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

Authors: Cai, Mingqian | Zhou, Ligang | Chen, Mingxian | Chen, Huayou

Article Type: Research Article

Abstract: Linguistic q-rung orthpair fuzzy sets (Lq-ROFSs) can facilitate describing the uncertainty and the vagueness information existing in the real world. Based on the advantages of Lq-ROFSs, this paper innovatively puts forward a new method to solve the multi-attribute group decision-making (MAGDM) problems when the attribute weight is completely unknown, and proves the feasibility and effectiveness of this method through illustrative examples. Firstly, we propose the linguistic q-rung orthopair fuzzy generalized power average (Lq-ROFGPA) operator, which considers not only the importance of the data itself, but also the interaction between the data, and prove its properties. In particular, the linguistic q-rung …orthopair fuzzy weighted generalized power average (Lq-ROFWGPA) operator takes into account the weight between data, which can better aggregate evaluation information. Then, we introduce decision making trial and evaluation laboratory (DEMATEL) method of the linguistic q-rung orthpair fuzzy numbers (Lq-ROFNs) to analyze the causal relationship and key elements of complex systems. Based on DEMATEL method, we further develop a weight model to calculate the attribute weights, which can make up for the deficiency which is the influence of the interaction between attributes that the existing weight determination method for Lq-ROFNs does not consider. Finally, we present a new MAGDM method based on the Lq-ROFWGPA operator and DEMATEL method. Further, several practical examples are given to illustrate the effectiveness and superiority of this new method in comparison with other existing MAGDM methods. Show more

Keywords: Multiple-attribute group decision-making, linguistic q-rung orthopair fuzzy numbers, generalized power average operator, DEMATEL

DOI: 10.3233/JIFS-230712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 639-658, 2024

Authors: Wang, Pingping | Chen, Jiahua

Article Type: Research Article

Abstract: As a decision information preference which includes membership degree (MD), non-membership degree (NMD), and probability, the probabilistic dual hesitant fuzzy set (PDHFS) is a crucial tool for effectively expressing uncertain information. In the domains of multi-attribute decision making (MADM) and multi-attribute group decision making (MAGDM), distance measures are extremely helpful tools. In this study, a novel PDHFS distance measure is put out, on which a MAGDM method that takes decision-makers’ (DMs’) psychological behavior into account is proposed. First, a novel distance measure is put forward to effectively assess the difference between different PDHFSs by adding consideration of the distances between …MDs and between NMDs. Second, a similarity-trust analysis method based on the new distance measure is employed to calculate expert weights for integrating group decisions, and the group satisfaction index and regret theory are extended to a probabilistic dual hesitant fuzzy information environment. A MAGDM method based on the novel distance measure and regret theory is proposed. Finally, the proposed method is applied to the selection of radiation protection strategies in nuclear power plants, and it is also determined through parametric analysis that DMs’ tendency to avoid regret has an impact on the outcomes of decisions. When the method proposed in this study is compared to existing approaches, the findings demonstrate that the method’s performance in resolving MAGDM issues in a PDHFS environment is superior. Show more

Keywords: Multi-attribute group decision making, probabilistic dual hesitant fuzzy set, distance measures, regret theory, EDAS, similarity-trust analysis

DOI: 10.3233/JIFS-233148

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 659-675, 2024

Authors: Quyang,

Article Type: Research Article

Abstract: The completion degree of sports training can not reach the corresponding standard, and the training effect will be greatly weakened. In order to improve the effect of sports training, the evaluation method of sports training completion degree based on deep residual network is studied. The image collector based on ARM is used to collect the action images of athletes in sports training, and the collected action images are preprocessed based on spatial scale filtering and regression factors. Construct a depth residual network, learn the implicit relationship between athletes’ state and the dynamic change process of sports training actions through off-line …training, and train the model; In the online application process, the preprocessed action images will be input into the trained evaluation model to evaluate the athletes’ sports training action completion in real time. At the same time, residual shrinkage unit and attention mechanism are used to optimize the depth residual network, which improves the training efficiency and evaluation performance of the network. The experimental results show that this method has good evaluation performance under the condition of setting parameters, and can effectively improve the effect of physical training. Show more

Keywords: Deep residual network, sports training, action completion degree, image acquisition, image denoising

DOI: 10.3233/JIFS-233773

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 677-691, 2024

Authors: Naik, N.V. | Hyma, J. | Prasad Reddy, P.V.G.D.

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-235991

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 693-709, 2024

Authors: Chunhong, Zhao | Jinglei, Nie | Shuwen, Yin | Dingyu, Zhang | Chengmo, Li

Article Type: Research Article

Abstract: The design and implementation of teachers’ classroom teaching strategies is the key to the success of second language classroom teaching. In order to improve the quality of second-language classroom teaching in universities and enhance the interactivity in the teaching process, the application of virtual reality technology in second-language classroom teaching in universities is studied. Firstly, an integrated ware platform is designed for second language classroom teaching in universities, which consists of an integrated ware library and a database. Then virtual reality technology is used to design an integrated ware library within the platform and collect information such as various teaching …media resources designed in the classroom teaching content into the integrated ware library; Utilize 3DS Max software in virtual reality technology to construct three-dimensional models of teaching scenes and entities; Choose to use linear difference method to render 3D model colors; From a visual perspective, enhance the realism of the rendered model through image enhancement technology and color contrast enhancement technology. According to the functions of the physical object, various interactive events are added to the created teaching scene and the three-dimensional model of the entity and stored in the integrable ware library to achieve panoramic roaming of the second language classroom teaching scene in universities. The experimental results show that the teaching platform designed by this method can accurately construct three-dimensional models of teaching scenes and objects with good visual effects, providing users with a more realistic sensory experience and effectively improving students’ mastery of teaching content. Show more

Keywords: Virtual reality technology, classroom teaching, 3D model, color rendering, panoramic walkthrough

DOI: 10.3233/JIFS-233210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 711-722, 2024

Authors: Kaijun, Zhao

Article Type: Research Article

Abstract: To enhance the psychological resilience of athletes, a method for evaluating the psychological resilience of High-intensity Interval Training (HIIT) athletes based on evolutionary neural networks is studied. From the six criteria of frustration coping, personal characteristics, self-promotion, self-regulation, internal protection and external protection, the evaluation index of psychological resilience of athletes in sports High-intensity Interval Training is selected; the audition indicators are qualitatively analyzed according to the principle of indicator selection, and the indicators that do not meet the requirements are eliminated; Cluster analysis and coefficient of variation analysis are used to carry out quantitative analysis on the remaining evaluation …indicators after qualitative analysis; the indicators after quantitative analysis are improved, to build the assessment index system of psychological resilience of athletes in high-intensity sports training. The Back Propagation (BP) neural network is optimized by a genetic algorithm, and the evolutionary neural network is constructed. The index data set is input into the evolutionary neural network as a sample, and the index weight value is output through training. The evaluation result and corresponding evaluation grade are determined based on the index weight value and membership degree. The experimental results show that when the number of hidden layers is 3, the calculation of evaluation index weights is the best; The weight of personal traits obtained from the evaluation results is the highest (0.206), while the weight of external protection is the lowest (0.151), and the evaluation results are basically consistent with the expert results. The above results show that this method can accurately evaluate the psychological resilience of athletes and significantly enhance their psychological resilience. Show more

