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

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