Journal of Intelligent & Fuzzy Systems - Volume 46, issue 3

Authors: Kang, Zhonghui

Article Type: Research Article

Abstract: Intangible cultural heritage can be said to be an important component of tourism resources. With the rapid development of society in today’s era, tourism development and intangible cultural heritage protection have gradually attracted attention from Chinese society, and in recent years, it has attracted high attention from relevant departments of the Chinese government. Tourism development has a “dual” impact on the protection of intangible cultural heritage, with both positive and negative impacts. The risk assessment of intangible cultural heritage tourism development is a MAGDM problems. Recently, the TODIM and GRA technique has been employed to manage MAGDM issues. The interval-valued …Pythagorean fuzzy sets (IVPFSs) are employed as a tool for characterizing uncertain information during the risk assessment of intangible cultural heritage tourism development. In this paper, the interval-valued Pythagorean fuzzy TODIM-GRA (IVPF-TODIM-GRA) technique is construct to manage the MAGDM under IVPFSs. Finally, a numerical case study for risk assessment of intangible cultural heritage tourism development is employed to validate the proposed technique. Show more

Keywords: Multiple-attribute group decision-making (MAGDM), interval-valued pythagorean fuzzy sets (IVPFSs), TODIM technique, GRA technique, intangible cultural heritage tourism development

DOI: 10.3233/JIFS-236937

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5811-5824, 2024

Authors: Chang, Zaibin | Mao, Lingling

Article Type: Research Article

Abstract: Fuzzy complementary β-neighborhoods (FCNs) are used to find information relevant to the target in data mining. Based on FCNs, there are six types of covering-based multigranulation fuzzy rough set (CMFRS) models have been constructed, which can be used to deal with the problem of multi-criteria information systems. These CMFRS models are calculated by set representations. However, it is time-consuming and error-prone when set representations are used to compute these CMFRS models in a large multi-criteria information system. Hence, it is important to present a novel method to compute them quickly, which is our motivation for this paper. In this paper, …we present the matrix representations of six types of CMFRS models on FCNs. Firstly, some new matrices and matrix operations are given in a multi-criteria information system. Then, matrix representations of three types of optimistic CMFRSs on FCNs are proposed. Moreover, matrix approaches are also used for computing three types of pessimistic CMFRSs on FCNs. Finally, some experiments are presented to show the effectiveness of our approaches. Show more

Keywords: Fuzzy rough set, covering, matrix, multigranulation

DOI: 10.3233/JIFS-224323

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5825-5839, 2024

Authors: Wang, Yong | Jiang, Zhipeng | Wang, Yihan | Yang, Chunyu | Zou, Liang

Article Type: Research Article

Abstract: The mining belt conveyor is one of the most important modules in coal mine, whose safety always be threatened by the foreign objects. Although the traditional target detection methods achieve promising results in various computer vision tasks, the performance heavily depends on sufficient labelled data. However, in real-world production scenario, it is difficult to acquire huge number of images with foreign objects. The obtained datasets lacking of capacity and diversity are not suitable for training supervised learning-based foreign objects detection models. To address this concern, we propose a novel method for detecting the foreign objects on the surface of underground …coal conveyor belt via improved GANomaly. The proposed foreign objects detection method employs generative adversarial networks (GAN) with attention gate to capture the distribution of normality in both high-dimensional image space and low-dimensional latent vector space. Only the normal images without foreign object are utilized to adversarially train the proposed network, including a U-shape generator to reconstruct the input image and a discriminator to classify real images from reconstructed ones. Then the combination of the difference between the input and generated images as well as the difference between latent representations are utilized as the anomaly score to evaluate whether the input image contain foreign objects. Experimental results over 707 images from real-world industrial scenarios demonstrate that the proposed method achieves an area under the receiver operating characteristic curve of 0.864 and is superior to the previous GAN-based anomaly detection methods. Show more

Keywords: Generative adversarial networks, anomaly detection, attention, industrial scenarios, mining belt conveyor

DOI: 10.3233/JIFS-230647

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5841-5851, 2024

Authors: Li, Keyuan | Zhang, Qinghua | Xie, Qin | Huang, Shuaishuai

Article Type: Research Article

Abstract: Medical image classification is an essential task in the fields of computer-aided diagnosis and medical image analysis. In recent years, researchers have made extensive work on medical image classification by computer vision techniques. However, most of the current work is based on deep learning methods, which still suffer from expensive hardware resources, long time consuming and a lot of parameters to be optimized. In this paper, a multi-granularity ensemble algorithm for medical image classification based on broad learning system is proposed, which is an end-to-end lightweight model. On the one hand, the proposed method is designed to address the problem …of weak image feature learning ability of broad learning system. The convolution module with fixed weights based on transfer learning is introduced as a feature extractor to extract fusion features of medical images. On the other hand, the multi-granularity ensemble framework is proposed, which learn the fusion features of medical images from fine-grained to coarse-grained respectively, and the prediction results at different granularity levels are integrated by ensemble learning. In this way, the bottom local features can be sufficiently considered, while the global features can also be taken into account. The experimental results show that on the MedMNIST dataset (containing 10 sub-datasets), the proposed method can shorten the training time by tens of times while having similar accuracy to deep convolutional neural networks. On the ChestXRay2017 dataset, the proposed method can achieve an accuracy of 92.5%, and the training time is also significantly better than other methods. Show more

Keywords: Broad learning system(BLS), multi-granularity, ensemble learning, medical image classification

DOI: 10.3233/JIFS-235725

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5853-5867, 2024

Authors: Shen, Hanhan | Zhang, Fu | Pan, Xiaodong | Sun, Xiaofei

Article Type: Research Article

Abstract: As significant carriers of the application of fuzzy set theories, fuzzy systems have been widely used in many fields. However, selecting fuzzifications, fuzzy reasoning engines, and defuzzifications is subjective for Mamdani fuzzy systems, and the fuzzy rule of Takagi-Sugeno-Kang fuzzy systems is less of a linguistic interpretation. Regarding these shortcomings, this paper proposes a fuzzy system based on vague partitions processing information directly from the fuzzy rule base, in which fuzzy rules have explicit semantics. Firstly, the n -dimensional vague partition of the n -dimensional universe is defined based on 1-dimensional vague partitions and the aggregation function, and its properties …are discussed. Based on these, we design the new fuzzy system, and investigate its approximation properties which is the theoretical guarantee for applying the fuzzy system. As an application, we combine the fuzzy system with PID control system to deal with autonomous vehicle path tracking control problems. A series of experiments are constructed, and experimental results indicate that the fuzzy system based on vague partitions makes the fuzzy PID control system strong robustness, and has obvious advantages compared with other traditional fuzzy systems for path tracking control problems. Show more

Keywords: Fuzzy systems, vague partitions, aggregation functions, fuzzy PID control, path tracking

DOI: 10.3233/JIFS-232903

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5869-5892, 2024

Authors: Wang, Chia-Hung | Ye, Qing | Cai, Jiongbiao | Suo, Yifan | Lin, Shengming | Yuan, Jinchen | Wu, Xiaojing

Article Type: Research Article

Abstract: The multi-feature and imbalanced nature of network data has always been a challenge to be overcome in the field of network intrusion detection. The redundant features in data could reduce the overall quality of network data and the accuracy of detection models, because imbalance could lead to a decrease in the detection rate for minority classes. To improve the detection accuracy for imbalanced intrusion data, we develop a data-driven integrated detection method, which utilizes Recursive Feature Elimination (RFE) for feature selection, and screens out features that are conducive to model recognition for improving the overall quality of data analysis. In …this work, we also apply the Adaptive Synthetic Sampling (ADASYN) method to generate the input data close to the original dataset, which aims to eliminate the data imbalance in the studied intrusion detection model. Besides, a novel VGG-ResNet classification algorithm is also proposed via integrating the convolutional block with the output feature map size of 128 from the Visual Geometry Group 16 (VGG16) of the deep learning algorithm and the residual block with output feature map size of 256 from the Residual Network 18 (ResNet18). Based on the numerical results conducted on the well-known NSL-KDD dataset and UNSW-NB15 dataset, it illustrates that our method can achieve the accuracy rates of 86.31% and 82.56% in those two test datasets, respectively. Moreover, it can be found that the present algorithm can achieve a better accuracy and performance in the experiments of comparing our method with several existing algorithms proposed in the recent three years. Show more

Keywords: Artificial Intelligence, Classification Algorithms, Deep Learning Algorithms, Network Intrusion Detection, Multi-class Pattern Classification

DOI: 10.3233/JIFS-234402

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5893-5910, 2024

Authors: Sundar, R. | Purushotham Reddy, M. | Sethy, Abhisek | Selvam, K. | Abidin, Shafiqul | Chakrabarti, Prasun | Nagarjuna, Valeti | Ravuri, Ananda | Selvan, P.