Keywords: Evolutionary neural network, evaluation of psychological resilience, index system construction, genetic algorithm, weight calculation

DOI: 10.3233/JIFS-233299

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 723-737, 2024

Authors: Lei, Hongquan | Li, Diquan | Jiang, Haidong

Article Type: Research Article

Abstract: Traditional sonar image target detection analysis has problems such as long detection time, low detection accuracy and slow detection speed. To solve these problems, this paper will use the multi-feature fusion sonar image target detection algorithm based on the particle swarm optimization algorithm to analyze the sonar image. This algorithm uses the particle swarm algorithm to optimize the combination of multiple feature vectors and realizes the adaptive selection and combination of features, thus improving the accuracy and efficiency of sonar image target detection. The results show that: when other conditions are the same, under the particle group optimization algorithm, the …sonar image multiple feature detection algorithm for three sonar image detection time between 4s-9.9s, and the sonar image single feature detection algorithm of three sonar image detection time between 12s-20.9s, shows that the PSO in multiple feature fusion sonar image target detection with better performance and practicability, can be effectively applied to the sonar image target detection field. Show more

Keywords: Sonar images, particle swarm optimization algorithm, target detection, multi-feature fusion, single multi-feature fusion

DOI: 10.3233/JIFS-234876

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 739-751, 2024

Authors: Kavitha, J.C. | Subitha, D.

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-235990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 753-767, 2024

Authors: Anisha, C.D. | Arulanand, N.

Article Type: Research Article

Abstract: The Spiral Drawing Test (SDT) has become a prominent clinical marker for the early diagnosis of Parkinson’s Disorder (PD) by capturing tremor symptoms. The integration of AI algorithms into a PD diagnosis system has proven to be a breakthrough objective assessment that aids professionals in decision-making. However, there is a need for improvisation of the workflow architectures of AI models to optimize the diagnosis system by reducing the misdiagnosis rate. The proposed system presents PD prediction using a Spiral Drawing Test (SDT) image modality integrated with an Artificial Intelligence (AI) algorithm. The proposed study presents three hybrid workflow architectures formed …by integrating three core layers: a data augmentation layer, Transfer Layer (TL)-based feature extraction layer, and Deep Learning (DL)-based classification layer. The results were analyzed by conducting 18 experiments based on the hyperparameter values and workflow architectures. The highest accuracy obtained by the proposed study is 98% for Hybrid Workflow Architecture II. Show more

Keywords: Parkinson disorder, transfer learning

DOI: 10.3233/JIFS-231202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 769-787, 2024

Authors: Li, Zehao | Wang, Shunli | Yu, Chunmei | Qi, Chuangshi | Shen, Xianfeng | Fernandez, Carlos

Article Type: Research Article

Abstract: The development of a secure battery management system (BMS) for electric vehicles depends heavily on the correct assessment of the online state-of-charge (SOC) of Li-ion batteries. The ternary lithium battery is used as the research object in this paper, and a second-order RC equivalent circuit model is developed to characterize the dynamic operating characteristics of the battery. In order to solve the problem that the adaptive unscented Kalman filter (AUKF) algorithm is easy to fail SOC estimation because the error covariance matrix is not positively definite due to the incomplete accuracy of the equivalent circuit model, a corresponding solution is …proposed. Considering the poor real-time battery SOC estimate caused by the battery model’s fixed parameters, therefore we propose the Variable Forgetting Factor Recursive Least Squares (VFFRLS) algorithm for joint estimation of Li-battery SOC and the Singular Value Decomposition-AUKF (SVD-AUKF) algorithm. The SVD-AUKF algorithm can accurately estimate the SOC of the battery when the error covariance is negative. The algorithm can be adaptively adjusted in both the parameter identification and SOC estimation stages, which can effectively solve the problem of poor estimation accuracy caused by fixed parameters. According to experiments, under two separate dynamic operating situations, the joint estimation algorithm’s error is less than 2%, and its stability has also been greatly enhanced. At the same time, when the initial SOC value is set incorrectly, the convergence time of the algorithm proposed in this paper can reach within 2.1 seconds for BBDST and DST conditions, which can be well adapted to complex working conditions. Show more

Keywords: Lithium-ion battery, second-order RC equivalent circuit model, charge state, adaptive unscented Kalman filter algorithm, variable forgetting factor recursive least squares, singular value decomposition, error covariance

DOI: 10.3233/JIFS-231433

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 789-803, 2024

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

Authors: Kumar, M. | Kavitha, A.

Article Type: Research Article

Abstract: An exponential growth of users demands ubiquitous connectivity, which requires the integration of new technology. Therefore, Device to Device (D2D) communication has been considered a promising technology that utilizes effective and efficient communication. Even though numerous studies have been conducted for establishing secure D2D communication, however, existing techniques face challenges like privacy, security threats, and poor generality. To overcome these issues a novel Deep-MAD model is proposed to preserve data privacy along with its access control in the D2D network and multiple attack detection in a fog environment. A Fully Homomorphic Elliptic Curve Cryptography (FHECC) is introduced to transmit data …securely in a D2D network. The data owner uses FHECC algorithm to encrypt the plain text into cipher text before storing it on the fog. Whenever the user requests data from the fog, the fog service provider confirm the user’s access control. Furthermore, the deep learning-based Bi-LSTM is used to differentiate the device as an authorized or unauthorized user. If the IP address is genuine then the inverse FHECC is used to decrypt the data for authorized users. Otherwise, the particular device is blocked and it is sent for further verification for classifying the types of attacks. The effectiveness of the proposed strategy is examined using several parameters, such as computational complexity, scalability, accuracy, and Execution time. The proposed technique improves the overall computational overhead of 31.77, 9.34, and 4.67 better than AKA protocol, lightweight cipher, and FHEEP respectively. Show more

Keywords: Bi-LSTM, device-to-device communication, cryptography, security, fog environment

