguishing characteristics of brain tissue from tumors. The use of fusion images allows for more accurate detection of tumor types. In comparison with other approaches, the proposed method has demonstrated superior results. Show more

Keywords: Deep learning, brain tumor, visual geometry group, CT scan, MRI images

DOI: 10.3233/JIFS-230850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2529-2536, 2023

Authors: Garg, Harish | Ünver, Mehmet | Aydoğan, Büşra | Olgun, Murat

Article Type: Research Article

Abstract: As an extension of the concepts of fuzzy set and intuitionistic fuzzy set, the concept of Pythagorean fuzzy set better models some real life problems. Distance, entropy, and similarity measures between Pythagorean fuzzy sets play important roles in decision making. In this paper, we give a new entropy measure for Pythagorean fuzzy sets via the Sugeno integral that uses fuzzy measures to model the interaction between criteria. Moreover, we provide a theoretical approach to construct a similarity measure based on entropies. Combining this theoretical approach with the proposed entropy, we define a distance measure that considers the interaction between criteria. …Finally, using the proposed distance measure, we provide an extended Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision making and apply the proposed technique to a real life problem from the literature. Finally, a comparative analysis is conducted to compare the results of this paper with those of previous studies in the literature. Show more

Keywords: Pythagorean fuzzy set, entropy measure, distance measure, extended TOPSIS, medical diagnosis

DOI: 10.3233/JIFS-231454

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2537-2549, 2023

Authors: Wang, Tengfei | Shi, Peng

Article Type: Research Article

Abstract: In this paper, the problems of expressing and fusing multi-channel uncertain digital information is studied. The concept of a special high-dimensional fuzzy number called multi-level linear fuzzy ellipsoid number is given, and a method of constructing such high dimensional fuzzy number to express multi-channel uncertain digital information is established. Then a calculation formula of the centroid of multistage linear fuzzy ellipsoid number is deduced. And then, as an application example of multi-channel uncertain digital information fusion, a specific example is given to show ranking some objects which are characterized by multi-channel uncertain digital information by using the obtained results and …the concept of fuzzy order on high dimensional fuzzy number space. Show more

Keywords: Fuzzy numbers, fuzzy ellipsoid numbers, multistage linear fuzzy ellipsoid number, constructing fuzzy numbers, expressing multi-channel uncertain digital information, multi-channel uncertain digital information fusion

DOI: 10.3233/JIFS-222761

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2551-2563, 2023

Authors: Memon, Abdul Sami | Laghari, J.A. | Bhayo, Muhammad Akram | Khokhar, Suhail | Chandio, Sadullah | Memon, Muhammad Saleem

Article Type: Research Article

Abstract: In the modern power system, the use of renewable energy sources is increasing rapidly, which makes the system more sensitive. Therefore, it requires effective controllers to operate within the allowable ranges. The existing techniques based on cascaded controllers implemented so far for load frequency control have the advantage of improving the system response. However, this makes the system a more complex and time-consuming process. This makes the system more straightforward, makes it easy to optimize PID parameters, and provides results in acceptable ranges. This paper attempts to solve the load frequency control (LFC) problem in an interconnected hybrid power system …with a classical PID controller employing the tunicate swarm algorithm (TSA). This algorithm is used for two areas of an interconnected hybrid power system: thermal, hydro, nuclear, and wind. The PID controller parameters are optimized by tunicate swarm algorithm using integral time absolute error (ITAE) based objective function. To show the robustness of the proposed TSA algorithm, a sensitivity analysis is performed for four case studies ranging from 20% to 30% load increments and decrements. The performance of the proposed TSA algorithm has been compared with the well-known optimization algorithms, particle swarm optimization (PSO), artificial bee colony (ABC), and arithmetic optimization algorithm (AOA) in terms of overshoot, undershoot, and settling time. The simulation results show that the proposed TSA has better optimization capability than PSO, ABC, and AOA in terms of overshoot, undershoot, and settling time. Show more

Keywords: Tunicate Swarm based Automatic generation control, hybrid power system, TSA based Optimized PID controller, Interconnected power system, multi-area power system.

DOI: 10.3233/JIFS-223227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2565-2578, 2023

Authors: Leng, Hongyong | Shao, Jinxin | Zhang, Zhe | Qian, Yurong | Ma, Mengnan | Li, Zichen

Article Type: Research Article

Abstract: To address the problem that single-channel neural networks cannot fully extract text semantic features in traditional user portrait construction methods, this paper proposes a dual-channel user portrait model based on DPCNN-BIGRU and attention mechanism. The model first uses Bidirectional Encoder Representation from Transformers(Bert) and CK-means+ to obtain the fusion vector of semantic features and topic features, and then feeds the vector into Deep Pyramid Convolutional Neural Networks (DPCNN) and Bidirectional Gated Recurrent Unit (BiGRU). Deep features and global features of the text are obtained simultaneously, and then weights are assigned by the attention mechanism. Finally, the output features of the …dual channels are fused and classified. It is tested on the Sogou user portrait datasets, and the experimental results prove that the dual-channel model outperforms the baseline model. Show more

Keywords: User profile, BERT, canopy, K-means, text classification

DOI: 10.3233/JIFS-224532

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2579-2591, 2023

Authors: Fathy, E. | Ammar, E. | Helmy, M.A.

Article Type: Research Article

Abstract: Due to the importance of the multi-level fully rough interval linear programming (MLFRILP) problem to address a wide range of management and optimization challenges in practical applications, such as policymaking, supply chain management, energy management, and so on, few researchers have specifically discussed this point. This paper presents an easy and systematic roadmap of studies of the currently available literature on rough multi-level programming problems and improvements related to group procedures in seven basic categories for future researchers and also introduces the concept of multi-level fully rough interval optimization. We start remodeling the problem into its sixteen crisp linear programming …LP problems using the interval method and slice sum method. All crisp LPs can be reduced to four crisp LPs. In addition, three different optimization techniques were used to solve the complex multi-level linear programming issues. A numerical example is also provided to further clarify each strategy. Finally, we have a comparison of the methods used for solving the MLFRILP problem. Show more

Keywords: Constraint method, interval arithmetic, interactive approach, fuzzy approach, rough interval programming, slice sum method

DOI: 10.3233/JIFS-230057

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2593-2610, 2023

Authors: Zhang, Zhaojun | Lu, Jiawei | Xu, Zhaoxiong | Xu, Tao

Article Type: Research Article

Abstract: To solve the problems of the ant colony optimization (ACO), such as slow convergence speed, easy to fall into local extremum and deadlock in path planning, this paper proposed an improved ACO, which was hybridized by PSO based on logistic chaotic mapping, called hybrid ant colony optimization (HACO). According to the number of obstacles around the next feasible node, HACO distributes the initial pheromones unevenly to avoid the ant getting stuck in deadlock. According to the orientation of the next node selected by the ant, the heuristic information is adaptively adjusted to guide the ant to the direction of the …target position. When updating the pheromone, the local and global search mechanism of the particle swarm optimization is used to improve the pheromone update rule and accelerate convergence speed. Finally, the grid method is used to construct the environment map, and simulation experiments are conducted in different environments. The experimental results verify the effectiveness and feasibility of the improved algorithm. Show more

Keywords: ant colony optimization, path planning, grid method, pheromone update

DOI: 10.3233/JIFS-231280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2611-2623, 2023

Authors: Sundarakumar, M.R. | Salangai Nayagi, D. | Vinodhini, V. | VinayagaPriya, S. | Marimuthu, M. | Basheer, Shajahan | Santhakumar , D. | Johny Renoald, A.

Article Type: Research Article

Abstract: Improving data processing in big data is a delicate procedure in our current digital era due to the massive amounts of data created by humans and machines in daily life. Handling this data, creating a repository for storage, and retrieving photos from internet platforms is a difficult issue for businesses and industries. Currently, clusters have been constructed for many types of data, such as text, documents, audio, and video files, but the extraction time and accuracy during data processing remain stressful. Hadoop Distributed File System (HDFS) is a system that provides a large storage area in big data for managing …large datasets, although the accuracy level is not as high as desired. Furthermore, query optimization was used to produce low latency and high throughput outcomes. To address these concerns, this study proposes a novel technique for query optimization termed the Enhanced Salp Swarm Algorithm (ESSA) in conjunction with the Modified K-Means Algorithm (MKM) for cluster construction. The process is separated into two stages: data collection and organization, followed by data extraction from the repository. Finally, numerous experiments with assessments were carried out, and the outcomes were compared. This strategy provides a more efficient method for enhancing data processing speed in a big data environment while maintaining an accuracy level of 98% while processing large amounts of data. Show more

Keywords: Hadoop distributed file system, latency, throughput, query optimization, hash algorithms clustering

DOI: 10.3233/JIFS-231389

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2625-2640, 2023

Authors: Chandana Mani, R.K. | Kamalakannan, J.