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-237948

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5911-5925, 2024

Authors: Wang, Kai | Bai, Yameng

Article Type: Research Article

Abstract: With the rapid development of science and technology, the flow of information has become more convenient, and society has entered the era of knowledge economy. In this era, technological innovation capability is becoming increasingly important and has become an important weapon for enterprises to survive in fierce competition, especially for technology-based small and medium-sized enterprises. Nowadays, technology-based small and medium-sized enterprises have developed many technological innovation achievements through continuous technological innovation, and have created a large number of high-tech products and services. Technological innovation has been proven to effectively improve the core competitiveness and economic benefits of technology-based small and …medium-sized enterprises. Therefore, evaluating the technological innovation capabilities of technology-based small and medium-sized enterprises has both theoretical and practical significance. The enterprise technological innovation capability evaluation from a low carbon perspective could be deemed as the multiple attribute group decision making (MAGDM) problem. Recently, the evaluation based on distance from average solution (EDAS) technique and cosine similarity measure (CSM) technique has been employed to manage MAGDM issues. The spherical fuzzy sets (SFSs) are used as an efficient tool for portraying uncertain information during the enterprise technological innovation capability evaluation from a low carbon perspective. In this paper, the spherical fuzzy number EDAS based on the CSM (SFN-CSM-EDAS) technique is cultivated to manage the MAGDM under SFSs. Finally, a numerical study for enterprise technological innovation capability evaluation from a low carbon perspective is supplied to validate the proposed technique. The main contributions of this paper are outlined: (1) the EDAS and CSM technique was extended to SFSs; (2) the CRITIC technique is used to derive weight based on CSM technique under SFSs. (3) the SFN-CSM-EDAS technique is founded to manage the MAGDM under SFSs; (4) a numerical case study for enterprise technological innovation capability evaluation from a low carbon perspective and some comparative analysis is supplied to validate the SFN-CSM-EDAS technique. Show more

Keywords: Multiple attribute group decision making (MAGDM), spherical fuzzy sets (SFSs), EDAS technique, CRITIC technique, enterprise technological innovation capability evaluation

DOI: 10.3233/JIFS-236778

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5927-5940, 2024

Authors: Yang, Guangfu | Xiao, Chunyun

Article Type: Research Article

Abstract: The employment of college graduates is related to the overall situation of China’s social development, and the difficulty of employment has become a social problem that cannot be ignored. Through the analysis of the current situation of employment, it is found that the lack of employment guidance in colleges and universities and the lack of employment concept of college students are important factors for the difficulty of college students’ employment, and college counselors play an irreplaceable role in college students’ career planning. Based on the characteristics of college counselors’ work, the paper constructs a career planning evaluation system, hoping to …provide new ideas for counselors’ employment guidance. The college students’ career planning evaluation is a multiple attributes group decision making (MAGDM). Recently, the TODIM and GRA technique has been employed to manage MAGDM. The probabilistic hesitant fuzzy sets (PHFSs) are employed as a useful tool for depicting uncertain information during the college students’ career planning evaluation. In this paper, the probabilistic hesitant fuzzy TODIM-GRA (PHF-TODIM-GRA) technique is built to manage the MAGDM under PHFSs. At last, the numerical example for college students’ career planning evaluation is employed to show the PHF-TODIM-GRA technique. The main contribution of this paper is outlined: (1) the TODIM technique based on GRA technique has been extended to PHFSs based on CRITIC technique; (2) the CRITIC technique is employed to derive weight values under PHFSs. (3) the PHF-TODIM-GRA technique is founded to manage the MAGDM under PHFSs; (4) a numerical case study for college students’ career planning evaluation and some comparative analysis is supplied to validate the proposed PHF-TODIM-GRA technique. Show more

Keywords: Multiple attributes group decision making (MAGDM), probabilistic hesitant fuzzy sets (PHFSs), TODIM technique, GRA technique, career planning evaluation

DOI: 10.3233/JIFS-232606

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5941-5956, 2024

Authors: Yin, Bingquan | Ouyang, Shaojuan | Hou, Yali | Ma, Jizhao

Article Type: Research Article

Abstract: Innovation and entrepreneurship education is an important component of cultivating the comprehensive quality of college students and an important force in promoting economic and social development. Meanwhile, due to changes in the social environment and economic structure, traditional university education is no longer able to meet the needs of contemporary society. Therefore, innovation and reform of innovation and entrepreneurship education for college students are urgent. Innovation and entrepreneurship education for college students needs to keep up with the times, constantly update concepts and techniques, in order to adapt to the ever-changing social and economic environment. The innovation and entrepreneurship education …evaluation in the application-oriented vocational colleges is a multiple-attribute decision-making (MADM) problem. Recently, the TODIM and TOPSIS technique has been used to cope with MADM issues. The Type-2 neutrosophic numbers (T2NNs) are employed as a technique for characterizing uncertain information during the innovation and entrepreneurship education evaluation in the application-oriented vocational colleges. In this paper, the Type-2 neutrosophic number TODIM-TOPSIS (T2NN-TODIM-TOPSIS) technique is implemented to solve the MADM under T2NNs. Finally, a numerical case study for innovation and entrepreneurship education evaluation in the application-oriented vocational colleges and several comparative analysis is implemented to validate the proposed T2NN-TODIM-TOPSIS technique. The main research contribution of this paper is managed: (1) the TODIM and TOPSIS technique was enhanced with T2NNs; (2) Entropy technique is enhanced to manage the weight values with T2NNs. (3) the T2NN-TODIM-TOPSIS technique is founded to manage the MADM with T2NNs; (4) Algorithm framework for innovation and entrepreneurship education evaluation in the application-oriented vocational colleges and several comparative analysis are constructed based on one numerical example to verify the effectiveness of the T2NN-TODIM-TOPSIS technique. Show more

Keywords: Multiple-attribute decision-making (MADM), Type-2 neutrosophic numbers (T2NNs), TODIM technique, TOPSIS technique, innovation and entrepreneurship education evaluation

DOI: 10.3233/JIFS-233811

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5957-5973, 2024

Authors: Yu, Zhao | Ye, Ting

Article Type: Research Article

Abstract: The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deep learning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deep learning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-time processing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. Our …approach involves generating a custom dataset with diverse traffic sign images, followed by conducting training, validation, and testing sets to ensure the robustness and generalization of the model. Experimental results and performance evaluation demonstrate the effectiveness of the proposed method. Extensive experiments show that our model achieved remarkable accuracy rates in traffic sign detection, meeting the real-time requirements of the input data. Show more

Keywords: Traffic sign detection, deep learning, YOLOv8 model, self-driving cars, real-time processing

DOI: 10.3233/JIFS-235863

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5975-5984, 2024

Authors: Simin, Wang | Yifei, Kang | Yixuan, Xu | Chunmiao, Ma | Jinyu, Wang | Weiguo, Wu