DOI: 10.3233/JIFS-235362

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 955-966, 2024

Authors: Rehman, Ubaid ur | Mahmood, Tahir

Article Type: Research Article

Abstract: Wireless sensor networks are flexible monitoring systems that save track of, data, and communicate multipoint digital information interpretations to other devices. Wireless sensor networks meaningly enhance the accuracy, breadth, and extent of local data collection, commonly doing away with the requirement for expensive data wiring and recurring manual checks at risky, remote, or inaccessible locations. As a result, it is utilized to keep an eye on systems and environmental or physical parameters. In this manuscript, we expand the Heronian mean operators in the model of bipolar complex fuzzy linguistic set to concoct bipolar complex fuzzy linguistic arithmetic Heronian mean, bipolar …complex fuzzy linguistic weighted arithmetic Heronian mean, bipolar complex fuzzy linguistic geometric Heronian mean and bipolar complex fuzzy linguistic weighted geometric Heronian mean operators. We also inspect the special cases of the invented bipolar complex fuzzy linguistic arithmetic Heronian mean and bipolar complex fuzzy linguistic geometric Heronian mean operators. Moreover, in this manuscript, we concoct a technique of decision-making in the model of a bipolar complex fuzzy linguistic set with the assistance of the invented operators. As the selection and prioritization of the various types of Wireless sensor networks is the decision-making dilemma, we prioritize various types of Wireless sensor networks by employing the concocted technique of decision-making and by taking artificial data in the model of the bipolar complex fuzzy linguistic set. To reveal the influence and excellence of the concocted work, a comparative study is given in this manuscript. Show more

Keywords: Wireless sensor networks, arithmetic/geometric Heronian mean operators, bipolar complex fuzzy linguistic set.

DOI: 10.3233/JIFS-232167

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 967-990, 2024

Authors: Zhang, Yuhua | Li, Yuerong | Che, Jinxing

Article Type: Research Article

Abstract: Accurate prediction of carbon price is of great value for production, operation, investment decisions and the establishment of carbon pricing mechanism. However, the large amount of data often limits the application of learning model with good predictive performance in carbon price prediction. Therefore, the development of learning algorithms with low computational complexity has become a research hotspot. Among them, subsampling integration technology is an effective method to reduce the computational complexity. However, lack of data representativeness in subsamples and ignorance of differences among submodels inhibit the prediction performance of the subsampled ensemble model. This project proposes an optimal weight random …forest ensemble model with cluster-based subsampling (FCM-OWSRFE) for carbon price forecasting. Firstly, Fuzzy C-means cluster-based subsampling to ensure the data representativeness of subsamples. Secondly, a series of sub-random forest models are built based on subsamples with data representativeness. Finally, an optimal weight ensemble model from these sub-models is derived. To verify the validity of the model, we test FCM-OWSRFE model with the carbon price of Guangzhou Emission Exchange and the carbon price of Hubei Carbon Emission Exchange, respectively. Experimental results show that Fuzzy C-means cluster-based subsampling and the optimal weight scheme can efficiently improve the prediction performance of the subsampled random forest ensemble model. Show more

Keywords: Random forest, Fuzzy C-means clustering, subsampling, optimal weight ensemble

DOI: 10.3233/JIFS-233422

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 991-1003, 2024

Authors: Chenmin, Ni | Marsani, Muhammad Fadhil | Shan, Fam Pei

Article Type: Research Article

Abstract: Traffic sign recognition is of great significance to promote traffic sustainability and maintain traffic safety. GPS monitoring systems and advanced autonomous vehicles are often heavily reliant on camera imagery. Algorithms based on dark channel prior are susceptible to color distortion when processing traffic images containing bright sky or high-brightness areas, which can negatively impact the identification of traffic signals and signage located in elevated positions. To address this issue, this paper proposes a dehazing algorithm (SRSTO) that combines sky region segmentation and transmittance optimization. Firstly, the gradient, brightness and saturation information are calculated, followed by the construction of a threshold …function used in area segmentation. This approach is utilized to partition the image into areas not containing sky highlights and the area that contains them. Subsequently, the dark channel images of the sky and the non-sky regions are acquired, morphological operations are further performed in layers and blocks, and then the atmospheric scattered light value is calculated. Secondly, the functional relationship between the transmittance of the sky region and the brightness of the image is constructed, the transmittance of the sky and the non-sky region are optimized, and the transmittance map is further improved by using guided filtering. A simulated annealing algorithm is employed to intelligently optimize parameters such as sky segmentation threshold and sky brightness area transmittance, followed by improving the adaptability of the algorithm. Finally, combined with Gaussian filtering and Sobel edge enhancement, the image brightness is further adjusted. Using Information Entropy and NIQE as objective evaluation indexes, combined with subjective evaluation, it is concluded that the proposed method has good convergence and self-adaptive ability, and the objective indexes and subjective effects are better, especially for the hazed images containing air traffic signs. Show more

Keywords: Haze removal, traffic image, sky region segmentation, transmission optimization, simulated annealing algorithm

DOI: 10.3233/JIFS-233433

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1005-1017, 2024

Authors: Yan, Li

Article Type: Research Article

Abstract: To improve the effect of immersive animation design, this paper combines digital media technology (DT) to establish an immersive animation design system and analyzes the media digital signal data processing algorithm. According to the advantages and disadvantages of the FHT algorithm and probabilistic algorithm, this paper proposes the FHT-SLM algorithm and the FHT-IPTS algorithm. Moreover, this paper analyzes the basic principle of TPWC transform and M-TPWC and the CO-OFDM system of cascaded FHT algorithm and M-TPWC algorithm. Finally, this paper simulates the CO-OFDM simulation system built by Matlab2018.a and Optisystem. Through the experimental analysis results, the reliability of the algorithm …and the system in this paper is verified, and the design effect of immersive animation is effectively improved. Show more

Keywords: DT, immersion, animation design, influence

DOI: 10.3233/JIFS-235793

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1019-1028, 2024

Authors: Khan, Sami Ullah | Al Ghour, Samer | Hussain, Shoukat | Saeed, Maha Mohammed | Hussain, Fiaz | Mehmood, Arif | Park, Choonkil

Article Type: Research Article

Abstract: The concept of the fuzzy graph (FG) and the simplification of its type have been designed with many real-world concerns, decision-making, network problems, and other types of impression in mind. This article is essentially a generalization of fuzzy graph theory with their association in settings where four organizations of membership grade are characterized by imprecision. The contemporary idea of domination in picture fuzzy graph (PFG) is given the generalization of fuzzy graph domination and intuitionistic fuzzy graph (IFG) domination is unit on the innovative idea of picture fuzzy graph. This article is the main focus on the graph of the …picture fuzzy set. Additionally, this research paper concludes with the concept of domination theory (DT) and double domination theory (DDT) in the concept of environment. Obviously, the framework of picture fuzzy graph is purposed and their connected theory is obtained together with presented examples. Supplementary the domination theory (DT) in the picture fuzzy graph. A few order, strength, cardinality, and domination of completeness, neighbors in a picture fuzzy graph, and the DDS and given some examples are likewise studied in the concept of picture fuzzy graph in the environment. The application of the politician campaign is novel exploiting the projected study and curious how to double dominate would relationship with the politician campaign. Finally, a comparison between the current effort and the prior effort is made, and the advantages of the work done in the context of a picture fuzzy graph are discussed. Show more

