Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

Authors: Li, Yue | Cai, Qiang | Wei, Guiwu

Article Type: Research Article

Abstract: In this paper, the author propose a unique multi-attribute group decision making(MAGDM) method SVN-CPT-GRA. The method takes the single-value neutrosophic environment as the decision-making environment and uses the entropy weighted-grey relational analysis method under cumulative prospect theory. First, based on the evaluation of decision-makers, the single-value neutrosophic decision matrix was obtained. The entropy weight method was used to calculate the attribute weights. Next, according to the distance between each SVNN and the negative ideal value, combining the gray relation analysis and the cumulative prospect theory, the correlation between each solution and the attribute is compared to determine the advantages and …disadvantages of each solution. Finally, the extended gray relational analysis method is demonstrated to be effectively applied to the decision-making process through a case study of investment choices in new energy vehicles and a comparison with other methods. The main innovations in this paper can be summarized as follows. Firstly, combining the cumulative prospect theory with the gray relational analysis for decision making can better reflect and represent the psychological changes and risk sensitivity of decision makers. Secondly, the entropy weight method is used to determine the attribute weights according to the distance between SVNN and the negative ideal value, which makes the attribute weights more objective and ensures the scientificity and reasonableness of the attribute weights. Thirdly, applying GRA method to the single-value neutrosophic environment, the original simple and practical GRA method to be more widely applied to the fuzzy environment, expanding the scope of application. Overall, the extended GRA method proposed in this paper can be more efficiently and scientifically adapted to MAGDM in fuzzy environments, providing more choices for decision-makers. Show more

Keywords: Single-valued neutrosophic sets (SVNSs), grey relational analysis (GRA), multi-attribute group decision making (MAGDM), CRITIC, cumulative prospect theory (CPT)

DOI: 10.3233/JIFS-231630

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 805-819, 2024

Authors: Zhang, Yanyu | Liu, Chunyang | Rao, Xinpeng | Zhang, Xibeng | Zhou, Yi

Article Type: Research Article

Abstract: Accurate forecasting of the load of electric vehicle (EV) charging stations is critical for EV users to choose the optimal charging stations and ensure the safe and efficient operation of the power grid. The charging load of different charging stations in the same area is interrelated. However, forecasting the charging load of individual charging station using traditional time series methods is insufficient. To fully consider the spatial-temporal correlation between charging stations, this paper proposes a new charging load forecasting framework based on the Adaptive Spatial-temporal Graph Neural Network with Transformer (ASTNet-T). First, an adaptive graph is constructed based on the …spatial relationship and historical information between charging stations, and the local spatial-temporal dependencies hidden therein are captured by the spatio-temporal convolutional network. Then, a Transformer network is introduced to capture the global spatial-temporal dependencies of charging loads and predict the future multilevel charging loads of charging stations. Finally, extensive experiments are conducted on two real-world charging load datasets. The effectiveness and robustness of the proposed algorithm are verified by experiments. In the Dundee City dataset, the MAE, MAPE, and RMSE values of the proposed model are improved by approximately 71%, 90%, and 67%, respectively, compared to the suboptimal baseline model, demonstrating that the proposed algorithm significantly improves the accuracy of load forecasting. Show more

Keywords: Electric vehicle, load forecasting, graph convolutional network, temporal convolutional network, transformer

DOI: 10.3233/JIFS-231775

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 821-836, 2024

Authors: Hong, Jiajun | Tsai, Rong-Guei | Chen, Xiaolan | Lin, Di | Yu, Yicong | Lin, Ying | Li, Ronghao

Article Type: Research Article

Abstract: Marine debris is a serious global problem that is not limited to areas where humans live but also drifts around the world with wind and currents. More than 10 million tons of plastic waste flow into the ocean every year, posing a major threat to humanity. This study designs a path planning algorithm for surface garbage-cleaning robots called U*, which aims to improve the efficiency of salvaging marine debris and reduce labor and time costs. The U* algorithm consists of two procedures: exploration and path-planning. The exploration procedure searches for marine debris, while the path-planning procedure predicts the possible location …of marine debris using the velocity and direction of ocean currents and finds the shortest path by using a genetic algorithm (GA) to collect the found marine debris. According to the experimental results, the U* method is more efficient in terms of reducing path length and time costs. Show more

Keywords: Path planning, shorted path, genetic algorithm, surface garbage-cleaning robots

DOI: 10.3233/JIFS-232137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 837-850, 2024