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-231320

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5985-5999, 2024

Authors: Tong, Mingjia

Article Type: Research Article

Abstract: How to explore the potential value of landscape, realize the organic combination of tourism landscape, enrich landscape elements and enhance tourism experience has become an important topic of tourism landscape planning and design, which is also a practical problem that needs to be solved urgently in the process of tourism landscape development and planning in different regions of China. The tourism landscape planning design scheme evaluation based on the virtual reality technology a typical multi-attribute group decision-making (MAGDM) problem. With the complexity of economic activities, uncertain information has an increasing impact on production activities. However, due to the ambiguity and …uncertainty of human cognition, the factors affecting the risk of things cannot be accurately expressed. Therefore, selecting spherical fuzzy sets (SFSs) can make the expression of information more accurate and complete. On basis of the TODIM method and the PROMETHEE method, in this study, spherical fuzzy number TOMIM-PROMETHEE (SFN-TOMIM-PROMETHEE) method is implemented to solve the MAGDM problem under SFSs. Furthermore, CRITIC method under SFSs is implemented to determine relative weights. Then a numerical example for tourism landscape planning design scheme evaluation based on the virtual reality technology is selected to illustrate the effectiveness and practicality of the method. Finally, the comparative analysis shows that the SFN-TOMIM-PROMETHEE method under SFSs is an effective method to deal with MAGDM problems. The main contribution of this paper is managed: (1) the TODIM and PROMETHEE technique was extended to SFSs; (2) CRITIC technique is employed to manage the weight values under SFSs. (3) the SFN-TOMIM-PROMETHEE technique is founded to manage the MAGDM under IVPFSs; (4) a numerical example for tourism landscape planning design scheme evaluation based on the virtual reality technology and comparison analysis are constructed to verify the feasibility and effectiveness of the SFN-TOMIM-PROMETHEE technique. Show more

Keywords: Multi-attribute group decision-making (MAGDM), TODIM-PROMETHEE method, spherical fuzzy sets, CRITIC method, tourism landscape planning design scheme

DOI: 10.3233/JIFS-233401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6001-6017, 2024

Authors: Tyagi, Pooja | Singh, Jaspreeti | Gosain, Anjana

Article Type: Research Article

Abstract: The contemporary real-world datasets often suffer from the problem of class imbalance as well as high dimensionality. For combating class imbalance, data resampling is a commonly used approach whereas for tackling high dimensionality feature selection is used. The aforesaid problems have been studied extensively as independent problems in the literature but the possible synergy between them is still not clear. This paper studies the effects of addressing both the issues in conjunction by using a combination of resampling and feature selection techniques on binary-class imbalance classification. In particular, the primary goal of this study is to prioritize the sequence or …pipeline of using these techniques and to analyze the performance of the two opposite pipelines that apply feature selection before or after resampling techniques i.e., F + S or S + F. For this, a comprehensive empirical study is carried out by conducting a total of 34,560 tests on 30 publicly available datasets using a combination of 12 resampling techniques for class imbalance and 12 feature selection methods, evaluating the performance on 4 different classifiers. Through the experiments we conclude that there is no specific pipeline that proves better than the other and both the pipelines should be considered for obtaining the best classification results on high dimensional imbalanced data. Additionally, while using Decision Tree (DT) or Random Forest (RF) as base learner the predominance of S + F over F + S is observed whereas in case of Support Vector Machine (SVM) and Logistic Regression (LR), F + S outperforms S + F in most cases. According to the mean ranking obtained from Friedman test the best combination of resampling and feature selection techniques for DT, SVM, LR and RF are SMOTE + RFE (Synthetic Minority Oversampling Technique and Recursive Feature Elimination), Least Absolute Shrinkage and Selection Operator (LASSO) + SMOTE, SMOTE + Embedded feature selection using RF and SMOTE + RFE respectively. Show more

Keywords: Imbalanced data, feature selection, machine learning, oversampling, undersampling

DOI: 10.3233/JIFS-233511

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6019-6040, 2024

Authors: Yuan, Songlin

Article Type: Research Article

Abstract: Since the dawn of the digital web era, web-based learning resources have become more and more significant in the field of education. To a certain extent, the visual communication design of these resources influences how well students learn. In view of this, the study proposes a deep learning-based approach to visual communication design. Convolutional neural networks are introduced to automatically construct the visual communication interface, a recommendation algorithm is used to develop the system’s recommendation function, and machine translation is used to translate the language description text. The study method’s efficacy was evaluated. According to the experimental results, the research …method’s runtime in a color environment was only about 37.7 seconds at 4k resolution; in a non-color environment, the method’s F1 value was 0.87 at a recommended list length of 35, which was higher than that of other methods; and when it came to the interface solutions in real terms, the research method produced 526 at 30 buttons. The aforementioned findings demonstrate that the suggested approach can successfully increase the visual communication’s design speed and performance in online learning materials and offer a suitable answer to the needs of real-world applications. Show more

Keywords: Visual communication design, convolutional neural networks, transformer, learning resources, teacher forcing

DOI: 10.3233/JIFS-233944

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6041-6052, 2024

Authors: Gou, Hongyuan | Zhang, Xianyong

Article Type: Research Article

Abstract: Multi-granularity rough sets facilitate knowledge-based granular computing, and their compromised models (called CMGRSs) outperform classical optimistic and pessimistic models with extremity. Three-level CMGRSs with statistic-optimization-location effectively process hierarchical granularities with attribute enlargements, and they are worth generalizing for general granularities with arbitrary feature subsets. Thus, three-level CMGRSs on knowledge, approximation, and accuracy are established for arbitrary granularities by using three-way decision (3WD). Corresponding 3WD-CMGRSs adopt statistic-optimization-3WD by adding optimistic and pessimistic bounds to the representative location, so they resort to optimal index sets to acquire the multi-granularity equilibrium and decision systematicness. As a result, multiple CMGRSs emerge within the three-level …and three-way framework, they improve the classical MGRSs and enrich 3WD as well as three-level analysis, and exhibit the good simulation, extension, effectiveness, improvement, and generalization. Firstly at the knowledge level, cardinality statistic-optimization improves previous label statistic-optimization for equilibrium realization, so CMGRSs are improved for hierarchical granularities while 3WD-CMGRSs are proposed for arbitrary granularities. Then at the approximation and accuracy levels, measure statistic-optimization determines optimal index sets, so 3WD-CMGRSs are similarly proposed to complete the simulation and extension. Furthermore, mathematical properties and computational algorithms of relevant models are investigated. Finally, three-level 3WD-CMGRSs are illustrated by table examples and are validated by data experiments. Show more

Keywords: Multi-granularity rough sets, compromised models, statistic-optimal equilibrium, three-way decision, three-level analysis

DOI: 10.3233/JIFS-236063

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6053-6081, 2024

Authors: Zhang, Hao | Sheng, Yuhong

Article Type: Research Article

Abstract: In this study, an innovative approach that combines least square support vector regression (LSSVR) with uncertainty theory to enhance its performance in dealing with low-quality or imprecise data from real-world be proposed. The resulting model, called uncertain least square support vector regression (ULSSVR), incorporates chance constraints and simplified parameter selection, which are critical to handle imprecise observations. A numerical algorithm called the conjugate residual method (CR) is introduced to reduce the computational complexity of the model solution. The experimental results using both small and medium-sized datasets demonstrate the superior performance of ULSSVR in terms of prediction accuracy and generalization ability …compared to other models such as uncertain support vector regression (USVR), uncertain linear lodel, uncertain polynomial model, and uncertain growth models. ULSSVR not only improves prediction accuracy by at least 28.49% but also demonstrates faster computational speed. Overall, ULSSVR presents a promising solution for data science and internet applications where dealing with imprecise and low-quality data is a common challenge. Show more

Keywords: Least square support vector regression, uncertainty theory, conjugate residual method, chance constraint