Keywords: Intuitionistic fuzzy set, intuitionistic fuzzy graph, picture fuzzy graph, domination, double domination

DOI: 10.3233/JIFS-230918

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1029-1041, 2024

Authors: Sharma, Rakesh | Sahay, Kuldeep | Singh, Satyendra

Article Type: Research Article

Abstract: The Challenge to stabilize the grid frequency increases with the increment of renewable energy resources. System inertia/frequency control is a significant concern for maintaining system stability with a fast response time during the load transition. This manuscript proposes surface-mounted permanent magnet synchronous generator (SPMSG) based wind energy conversion system (WECS) with a frequency support DC-link controlling loop and a converter protective DC-link voltage-controller frequency support system. To achieve power exchange, the frequency control system (FCS) for the SPMSG-based wind turbine supports the grid-side virtual inertia. The DC-link voltage is disturbed during the load transition to maintain the frequency, while the …electrolytic capacitor requires extra care regarding the DC bus voltage. This manuscript incorporates the FCS system with a supercapacitor and dynamic voltage limiter to avoid additional care of the DC bus voltage. A detailed analysis of system frequency with the additional load increment, normal connected load decrement, and various fault scenarios has been done. The proposed system is compared with the existing virtual inertia support (VIS). The simulation results show that the proposed frequency control system-based VIS efficiently limits the frequency deviations & DC-link voltage. The results are verified in the OPAL-RT 4510 real-time simulator environment to ensure the efficacy of the proposed system. Show more

Keywords: Frequency control system, wind energy conversion system, surface-mounted permanent magnet synchronous generator (SPMSG), variable speed wind turbine system, virtual inertia support.

DOI: 10.3233/JIFS-233729

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1043-1057, 2024

Authors: Pu, Tongzheng | Huang, Chongxing | Yang, Yifei | Yang, Jingjing | Huang, Ming

Article Type: Research Article

Abstract: Hybrid tabular-textual question answering (QA) is a crucial task in natural language processing that involves reasoning and locating answers from various information sources, primarily through numerical reasoning and span extraction. Cur-rent techniques in numerical reasoning often rely on autoregressive models to decode program sequences. However, these methods suffer from exposure bias and error propagation, which can significantly decrease the accuracy of program generation as the decoding process unfolds. To address these challenges, this paper proposes a novel multitasking hybrid tabular-textual question answering (MHTTQA) framework. Instead of generating operators and operands step by step, this framework can independently generate entire program …tuples in parallel. This innovative approach solves the problem of error propagation and greatly improves the speed of program generation. The effectiveness of the method is demonstrated through experiments using the ConvFinQA and MultiHiertt datasets. Our proposed model outperforms the strong FinQANet baselines by 7% and 7.2% Exe/Prog Acc and the MT2Net baselines by 20.9% and 9.4% EM/F1. In addition, the program generation rate of our method far exceeds that of the baseline method. Additionally, our non-autoregressive program generation method exhibits greater resilience to an increasing number of numerical reasoning steps, further highlighting the advantages of our proposed framework in the field of hybrid tabular-textual QA. Show more

Keywords: Tabular-textual question answering, numerical reasoning, program generation

DOI: 10.3233/JIFS-234719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1059-1068, 2024

Authors: Rajasekaran, P. | Duraipandian, M.

Article Type: Research Article

Abstract: Internet of Things (IoT), a distributed healthcare system has integrated different medical resources with sensors and actuators. In this research paper proposes a secure healthcare monitoring system for IoT based distributed healthcare systems in the cloud using blockchain and deep learning (DL) mechanisms. The proposed system involved three phases: secure data transmission, data storage, and disease classification system. Initially, the patients are authenticated via blockchain mechanism and their data is encrypted via Effective Key-based Rivest Shamir Adelman (EKRSA), in which the keys are generated using Circle chaotic map and Linear inertia weight-based Honey Badger Optimization (CLHBO) algorithm. Next, in the …data storage phase, these encrypted IoT data are securely stored in the cloud using blockchain technology in a distributed manner. Finally, in the disease classification, the data are gathered from the publicly available dataset, and these collected datasets are preprocessed to handle missing values and data normalization. After that, the proposed system applies a radial basis kernel-based linear discriminant analysis (RBKLDA) model to reduce the dimensionality of the dataset. At last, the disease classification is done by optimal parameter-centered bidirectional long short-term memory (OPCBLSTM). The proposed EKRSA system archives maximum throughput of 99.05% and reliability of 99.66, which is superior to the existing approaches. The OPCBLSTM is investigated for its disease classification process, the proposed one achieves 99.64% accuracy with less processing time of 6 ms, which is superior to the existing classifiers. The experimental analysis proves that the system attained better security and classification metrics results than the existing methods. Show more

Keywords: Internet of Things (IoT), healthcare monitoring, secure data transmission, blockchain, disease prediction, machine learning (ML), deep learning (DL)

DOI: 10.3233/JIFS-234884

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1069-1084, 2024

Authors: Xu, Xueyan | Wang, Jiayin

Article Type: Research Article

Abstract: In this study, we propose a new classification method by adopting some ideas originating from the fuzzy comprehensive evaluation (FCE). To make the FCE be a classifier, the class labels in classification problems are regarded as the evaluation remarks in the FCE, and the attributes in these two domains are regarded to be consistent. Then, to implement the FCE model B  = W ∘ R and obtain an accurate classification result, on the one hand, a learning algorithm, which is based on the joint distribution of attribute values and is dynamic, is proposed to construct the fuzzy relational matrix R …; on the other hand, equal weight is considered to constitute the weight vector W. Meanwhile, for a continuous dataset, the discretization method and the determination of the discretization class number corresponding to the proposed classifier are discussed. The proposed classifier not only innovatively extends the FCE to data mining but also has its own classification advantages, that is, it is easy to operate and has good interpretability. Finally, we perform some numerical experiments using publicly available datasets, and the experimental results demonstrate that the proposed classifier outperforms some existing classifiers. Show more