Authors: Gao, Yuchen | Yang, Qing | Meng, Huijuan | Gao, Dexin

Article Type: Research Article

Abstract: Flame and smoke detection is a critical issue that has been widely used in various unmanned security monitoring scenarios. However, existing flame smoke detection methods suffer from low accuracy and slow speed, and these problems reduce the efficiency of real-time detection. To solve the above problems, we propose an improved YOLOv7(You Only Look Once) algorithm for flame smoke mobile detection. The algorithm uses the Kmeans algorithm to cluster the prior frames in the dataset and uses a lightweight CNeB(ConvNext Block) module to replace part of the traditional ELAN module to accelerate the detection speed while ensuring high accuracy. In addition, …we propose an improved CIoU loss function to further enhance the detection effect. The experimental results show that, compared with the original algorithm, our algorithm improves the accuracy by 4.5% and the speed by 39.87%. This indicates that our algorithm meets the real-time monitoring requirements and can be practically applied to field detection on mobile edge computing devices. Show more

Keywords: YOLO, fire detect, smoke detect, NVIDIA Jetson

DOI: 10.3233/JIFS-232650

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 851-861, 2024

Authors: Monteiro, Ana Shirley | Santiago, Regivan | Bedregal, Benjamín | Palmeira, Eduardo | Araújo, Juscelino

Article Type: Research Article

Abstract: Saminger-Platz, Klement, and Mesiar (2008) extended t -norms from a complete sublattice to its respective lattice using the conventional definition of sublattice. In contrast, Palmeira and Bedregal (2012) introduced a more inclusive sublattice definition, via retractions. They expanded various important mathematical operators, including t -norms, t -conorms, fuzzy negations, and automorphisms. They also introduced De Morgan triples (semi-triples) for these operators and provided their extensions in their groundbreaking work. In this paper, we propose a method of extending quasi-overlap functions and quasi-grouping functions defined on bounded sublattices (in a broad sense) to a bounded superlattice. To achieve that, we use …the technique proposed by Palmeira and Bedregal. We also define: quasi-overlap (resp . quasi-grouping) functions generated from quasi-grouping (resp . quasi-overlap) functions and frontier fuzzy negations, De Morgan (semi)triples for the classes of quasi-overlap functions, quasi-grouping functions and fuzzy negations, as well as its respective extensions. Finally we study properties of all extensions defined. Show more

Keywords: Retractions, extensions, quasi-overlap, quasi-grouping, bounded lattices

DOI: 10.3233/JIFS-232805

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 863-877, 2024

Authors: Hao, Xiaofan

Article Type: Research Article

Abstract: From a management perspective, performance is the desired outcome of an organization, and it is an effective output that an organization exhibits at different levels to achieve its goals. Sports event performance refers to the results and effects generated by sports events, and is a comprehensive assessment category in sports event management. It refers not only to the concept of economic level, but also to the public satisfaction of sports events and a series of social effects caemployed by them. It focuses not only on the quality and economic value of sports events themselves, but also on the achievements and …effects of sports events and society, sports events and citizens, sports events and the environment. The performance evaluation of intangible assets operation and management (IAOM) in sports events is the MAGDM. Recently, the TODIM and TOPSIS technique has been employed to manage MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are employed as a useful tool for depicting uncertain information during the performance evaluation of IAOM in sports events. In this paper, the interval-valued intuitionistic fuzzy TODIM-TOPSIS (IVIF-TODIM-TOPSIS) technique is built to manage the MAGDM under IVIFSs. At last, the numerical example for sports events performance evaluation of IAOM is employed to show the IVIF-TODIM-TOPSIS decision technique. The main contribution of this paper is outlined: (1) the TODIM technique based on TOPSIS has been extended to IVIFSs based on information Entropy; (2) the information Entropy technique is employed to derive weight based on core values under IVIFSs. (3) the IVIF-TODIM-TOPSIS technique is founded to manage the MAGDM under IVIFSs; (4) a numerical case study for performance evaluation of IAOM in sports events and some comparative analysis is supplied to validate the proposed technique. Show more

Keywords: Multiple-attribute group decision-making (MAGDM), interval-valued intuitionistic fuzzy sets (IVIFSs), TODIM, TOPSIS, performance evaluation

DOI: 10.3233/JIFS-233465

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 879-890, 2024

Authors: Cui, Qian | Rong, Shuai | Zhang, Fei | Wang, Xiaodan

Article Type: Research Article

Abstract: The consumer price index (CPI) is an important indicator to measure inflation or deflation, which is closely related to residents’ lives and affects the direction of national macroeconomic policy formulation. It is a common method to discuss CPI from the perspective of economic analysis, but the statistical principles and influencing factors related to CPI are often ignored. Thus, the impact of different types of CPI on China’s overall CPI was discussed from three aspects: statistical simulation, machine learning prediction and correlation analysis of various types of influencing factors and CPI in this study. Realistic data from the National Bureau of …Statistics from 2010 to 2022 were selected as the analysis object. The Statistical analysis showed that in 2015 and 2020, CPI had a fluctuating trend due to the impact of education and transportation. Four types of statistical models including Gauss, Lorentz, Extreme and Pearson were compared. It was determined that the R2 fitted by Extreme model was higher (R2  = 0.81), and the optimal year of simulation was around 2019, which was close to reality. To accurately predict the CPI, the results of Support Vector Machine, Regression decision tree and Gaussian regression (GPR) were compared, and the GPR was determined to be the optimal model (R2  = 0.99). In addition, Spearman matrix analyzed the correlation between CPI and various influencing factors. Herein, this study provided a new method to determine and predict the changing trend of CPI by using big data analysis. Show more