DOI: 10.3233/JIFS-236849

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6083-6092, 2024

Authors: Shi, Lin

Article Type: Research Article

Abstract: With the improvement of the public’s aesthetic level, product appearance has become an important influencing factor for consumers to make purchasing decisions. Product styling design is based on this market demand, combining the aesthetic and functional aspects of the product to create a personalized product appearance, in order to better attract consumers, improve the competitiveness and added value of the product. Usually, product styling design involves multiple elements such as product form, color, proportion, etc. The quality evaluation of product styling design is a MAGDM problems. Recently, the TODIM and EDAS technique has been employed to manage MAGDM issues. The …interval-valued Pythagorean fuzzy sets (IVPFSs) are employed as a tool for characterizing uncertain information during the quality evaluation of product styling design. In this paper, the interval-valued Pythagorean fuzzy TODIM-EDAS (IVPF-TODIM-EDAS) technique is construct to manage the MAGDM under IVPFSs. Finally, a numerical case study for quality evaluation of product styling design is employed to validate the proposed technique. The main contribution of this paper is managed: (1) the TODIM and EDAS technique was extended to IVPFSs; (2) Entropy technique is employed to manage the weight values under IVPFSs. (3) the IVPF-TODIM-EDAS technique is founded to manage the MAGDM under IVPFSs; (4) Algorithm analysis for quality evaluation of product styling design and comparison analysis are constructed based on one numerical example to verify the feasibility and effectiveness of the IVPF-TODIM-EDAS technique. Show more

Keywords: Multiple-attribute group decision-making (MAGDM), Interval-valued Pythagorean fuzzy sets (IVPFSs), TODIM technique, EDAS technique, product styling design

DOI: 10.3233/JIFS-236947

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6093-6108, 2024

Authors: Weng, Shizhou | Huang, Zhengwei | Lv, Yuejin

Article Type: Research Article

Abstract: In the face of increasingly complex data forms and decision-making problems, the uncertainty of information poses a major challenge to multi-attribute decision-making methods. How to effectively organize information and serve realistic decision-making problems has attracted extensive attention in the academic circles. In view of this, based on the distribution law of random variables, we put forward the basic concept of probability numbers and construct a general framework, including the concepts of type, order, item, isomorphism and isomerism, same domain and same distribution of probability numbers. On this basis, we further define the expectation and variance formula of probability numbers, and …its operation rules are defined for the same type of probability numbers. To compare the dominance and inferiority of probability numbers further accurately, we put forward the concepts of dominance degree and comparability degree of probability numbers, so that decision makers can realize the ranking of probability numbers by calculating the comprehensive dominance degree. In view of the related concepts of probability numbers, we summarize the properties and theorems of probability numbers and prove them. In addition, a probability numbers-based multi-attribute decision-making framework model is proposed to solve the multi-attribute decision-making problem. Decision makers can select appropriate sub-models to construct personalized multi-attribute decision-making methods according to actual needs. At the end of the paper, we apply the method to the multi-attribute decision case of campus express stations evaluation and verify the scientificity and rationality of the evaluation method. The concept of probability numbers and its decision model proposed in this paper extend the concept category of numbers, enrich the multi-attribute decision-making method based on probability numbers, and have certain reference significance for further research of uncertain decision theory and method. Show more

Keywords: Probability numbers, calculation rule, dominance degree, ranking method, multi-attribute decision-making

DOI: 10.3233/JIFS-223565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6109-6132, 2024

Authors: Jiang, Yanping | Tang, Zhenpeng | Song, Xinchao | Shao, Xinran

Article Type: Research Article

Abstract: There has been widespread and growing concern about parking. This paper attempts to provide decision support for a shared parking system to reduce parking difficulty. We study a many-to-many matching problem between shared private idle parking spaces and their demanders. A novelty is that the demanders are allowed to use different parking spaces successively in parking relocation service support. This can further reintegrate the idle time of the parking spaces and improve their utilization rate. A multi-objective optimization model is constructed to maximize the number of matched demanders, the total priority of the parking spaces, and the total priority of …the demanders. More importantly, the priorities of the parking spaces and the demanders are innovatively considered. Each of the parking spaces and the demanders is given a priority for the matching and the priority of a parking space or a demander will be increased if the parking space or demander rarely gets matched successfully. This helps reduce the withdrawal of parking spaces and the demanders from the parking platform. In addition, an NSGA-II algorithm is designed to solve the model efficiently. Finally, the feasibility of the proposed method is illustrated via an example. Show more

Keywords: Sharing economy, shared private idle parking space, many-to-many matching, parking priority, improved NSGA-II algorithm

DOI: 10.3233/JIFS-223789

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6133-6148, 2024

Authors: Wang, Daiwen | Sun, Jie | Wei, Cuiping

Article Type: Research Article

Abstract: Spent lithium-ion battery (LIB) recycling can create great pollution to the environment. Understanding the safety, environment, technique, and regulation factors’ impact on the recycling process is crucial. Due to the complexity of the relevant factors, and there is a certain degree of correlation and dependence between the factors, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyze the factors’ degree of impact in this study. As the experts are ambiguous about some relations between the factors, it is impossible to conduct integrated evaluation. The improved DEMATEL method is proposed in this study to make up the …missing relations. Further, the weights of the factors will be calculated. In the improved DEMATEL method, the numerical scale of a linguistic term set is introduced. Therefore, the numerical scale used by experts can not only be uniform and symmetrical, but can also be non-uniform symmetric, non-uniform asymmetric, etc. Finally, both reusing and recycling companies are included in this study and their factors’ importance weights were analyzed with the fuzzy comprehensive evaluation method. Show more

Keywords: Automotive waste lithium-ion battery recycling, numerical scale function, DEMATEL method, fuzzy comprehensive evaluation

DOI: 10.3233/JIFS-224124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6149-6169, 2024

Authors: Mohan Raj, K.R. | Katiravan, Jeevaa

Article Type: Research Article

Abstract: Recently, security has been necessary in this computer world due to the fast development of technology and enormous user strength. The different kinds of security mechanisms including the Intrusion Detection System (IDS) were developed by many researchers to confirm the security of the data in the communication process. In general, the IDS are used to detect anomalous nodes, and attacks and increase the security level. Even though, the various disadvantages are available to ensure the data reliability on different kinds of applications. For this purpose, this work proposes a cross-layer IDS that is a combination of the trust-based secure routing …method, attribute selection and classification algorithms. This study introduces a novel attribute selection approach known as the Weighted Genetic Feature Selection Algorithm (WGFSA). This method is designed to identify and prioritize valuable attributes within the context of network, physical, and data link layers. And introduce a deep classifier called the Hyperparameter-Tuned Fuzzy Temporal Convolutional Neural Network (HFT-CNN) for efficient categorization. Additionally, we propose a pioneering secure routing algorithm known as the Fuzzy Logic and Time-Constrained Dynamic Trusted Cross-Layer-Based Secure Routing Algorithm (FCSRA) to ensure the secure transmission of data packets. The effectiveness of the newly developed system is proved by conducting experiments with the network, standard Aegean Wi-Fi intrusion dataset (AWID) and proved superior to other systems in delay, energy consumption, packet delivery rate, and prediction accuracy. Show more

Keywords: Fuzzy temporal logic, intrusion detection systems, trust score, cross-layer, deep learning, attribute selection, secure routing

DOI: 10.3233/JIFS-233275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6171-6183, 2024

Authors: Wei, Ying | Gong, Kaixin | Chen, Chunfang | Zhu, Xianghong

Article Type: Research Article

Abstract: This research proposes a new method to solve group decision-making(GDM) problems with intuitionistic fuzzy preference relations(IFPRs). First, a new definition of multiplicative consistency of IFPR is presented to address the defects of the existing consistency definitions. Then, two programming models are established to obtain the most optimistic and pessimistic consistent IFPRs and corresponding intuitionistic fuzzy priority weights. Also, in order to improve the accuracy of aggregate information, a new method to determine the weights of decision-makers(DMs) is offered by considering the interaction among DMs. Subsequently, by combining the vagueness and non-vagueness of the aggregated information, a multiplicative consistency definition of …the collective IFPR is provided. Moreover, to simplify the GDM process, a programming model for solving the priority weight is established, which effectively avoids the consistency test and correction of IFPRs. Finally, the values of the proposed method are illustrated by comparative analysis. Show more

Keywords: Group decision-making, interaction, risk preference, intuitionistic fuzzy preference relation, consistency

DOI: 10.3233/JIFS-233543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6185-6199, 2024

Authors: Hou, Xianyu | Chen, Yumin | Wu, Keshou | Zhou, Ying | Lu, Junwen | Weng, Xuan