Keywords: Fuzzy set, fuzzy comprehensive evaluation, classification, data mining

DOI: 10.3233/JIFS-232622

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1085-1100, 2024

Authors: Tiwari, Devendra | Gupta, Anand

Article Type: Research Article

Abstract: Tables are commonly used for effective and compact representation of relational information across the data in diverse document classes like scientific papers, financial statements, newspaper articles, invoices, or product descriptions. However, table structure detection is a relatively simple process for humans, but recognizing precise table structure is still a computer vision challenge. Further, innumerable possible table layouts increase the risk of automatic topic modeling and understanding the capability of each table from the generic document. This paper develops the framework to recognize the table structure from the Compound Document Image(CDI). Initially, the bilateral filter is designed for image transformation, enhancing …CDI quality. An improved binarization-Sauvola algorithm (IBSA) is proposed to degrade the tables with uneven illumination, low contrast, and uniform background. The morphological Thinning method extracts the line from the table. The masking approach extracts the row and column from the table. Finally, the ResNet Attention model optimized over Black Widow optimization-based mutual exclusion (BWME) is developed to recognize the table structure from the document images. The UNLV, TableBank, and ICDAR-2013 table competition datasets are used to evaluate the proposed framework’s performance. Precision and accuracy are the metrics considered for evaluating the proposed framework performance. From the experimental results, the proposed framework achieved a precision value of 96.62 and the accuracy value of 94.34, which shows the effectiveness of the proposed approach’s performance. Show more

Keywords: Image transformation, table extraction, ResNet Attention model, table structure recognition

DOI: 10.3233/JIFS-232646

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1101-1114, 2024

Authors: Zhang, Yu | Shen, Bo | Zhang, Jinglin | Zhang, Zhiyuan

Article Type: Research Article

Abstract:  The task of conversational machine reading comprehension (CMRC) is an extension of single-turn machine reading comprehension to multi-turn settings, to relflect the conversational way in which people seek information. The correlations between multiple rounds of questions mean that the conversation history is critical to solving the CMRC task. However, existing CMRC models ignore the interference that arises from using excessive historical information to answer the current question when incorporating the dialogue history into the current question. In this paper, an effective Question Selection Module (QSM) is designed to select most relevant historical dialogues when answering the current question through question …coupling and coarse-to-fine matching. In addition, most existing approaches perform memory inference by stacked RNNs at context word level, without considering semantic information flowing in the direction of conversation flow. In view of this problem, we implement sequential recurrent reasoning at the turn level of the dialogue, where the turn information contains all the filtered historical semantics for the current step. We conduct experiments on two benchmark datasets, QuAC and CoQA, released by Stanford University. The results confirm that our model satisfactorily captures the valid history and performs recurrent reasoning, and our model achieves an F1-score of 83.0% on CoQA dataset and 67.8% on QuAC dataset, outperforming the best alternative model by 4.6% on CoQA and 2.7% on QuAC. Show more

Keywords: Conversational machine reading comprehension, conversation history, recurrent reasoning, attention mechanism

DOI: 10.3233/JIFS-233828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1115-1128, 2024

Authors: Li, Ping | Ni, Zhiwei | Zhu, Xuhui | Song, Juan | Liu, Wentao

Article Type: Research Article

Abstract: The histopathological image classification method, based on deep learning, can be used to assist pathologists in cancer recognition in colon histopathology. The popularization of automatic and accurate histopathological image classification methods in this way is of great significance. However, smaller medical institutions with limited medical resources may lack colon histopathology image training sets with reliable labeled information; thus they may be unable to meet the needs of deep learning for many labeled training samples. Therefore, in this paper, the colon histopathological image set with rich label information from a certain medical institution is taken as the source domain; the colon …histopathological image set from a smaller medical institution with limited medical resources is taken as the target domain. Considering the potential differences between histopathological images obtained by different institutions, this paper proposes a classification learning framework, namely unsupervised domain adaptation with local structure preservation for colon histopathological image classification, which can learn an adaptive classifier by performing distribution alignment and preserving intra-domain local structure to predict the labels of the colon histopathological images from institutions with lower medical resources. Extensive experiments demonstrate that the proposed framework shows significant improvement in accuracy and specificity of colon histopathological images without reliable labeled information compared to models without unsupervised domain adaptation. Specifically, in an affiliated hospital in Fuyang City, Anhui Province, the classification accuracy of benign and malignant colon histopathological images reaches 96.21%. The results of comparative experiments also show promising classification performance of our method in comparison with other unsupervised domain adaptation methods. Show more

Keywords: Colon cancer, histopathological image, cross-domain classification, unsupervised domain adaptation, transfer learning

DOI: 10.3233/JIFS-234920

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1129-1142, 2024

Authors: Zhang, Kai | Wang, Yixiang | Hu, Zhicheng | Zhou, Ligang

Article Type: Research Article

Abstract: Combination forecasting is an effective tool to improve the forecasting rate by combining single forecasting methods. The purpose of this paper is to apply a new combination forecasting model to predicting the BRT crude oil price based on the dispersion degree of two triangular fuzzy numbers with the circumcenter distance and radius of the circumcircle. First, a dispersion degree of two triangular fuzzy numbers is proposed to measure the triangular fuzzy numbers with the circumcenter distance and radius of the circumcircle, which can be used to predict the fluctuating trend and is suitable for crude oil futures price. Second, three …single prediction methods (ARIMA, LSSVR and GRNN) are then presented to combine traditional statistical time set prediction with the latest machine learning time prediction methods which can strengthen the advantage and weaken the disadvantage. Finally, the practical example of crude oil price forecasting for London Brent crude futures is employed to illustrate the validity of the proposed forecasting method. The experimental results show that the proposed forecasting method produces much better forecasting performance than some existing triangular fuzzy models. The prediction error is reduced to 2.7 from 3–5 in oil price combination forecasting, in another comparison experiment the error is reduced to 0.0135 from 1. The proposed combination forecasting method, which fully capitalizes on the time sets forecasting model and intelligent algorithm, makes the triangular fuzzy prediction more accurate than before and has effective applicability. Show more

Keywords: Oil price forecasting, dispersion degree of two triangular fuzzy numbers, ARIMA, LSSVR, GRNN

DOI: 10.3233/JIFS-230741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1143-1166, 2024

Authors: Narayanan, Badri | Muthusamy, Sreekumar

Article Type: Research Article

Abstract:  The performance of Interval type-2 fuzzy logic system (IT2FLS) can be affected by many factors including the type of reduction methodology followed and the kind of membership function applied. Further, a particular membership function is influenced by its construction, the type of optimisation and adaptiveness applied, and the learning scheme adopted. The available literature lags in providing detailed information about such factors affecting the performance of IT2FLS. In this work, an attempt has been made to comprehensively study the factors affecting the performance of IT2FLS by introducing a new trapezoidal-triangular membership function (TTMF). A real-time application of drilling operation has …been considered as an example for predicting temperature of the job, which is considered as one of the key state variables to evaluate. A detailed comparison based on membership functions (MFs) such as triangular membership function (TrMF), trapezoidal membership function (TMF), the newly introduced trapezoidal-triangular membership function (TTMF), semi-elliptic membership function (SEMF), and Gaussian membership function (GMF) has been performed and presented. Further, the average error rate obtained with two “type-reduction” methods such as “Wu-Mendel” uncertainty bounds and Center of sets type reduction (COS TR) has also been discussed. This study provides information for selecting a particular MF and “type reduction” scheme for the implementation of IT2FLS. Also, concludes that MF having fewer parameters such as GMF and SEMF possess significant advantages in terms of computation complexity compared to others. Show more