Article Type: Research Article

Abstract: Breast cancer (BC) is categorized as the most widespread cancer among women throughout the world. The earlier analysis of BC assists to increase the survival rate of the disease. BC diagnosis on histopathology images (HIS) is a tedious process that includes recognizing cancerous regions within the microscopic image of breast tissue. There are various methods to discovering BC on HSI, namely deep learning (DL) based methods, classical image processing techniques, and machine learning (ML) based methods. The major problems in BC diagnosis on HSI are the larger size of images and the high degree of variability in the appearance of …tumorous regions. With this motivation, this study develops a computer-aided diagnosis using a white shark optimizer with attention-based deep learning for the breast cancer classification (WSO-ABDLBCC) model. The presented WSO-ABDLBCC technique performs accurate classification the breast cancer using DL techniques. In the WSO-ABDLBCC technique, the Guided filtering (GF) based noise removal is applied to improve the image quality. Next, the Faster SqueezeNet model with WSO-based hyperparameter tuning performs the feature vector generation process. Finally, the classification of histopathological images takes place using attention-based bidirectional long short-term memory (ABiLSTM). A detailed experimental validation of the WSO-ABDLBCC occurs utilizing the benchmark Breakhis database. The proposed model achieved an accuracy of 95.2%. The experimental outcomes portrayed that the WSO-ABDLBCC technique accomplishes improved performance compared to other existing models. Show more

Keywords: Breast cancer, computer-aided diagnosis, histopathological images, deep learning, white shark optimizer

DOI: 10.3233/JIFS-231776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2641-2655, 2023

Authors: Prajitha, C. | Sridhar, K.P. | Baskar, S.

Article Type: Research Article

Abstract: Electrocardiogram (ECG) signal analyses can enhance human life in various ways, from detecting and treating heart illness to controlling the lives of cardiac-diseased people. ECG analysis has become crucial in medical studies for accurately detecting cardiovascular diseases (CVDs). Cardiac Arrhythmia is one of the major life-threatening diseases. Analyzing ECG signals is the easiest way to detect Arrhythmia. Different noises often corrupt the ECG signals, like power line interference, electromyographic (EMG) noise, and electrode motion artifact noise. Such noises make it difficult to identify the various peaks in the ECG signal for arrhythmia classification. To overcome such problems, Noise Removal-based Thresholding …(NRT) framework has been introduced to remove noises from ECG signals and accurately classify Arrhythmia. Discrete Wavelet transform reduces noise from ECG signals in the pre-processing stage. The noise-removed signal is segmented by K-means clustering for R-peak detection by finding all local maximum points from the signal. The signal features are extracted by Burg’s method to obtain good frequency resolution and quick integration for short-time signals in the form of a cumulative distribution function. All features collected from R-peak are fed to the Iterative Convolutional Neural Network (ICNN) and classified the arrhythmia types based on the alignment of a few variables to work well with the Euclidean distance metric. The NRT framework is evaluated based on the data obtained from the MIT-BIH Arrhythmia dataset and achieves the Accuracy of 99.45 %, Positive Prediction of 98.92%, F1-Score of 98.95%, SNR of 35 dB, MSE of 0.001, RMSE of 0.002 Show more

Keywords: K-means clustering, Iterative Convolutional Neural Network, arrhythmia classification, R-peak

DOI: 10.3233/JIFS-223719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2657-2668, 2023

Authors: Huang, Jr-Jen | Yang, Cheng-Ying | Lin, Yi-Nan | Shen, Victor R.L. | Lin, Chia-Tsai | Shen, Frank H.C.

Article Type: Research Article

Abstract: Human faces have been naturally viewed as a central part in each image. One interesting task is to classify each face into different categories based on the emotion shown in the facial expression. In addition, an awareness of emotion during work on a project and how affective states are presented in the communication style might help system developers work more effectively, thus improving the performance of a collaborative team. Currently, the feasibility and portability of emotion recognition in the platform with Raspberry PI are insufficient. Hereby, a novel emotion recognition system in real time using the edge computing platform with …deep learning has been implemented successfully. The feature values of objects are calculated by a high computing processor on the embedded platform. When an object with the matching features is detected, it is drawn as a rectangular bounding box and the results are displayed on the screen. In the proposed system, it first annotates the image datasets and saves them in the corresponding input data format for model training. Thus, the You Only Look Once (YOLOv5) model has been employed for training because it is a state-of-the-art object detection system. In other words, a fast and accurate emotion recognition is the main benefits of choosing YOLOv5 model. Then, the correctly trained YOLOv5 model file is loaded into an edge computing platform; and the feature values of objects are analyzed by a high computing processor. Finally, the experimental results show that the promising mean Average Precision (mAP), 92.6%, and recognition speed in Frames Per Second (FPS), 40, are obtained, which outperforms other existing systems. Show more

Keywords: Deep learning, emotion recognition, high computing platform, face recognition, image recognition, object detection

DOI: 10.3233/JIFS-223801

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2669-2683, 2023

Authors: Arivalagan, Divya | Bhoopathy Began, K. | Ewins Pon Pushpa, S. | Rajendran, Kiruthiga

Article Type: Research Article

Abstract: Fingerprints are widely used as effective personal authentication systems, because they constitute unique, robust, and risk-free evidence. Fingerprinting techniques refer to biometric procedures used for identifying individuals based on their physical characteristics. A fingerprint image contains ridges and valleys forming a directionally-oriented pattern. The robustness of the fingerprint authentication technique determines the quality of the fingerprint image. This study proposed an intelligent 12-layered Convolutional Neural Network (CNN) model using Deep learning (DL) for gender determination based on fingerprints. Further, the study compared the performance of this model to existing state-of-the-art methods. The primary goal of this study was to reduce …the number of comparisons within a large database obtained from automatic fingerprint recognition systems. The classification process was found to be swifter and more accurate when analysis of the DL algorithm was performed. With reference to the criteria of precision, recall, and accuracy evaluation during classification, this proposed 12-layered CNN model outperformed the Residual Neural Network with 50 Layers (ResNet-50) and Dense Convolutional Network with 201 Layers (DenseNet-201) models. The accuracies obtained were 97.0%, 95.8%, 98.0%, and 96.8% for female-left, female-right, male-left, and male-right classes respectively, while achieving an overall accuracy of 94.0%. Show more

Keywords: Fingerprint image, intelligent system, authentication, convolutional neural network, deep learning algorithm, precision, recall, accuracy, DenseNet201, ResNet-50

DOI: 10.3233/JIFS-224284

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2685-2706, 2023

Authors: Zhang, Linzi | Shi, Yong

Article Type: Research Article

Abstract: Classical supply chain finance (SCF) primarily focuses on the financial service among all upstream and downstream supply chain participants. Due to the continuously deteriorating of the ecological environment, an environmental-friendly SCF system is urgently needed. In this paper, we propose a novel SCF design scheme with environmental concerns, i.e., green supply chain finance (GSCF), consider the financing channels both from banks and from consumers, and design a bi-objective optimization model that depicts the trade-off between the benefit and the emission. Further, an improved normalized normal constraint (INNC) Pareto method is developed to address the optimal financing strategy of the bi-objective …model. We then conduct a numerical case of a Taiwanese steel firm to verify the effectiveness and efficiency of our method. Results show that our model provides a portfolio of optimal solutions on Pareto frontier which can be applied as an effective decision support system when designing a GSCF. Furthermore, the sensitivity analysis also presents the impact of environmental investment cost, technological ratio of companies and the interest rate of trade credit on the optimal configuration of the GSCF. Show more

Keywords: Green supply chain finance, Multi-objective optimization, Network design, Pareto frontiers, Trade credit

DOI: 10.3233/JIFS-230270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2707-2721, 2023

Authors: Al-Jamaan, Rawabe | Ykhlef, Mourad | Alothaim, Abdulrahman

Article Type: Research Article

Abstract: Social networks like Twitter are extremely popular and widely used, which has increased interest in studying the information posted there. One such analytical application is extracting location information of users for real-time monitoring of the objects and events of interest, such as political and social events, disease surveillance, natural calamities, and crime prevention. Identifying geographic location is a nontrivial task, as user profiles contain outdated and inaccurate location information. Furthermore, extracting geographical information from Arabic tweets is challenging since they contain many nonstandard data (dialects), complex structures, abbreviations, grammatical and spelling mistakes, etc. This study focuses on the localization of …Saudi Arabian users who tweet in Arabic. This study proposes a convolutional neural network-based deep learning model to predict a Twitter user’s region-level location using user profiles, text texts, place attachments, and historical tweets. The model was evaluated empirically on a dataset of 95,739 tweets written in Arabic and produced by 4,331 users from Saudi Arabia cities. Regarding classification accuracy, the proposed CNN model outperformed machine learning classifiers such as NB, LR, and SVM with a 60% accuracy on the test set. This study is the first of its kind, aimed at localizing Saudi users based on their tweets. Show more

Keywords: Convolutional neural network, location estimation, machine learning, natural language processing, Twitter