Article Type: Research Article

Abstract: Neighborhood granulation is a classical granulation method. Although it is adequate for clustering and classification tasks, its granules are more complex, and the data representation is binary. This paper proposes a new granulation method based on the neighborhood granulation. Firstly, a detailed definition of the granular form is given with fuzzy rough set theory. Then, a modified fuzzy rough discriminant function is proposed based on neighborhood systems. The samples are globally granulated on single features to construct granules and on multiple features to construct granular vectors. Also, a feature selection technique based on the Chi-square, which strikingly reduces the complexity …of the fuzzy rough granular vectors, is introduced to address the disadvantage of the fuzzy rough granular vectors. An ensemble model structure is also proposed in the paper for the mixed nature of fuzzy rough granular vectors. The paper makes a detailed comparison between the fuzzy rough granulation and the neighborhood granulation. The results show that fuzzy rough granulation has higher computational efficiency and classification performance. Finally, a detailed comparison is made between the fuzzy rough granular ensemble model and various classical ensemble algorithms. The final results show that the fuzzy rough granular ensemble model has better robustness and generalization. Show more

Keywords: Granular computing, fuzzy rough granulation, neighborhood granulation, granular ensemble learning, granular selection

DOI: 10.3233/JIFS-234510

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6201-6217, 2024

Authors: Sun, Hong | Zhang, Xianyong

Article Type: Research Article

Abstract: Z-numbers contain fuzzy restrictions, credibility measures, and probability distributions to effectively represent uncertain information. Converting Z-numbers to fuzzy numbers facilitates extensive applications (such as multi-attribute decision-making (MADM)), thus becoming valuable for research purposes. Regarding Z-number conversions, the original method never considers the association probability, while probabilistic strategies offer better informatization. Recently, a probability-driven conversion starts with a linear transformation of the centroid difference between the fuzzy restriction and probabilistic distribution. However, it has the invalidation weakness of edge information due to underlying non-normalization. To improve this probability-linear conversion, a Z-number conversion is proposed by using underlying probability-exponential descriptions, and this …new method is further applied to MADM. At first, the current probability-linear conversion is analyzed based on the initial non-probabilistic conversion, and its intrinsic weakness and correctional improvement are revealed. Then, the novel probability-exponential conversion resorts to an exponential characterization of centroid difference between the restriction and distribution, and it gains information enrichment due to underlying normalization. The refined method preserves the inherent characteristics of Z-numbers more effectively, facilitating their application in subsequent engineering practices. This is especially pertinent in decision-making systems based on expert input and initial value problems. The proposed method for converting Z-numbers aims to minimize information loss in transitions between Z-numbers and classical fuzzy numbers. This approach will be further explored in future research. Furthermore, the probability-exponential conversion induces an ExpTODIM algorithm for MADM, called PE-ExpTODIM. Three Z-number conversions (i.e., the non-probabilistic, probability-linear, and probability-exponential types) and three decision algorithms (i.e., ExpTODIM, EDAS, MOORA) are combined to establish a 3 × 3 framework of Z-number-driven MADM. Finally, the systematical 9 algorithms are applied to the problem of site selection of carbon storage. They are validated by criss-cross contrast analyses and statistical significance tests. Thus, PE-ExpTODIM exhibits the desired optimization. The last technology of statistical testing is original, ingenious, and valuable for MADM. Show more

Keywords: Z-numbers, fuzzy numbers, probability-exponential conversion, multi-attribute decision making, ExpTODIM/EDAS/MOORA

DOI: 10.3233/JIFS-235304

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6219-6233, 2024

Authors: Kavitha, D. | Radha, V.

Article Type: Research Article

Abstract: This work proposes an unique hardware design for a multi-line Refreshable Braille Display (RBD) device using Imprint Punch Head Technology with Optical Character Recognition (OCR) capabilities for learning and reading text in Braille codes. The device uses a microprocessor board to seamlessly integrate stepper/servo motors with OCR algorithms. A thin flexible metal sheet coated with rubber is used as a display surface on which the raised points for Braille codes are repeatedly formed and deformed. The device is designed in such a way that the material used for its construction are low cost which makes them economical and affordable. The …device was evaluated in the lab setup and showed promising results, and had prospects of becoming a vital Assistive Technology for vision impaired people. Show more

Keywords: OCR, refreshable braille display, raspberry pi, text detection

DOI: 10.3233/JIFS-236527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6235-6248, 2024

Authors: Li, Heng

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-237196

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6249-6263, 2024

Authors: Li, Bo | Lu, TongWei | Min, Feng

Article Type: Research Article

Abstract: 3D point cloud has irregularity and disorder, which pose challenges for point cloud analysis. In the past, the projection or point cloud voxelization methods often used were insufficient in accuracy and speed. In recent years, the methods using Transformer in the NLP field or ResNet in the deep learning field have shown promising results. This article expands these ideas and introduces a novel approach. This paper designs a model AaDR-PointCloud that combines self-attention blocks and deep residual point blocks and operates iteratively to extract point cloud information. The self-attention blocks used in the model are particularly suitable for point cloud …processing because of their order independence. The deep residual point blocks used provide the expression of depth features. The model performs point cloud classification and segmentation tests on two shape classification datasets and an object part segmentation dataset, achieving higher accuracy on these benchmarks. Show more

Keywords: PointCloud, transformer, ResNet, point cloud classification, point cloud part segmentation

DOI: 10.3233/JIFS-231997

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6265-6277, 2024

Authors: Yu, Huan

Article Type: Research Article

Abstract: With the acceleration of urbanization and the significant improvement of people’s living standards, the motorization of urban transportation in China has developed rapidly, and the number of urban motor vehicles has sharply increased. This has also caused a series of problems such as increasingly severe urban road traffic congestion, increased traffic energy consumption, and atmospheric environmental pollution. Unprecedented social and environmental pressures have put forward higher requirements for the development model of urban transportation. Against the backdrop of increasingly severe conflicts between urban transportation and resource environment in China, green transportation with the goal of “meeting maximum demand with minimum …consumption” has gradually received widespread attention from the academic community. The urban green transportation development level evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional geometric BM (GBM) operator and generalized weighted GBM (GWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy generalized GBM (TPFGGBM) operator and triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for urban green transportation development level evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity. Show more

Keywords: Multiple attribute decision making (MADM), Triangular Pythagorean fuzzy set, geometric BM (GBM) operator, Triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM), green transportation development level

DOI: 10.3233/JIFS-232579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6279-6297, 2024

Authors: Bolourchi, Pouya | Gholami, Mohammadreza

Article Type: Research Article

Abstract: Alzheimer’s disease (AD) is the most prevalent brain disorder which affects millions of people worldwide. Early detection is crucial for possible treatment. In this regard, machine learning (ML) approaches are widely utilized for AD detection. In this paper, we propose an ML-based method that drastically reduces the dimensionality of features while maintaining the relevant features and boosting the overall performance. To remove irrelevant features, first statistical feature extraction method is applied, and then further reduction among remaining features is applied by utilizing the harmony search method (HSM). The selected features are the most informative features that are fed to the …different classifiers. To test the effectiveness of the proposed method, we deployed three classification techniques including support vector machine (SVM), k -nearest neighbor (k -NN), and decision tree (DT). The experimental results show that the proposed method has a higher performance while decreasing the dimensionality of feature space. To guarantee that the performance of the proposed method is accurate, we applied an ensemble of three classifiers (SVM, KNN, and DT) for classification. The results of the proposed method verify that this method can be successfully deployed for AD detection, due to its high performance and low dimensional features, and can help improve the accuracy and efficiency of Alzheimer’s disease diagnosis. The proposed method demonstrated a significant improvement, achieving high performance in AD/HC classification, with accuracy, sensitivity, specificity, F1 -score, MCC , and Cohen’s Kappa rates reaching 95.5%, 97%, 94%, 95.56%, 0.9104, and 0.9109, respectively. AD/HC classification displayed the highest performance. Additionally, in the more challenging pMCI/sMCI classification, the method achieved an accuracy of 78.50%, sensitivity of 84.00%, specificity of 73.00%, F1 -score of 79.62%, MCC of 0.57, and Cohen’s Kappa of 0.59. Show more