Keywords: Interval type-2 fuzzy logic system, semi-elliptic membership function, trapezoidal membership function, trapezoidal-triangular membership function, center of sets type reduction, Wu-Mendel uncertainty bound

DOI: 10.3233/JIFS-231412

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1167-1182, 2024

Authors: Fan, Jianping | Zhu, Qianwei | Wu, Meiqin

Article Type: Research Article

Abstract: Failure mode and effect analysis (FMEA) is an effective quality management tool used to improve product quality and reliability. However, with the application of FMEA, its shortcomings are exposed regarding risk assessment, weight determination, and failure mode risk prioritization. This paper proposes a new FMEA model using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method based on the Interval-valued linguistic Z-numbers (IVLZNs). Specifically, IVLZNs and the Interval-valued linguistic Z-numbers weighted arithmetic averaging (IVLZNWAA) operator are used to evaluate and aggregate risk information of failure modes; the maximum deviation method is used to determine the weight of risk factors; the IVLZNs-VIKOR method …is used to determine the risk priority of failure modes. Then, a numerical example is given to verify the effectiveness of the proposed model. Finally, a comparative analysis is made to demonstrate the feasibility and rationality of the proposed method. Show more

Keywords: Interval-valued linguistic Z-numbers (IVLZNs), interval-valued linguistic Z-numbers weighted arithmetic averaging (IVLZNWAA) operator, failure mode and effect analysis (FMEA), VIKOR method

DOI: 10.3233/JIFS-231527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1183-1199, 2024

Authors: Nguyen, Anh Tu | Bui, Thanh Lam | Bui, Huy Anh | Nguyen, Sy Tai | Nguyen, Xuan Thuan

Article Type: Research Article

Abstract: With the superior development of technology, mobile robots have become an essential part of humans’ daily life. Consequently, interacting and dealing with them pushes us to develop and propose different suitable Human-Robot Interaction (HRI) systems that can detect the interacted user’s actions and achieve the desired output in real-time. In this paper, we propose a closed-loop smart mechanism for two main agents: the hand gloves’ controller and the mobile robot. To be more specific, the developed model employs flex sensors to measure the curve of the finger. The sensor signals are then processed by aiding the Hedge Algebras Algorithm to …control the movement direction and customize the speed of the mobile robot via wireless communication. Numerical simulation and experiments demonstrate that the mobile robot could operate reliably, respond rapidly to control signals, and vary its speed continually based on the different finger gestures. Besides, the control results are also compared with those obtained from the traditional fuzzy controller to prove the superiority and efficiency of the proposed method. Show more

Keywords: Hedge algebras algorithm, hand gestures, mobile robot, fuzzy controller, wireless protocol

DOI: 10.3233/JIFS-232116

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1201-1212, 2024

Authors: Li, Jing | Jia, Bin | Fan, Jiulun | Yu, Haiyan | Hu, Yifan | Zhao, Feng

Article Type: Research Article

Abstract: The relative entropy fuzzy c-means (REFCM) clustering algorithm improves the robustness of the fuzzy c-means (FCM) algorithm against noise. However, its increased complexity results in slower convergence. To address this issue, we have proposed a suppressed REFCM (SREFCM) algorithm, in which a constant suppression rate, α, is selected. However, in cases where external factors, such as changes in the data structure, are present, relying on a fixed α value may result in a decline in algorithm performance, which is clearly unsuitable. Therefore, the adaptive selection of parameters is a critical step. Based on the data structure itself, this paper proposes …an algorithm for adaptive parameter selection utilizing partition entropy coefficient and alternating modified partition coefficient, and compares it to six parameter selection algorithms based on generalized rules: θ ′ type, ρ type, β type, τ type, σ type and ξ type. Empirical findings indicate that adapting parameters can enhance the partitioning capability of the algorithm while ensuring a rapid convergence rate. Show more

Keywords: Suppressed relative entropy fuzzy c-means clustering algorithm, suppression rate, partition entropy coefficient, alternating modified partition coefficient, adaptive parameter selection

DOI: 10.3233/JIFS-232999

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1213-1228, 2024

Authors: Tang, Shangjie | Zhong, Youkun

Article Type: Research Article

Abstract: The development of rural preschool education (RPE) is not only related to the healthy growth of rural preschool children, but also to social fairness and sustainable development. Therefore, the development of RPE not only involves the expansion of quantity, but also the improvement of its quality. At present, in China’s RPE, the determination of value goals There are still many obstacles in terms of source supply, institutional mechanism construction, development mode selection, and external environment construction, which make the high-quality development of RPE lack good internal motivation and external support. In view of this situation, some researchers have begun to …explore the high-quality and sustainable development of RPE differently. However, the high-quality development of RPE is a systematic reform project that needs to start from the present. From multiple perspectives such as reality and history, internal and external education, this paper examines the systematic and global nature of RPE reform and development. The development level evaluation of RPE is a MADM. In this paper, the generalized weighted Bonferroni mean (GWBM) decision operator and power average (PA) is designed to manage the MADM under single-valued neutrosophic sets (SVNSs). Then, the generalized single-valued neutrosophic number power WBM (GSVNNPWBM) decision operator is constructed and the MADM model are constructed based on GSVNNPWBM decision operator. Finally, a decision example for development level evaluation of RPE and some useful comparative studies were constructed to verify the GSVNNPWBM decision operator. Show more

Keywords: MADM, single-valued neutrosophic sets (SVNSs), GWBM operator, PA operator, development level evaluation

DOI: 10.3233/JIFS-233121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1229-1244, 2024