DOI: 10.3233/JIFS-230518

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2723-2734, 2023

Authors: Özer, N. Ceyda | Tuzkaya, U. Rıfat

Article Type: Research Article

Abstract: City logistics approaches and modeling struggles have a significant role in urban areas in increasing the efficiency of logistics operations and reducing traffic jams and their environmental effects. By developing an effective distribution network for cities, it is possible to compete with the changing world and satisfy flexible customer requirements. In this study, as a real-world case, a city logistics model for Istanbul metropolitan area is designed using multi-objective linear programming that considers the different objectives of the stakeholders in cities by integrating the fuzzy Choquet integral technique in a multi-level distribution network for the automotive spare part industry. This …paper makes decisions regarding the amount of product flowing among the echelons, the amount of stock to be kept in the warehouses, and the product delays allowed. While minimizing the transportation cost, holding cost and emission levels during these decisions, the study also aims to maximize the service quality in the warehouses. The model is applied to a logistics network of fifty demand points and thirty time periods which can be considered a middle or large-scale problem. In the model, it is also decided to transport the products with electric or fuel vehicles. In the transport sector, electric vehicles are the key to meet future needs for social, health and other human services. The results are discussed under different scenarios. This research allows the use of such a model in making strategic decisions for the distribution network design in big cities. Show more

Keywords: Fuzzy Choquet integral, electric vehicles, multi-criteria decision making, city logistics, mathematical modeling

DOI: 10.3233/JIFS-223282

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2735-2752, 2023

Authors: Yang, Jun | Qiao, Linke | Li, Changjiang | Wu, Xing

Article Type: Research Article

Abstract: Roof collapse is the most frequent production accident in the mine production process, which seriously threatens the efficient and safe production of the mine. Therefore, it is urgent to carry out practical research on the roof collapse tendency of the roadway. After searching and analyzing the relevant documents, the primary influencing factors of roof collapse risk based on AHP are determined, namely engineering geology, rock mass support, construction management and natural environment. After refining the main influencing factors, the evaluation factor set is obtained, the fuzzy comprehensive evaluation relationship matrix is established, and the fuzzy comprehensive evaluation model of roof …collapse risk is obtained. Finally, the quantitative evaluation of no collapse risk, weak collapse risk, medium collapse risk and high collapse risk is carried out. Taking a metal mine as an example, the risk of roof collapse of its C11 haulage roadway is selected for fuzzy evaluation. The evaluation result is high collapse risk, which is consistent with the evaluation result of the current specification, indicating that the model can be used for mine roof collapse risk evaluation. This method of estimating roof collapse has been applied on-site, which is consistent with the actual situation and has achieved good results. It has guiding significance for predicting the stability of tunnels and supporting operations. Show more

Keywords: Analytic hierarchy process, risk assessment, roof collapse, fuzzy theory

DOI: 10.3233/JIFS-224146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2753-2762, 2023

Authors: Liu, Pingqing | Wang, Hongjun | Wei, Guiwu

Article Type: Research Article

Abstract: Generalized hesitant fuzzy numbers (GHFNs) can reflect the real situation of the event, in which we may encounter limited known values and known values of the set of the degree of doubt, as a quantitative approximation of uncertainty or quantification of linguistic expressions. The score function and weighting method of GHFNs are of great significance in multi-attribute decision-making (MADM) problems. In different ambiguous environments, many scholars have proposed score functions and entropy measures for different fuzzy sets. Firstly, the existed score function of GHFNs was analyzed in detail and a new score function of GHFNs was established by combining previous …references. Secondly, a combined weighting method is built based on the minimum identification information principle by fusing GHF entropy and Method based on the Removal Effects of Criteria (MEREC). Thirdly, a novel GHF MADM method (GHF-EDAS) is built by extending evaluation based on distance from average solution (EDAS) method to the GHF environment to solve the issue that the decision attribute information is GHFNs. Finally, the validity and usefulness of the technique are verified by applying the GHF-EDAS technique to energy projects selection and comparing with the existing GHF-MADM method, the practicability and effectiveness of the model are verified, which offer a new way to solve the MADM problem of GHFNs. Show more

Keywords: Multi-attribute decision-making (MADM), generalized hesitant fuzzy numbers (GHFNs), EDAS method, MEREC method, energy project selection

DOI: 10.3233/JIFS-230105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2763-2779, 2023

Authors: Chen, Mengxing | Dou, Jun | Fan, Yali | Song, Yan

Article Type: Research Article

Abstract:  Self-training semi-supervised classification has grown in popularity as a research topic. However, when faced with several challenges including outliers, imbalanced class, and incomplete data in reality, the traditional self-training semi-supervised methods might adversely damage the classification accuracy. In this research, we develop a two-step robust semi-supervised self-training classification algorithm that works with imbalanced and incomplete data. The proposed method varies from traditional self-training semi-supervised methods in three major ways: (1) The method in this paper does not necessitate the balance and complete assumption in traditional semi-supervised self-training methods, since it can complete and rebalance the dataset simultaneously. (2) This method …is compatible with many classifiers, so it can handle multi-classification and non-linear classification cases. (3) The classifier in this paper is resistant to outliers during semi-supervised classification. Furthermore, several numerical simulations were performed in this research to illustrate the quality of our method to synthesized data, as well as multiple experiments to demonstrate our method superior classification performance on various real datasets. Show more

Keywords: Semi-supervised classification, robust classification, fuzzy information decomposition, imbalanced class, incomplete data

DOI: 10.3233/JIFS-230658

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2781-2797, 2023

Authors: Hu, Guanghua

Article Type: Research Article

Abstract: Classroom teaching is an important link related to the quality of teaching and talent cultivation. In the implementation of classroom teaching, we should fully attach importance to the main position of students, the role of educational technology and information technology in teaching activities, make use of the latest educational ideas and educational concepts, and combine the actual situation of college English teaching and college English teaching in China, and attach importance to foreign language teaching theories and practices at home and abroad, Establish monitoring indicators and monitoring system for college English teaching quality. Under the guidance of effective monitoring indicators, …teachers’ teaching concepts can be updated and improved in real time to achieve better teaching results. At the same time, the quality assurance and monitoring system of college English teaching can be continuously improved to make it more perfect. The English classroom teaching quality evaluation could be deemed as a classic multiple attribute group decision making (MAGDM) problem. Spherical fuzzy sets (SFSs) can excavate the uncertainty and fuzziness in MAGDM more effectively and deeply. This article we first present a novel score function to compare spherical fuzzy numbers (SFNs) more directly and efficiently. Then, on basis of evaluation based on distance from average solution (EDAS), a novel spherical fuzzy EDAS (SF-EDAS) method is built for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the MEthod based on the Removal Effects of Criteria (MEREC) is extended to spherical fuzzy environment (SFE) to reasonably acquire the attribute weights. Finally, SF-EDAS approach is used for English classroom teaching quality evaluation to prove practicability of the developed method and compare SF-EDAS method with existing methods to further demonstrate its legitimacy and superiority. Show more

Keywords: Multiple attribute group decision making, spherical fuzzy sets, EDAS, MEREC, English classroom teaching quality evaluation

DOI: 10.3233/JIFS-230962

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2799-2811, 2023

Authors: Du, Kanghua | Du, Yuming

Article Type: Research Article

Abstract: At present, there is generally little research on intangible assets of sports events, and most of the research content focuses on general research on the meaning and content of intangible assets of sports events, the operation status of regional sports intangible assets, and development strategies of sports intangible assets. From the perspective of research results, only attention has been paid to the research on process management and process control of intangible assets of sports events, However, there is a lack of systematic research on the performance evaluation of intangible assets operation and management. It is necessary to build a scientific …and reasonable performance evaluation system for intangible assets operation and management of sports events based on the content of intangible assets in sports events. The performance evaluation of intangible assets operation and management in sports events is multiple attribute decision making (MADM). Recently, the TODIM and VIKOR method has been used to cope with MADM issues. The double-valued neutrosophic sets (DVNSs) are used as a tool for characterizing uncertain information during the performance evaluation of intangible assets operation and management in sports events. In this manuscript, the double-valued neutrosophic number TODIM-VIKOR (DVNN-TODIM-VIKOR) method is built to solve the MADM under DVNSs. In the end, a numerical case study for performance evaluation of intangible assets operation and management in sports events is given to validate the proposed method. Show more

Keywords: Multiple attribute decision making (MADM), double-valued neutrosophic sets (DVNSs), TODIM, VIKOR, performance evaluation, intangible assets operation and management

DOI: 10.3233/JIFS-231467

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2813-2822, 2023

Authors: Han, Yongguang | Xu, Xinrui

Article Type: Research Article

Abstract: As an important way to cultivate talents, school-enterprise cooperation is highly praised by the educational and economic circles of all countries. Vocational education spans enterprises and schools, and is an education of cross-border innovation. Vocational schools should make the people they cultivate meet the needs of the market and enterprises, and carry out in-depth school-enterprise cooperation is one of the effective ways. The school-enterprise cooperation between vocational colleges and enterprises makes vocational education more targeted and practical, and plays an important role in the training of skilled talents. It is not only an inevitable requirement for enterprise development and participation …in competition, but also an important direction of China’s vocational education reform, and is the fundamental outlet for the development of China’s vocational education. The performance evaluation of school-enterprise cooperation in vocational colleges is classical multiple-attribute group decision-making (MAGDM) issues. Recently, the TODIM and VIKOR method has been used to solve MAGDM issues. The 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs) are used as a tool for characterizing uncertain information during the performance evaluation of school-enterprise cooperation in vocational colleges. In this manuscript, we design the 2-tuple linguistic Pythagorean fuzzy TODIM-VIKOR(2TLPF-TODIM-VIKOR) method to solve the MAGDM under 2TLPFSs. In the end, a numerical case study for performance evaluation of school-enterprise cooperation in vocational colleges is given to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic Pythagorean fuzzy sets (2TLPFNs), TODIM-VIKOR method, performance evaluation, school-enterprise cooperation