Keywords: Consumer price index, statistics, mathematical, machine learning, Spearman

DOI: 10.3233/JIFS-234102

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 891-901, 2024

Authors: Xu, Yue | Afzal, Mansour

Article Type: Research Article

Abstract: Accurately estimating concrete mechanical parameters using artificial intelligence-based methods can save time and energy. Existing nonlinear relationships between concrete components have entered uncertainty in the estimation of hardness properties of the slump and compressive strength as one of the most important parameters in concrete design. Employing regular approaches to use AI models individually in estimating dependent variables has been adopted in many studies. Therefore, the current study has aimed to develop predictive models in two categories of ensemble and hybrid frameworks to predict the hardness properties of high-performance concrete (HPC). In this regard, models based on Support Vector Regression, Decision …Tree, and AdaBoost Machine learning were coupled with a metaheuristic optimization algorithm Chaos game optimizer (CGO). Linking three predictive models as well as tuning their internal settings via optimization algorithm could generate various types of hybrid and ensemble models. By assessing the results of the proposed models for compressive strength, the performance of ADA-CGO hybrid models was calculated higher than the ensemble model of SVR-ADA-DT, with 1.22% and 166% percent difference in terms of R2 and RMSE, respectively. Also, for predicting Slump, other hybrid models appeared with weaker performance than the ensemble model, with an average difference of 40.66% in terms of the MAE index. Generally, using advanced types of individual models, including ensemble and hybrid, indicated boosted performance accompanied by low-cost modeling processes. Show more

Keywords: High-performance concrete compressive strength and slump, AdaBoost, support vector regression, decision tree, Chaos game optimizer.

DOI: 10.3233/JIFS-234409

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 903-921, 2024

Authors: Shanyong, Xu | Jicheng, Deng | Yourui, Huang | Tao, Han

Article Type: Research Article

Abstract: Aiming at the problems of poor accuracy of insulator defects, bird’s nests and foreign objects detection in transmission lines, and the difficulty of algorithm hardware deployment, this paper proposes an improved YOLOv5s multi-hidden target detection algorithm for transmission lines, firstly, in backbone, the CA attention(Coordinate attention) mechanism is integrated into the C3 module to form the C3CA module, which replaces the C3 module of the sixth and the eighth layers, and enhances the feature fusion capability; secondly, in the neck, the GSConv convolution and VoVGSCSP modules are used to replace the standard convolution and C3 modules to form a BiFPN …network, which reduces the floating-point operations of the network; finally, the improved algorithm is deployed into Raspberry Pi and accelerated by OpenVINO to realize the hardware deployment of the algorithm, which is demonstrated by experiments that: the mAP value of the algorithm is comparable to that of YOLOv3, YOLOv5 and YOLOv7 by 4.7%, 1.1%, and 1.2%, respectively. The model size is 14.2MB, and the average time to detect an image in Raspberry Pi is 78.2 milliseconds, which meets the real-time detection requirements. Show more

Keywords: Improved YOLOv5s, transmission line inspection, GSConv convolutional, raspberry Pi, OpenVINO

DOI: 10.3233/JIFS-234732

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 923-939, 2024

Authors: Yang, Xingyao | Dang, Zibo | Yu, Jiong | Zhong, Zhiqiang | Chang, Mengxue | Zhang, Zulian

Article Type: Research Article

Abstract: In existing sequential recommendation systems, user behavior data are directly used as training data for the model to complete the training process and address recommendation tasks. However, user-generated behavioral data inevitably contains noise, and the use of the Transformer’s recommendation model may lead to overfitting on such noisy data. To address this issue, we introduce a sequence recommendation algorithm model named FAT-Rec, which incorporates fusion filters and converters through joint training. By employing joint training methods, we establish both a transformer prediction layer and a CTR prediction layer. Toward the end of the model, we assign weights and sum up …the losses from the Transformer and CTR prediction layers to derive the final loss function. Experimental results on two widely used datasets, MovieLens and Goodbooks, demonstrate a significant enhancement in the performance of the proposed FAT-Rec recommendation algorithm compared with seven comparative models. This validates the efficacy of the fusion filter and transformer within the context of sequence recommendation tasks under the joint training mechanism. Show more

Keywords: Filter, self-attention mechanism, transformer, joint training, user sequence

DOI: 10.3233/JIFS-235318

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 941-953, 2024