Authors: Fu, Liping

Article Type: Research Article

Abstract: Today, information technology has penetrated into various fields of universities, and the development of information technology in teaching, scientific research, management, and services has become a catalyst for promoting changes in universities. In terms of teaching informatization, the Internet provides a powerful tool for knowledge dissemination and a huge platform for learning and communication between university teachers and students. Knowledge sharing has become easier, and the era of mutual interaction between teachers and students has arrived. University teachers need to quickly face this challenge, adapt to the new teaching and learning environment, improve their own literacy, enhance their information-based teaching …ability, change their teaching behavior, and thereby improve the quality of university education and meet the needs of society for talent cultivation. The informationization teaching ability evaluation of university teachers is a classical MAGDM problems. Recently, the Exponential TODIM(ExpTODIM) and (grey relational analysis) GRA method has been used to cope with MAGDM issues. The interval neutrosophic sets (INSs) are used as a tool for characterizing uncertain information during the informationization teaching ability evaluation of university teachers. In this manuscript, the interval neutrosophic number Exponential TODIM-GRA (INN-ExpTODIM-GRA) method is built to solve the MAGDM under INSs. In the end, a numerical case study for informationization teaching ability evaluation of university teachers is given to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), interval neutrosophic sets (INSs), ExpTODIM, GRA, informationization teaching ability evaluation

DOI: 10.3233/JIFS-233192

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1245-1258, 2024

Authors: Kumar, Ajay | Singh, Anuj Kumar | Garg, Ankit

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-233443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1259-1273, 2024

Authors: Jia, Xiaoying

Article Type: Research Article

Abstract: Under the premise of today’s socialist modernization construction, the development and progress of the country require more outstanding talents such as aspiring youth and intellectuals to participate, which puts forward higher requirements for education quality indicators and various aspects of operation. As the main institution and environment for implementing educational activities, the effectiveness of school management organization has a direct impact and even a decisive role on the quality of education. Therefore, how to improve the quality management of school education has become a hot topic in the education industry. The education quality management evaluation in higher education institutions is …viewed as the multiple-attribute decision-making (MADM) issue. In this paper, the interval-valued neutrosophic number cross-entropy (IVNN-CE) technique is built under interval-valued neutrosophic sets (IVNSs) based on the traditional cross-entropy technique. Then, combine traditional cross-entropy technique with IVNSs, the IVNN-CE technique is constructed for MADM under IVNSs. Finally, the numerical example for education quality management evaluation in higher education institutions was constructed and some comparisons is employed to verify advantages of IVNN-CE technique. The main contribution of this paper is constructed: (1) the cross-entropy model is extended to IVNSs; (1) the CRITIC technique is employed to construct the attribute weights under IVNSs; (3) the IVNN-CE technique is constructed to manage the MADM under IVNSs; (4) a case study about education quality management evaluation in higher education institutions is constructed to show the built technique; (5) some comparative algorithms are constructed to verify the rationality of IVNN-CE technique. Show more

Keywords: Multiple attribute decision making (MADM), interval-valued neutrosophic sets (IVNSs), cross-entropy technique, education quality management evaluation

DOI: 10.3233/JIFS-233481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1275-1286, 2024

Authors: Liu, Yayun | Ning, Kuangfeng

Article Type: Research Article

Abstract: The adaptive fusion module with an attention mechanism functions by employing a dual-channel graph convolutional network to aggregate neighborhood information. The resulting embeddings are then utilized to calculate interaction terms, thereby incorporating additional information. To enhance the relevance of fusion information, an adaptive fusion module with an attention mechanism is constructed. This module selectively combines the neighborhood aggregation and interaction terms, prioritizing the most pertinent information. Through this adaptive fusion process, the algorithm effectively captures both neighborhood features and other nonlinear information, leading to improved overall performance. Neighborhood Aggregation Interaction Graph Convolutional Network Adaptive Fusion (NAIGCNAF) is a graph representation …learning algorithm designed to obtain low-dimensional node representations while preserving graph properties. It addresses the limitations of existing algorithms, which tend to focus solely on aggregating neighborhood features and overlook other nonlinear information. NAIGCNAF utilizes a dual-channel graph convolutional network for neighborhood aggregation and calculates interaction terms based on the resulting embeddings. Additionally, it incorporates an adaptive fusion module with an attention mechanism to enhance the relevance of fusion information. Extensive evaluations on three citation datasets demonstrate that NAIGCNAF outperforms other algorithms such as GCN, Neighborhood Aggregation, and AIR-GCN. NAIGCNAF achieves notable improvements in classification accuracy, ranging from 1.0 to 1.6 percentage points on the Cora dataset, 1.1 to 2.4 percentage points on the Citeseer dataset, and 0.3 to 0.9 percentage points on the Pubmed dataset. Moreover, in visualization tasks, NAIGCNAF exhibits clearer boundaries and stronger aggregation within clusters, enhancing its effectiveness. Additionally, the algorithm showcases faster convergence rates and smoother accuracy curves, further emphasizing its ability to improve benchmark algorithm performance. Show more

Keywords: Graph representation learning, graph convolutional neural network (GCNN), attention mechanism, node classification

DOI: 10.3233/JIFS-234086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1287-1314, 2024

Authors: Lv, Jingjing

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-234212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1315-1328, 2024

Authors: Jiang, Zhujun | Zhou, Jieyong | He, Qixiang

Article Type: Research Article

Abstract: Fuzzy singular Lyapunov matrix equations have many applications, but feasible numerical methods to solve them are absent. In this paper, we propose an efficient numerical method for fuzzy singular Lyapunov matrix equations, where A is crisp and semi-stable. In our method, we transform fuzzy singular Lyapunov matrix equation into two crisp Lyapunov matrix equations. Then we solve the least squares solutions of the two crisp Lyapunov matrix equations, respectively. The existence of fuzzy solution is also considered. At last, two small examples are presented to illustrate the validate of the method and two large scale examples that the existing method …fails to slove are presented to show the efficiency of the method. Show more

Keywords: Fuzzy, singular Lyapunov matrix equations, semi-stable, Extension method, the least squares solutions

DOI: 10.3233/JIFS-230990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1329-1340, 2024

Authors: Geetha, M.P. | Karthika Renuka, D.

Article Type: Research Article

Abstract: A recommendation system serves as a distributed information filter, predicting customer preferences in reviews, ratings, and comments. Analysing customer behaviour aids in understanding needs and predicting intentions. E-commerce tracks product usage and sentiment to provide a personalized network based on consumer preference modelling. The challenge lies in optimizing item selection for suitable consumers to enhance performance. To address this, an imperative is the item recommendation approach for modelling future consumer behaviour. However, traditional machine learning methods often overlook dynamic product recommendations due to evolving user interests and changes in preferences reflected in customer ratings, causing cold-start issues. To overcome these …challenges, a comprehensive deep learning approach is introduced. This approach incorporates a deep neural network for consumer preference prediction, utilizing a multi-task learning paradigm to accommodate variations in consumer ratings. The research contribution lies in applying this network to predict consumer preference scores based on latent multimodal information and item characteristics. Initially, the architecture manages changing consumer aspects and preferences by extracting features and latent factors from customer review rating data. These latent factors include customer demographic information and other concealed features that signify preferences based on experiences and behaviours. Extracted latent features are processed using a sentiment analysis model to generate embedding latent features. A finely-tuned deep neural network with hyper-parameter adjustments serves as a prediction network, forming a customer performance-oriented recommendation system. It processes embedded latent features along with associated sentiments to achieve high prediction accuracy, reliability, and latency. The deep learning architecture, enriched with consumer-specific discriminative information, generates an objective function for item recommendations with minimal error, significantly enhancing predictive performance. Empirical experiments on Amazon review datasets validate the proposed model’s performance, showcasing its enhanced effectiveness and scalability in handling substantial data volumes. Show more