DOI: 10.3233/JIFS-231575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2823-2834, 2023

Authors: Wang, Rong | Rong, Xia

Article Type: Research Article

Abstract: Mental health education (MHE) is an indispensable and fundamental content for schools to cultivate high-quality, high-standard, and high-quality talents, which has attracted sufficient attention and widespread attention in the educational community. Due to the impact of various factors such as society, family, and oneself, the overall psychological quality of Chinese college students is poor, their thinking is relatively childish, they cannot calmly face new problems and situations in life, and they feel vague and confused about life, ideals, and goals Confusion and other psychological barriers are becoming more and more common. If effective psychological crisis intervention is not available, it …will seriously affect their learning and personality development, and is not conducive to talent growth. In recent years, Huaibei Vocational and Technical College has placed psychological quality education at the top of its list, providing good psychological quality training for college students, improving their psychological endurance, eliminating negative attitudes and psychological barriers, and promoting their mental health and sustainable development. The quality evaluation of MHE of college students is a classical multiple attribute decision making (MADM) problems. Recently, the TODIM and VIKOR method has been used to cope with MAGDM issues. The hesitant triangular fuzzy sets (HTFSs) are used as a tool for characterizing uncertain information during the quality evaluation of MHE of college students. In this manuscript, the hesitant triangular fuzzy TODIM-VIKOR (HTF-TODIM-VIKOR) method is built to solve the MADM under HTFSs. In the end, a numerical case study for quality evaluation of MHE of college students is given to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), hesitant triangular fuzzy sets (HTFSs), TODIM, VIKOR, quality evaluation of MHE

DOI: 10.3233/JIFS-231719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2835-2845, 2023

Authors: Madhu, D. | Vasuhi, S.

Article Type: Research Article

Abstract: The role of communication technologies has become increasingly vital in various fields such as industrial communication, surveillance and monitoring, healthcare, and data communication, leading to a surge in demand for these technologies in recent years. In this research paper, the LEMARS model is proposed as a novel and robust lightweight encryption assisted Man-in-The-Middle (MITM) attack-resilient steganography model for secure satellite imagery services. The proposed solution combines lightweight encryption and attack-resilient embedding to achieve higher attack-resilience with low computational cost and optimal reconstruction quality. The use of Feistel architecture-driven substitution and permutation-based block-cipher encryption and heuristic-driven pixel adjustment method for MITM-Adaptive …B-Channel Embedding results in higher imperceptibility and superior attack-resilience under uncertain channels. The strategic amalgamation of FSPN-BCE, L2D-IWT, and HD-OMABE enabled attack-resilient steganography for secure satellite communication. The performance of LEMARS has been evaluated using Peak Signal-to-Noise Ratio (PSNR), entropy, Non-Parametric Change Rate (NPCR), Unified Average Change Intensity (UACI), and regular and singular block pattern changes. LEMARS achieved a PSNR value of 58.02 dB, an entropy value of 6.15 dB, an NPCR value of 99.84%, a UACI value of 33.70, and exhibited minimal block pattern changes. These results indicate that the proposed model achieved higher attack-resilience with low computational cost and optimal reconstruction quality. The research aimed to improve encryption and embedding methods to achieve an MITM-attack resilient embedding solution for secure satellite imagery services. The proposed LEMARS model exhibited superiority over other state-of-the-art methods, affirming the robustness of the proposed LEMARS model for real-time multimedia data security purposes, including remote sensing, satellite imagery services, telemedicine, and EHR. In conclusion, the LEMARS model offers an optimal solution for multimedia data security, combining lightweight encryption and attack-resilient embedding to achieve higher attack-resilience with low computational cost and optimal reconstruction quality. Show more

Keywords: Steganography, lightweight encryption, heuristic-driven pixel adjustment, MITMattack resilient cipher embedding

DOI: 10.3233/JIFS-223329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2847-2869, 2023

Authors: Rong, Xianghong

Article Type: Research Article

Abstract: At the stage of college education, English is a very important subject, which is directly related to the enhancement of college students’ English literacy and the improvement of their English level. From the perspective of mobile internet, the construction and application of college English intelligent classroom teaching mode can promote the reform and improvement of traditional English teaching mode. By giving full play to students’ subjectivity, English teaching can be carried out in a modern teaching classroom environment, which is conducive to strengthening students’ comprehensive English ability, which is also very important and meaningful for building an efficient college English …classroom. The college English smart classroom (CESC) teaching quality evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy set (TPFS) and investigate the multiple attribute decision making problems under it. Accordingly, we take advantage of traditional Bonferroni mean (BM) operator to develop some triangular Pythagorean fuzzy information operators: triangular Pythagorean fuzzy Bonferroni mean (TPFBM) operator and triangular Pythagorean fuzzy weighted Bonferroni mean (TPFWBM) operator. The dominating natures of these operators are researched. Accordingly, the TPFWBM operator is built for triangular Pythagorean fuzzy MADM. Ultimately, a practical example for CESC teaching quality evaluation is took advantage of to validate the developed approach. Show more

Keywords: Multiple attribute decision making (MADM), Triangular Pythagorean fuzzy set, Bonferroni mean (BM) operator, triangular Pythagorean fuzzy weighted Bonferroni mean (TPFWBM) operator, CESC, teaching quality evaluation

DOI: 10.3233/JIFS-232002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2871-2886, 2023

Authors: Zhang, Jiarui

Article Type: Research Article

Abstract: The design of the emergency communication vehicle is centered around the overall layout, which is characterized by the fact that the vehicle chassis is mostly purchased parts and is equipped with an insulated and insulated box, mainly used for instrument operation and equipment placement. The emergency communication vehicle cabin environment includes: cabin seats; seat cover; control cabinet, including operation keyboard, display screen, etc; The ceiling and carpet that make up the interior space of the car; Storage box for storage equipment; tool cabinet; cabin control system facilities; Fire prevention and extinguishing facilities in the cabin; various handrails, coat racks, short …ladders, lighting facilities, etc. inside the car. The design of emergency communication vehicle interior environment involves multiple disciplines, and the selection of design schemes requires scientific and reasonable evaluation methods to select reasonable schemes for optimization design. The evaluation of emergency communication vehicle cabin’s internal environment design is classical multiple attribute decision making (MADM) problems. In this article, based on bidirectional projection and grey relational projection measure, we shall propose some projection models with q-rung orthopair fuzzy sets (q-ROFSs). First of all, the definition of q-ROFSs is introduced. Furthermore, some projection models with q-ROFSs are proposed based on the bidirectional projection and grey relational projection model. Based on developed weighted projection models, the MADM model is established and all computing steps are simply depicted. Finally, a numerical example for evaluation of emergency communication vehicle cabin’s internal environment design is given to illustrate this new model and some comparisons are conducted to illustrate advantages of the new built method. Show more

Keywords: MADM problems, q-rung orthopair fuzzy sets (q-ROFSs), projection model, emergency communication vehicle, cabin’s internal environment design

DOI: 10.3233/JIFS-232198

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2887-2898, 2023

Authors: Oh, Ju-Mok

Article Type: Research Article

Abstract: In this paper, we present the notion of commutative coquantale frames as logical relational systems. We introduce an approach to study these frames through the utilization of distance functions in place of the commonly used fuzzy partially orders. We show that a commutative coquantale frame can be regarded as an algebraic structure that induces a commutative coquantale, while a commutative coquantale with q -distance function leads to the development of a commutative coquantale frame. Moreover, we provide several examples pertaining to these notions.