Keywords: Alzheimer’s disease, ensemble of classifiers, harmony search, statistical feature extraction, sMRI

DOI: 10.3233/JIFS-233000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6299-6312, 2024

Authors: Khenglawt, Vanlalmuansangi | Laskar, Sahinur Rahman | Pakray, Partha | Khan, Ajoy Kumar

Article Type: Research Article

Abstract: Low-resource language in machine translation systems poses multiple complications regarding accuracy in translation due to insufficient incorporation of linguistic information. The difference in the linguistic information between the language pair also significantly impacts the dataset creation for improving translation accuracy. Although neural machine translation achieves a state-of-the-art approach, dealing with low-resource language is challenging since it struggled with limited resources. This paper attempts to address the data scarcity problem using augmentation of synthetic parallel sentences, source-target phrase pairs, and language models at the target side for English-to-Mizo and Mizo-to-English translation via transformer-based neural machine translation. We have attained state-of-the-art results …for both directions of translation. Show more

Keywords: English–Mizo, NMT, transformer, augmentation, language model

DOI: 10.3233/JIFS-235740

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6313-6323, 2024

Authors: Jin, Yongbing | Ran, Teng | Yuan, Liang | Lv, Kai | Wang, Guoliang | Xiao, Wendong

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-237275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6325-6335, 2024

Authors: You, Haoyang

Article Type: Research Article

Abstract: Students’ English learning ability depends on the knowledge and practice provided during the teaching sessions. Besides, language usage improves the self-ability to scale up the learning levels for professional communication. Therefore, the appraisal identification and ability estimation are expected to be consistent for different English learning levels. This paper introduces Performance Data-based Appraisal Identification Model (PDAIM) to support such reference. This proposed model is computed using fuzzy logic to identify learning level lags. The lag in performance and retains in scaling-up are identified using different fuzzification levels. The study suggests a fuzzy logic model pinpointing learning level gaps and consistently …evaluating performance across various English learning levels. The PDAIM model gathers high and low degrees of variance in the learning process to give students flexible learning knowledge. Based on the student’s performance and capacity for knowledge retention, it enables scaling up the learning levels for professional communication. The performance measure in the model is adjusted to accommodate the student’s diverse grades within discernible assessment boundaries. This individualized method offers focused education and advancement to students’ unique requirements and skills. The model contains continuous normalization to enhance the fuzzification process by employing prior lags and retentions. Several indicators, including appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time, are used to validate the PDAIM model’s performance. The model may adjust to the various performance levels and offer pertinent feedback using fuzzification. The high and low variation levels in the learning process are accumulated to provide adaptable learning knowledge to the students. Therefore, the performance measure is modified to fit the student’s various grades under distinguishable appraisal limits. If a consistent appraisal level from the fuzzification is observed for continuous sessions, then the learning is scaled up to the next level, failing, which results in retention. This proposed model occupies constant normalization for improving the fuzzification using previous lags and retentions. Hence the performance of this model is validated using appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time. Show more

Keywords: Appraisal model, big data, English learning, fuzzy logic and fuzzification

DOI: 10.3233/JIFS-233414

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6337-6353, 2024

Authors: Men, Rui | FAN, Xiumei | Yan, Jun | Shan, Axida | Fan, Shujia

Article Type: Research Article

Abstract: Vehicle Edge Computing (VEC) is a promising technique to improve the quality of service (QoS) and quality of experience (QoE) in autonomous driving by exploiting the resources at the network edge. However, the high mobility of the vehicles leads to stochastic communication link duration, and the tasks generated by various applications in autonomous driving incur fierce competition for resources. These challenges cause excessive task completion delays. In this paper, we propose a vehicle-to-vehicle (V2V) partial computation offloading scheme that leverages the prediction results of the communication link lifetime between vehicles. A History track, Current interactions and Future planning trajectory-aware Gated …Recurrent Units (HCF-GRU) network is built to capture the essential factors to improve the prediction accuracy. Then, we design a GRU-based Proximal Policy Optimization (GRU-PPO) algorithm to obtain an optimal one-to-many offloading decision to minimize the task execution cost. The HCF-GRU prediction algorithm is evaluated on a real world vehicle trajectory dataset, and the performance of the GRU-PPO algorithm is analyzed on extensive numerical simulations. Experimental results demonstrate that our prediction network and offloading decision algorithm outperform the baseline methods in terms of prediction accuracy and task execution cost. Show more

Keywords: Communication link lifetime prediction, partial offloading decision, machine learning, autonomous driving

DOI: 10.3233/JIFS-235954

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6355-6368, 2024

Authors: Amin, Umair | Fahmi, Aliya | Yaqoob, Naveed | Farid, Aqsa | Hassan, Muhammad Arshad Shehzad

Article Type: Research Article

Abstract: The concept of domination in graphs is very ancient. Several types of notions of domination in graphs have been discussed by many researchers. In this work, the concept of domination and some notions of domination sets, minimal dominating sets, independence sets, and maximal independence sets are introduced in bipolar fuzzy soft graphs. Additionally, several properties of dominating sets are discussed and some theorems in bipolar fuzzy soft graphs are proved.

Keywords: Domination in bipolar fuzzy soft graphs, minimal domination set in bipolar fuzzy soft graphs, maximal independence set in bipolar fuzzy soft graphs

DOI: 10.3233/JIFS-236485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6369-6382, 2024

Authors: Xu, Fang

Article Type: Research Article

Abstract: In the context of globalization, cross-border e-commerce platforms have become the main way for enterprises to achieve international trade transformation and overseas investment. From this, it can be seen that cross-border e-commerce platforms are of great importance to the development of enterprises, and the development of cross-border e-commerce platforms is also a necessary choice for the development of the times. In the new era, in order to make cross-border e-commerce platforms better serve enterprises and bring economic benefits to their development. The sustainable development capability evaluation of third-party cross-border e-commerce (TPCBEC) platform is a MAGDM. Recently, the Exponential TODIM (ExpTODIM) …technique and Evaluation Based on Distance from Average Solution (EDAS) technique has been employed to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are employed as a tool for portraying uncertain information during the sustainable development capability evaluation of TPCBEC platform. In this paper, the 2-tuple linguistic neutrosophic number Exponential TODIM-EDAS (2TLNN-ExpTODIM-EDAS) technique is implemented to manage the MAGDM under 2TLNSs. Finally, a numerical study for sustainable development capability evaluation of TPCBEC platform is constructed to validate the implemented technique. Thus, the main advantages of the proposed 2TLNN-ExpTODIM-EDAS technique are outlined: (1) the proposed 2TLNN-ExpTODIM-EDAS technique not only handles the distances information from the 2TLNNAS, but also portrays the DMs’ psychological behavior during the sustainable development capability evaluation of TPCBEC platform. (2) the proposed 2TLNN-ExpTODIM-EDAS technique analyze the behavior of the TODIM technique and EDAS technique as MADM techniques when they are hybridized. Show more

Keywords: Multiple-attribute group decision-making (MAGDM), 2-tuple linguistic neutrosophic sets (2TLNSs), Exponential TODIM (ExpTODIM) technique, EDAS technique, sustainable development capability evaluation

DOI: 10.3233/JIFS-237170

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6383-6398, 2024

Authors: Gao, Jun | Peng, Zhiyuan | Cao, Qiang | Zhang, Jie

Article Type: Research Article

Abstract: The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge …was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study. Show more

Keywords: Energy management strategy, Markov chain, minimum principle, optimal control

DOI: 10.3233/JIFS-238713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6399-6409, 2024

Authors: Zhang, Nan | Yin, Jiayi | Zhang, Ning | Sun, Tongtong | Yin, Shi | Wan, Lijun

