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

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Abstract: This article has been retracted.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