Keywords: Commutative coquantales, commutative coquantale frames, distance functions, Alexandrov topologies

DOI: 10.3233/JIFS-223367

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2899-2917, 2023

Authors: Li, Zhao | Sun, Weiyi | Li, Dongze

Article Type: Research Article

Abstract: With the implementation of China’s “two-carbon” target strategy, the status of renewable energy in the power system is gradually improving. In order to improve the carbon emission control level of power grid, it is necessary to predict the peak carbon emission of power grid. A prediction method of grid carbon emission peak based on energy elasticity coefficient is proposed. A time series model of grid carbon emission samples was constructed, and a combination of combinatorial sorting and machine learning was used to reconstruct the time series of grid carbon emission peaks. Based on the results of time series reconstruction, feature …extraction and classification training of grid carbon emission peak are carried out, and the measurement error correction of grid carbon emission under the constraint of energy consumption elasticity is realized. Combined with the association rule mining method, the fusion processing of grid carbon emission peak samples under the elastic constraint of energy consumption is realized. Through characteristic detection and fusion processing of energy consumption elasticity coefficient, the reconstituted carbon emission samples of power grid constrained by energy consumption elasticity were reconstructed, and the peak carbon emission of power grid was predicted. The simulation results show that this method has high accuracy and convergence in predicting the peak carbon emission of power grid, and improves the error correction ability in the prediction process. Show more

Keywords: Energy consumption, elastic coefficient, power grid, carbon emissions, peak value prediction, association rule mining

DOI: 10.3233/JIFS-224599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2919-2930, 2023

Authors: Lei, Chang

Article Type: Research Article

Abstract: Cloud computing has emerged as one of the most promising technologies for meeting rising computing needs. However, high-performance computing systems are more likely to fail due to the proliferation of components and servers. If a sub-system fails, the entire system may not be functional. In this regard, the occurrence of faults is tolerable using an efficient fault-tolerant method. Since cloud computing involves storing data on a remote network, system failures and congestion are the most common causes of faults. The paper presents a new approach to adopting a fault-tolerant mechanism that adaptively monitors health to detect faults, handles faults using …a migration technique, and avoids network congestion. With the advantage of the Ant Colony Optimization (ACO) algorithm and active clustering, the load is distributed evenly in data centers. Simulation results indicate that our algorithm outperforms previous algorithms regarding total execution time and imbalance degree up to 10% and 17%, respectively. Show more

Keywords: Cloud computing, fault tolerance, load balancing, energy efficiency

DOI: 10.3233/JIFS-230102

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2931-2948, 2023

Authors: Long, Xiaoqing | Gao, Fei

Article Type: Research Article

Abstract:  Cooperative attack with unmanned aerial vehicles (UAVs) plays a critical role in modern military warfare. To achieve multi-swarm cooperative attack with obstacle avoidance of formation, this paper proposes a cooperative control strategy that integrates flight control and autonomous marshaling. Firstly, an improved dynamics model with virtual leader-following mode is constructed to achieve obstacle avoidance of the formation. And an improved interference fluid dynamic system (IIFDS) is applied to improve path selectivity during multi-swarm attack. Secondly, a two-layer attack framework based on distributed swarm coordinated trajectory tracking with heading angle constraints is designed to achieve autonomous clustering of the UAVs and …target striking. Finally, the proposed improved dynamics model is compared with the particle swarm optimization (PSO) algorithm and artificial potential field (APF) method in terms of obstacle avoidance of formation to demonstrate its superiority, which can obtain better benefits. Furthermore, two simulations of multi-swarm cooperative attack are conducted to validate the effectiveness of the control strategy. The proposed method expands the application of UAVs attack with obstacle avoidance of formation and provides a valuable reference for modern military operations. Show more

Keywords: Multi-swarm cooperative attack, autonomous marshalling, coordinated trajectory tracking, IIFDS

DOI: 10.3233/JIFS-231180

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2949-2965, 2023

Authors: Wang, Chu | Wang, Jian

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-231449

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2967-2977, 2023

Authors: Kala, R. | Deepa, P.

Article Type: Research Article

Abstract: Brain tumor is an anomalous growth of brain cells. Segmentation of brain tumors is currently the most important surgical and pharmaceutical procedure. However, manually segmenting the brain tumor is a challenging task due to the complex structure of brain. In recent years, artificial intelligence techniques with the fuzzy logic have shown better results in the field of medicine. In this work, a novel deep learning classification network with fuzzy hexagonal membership function (DLC-FHMF) model has been proposed for accurately segmenting brain tumors. The different MRI modalities namely T1, T1-c, T2 and Flair images are preprocessed using a fuzzy hexagonal trilateral …and median filter to eliminate the Rician noise. Afterwards, the DLC-FHMF model is used for segmenting the tumor portion by using the multimodal composition of MRI as input. The fuzzy weights are determined with hexagonal membership functions and convoluted with the corresponding MRI images. The quantitative examination is carried out using the performance metrics namely accuracy, specificity, precision, sensitivity, incorrect segmentation, under-segmentation, and over-segmentation. In addition to the above metrics, the pre-processing metrics include PSNR, RMSE, and SSIM. The experimental fallout portrayals that the proposed DLC-FHMF approach attains a better accuracy range of 99% for detecting brain tumors using the BRATS 2013 dataset. The proposed DLC-FHMF model improves the overall accuracy by 15.1%, 11.1%, 3.0%, 21.2% and 0.5% better than ANN, SVM, NB, DNN and DAE respectively. Show more

Keywords: Brain tumor, magnetic resonance image, fuzzy logic, deep learning, segmentation

DOI: 10.3233/JIFS-221990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2979-2992, 2023

Authors: Zhang, Yongliang | Lu, Yang | Zhu, Wuqiang | Wei, Xing | Wei, Zhen

Article Type: Research Article

Abstract: Deep learning has dominated the research field of traffic sign detection, but the traffic sign detection algorithms based on deep learning have difficulty in solving the two tasks of localization and classification simultaneously when performing traffic sign detection on realistic and complex traffic scene images, and the images or the types of traffic signs provided by the public dataset used by the relevant algorithm cannot meet the situations encountered in realistic traffic scenes.To solve the above problems, this paper creates a new road traffic sign dataset, and based on the YOLOv4 algorithm, designs a multi-size feature extraction module and an …enhanced feature fusion module to improve the algorithm’s ability to locate and classify traffic signs simultaneously, in view of the complexity of realistic traffic scene images and the large variation of traffic sign sizes in the images. The experimental results on the newly created dataset show that the improved algorithm achieves 83.63% mean Average Precision (mAP), which is higher than several major object detection algorithms based on deep learning for the same type of task at present. The newly created dataset in this paper is publicly available at https://github.com/zhang1018/Traffic-sign-dataset-for-public . Show more

Keywords: Traffic sign detection and recognition, traffic sign datasets, autonomous driving, convolutional neural networks, intelligent traffic system

DOI: 10.3233/JIFS-210838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2993-3004, 2023

Authors: Jayashree, P. | Laila, K. | Amuthan, Aara

Article Type: Research Article

Abstract: The large flux of online products in today’s world makes business reviews a valuable source for consumers for making sound decisions before making online purchases. Reviews are useful for readers in learning more about the product and gauge its quality. Fake reviews and reviewers form the bulk of the review corpus, making review spamming an open research challenge. These spam reviews require detection to nullify their contribution to product recommendations. In the past, researchers and communities have taken spam detection problems as a matter of serious concern. Yet, for all that, there is space for the performance of exploration on …large-scale complex datasets. The work contributes towards robust feature selection with derived features that provide more details on malicious reviews and spammers. Ensemble and other standard machine learning techniques are trained and evaluated over optimal feature sets. In addition, the Metapath-based Graph Convolution Network (M-GCN) framework is proposed, which is an implicit knowledge extraction method to automatically capture the complex semantic meaning of reviews from the heterogeneous network. It makes analysis of triplet (users, reviews, and products) relationships in e-commerce sites through examination of Top-n feature sets in a mutually reinforcing manner. The proposed model is demonstrated on Yelp and Amazon benchmark datasets for evaluation of efficacy and it is shown outperforming state-of-the-art techniques with and without graph-utilization, providing an accuracy of 96% in the prediction task. Show more

Keywords: Spam review detection, feature sets derivation, machine learning, Metapath, graph convolution network

DOI: 10.3233/JIFS-223136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3005-3023, 2023

Authors: Jadhav, Ranjana S. | Dhore, Manikrao Laxmanrao

Article Type: Research Article

Abstract: Transliteration is phonetically translating a language’s words into an international or non-native screenplay. The machine translation process now plays an essential role in scholarly research. The most crucial complement criterion of the English translation system is preserving the phonetic qualities of the language specification after English translation in the chosen language. However, a suitable bilingual text corpus is necessary for statistical models to attain improved transliteration accuracy. Marathi-to-English direct machine translation is done through a cross-language information retrieval system using the CNN classifier model in this proposed research. The proposed method considers a sequence labelling issue brought on by the …split transliteration units used in the process. All half-consonant clusters in the Devanagari script are effectively mapped as half-consonant “a” s and labelled using the Modified Intermediate Phonetic Code (MIPC). After generating the phonetic units for each feature in the base and aim languages, the weight is assigned to a phonetic unit in both languages, and individual phonetic unit probabilities are computed. If the probability is zero, then segments are established and recalculated for each segment based on the target phonetic unit location in the word. Therefore, the proposed approach classifies the required phonetic unit with a high accuracy rate. Show more

Keywords: Machine Transliteration, phonetic unit, Devanagari, syllabification, N-grams, CNN classifier model

DOI: 10.3233/JIFS-223591

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3025-3037, 2023

Authors: Tang, Bingsong | Li, Nan

Article Type: Research Article

Abstract: Disputes are inevitable in construction. Tremendous losses caused by dispute are so amazing that professionals try to figure out how to manage it. It is a practical way to study the dispute problem from the perspective of governance theory. In this study, the paper intends to investigate the characteristics of contractual governance for disputes. Based on governance theory, the framework of contractual governance for dispute is constituted of governance structure (GS) and governance mechanism (GM). The flexibility of GS and GMs are all explored so as to better draft the contracts. By a multiple cases study, a new conceptual model …instructing governance picture for construction disputes was proposed which was mainly inspired from literature. The cases study shows that the GS determination is rigidly drafted and executed while the mostly GMs are flexibly designed. The rigid GS has an advantage to a stable foundation and the flexible GMs are apt to coordinate the disputes. Show more