Article Type: Research Article

Abstract: Digital technologies, such as big data, the Internet, and artificial intelligence, are rapidly advancing. Photovoltaic building materials enterprises (PBMEs) have been leveraging digital transformation to enhance their technological innovation capabilities and gain a competitive edge. In the global context of transitioning towards a low-carbon economy, the deep integration of digital technology offers a new solution for the green transformation of PBMEs. The synergy between green traction digitalization and digitalization enables green practices, making collaborative integration crucial for the far-reaching development of PBMEs. Within the framework of China’s “double carbon” policy, domestic PBMEs are experiencing exponential growth, where digital green innovation …(DGI) has become their primary objective. In this DGI context, selecting the right partners is the first step that significantly impacts the efficiency and effectiveness of DGI implementation. Therefore, the purpose of this study is to assist PBMEs in selecting high-quality partners, promoting the DGI process, enhancing technological innovation capabilities, and gaining a competitive advantage. To achieve this, the paper proposes constructing a theoretical framework for evaluating the DGI cooperation ability of PBMEs using the theory of ecological reciprocity. Based on this framework, an evaluation index system is established to assess the DGI cooperation ability of potential partners The interval intuitionistic fuzzy evaluation method, combined with a double combination weighting approach, is employed to evaluate the DGI ability of selected partners. Furthermore, by applying field theory, a dynamic selection model for strategic alliance partners is developed to aid PBMEs in selecting high-quality partners for DGI and facilitating the DGI process. The research findings indicate that: i) The evaluation standard framework for DGI cooperation ability of PBMEs encompasses “symbiosis,” “mutualism,” and “regeneration,” along with the crucial environmental element of mutual trust. ii) The evaluation method based on double combination weighting effectively assesses the comprehensive DGI capabilities of selected PBME partners. The application of field theory enables scientific and effective dynamic partner selection for PBMEs through resource complementarity. iii) The proposed framework and partner selection model can be employed in real partner selection scenarios for PBMEs, allowing them to choose high-quality partners, enhance their DGI capabilities, and attain practical selection outcomes. This paper presents novel partner selection model that integrates decision rules and resource complementarity, enabling PBMEs to efficiently select DGI partners from a pool of potential candidates and improve their innovation efficiency. The utilization of the double combination weighting method and field theory in the partner selection paradigm of D extends the theoretical foundation, while the establishment of the DGI capability evaluation index system for PBME partners contributes to empirical applications. Show more

Keywords: Photovoltaic building materials enterprises, digital green innovation, partner selection, double combination weighting, field theory

DOI: 10.3233/JIFS-234838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6411-6437, 2024

Authors: You, Fang | Li, Yaru | Fu, Qianwen | Zhang, Jun

Article Type: Research Article

Abstract: With the increasing levels of intelligence and automation, the relationship between humans and vehicles has evolved from a utilitarian perspective to a partnership. Among the crucial factors for enhancing user experiences are the analysis of driving tasks, the construction of user needs models, and the design of intelligent interfaces. Based on this background, this paper proposes a cognitive task analysis model using intelligent steering wheel information interaction design as the vehicle. The model aims to extract key design elements to assist designers in making design decisions, thereby improving the human-machine cooperation performance of intelligent automobiles and enhancing user perceptual experiences. …Firstly, within the context of human-machine cooperation systems, a cognitive task analysis method integrating the SRK model is proposed. By analyzing the behavioral decision characteristics between the vehicle and the user, a framework for the human-machine interface (HMI) logic of the steering wheel and a dynamic layout prototype are established. Secondly, the design of the steering wheel’s HMI interaction is based on an analysis of users’ affective needs and rational physiological characteristics. This paper integrates the analysis of users’ affective needs to identify design elements that align with a high level of user satisfaction. Lastly, the design methodology model is applied to a navigation scenario, resulting in the creation of a steering wheel HMI prototype within a human-machine cooperation system. The prototype is then subjected to a combined subjective and objective experimental analysis, thereby validating the superiority of the steering wheel HMI’s detection indicators over those of the central control HMI and establishing the design pattern for the steering wheel HMI. Show more

Keywords: Intelligent cockpit, steering wheel, cognitive task analysis, human-machine interaction interface

DOI: 10.3233/JIFS-233500

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6439-6464, 2024

Authors: Zhao, Dongping

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-235734

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6465-6478, 2024

Authors: Ouyang, Zhiyuan | Wan, Yanling | Zhang, Tao | Wu, Wen-Ze

Article Type: Research Article

Abstract: The introduction of fractional order accumulation has played a crucial role in the development of grey forecasting methods. However, accurately identifying a single fractional order accumulation for modeling diverse sequences is challenging due to the dependence of different fractional order accumulations on data structure over time. To address this issue, we propose a novel fractional grey model abbreviated as FGMMA, incorporating a model averaging method. The new model combines existing fractional grey models by using four judgment criteria, including Akaike information criteria, Bayesian information criteria, Mallows criteria, and Jackknife criteria. Meanwhile, the cutting-edge algorithm named breed particle swarm optimization is …employed to search the optimal fractional order for each candidate model to enhance the effectiveness of the designed model. Subsequently, we conduct a Monte Carlo simulation for verification and validation purposes. Finally, empirical analysis based on energy consumption in three countries is conducted to verify the applicability of the proposed model. Compared with other benchmark models, we can conclude that the proposed model outperforms the other competitive models. Show more

Keywords: Grey forecasting model, fractional order accumulation, model averaging, breed particle swarm optimization

DOI: 10.3233/JIFS-237479

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6479-6490, 2024

Authors: Zhang, Yihao | Wang, Yuhao | Lan, Pengxiang | Xiang, Haoran | Zhu, Junlin | Yuan, Meng

Article Type: Research Article

Abstract: Conversational recommender systems use natural language conversations to elicit user preferences and recommend items proactively. Existing methods based on graph neural networks have been proven to be effective in exploiting knowledge graphs. However, node positions are often treated as constants, which leads to the neglect of graph connectivity due to fuzzy processing. In addition, although the transformer has significant advantages in understanding the text, its secondary computational complexity may be incapable when dealing with long texts. In order to solve these problems, we propose an additive positional conversational recommender model called APCR. This model converts the pair product of transformer …into a linear operation, and uses the Laplacian eigenvector to build a location graph. The extended graph neural network captures the topology structure of the location knowledge graph. Specifically, we design an encoder based on additive attention to break through the bottleneck of long text. Furthermore, we develop a recommendation model based on a positional graph neural network to match items with dialogue context, thereby capturing the graph topology. Extensive experiments on the REDIAL dataset show significant improvements in our proposed model over the state-of-the-art methods in recommendation and dialogue generation evaluations. Show more

Keywords: Interactive recommender systems, graph neural networks, knowledge graphs, additive attention

DOI: 10.3233/JIFS-230905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6491-6503, 2024

Authors: Qian, Jin | Wang, Taotao | Lu, Yuehua | Yu, Ying

Article Type: Research Article

Abstract: Multi-granularity hesitant fuzzy linguistic terms set is an effective expression of linguistic information, which can utilize some fuzzy linguistic terms to evaluate various common qualitative information and plays an important role when experts provide linguistic information to express hesitancy. Since the alternative description in the decision-making information system is characterized by multi-granularity, uncertainty, and vagueness, this paper proposes a multi-granularity hesitant fuzzy linguistic decision-making VIKOR method based on entropy weight and information transformation. Specifically, this paper firstly adopts fuzzy information entropy to obtain the weights of different attributes and introduces a multi-granularity hesitant fuzzy linguistic term set conversion method to …realize the semantic information conversion between different granularities. Then for the converted affiliation linguistic decision matrix, the entropy weighting method is used to obtain the weights of different affiliation granularity layers, and a weight optimization VIKOR method based on the affiliation linguistic decision matrix is further proposed to rank the alternatives. Finally, the feasibility of the proposed method verified by arithmetic examples, experimental analysis is carried out in terms of parameter sensitivity analysis and comparison with other methods. The experimental results prove the rationality and effectiveness of the proposed method. Show more

Keywords: Multi-granularity hesitant fuzzy term set, affiliation degree, information transformation, VIKOR method

DOI: 10.3233/JIFS-237951

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6505-6516, 2024

Authors: Prasath, J.S. | Shyja, V. Irine | Chandrakanth, P. | Kumar, Boddepalli Kiran | Raja Basha, Adam