Keywords: Product recommendation, multitask learning, consumer buying behaviour analysis, user preference modelling

DOI: 10.3233/JIFS-231116

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1341-1357, 2024

Authors: Liu, Feng-Lang | Chien, Li-Chih | Chang, Ting-Yu | Ku, Cooper Cheng-Yuan | Chang, Ching-Ter

Article Type: Research Article

Abstract: Improving technological innovation (TI) capabilities is an integral component of government policies aimed at improving the competitiveness of small and medium enterprises (SMEs). This study aims to address implementation challenges arising from the use of Qualitative Forecasting Method (QFM) in new product development programs and proposes a novel method to aid decision makers (DMs) in their decision-making process. To tackle this issue, a hybrid method is proposed, incorporating Fuzzy Delphi method (FDM), Fuzzy Analytic Hierarchy Process (FAHP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Multi-Choice Goal Programming with utility function (MCGP-U), while introducing prospect theory …as a novel approach. is proposed. The proposed method offers several advantages, including effective early planning, accurate identification of key success factors (KSFs), selection of the most suitable project leader, and estimation of the most reasonable resource investment, all of which are critical factors for success in TI for enterprises. The research results show that (1) the proposed method reduces project execution time by 20% compared to the original manual planning, (2) it facilitates the acquisition of KSFs using a rational approach to ensure project success, and (3) it increases the financial returns of the company by 17% compared to the company’s forecast. In summary, this paper makes a significant contribution to practical applications and additionally contributes to decision-making field by introducing prospect theory into the proposed hybrid method. Show more

Keywords: Technological innovation, decision-making model, fuzzy, MCGP

DOI: 10.3233/JIFS-234327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1359-1378, 2024

Authors: Liu, Die | Xu, MengDie | Li, ZhiTing | He, Yingying | Zheng, Long | Xue, Pengpeng | Wu, Xiaodong

Article Type: Research Article

Abstract: Concrete surface crack detection plays a crucial role in ensuring concrete safety. However, manual crack detection is time-consuming, necessitating the development of an automatic method to streamline the process. Nonetheless, detecting concrete cracks automatically remains challenging due to the heterogeneous strength of cracks and the complex background. To address this issue, we propose a multi-scale residual encoding network for concrete crack segmentation. This network leverages the U-NET basic network structure to merge feature maps from different levels into low-level features, thus enhancing the utilization of predicted feature maps. The primary contribution of this research is the enhancement of the U-NET …coding network through the incorporation of a residual structure. This modification improves the coding network’s ability to extract features related to small cracks. Furthermore, an attention mechanism is utilized within the network to enhance the perceptual field information of the crack feature map. The integration of this mechanism enhances the accuracy of crack detection across various scales. Furthermore, we introduce a specially designed loss function tailored to crack datasets to tackle the problem of imbalanced positive and negative samples in concrete crack images caused by data imbalance. This loss function helps improve the prediction accuracy of crack pixels. To demonstrate the superiority and universality of our proposed method, we conducted a comparative evaluation against state-of-the-art edge detection and semantic segmentation methods using a standardized evaluation approach. Experimental results on the SDNET2018 dataset demonstrate the effectiveness of our method, achieving mIOU, F1-score, Precision, and Recall scores of 0.862, 0.941, 0.945, and 0.9394, respectively. Show more

Keywords: Crack segmentation, U-NET, residual structure, attention mechanism

DOI: 10.3233/JIFS-231736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1379-1392, 2024

Authors: Zhang, Bin | Li, Jianqi | Li, Zewen | Sun, Jian | Xia, Yixiang | Zou, Pinlong

Article Type: Research Article

Abstract: The prediction of power demand for unmanned aerial vehicles (UAV) is an essential basis to ensure the rational distribution of the energy system and stable economic flight. In order to accurately predict the demand power of oil-electric hybrid UAV, a method based on variational mode decomposition (VMD) and Sparrow Search Algorithm (SSA) is proposed to optimize the hybrid prediction model composed of long-short term memory (LSTM) and Least Squares Support Vector Machine (LSSVM). Firstly, perform VMD decomposition on the raw demand power data and use the sample entropy method to classify the feature-distinct mode components into high-frequency and low-frequency categories. …Then, each modality component was separately input into the mixed model for rolling prediction. The LSSVM model and LSTM model were used to process low-frequency and high-frequency components, respectively. Finally, the predicted values for each modal component are linearly combined to obtain the final predicted value for power demand. Compared with the current models, the prediction model constructed in this paper stands out for its superior ability to track the changing trends of power demand and achieve the highest level of prediction accuracy. Show more

Keywords: UAV demand power, variational mode decomposition, sparrow search algorithm, long-short term memory, long-short term memory

DOI: 10.3233/JIFS-234263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1393-1406, 2024

Authors: Hou, Junjian | Xu, Yaxiong | He, Wenbin | Zhong, Yudong | Zhao, Dengfeng | Zhou, Fang | Zhao, Mingyuan | Dong, Shesen

Article Type: Research Article

Abstract: Fatigue driving is one of the primary causative factors of road accidents. It is of great significance to discern, identify and warn drivers in time for traffic safety and reduce traffic accidents. In this paper, a systematic review for the fatigue driving behavior recognition method is developed to analyze its research status and development trends. Firstly, the data information and its application scenarios related to fatigue driving is detailed. Three driving behavior recognition methods based on different types of signal data are summarized and analyzed, and this signal data can be divided into physiological signal characteristics, visual signal characteristics, vehicle …sensor data characteristics and multi-data information fusion. By summarizing and comparing the recognition effect of existing fatigue driving recognition methods, combined with deep learning technology, the paper concludes the fatigue driving behavior recognition method based on single data source has some shortcomings such as low accuracy and easy to be affected by external factors, but the recognition method based on multi-feature information fusion can achieve a exhilarated recognition result. Finally, some prospects are given to analyze the development trend of fatigue driving behavior recognition in the future. Show more

Keywords: Fatigue driving, information fusion, physiological signals, deep learning, vehicle sensors

DOI: 10.3233/JIFS-235075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1407-1427, 2024