Keywords: Contract flexibility, dispute resolution, governance theory, rigid governance, flexible governance

DOI: 10.3233/JIFS-224227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3039-3051, 2023

Authors: Hu, Shan | Jiang, Weitao | Rong, Lingda | Hu, Shixuan | Zhong, Xiaoying | Wei, Yaxin

Article Type: Research Article

Abstract: Accessible products play an essential role in the lives of people with disabilities. This paper aims to identify key user satisfaction with accessible products factors affecting the use of accessible products by people with disabilities that influence user satisfaction. The extended model incorporates the essential elements of the TAM, TPB, and PR models and user satisfaction as an external variable. Data were collected from 339 users of accessible products. Structural equation modeling was used to identify significant variables in this study. SEM considered “behavioral intention” to be the most important among them. This study generated design strategies based on significant …factors analyzed in the findings and validated the design cases using the PSSUQ questionnaire, which showed that users had better user satisfaction when using accessible products with the new design strategies. Show more

Keywords: Accessibility, user satisfaction, structural equation modeling (SEM), technology acceptance model (TAM), theory of planned behavior (TPB), theory of perceived risk (PR)

DOI: 10.3233/JIFS-231121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3053-3075, 2023

Authors: Liu, Yu

Article Type: Research Article

Abstract: The “One Belt and One Road” is a major strategic deployment proposed by Chinese President Xi Jinping in 2013, and it is important to study the construction of green financial system under the “One Belt and One Road” initiative, especially the quality assessment of green finance to promote high-quality economic development along the route. At the same time, green finance and green “Belt and Road” have become a hot academic topic in the world. In this study, firstly, on the basis of the existing research on green finance, focusing on the evaluation of economic quality of green finance, we innovatively …draw on the quality function deployment theory in marketing to logically transform our research ideas and propose a combined comprehensive evaluation method based on the hierarchical analysis (AHP) and entropy method in fuzzy mathematical theory, which makes up for the traditional single fuzzy evaluation method’s influence on the evaluation results. This method makes up for the shortcomings of the traditional single fuzzy evaluation method to evaluate the results of subjective or objective weighting results. In this study, we apply this method to the assessment of the quality of green finance development in “One Belt, One Road”, and it is important that we construct a system of “One Belt, One Road” green finance quality assessment indicators, including one primary indicator, four secondary indicators and 12 tertiary indicators. It is worth noting that our indicator system is different from the traditional quality system of financial quality assessment in that we take green factors into account in the construction of the tertiary indicators, and then use this assessment method to calculate and rank the weights (importance) of the 12 tertiary indicators, taking the actual situation in China as an example. More importantly, our study not only extends the academic research results of economic quality evaluation, but also combines quantitative research with qualitative analysis to propose three targeted countermeasures for the development of green finance in the countries along the Belt and Road. This study can also provide theoretical support for the quality assessment of green finance in countries along the Belt and Road, and promote the high-quality development of green finance in countries along the Belt and Road. Show more

Keywords: Green finance, belt and road, financial quality assessment, quality function deployment theory, hierarchical analysis, entropy method

DOI: 10.3233/JIFS-223257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3077-3095, 2023

Authors: Jeevitha, Kannan | Garg, Harish | Vimala, Jayakumar | Aljuaid, Hanan | Abdel-Aty, Abdel-Haleem

Article Type: Research Article

Abstract: Digital transformation is the significant phenomena in contemporary global environment. All the conventional fuzzy sets are extended by the Linear Diophantine Fuzzy Set (LDFS). LDFS is the most viable adaptable method for decision makers to choose their grade values as it includes reference parameters. The foremost vision is to promote the resilient integration of Linear Diophantine Multi-Fuzzy Set (LDMFS) as a model for constructing decisions in order to identify the appropriate standards for digital transformation. Aggregation Operators are crucial in fuzzy systems for fusing information. To aggregate the LDMF, a number of operators have been devised, such as the Linear …Diophantine Multi-Fuzzy Weighted Geometric Operator (LDMFWGO), Linear Diophantine Multi-Fuzzy Ordered Weighted Geometric Operator (LDMFOWGO), Linear Diophantine Multi-Fuzzy Weighted Averaging Operator (LDMFWGO) and Linear Diophantine Multi-Fuzzy Ordered Weighted Averaging Operator (LDMFOWAO). By integrating preferred aggregating operations, a novel method for MCDM with LDMF data is studied. The best option from the current alternatives can be determined using these operators. Moreover, a comparison of LDMF operators is made. Additionally, the idea of a scoring function for LDF is designed to examine the rank of viable alternaties. Additionally, a novel approach to solving LDMF sets is suggested. The annals on organisational digital transformation is presented as the final section to test the supremacy of the theory. Accurate rankings for digital transformation are provided by the outcome. To exhibit the robustness of the MCDM methodology, a prompt comparative analysis is established between the suggested concept and the currently used approaches. Show more

Keywords: LDMFWG operator, LDMFWA operator, LDMFWA operator, LDMFOWA operator, LDMFOWG operator, score function, MCDM problem

DOI: 10.3233/JIFS-223844

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3097-3107, 2023

Authors: Xiao, Huimin | Yang, Peng | Ma, Xifeng | Wei, Meng

Article Type: Research Article

Abstract: A decision matrix is typically used to express hesitant information when solving multi-attribute decision problems in an uncertain environment. To further investigate the decision problem, this paper takes the property of matrix rank as the starting point, introduces it into the hesitant fuzzy theory, presents the concept of the rank of the hesitant fuzzy decision matrix and discusses the related properties, and then studies the hesitant fuzzy linear relation, obtaining the attribute reduction method based on the hesitant fuzzy linear relation and applying it to the multi-attribute decision making. It adds to the theoretical understanding of the hesitant fuzzy decision …matrix. The aggregation operator first transforms the hesitant fuzzy information into a comprehensive decision matrix, and the row echelon transformation determines the rank of the matrix. Second, the hesitant fuzzy linear relationship is obtained using the rank property. A new hesitant fuzzy matrix is obtained after attribute reduction based on the hesitant fuzzy linear relationship, and the alternatives are sorted using the TOPSIS method. Finally, the effectiveness and superiority of the proposed method are demonstrated through a comparison of actual case analysis and existing methods, and the expected research purpose is met. Show more

Keywords: Hesitant fuzzy set, hesitant fuzzy decision matrix, rank, attribute reduction

DOI: 10.3233/JIFS-224231

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3109-3121, 2023

Authors: Hu, Nanyan | Li, Xuexue | Li, Yufei | Ye, Yicheng | Wu, Menglong

Article Type: Research Article

Abstract: In order to address the issues of emergency decision-making and optimization (EDMO) of fire accidents in colleges, this paper proposes the EDMO way to take into account the synergies among divergent divisions replacements and the psychology of decision makers (DMs) on the basis of the best-worst method (BWM) and VIKOR within an interval 2-tuple linguistic (ITL) surroundings and cumulative prospect theory (CPT). First, DMs use the ITL to evaluate the degree of synergy among replacements from divergent divisions, the language information can be processed accurately and the information loss can be avoided. Then, the multi-alternative amalgamations consisted of divergent divisions …replacements are built. On the grounds of the DMs’ value assignment, the collaborative decision matrix of multi-alternative amalgamations can be gained. And the optimal weight of the evaluation standards can be computed based on the ITL-BWM method. The CPT is extended into VIKOR to think about the effect of the DMs’ psychological behavior on the decision result. Furthermore, the positive and negative utility matrices can be computed through the value function of CPT. On the grounds of the positive and negative utility matrices, the distance from the utility value of multi-alternative amalgamations to the desired right solution of positive and negative utility can be obtained, and the cumulative foreground value function is used to replace the distance among each replacement to the positive and negative right desired solutions, it can avoid ignoring the effect of the correlations among different attributes on the outcome. Furthermore, the model is applied to the example and an analysis of the sensitivity of the factors of the decision-making mechanism coefficient and the weights of synergistic indicators is carried out to prove the validity and stability of the model. Show more

Keywords: Emergency decision-making and optimization, college fire, interval 2-tuple linguistic, best-worst method, VIKOR

DOI: 10.3233/JIFS-224322

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3123-3136, 2023

Authors: Josephin Shermila, P. | Ahilan, A. | Jasmine Gnana Malar, A. | Jothin, R.