Article Type: Research Article

Abstract: Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of Internet users over the past two decades has increased the need for cyber security. Users have provided new opportunities for attackers to do harm. Limited security budgets leave IoT devices vulnerable and easily hacked to launch distributed denial-of-service (DDoS) attacks, with disastrous results. Unfortunately, due to the unique nature of the Internet of Things environment, most security solutions and intrusion detection systems (IDS) cannot be directly adapted to the …IoT with acceptable security performance and are vulnerable to various attacks that do not benefit. In this paper we propose an optimal secure defense mechanism for DDoS in IoT network using feature optimization and intrusion detection system (OSD-IDS). In OSD-IDS mechanism, first we introduce an enhanced ResNet architecture for feature extraction which extracts more deep features from given traffic traces. An improved quantum query optimization (IQQO) algorithm for is used feature selection to selects optimal best among multiple features which reduces the data dimensionality issues. The selected features have given to the detection and classification module to classify the traffic traces are affected by intrusion or not. For this, we design a fast and accurate intrusion detection mechanism, named as hybrid deep learning technique which combines convolutional neural network (CNN) and diagonal XG boosting (CNN-DigXG) for the fast and accurate intrusion detection in IoT network. Finally, we validate the performance of proposed technique by using different benchmark datasets are BoNeSi-SlowHTTPtest and CIC-DDoS2019. The simulation results of proposed IDS mechanism are compared with the existing state-of-art IDS mechanism and analyze the performance with respects to different statistical measures. The results show that the DDoS detection accuracy of proposed OSD-IDS mechanism is high as 99.476% and 99.078% for BoNeSi-SlowHTTPtest, CICDDoS2019, respectively. Show more

Keywords: Defense mechanism, DDoS intrusion, intrusion detection system, feature selection, IoT

DOI: 10.3233/JIFS-235529

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6517-6534, 2024

Authors: Ye, Qing | Song, Zihan | Zhao, Yuqi | Zhang, Yongmei

Article Type: Research Article

Abstract: Video anomaly detection refers to the automatic identification of abnormal behaviors, objects, or events in videos. However, current methods for anomaly detection based on original frames lack a comprehensive understanding of the importance of foreground information, making it challenging to efficiently address video anomaly detection in the presence of complex background interference. In this paper, we propose a video anomaly detection algorithm based on Background Separation Network (BSN) to address this issue. Firstly, we utilize a video stabilization algorithm to reduce video jitter and enhance the quality of input video frames. Secondly, BSN shifts the focus from the entire frame …to the foreground region with higher anomaly detection value. BSN utilizes the motion pixel distribution of the video as the basis for foreground extraction, enabling pixel-level background separation to obtain more accurate and complete foreground targets. Lastly, a certain proportion of foreground targets in the foreground image are masked as background, reducing the interference caused by redundant targets on the detection results. The proposed method achieves an accuracy of 96.2% on the UCSD ped2 dataset, demonstrating its effectiveness. This method contributes to accurately detecting abnormal behaviors in real-world surveillance videos to protect the safety of public lives and assets. Show more

Keywords: Video anomaly detection, auto encoder, background separation network, video jitter elimination

DOI: 10.3233/JIFS-235717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6535-6551, 2024

Authors: Sheikh, Ansar Isak | Sadish Sendil, M. | Sridhar, P. | Thariq Hussan, M.I. | Abidin, Shafiqul | Kumar, Ravi | Irshad, Reyazur Rashid | Muniyandy, Elangovan | Phani Kumar, Solleti

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-237474

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6553-6564, 2024

Authors: Runkler, Thomas A.

Article Type: Research Article

Abstract: Pairwise fuzzy preference matrices can be constructed using expert ratings. The number of pairwise preference values to be specified by the experts increases quadratically with the number of options. Consistency (transitivity) allows to reduce this quadratic complexity to linear complexity which makes this approach feasible also for large scale applications. Preference values are usually expected to be on a fixed finite interval. Additive preference is defined on such a finite interval. However, completing preference matrices using additive consistency may yield preferences outside this finite interval. Multiplicative preference is defined on an infinite interval and is therefore not suitable here. …To overcome this problem we extend the concept of consistency beyond additive and multiplicative to arbitrary commutative, associative, and invertible operators. Infinitely many of such operators induce infinitely many types of consistency. As one example, we examine Einstein consistency, which is induced by the Einstein sum operator. Completing preference matrices using Einstein consistency always yields preferences inside the finite interval, which yields the first method that allows to construct large scale finite preference matrices using expert ratings. A case study with the real–world car preference data set indicates that Einstein consistency also yields more accurate preference estimates than additive or multiplicative consistency. Show more

Keywords: Fuzzy preference relations, consistent preference, additive preference, multiplicative preference, Einstein sum

DOI: 10.3233/JIFS-224179

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6565-6576, 2024

Authors: Bu, Yanbin | Chen, Ting | Duan, Hongxiu | Liu, Mei | Xue, Yandan

Article Type: Research Article

Abstract: In the modern world, structured and semi-structured knowledge bases hold a considerable amount of data. There-fore, people who are familiar with formal query languages should not be the only ones who can efficiently and clearly query them. Semantic Parsing (SP) is converting natural language utterances into formal meaning representations. The paper suggests a model for SP that uses a novel method of utilizing the Semi-Supervised Generative Adversarial Network (SS-GAN) to enhance the classifier performance. The proposed SS-GAN extends the fine-tuning of word embedding architectures using unlabeled examples in a generative adversarial environment. We provide a regularization strategy for addressing the …mode missing problem and unstable training in SS-GAN. The main viewpoint is to use the extracted feature vectors from the discriminator. Hence, the generator produces outputs by aiding the discriminator’s learned features. A reconstruction loss is added to the loss function of the SS-GAN to drive the genera-tor to reconstruct outputs from the discriminator’s features, hence steering the generator toward actual data configurations. The proposed reconstruction loss improves the performance of SS-GAN, produces high-quality outputs, and may be combined with other regularization loss functions to improve the performance of diverse GANs. We employ BERT word embedding for our model, which can be included in a downstream task and fine-tuned as a model, while the pre-trained BERT model can capture various linguistic properties. We examine the suggested model using the WikiSQL and SparC datasets, and the analysis findings reveal our model outperforms its rivals. The findings from our experiments indicate that the need for labeled samples can be minimized, down to as few as 100 instances, while still achieving commendable classification outcomes. Show more

Keywords: Semantic parsing, generative adversarial network, semi-supervised learning, BERT

DOI: 10.3233/JIFS-233212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6577-6588, 2024

Authors: Li, Feng | Zhu, Mozhong | Lin, Ling

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-234686

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6589-6605, 2024

Authors: Ma, Fanglan | Zhu, Changsheng | Liu, Dukui

Article Type: Research Article

Abstract: Knowledge tracing (KT), which aims to trace human knowledge learning process by using machines, has widely applied in online learning systems. It dynamically models student’s knowledge states in relation to different learning factors through their learning interactions. Recently, KT has attracted many researches attention due to its good performance to using deep learning. Although most of KT models have shown outstanding results, they have limitations: either ignore the human cognitive law and learning behavior, or lack the ability to go deeper modeling to trace knowledge state. In this paper, we propose a deeper knowledge tracking model integrating cognitive theory and …learning behavior (CLDKT). It united the advantages of memory network and recurrent neural network of the existing deep learning KT models for modeling student learning. To better implement CLDKT, we add the residual network (ResNet) to realize the deep modeling of learning behaviors. Extensive experiments on three open benchmark datasets to evaluate our model. Experimental results demonstrate that (I) CLDKT outperforms the state-of-the-art KT models on students’ performance prediction. (II) CLDKT can deeper modeling to trace knowledge state owing to the ResNet import. (III) CLDKT has better interpretability and predictability, which proves the effectiveness of the knowledge tracing model integrating cognitive law and learning behavior. Show more

Keywords: Knowledge tracing, cognitive law, learning behavior, ResNet, deep learning

DOI: 10.3233/JIFS-235723

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6607-6617, 2024