Article Type: Research Article

Abstract: Foods are very essential for living beings for providing energy, development and preserve their existence. It plays a vital role in promoting health and preventing illness. Nowadays, many people are suffered from obesity, they tend to maintain their body weight by consuming a sufficient number of calories in their routine life. In this research, a novel Modified Deep Learning-based Food Item Classification (MDEEPFIC) approach has been proposed to categorize the different food items from the dataset with their calorie values. Initially, the images are processed using the sigmoid stretching method to enhance the image quality and remove the noises. Consequently, …the pre-processed images are segmented using Improved Watershed Segmentation (IWS2) algorithm. Recurrent Neural Network (RNN) is used to extract features like shape, size, textures, and color. The extracted features are then normalized using the modified dragonfly technique for same food calorie calculation. Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify food products based on these pertinent aspects. Finally, using food area volume and calorie and nutrition measures based on mass value, the calorie value of the categorized food item is calculated. The efficiency of the proposed method was calculated in terms of specificity, precision, accuracy, and recall F-measure. The proposed method improves the overall accuracy of 4.99%, 8.72%, and 10.4% better than existing Deep Convolution Neural Network (DCNN), Faster Recurrent convolution neural network (FRCNN), Local Variation Segmentation based Support Vector Machine (LSV-SVM) method respectively. Show more

Keywords: Food, bidirectional long short-term memory, improved watershed, recurrent neural network, dragon fly algorithm

DOI: 10.3233/JIFS-230193

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3137-3148, 2023

Authors: Fang, Chang | Chen, Yu | Wang, Yi | Wang, Weizhong | Yu, Qianping

Article Type: Research Article

Abstract: The Fine-Kinney (F-K) model has been broadly employed for evaluating and ranking risk in various fields. The risk scoring information expression and priority ranking are two significant operations for its application. Numerous approaches have been extended to the two operations to improve the performance of conventional Fine-Kinney for risk analysis. Nevertheless, current literature on the F-K framework seldom considers the collective and individual risk attitudes in ranking potential hazards, especially with Fermatean fuzzy-based -risk scoring information. This paper generates a new ranking approach for risk prioritization in F-K to fulfill this gap by integrating the Fermatean fuzzy sets with the …GLDS (gained and lost dominance score) method. First, the Fermatean fuzzy sets-based risk scale is introduced to acquire risk scores. Then, a new collective risk scoring matrix establishment approach based on Fermatean fuzzy Bonferroni mean (BM) operator is built for considering the interactive effects between experts. Next, an extended Fermatean fuzzy GLDS method with CRITIC (Criteria Importance Through Inter-criteria Correlation)is proposed to rank the potential hazards, in which the Fermatean fuzzy CRITIC method is adopted to determine the weights. Especially, this developed weighting method can depict the inter-correlation among risk parameters. Finally, this paper presents a risk evaluation case of professional hazards for construction operations to display the application and advantages of this improved hybrid risk ranking model in the F-K framework. The result reveals that the enhanced framework can effectively rank potential hazards with complex risk information. Show more

Keywords: Fine-Kinney, fermatean fuzzy set, risk prioritization, GLDS approach, CRITIC

DOI: 10.3233/JIFS-230423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3149-3163, 2023

Authors: Liang, Bingjie | Bi, Jun | Ran, Bin

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-231253

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3165-3179, 2023

Authors: Wang, Ji | Xu, Chunming

Article Type: Research Article

Abstract: This paper explores the issue of patent race in which 5G enterprises invest the patent package in the field of new spectrum to research and develop some core technologies. Based on the comprehensive interaction of expected profit, investment risk, and withdrawal cost, this paper aims to achieve the two objectives of maximizing the profit and minimizing the investment risk for a lagging firm. By numerical experiment analysis, the optimal portfolio strategy of a lagging firm is obtained, followed by the phenomenon in patent race of investment disinvestment. The result shows that the lagging firm can focus on certain self-interested technologies …to realize the leap of key technologies in research and development (R&D) under the high degree selection condition independently. In addition, different initial investment shares affect the portfolio strategy. Show more

Keywords: Patent race, lagging firms, new spectrum, portfolio strategy, R&D

DOI: 10.3233/JIFS-223463

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3181-3200, 2023

Authors: Angel Sajani, J. | Ahilan, A.

Article Type: Research Article

Abstract: Brain diseases is a wide range of disorders and diseases that affect the brain. They can change a person’s behavior, personality, and capacity for thought and function. CT images are more essential than conventional clinical tests for detecting brain hemorrhage accurately. MRI images of the brain can reveal even small abnormalities in the cranial region, helping providers diagnose a wide variety of conditions, ranging from brain stroke, cancers, aneurysms, and Alzheimer’s. This paper proposes a novel Fused dual neural (FDN) network for detecting brain cancer, stroke, aneurysms, and Alzheimer using Brain Medical Images (BMI) the combination of MRI and CT. …In BMI, the adaptive bilateral filter reduces noise artifacts. Google Net is used to extract features from pre-processed MRI images, and Mobile Net is used to extract features from pre-processed CT images. The integration of extracted features from Google Net and Mobile Net is fused by the Wrapper method. Finally, the Deep Belief Network is employed for classifying brain stroke, cancer, Aneurysm, and Alzheimer’s diseases using BMI images. The quantitative analysis of the suggested method is determined using the parameters like specificity, recall, precision, F1 score, and accuracy. The proposed FDN achieves a high classification accuracy rate of 98.19%, 97.68%, 94.31%, and 93.82% for detecting stroke, cancer, Aneurysm, and Alzheimer respectively. The proposed FDN model improves the overall accuracy by 5.35%, 3.14%, 9.48%, 5.33%, and 0.55% better than Faster R-CNN, CNN, Inception-V3, DCNN, and Fine-tuning Network respectively. Show more

Keywords: Brain disease, classification, Google Net, mobile net, deep belief network, deep learning

DOI: 10.3233/JIFS-230090

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3201-3211, 2023

Authors: Ren, Weijia | Du, Yuhong | Sun, Ronglu | Du, Yuqin | Lü, Mubo

Article Type: Research Article

Abstract: To improve the accuracy of decision results in complex fuzzy environments, complex cubic fuzzy sets are studied, which can not only measure the periodicity of decision-making data, but also use interval values and single values to act together on the data. However, the fuzzy sets do not provide a reasonable explanation for some special cases of input arguments. Thus, the power average operator is used to eliminate the influence of extreme input arguments on decision results, and the Maclaurin symmetric mean operator considers the correlation between inputs in this paper. Firstly, we define the operation rules, distance measures, evaluation index …function, and evaluation criteria in a complex cubic q -rung orthopair fuzzy environment. Then, some aggregation operators are proposed to aggregate complex cubic q -rung orthopair fuzzy numbers, and their desirable properties and some special cases are discussed. Next, we use the subjective and objective fusion method to determine the weight of attributes. Further, a multi-attribute decision-making method is established by combining aggregation operator, evaluation function, and weight determination method. Finally, the proposed method is applied to a specific quality evaluation problem, and the effectiveness and practicability of the proposed method are illustrated by other methods and parameter analysis. Show more

Keywords: Complex cubic fuzzy set, aggregation operator, multi-attribute group decision-making, application

DOI: 10.3233/JIFS-230402

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3213-3231, 2023

Authors: Chen, Xu

Article Type: Research Article

Abstract: With the rapid development of China’s construction industry, the competition in the construction industry is becoming increasingly fierce. Enterprises need to continuously improve their competitiveness in the market. Some non-core businesses can be outsourced to professional contractors. At present, contractors have more and more influence on the operation and development of enterprises. Whether it is the construction period or the quality of the project, it will have a greater impact on the operation of the construction project. In the environment of increasingly fierce market competition and increasing project quality requirements, for the construction project contracting enterprises, in order to achieve …the goal of low cost and high quality, it is necessary to select the most suitable contractor on the basis of comprehensive consideration of multiple factors. The construction enterprise contractor selection is a classical multiple attribute group decision making (MAGDM) problem. In recent years, the MAGDM problem has become an important research field in modern decision science. This paper extends the EDAS method to the 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). On the basis of the original EDAS method, 2-tuple linguistic Pythagorean fuzzy EDAS (2TLPF-EDAS) is built for MAGDM. Finally, a case study for construction enterprise contractor selection and some comparative analysis with the other methods show that the new method proposed in this paper is effective, reasonable and accurate. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs), EDAS method, construction enterprise contractor selection

DOI: 10.3233/JIFS-231063

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3233-3245, 2023

Authors: Suba, M. | Susan, D.

Article Type: Research Article

Abstract: A key component of cognitive radio technology is spectrum sensing, which finds and accesses unused frequency bands to efficiently use the underutilized spectrum. A potential method for spectrum sensing called cyclostationary feature detection (CFD) uses the cyclostationary characteristics of signals to distinguish between the signal and noise. Artificial neural networks (ANNs) have been suggested in recent years as a method for CFD based spectrum detection, which increases detection accuracy and decreases complexity. However, the variable signal to noise ratio (SNR) and noise variance have an impact on the effectiveness of ANNs for CFD-based spectrum sensing. The effectiveness of ANNs for …CFD based spectrum sensing under different SNR and noise variance conditions is evaluated in this work for the determination of threshold value in a dynamic way. We look into how SNR and noise variance affect the precision of probability of detection (Pd) and system complexity. Out analysis show how well ANNs work for CFD based spectrum detection with dynamic threshold value in the presence of changing SNR and noise variation. The findings demonstrate that ANNs may still obtain high Pd values with low SNR and large noise variance while maintaining a modest level of system complexity. According to our research, for a variety of SNR and noise variance situations, ANNs may be a viable option for CFD based spectrum detection in cognitive radio (CR) networks. The proposed approach can significantly improve the detection accuracy and reduce the complexity of the system, thereby enhancing the overall performance of cognitive radio networks. Based on the proposed work, it is determined that MPSK modulation function well with additive white Gaussian noise (AWGN), Rayleigh, and Rician channels up to a lower SNR value of – 30 dB and MQAM supports a lower SNR value of up to – 20 dB. Show more

Keywords: Cyclostationary feature detection, ANN, varying SNR, noise variance, dynamic thresholding, probability of detection

DOI: 10.3233/JIFS-232610

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3247-3257, 2023

Authors: Wei, Lin

Article Type: Research Article

Abstract: Blended teaching, which combines the advantages of face-to-face teaching and online learning, has become an important breakthrough in the current higher education teaching reform and innovation, and the construction of a blended teaching quality evaluation in college English courses system is of great significance and value to ensure the sustainable development of blended teaching activities. The blended teaching quality evaluation in college English courses is regarded as the defined multiple attribute decision making (MADM). In this paper, the Combined Compromise Solution (CoCoSo) method is constructed for MADM under double-valued neutrosophic sets (DVNSs). Then, the double-valued neutrosophic numbers CoCoSo (DVNN-CoCoSo) method …is built for MADM. Finally, a practical numerical example for blended teaching quality evaluation in college English courses is supplied to show the DVNN-CoCoSo method. The main contributions of this constructed paper are: (1) This paper builds the novel MADM method based on CoCoSo decision methods under DVNSs, which extends the classification CoCoSo method. (2) The new MADM method for blended teaching quality evaluation in college English courses based on DVNN-CoCoSo is proposed. Show more

Keywords: MADM, double-valued neutrosophic sets (DVNSs), CoCoSo method, blended teaching quality evaluation

DOI: 10.3233/JIFS-224389

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3259-3266, 2023

Authors: Lei, Yingzi

Article Type: Research Article

Abstract: The goal of internationalization, the actual needs of the new generation of college students, and the innovation of information technology have led to a shift from teaching English for general purposes (EGP) to teaching English courses. The blended teaching effectiveness evaluation of English courses in universities in an output-oriented perspective is viewed as the multiple attribute decision making (MADM). In this paper, Taxonomy method is designed for MADM under interval neutrosophic sets (INSs). Then, the interval neutrosophic numbers Taxonomy (INN-Taxonomy) method is formed to cope with MADM problem. Finally, a numerical decision example for blended teaching effectiveness evaluation of English …courses in universities in an output-oriented perspective is given to demonstrate the INN-Taxonomy method. Show more

Keywords: MADM, interval neutrosophic sets (INSs), taxonomy method, blended teaching effectiveness evaluation

DOI: 10.3233/JIFS-224475

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3267-3277, 2023

Authors: Zhang, Xin | Feng, Tao | Zhang, Shao-Pu

Article Type: Research Article

Abstract: Rough set theory is a powerful tool for handling uncertainty and vagueness in various fields. The hesitant fuzzy rough set, as a generalization of rough sets, can solve more complex problems. However, existing hesitant fuzzy rough sets do not satisfy the inclusive property. To address this issue, a novel hesitant fuzzy rough set model based on dual score functions is proposed. Four generalized hesitant fuzzy rough sets and their discernibility matrices are also presented. Additionally, the lower approximation distribution reductions can be obtained by the discernibility matrix. Meanwhile, hypergraphs provide an accurate description of relationships between multiple objects and offer …a concise operational approach. Then it is discovered that finding the lower approximation distribution reductions of a hesitant fuzzy decision system is equivalent to finding the minimal transversals of its hypergraph. Moreover, an improved algorithm for hesitant fuzzy decision systems based on hypergraphs is presented to accelerate the reduction process. Finally, the proposed algorithm is applied to the hybrid data of Hepatitis C Virus from UCI to demonstrate its feasibility. Show more

Keywords: Attribute reduction, hesitant fuzzy rough set, hypergraph, hesitant fuzzy decision system, approximation operator

DOI: 10.3233/JIFS-230460

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3279-3304, 2023

Authors: Wang, Rong | Rong, Xia

Article Type: Research Article

Abstract: With the rapid development of society, ideological and political education courses have occupied a very important position in various courses in major universities, playing a series of important functions and roles in student quality education, excellent quality cultivation, and other aspects. In the new era, the evaluation and assessment of ideological and political education quality is not only the primary factor to improve the teaching quality of ideological and political education courses in universities, but also an important means to promote the deepening reform of ideological and political education. However, there are many problems in the process of evaluating the …quality of ideological and political education in colleges and universities at present, such as the deviation in understanding the importance of evaluation, the relatively single evaluation method, and the low quality of application of evaluation results. The teaching quality evaluation of ideological and political courses in universities is a classical multiple attribute group decision making (MAGDM). Spherical fuzzy sets (SFSs) provide more free space for decision makers (DMs) to express preference information during the teaching quality evaluation of ideological and political courses in universities. Therefore, this paper we first extend partitioned Maclaurin symmetric mean (PMSM) operator and IOWA operator to SFSs and develop induced spherical fuzzy weighted PMSM (I-SFWPMSM) operator. Subsequently, a new MAGDM method is established based on I-SFWPMSM operator and SFNWG operator under SFSs. Finally, a numerical example for teaching quality evaluation of ideological and political courses in universities is used to illustrate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), spherical fuzzy sets, I-SFWPMSM operator, teaching quality evaluation

DOI: 10.3233/JIFS-231714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3305-3319, 2023

Authors: Zhang, Xingfu

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-232839

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3321-3331, 2023

Authors: Huang, Lixun | Sun, Lijun | Chen, Tianfei | Zhang, Qiuwen | Huo, Linlin | Liu, Weihua

Article Type: Research Article

Abstract: Hold-up compensation decelerates the convergence of iterative learning control (ILC) systems with data dropouts and time delays. Only depending on the prior knowledge of both ILC controllers and transmission channels, this paper develops a predictor to calculate the input not received on time due to data dropouts and time delays. First, a controller adopting the proportional learning strategy is considered directly, which is appropriate for objects in ideal communication conditions. After that, two data-receiving equations are given to describe the effect of data dropouts and one-step time delays. Finally, a predictor is designed according to the innovation analysis approach. Since …the prediction uses all historical input at the identical time index in previous iterations, the predicted input is more approximate to the one not received on time than the input held up for compensation. Simulation results show the object with prediction compensation tracks the expected trajectory faster than that with input-hold compensation. Show more

Keywords: Iterative learning control, convergence, input predictor, data dropout, time delay

DOI: 10.3233/JIFS-223074

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3333-3344, 2023

Authors: Jafari, Aya | Al-Mousa, Amjed | Jafar, Iyad

Article Type: Research Article

Abstract: The advent use of smart devices has enabled the emergence of many applications that facilitate user interaction through speech. However, speech reveals private and sensitive information about the user’s identity, posing several security risks. For example, a speaker’s speech can be acquired and used in speech synthesis systems to generate fake speech recordings that can be used to attack that speaker’s verification system. One solution is to anonymize the speaker’s identity from speech before using it. Existing anonymization schemes rely on using a pool of real speakers’ identities for anonymization, which may result in associating a speaker’s speech with an …existing speaker. Hence, this paper investigates the use of Generative Adversarial Networks (GAN) to generate a pool of fake identities that are used for anonymization. Several GAN types were considered for this purpose, and the Conditional Tabular GAN (CTGAN) showed the best performance among all GAN types according to different metrics that measure the naturalness of the anonymized speech and its linguistic content. Show more

Keywords: Speaker anonymization, voice privacy, generative adversarial networks, CTGAN, x-vector

DOI: 10.3233/JIFS-223642

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3345-3359, 2023

Authors: Shen, Xin | Xu, Qianhui | Liu, Qiao | Leibercht, Markus

Article Type: Research Article

Abstract: With the acceleration of technological change and globalization, companies face increasing environmental uncertainty and complexity. The COVID-19 pandemic has severely damaged the global supply chain and aggravated the operational risks of supply chains. Industry and academia have conducted studies on the construction of resilient and integrated supply chains, and to date a bulk of empirical literature has already been accumulated. A notable feature of existing literature is the heterogeneity in the characterization of the relationship between supply chain resilience, supply chain integration, and supply chain performance. In this study meta-analysis and structural equation modeling (MASEM) methods are integrated to construct …a theoretical framework of supply chain resilience, supply chain integration, and supply chain performance. 45 empirical studies (73 effect size data, 2092 samples) are selected from 10,623 papers published over the years 2013 to 2021 to explore the transmission mechanisms, the role of mediator variable, and boundary conditions of the relationship between supply chain resilience and supply chain performance. The results show that supply chain resilience can promote supply chain performance. Moreover, supply chain integration (supplier integration, internal integration, and customer integration) plays a partial mediating role for the impact of supply chain resilience on supply chain performance. Situations and measurement factors such as industry type, national culture (power distance), sampling area, and logistics performance have a certain impact on the relationship, and the usage of different indicators may lead to marked differences in conclusions regarding the relationship. By extracting the conclusions of existing empirical studies, this study proposes new insights into the mechanism of action of supply chain resilience, supply chain integration, and supply chain performance and provides specific suggestions for future supply chain management. Show more

Keywords: Supply chain resilience, supply chain integration, supply chain performance, meta-analysis, structural equation model

DOI: 10.3233/JIFS-220649

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3361-3377, 2023