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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Article Type: Research Article
Abstract: The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deep learning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deep learning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-time processing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. Our …approach involves generating a custom dataset with diverse traffic sign images, followed by conducting training, validation, and testing sets to ensure the robustness and generalization of the model. Experimental results and performance evaluation demonstrate the effectiveness of the proposed method. Extensive experiments show that our model achieved remarkable accuracy rates in traffic sign detection, meeting the real-time requirements of the input data. Show more
Keywords: Traffic sign detection, deep learning, YOLOv8 model, self-driving cars, real-time processing
DOI: 10.3233/JIFS-235863
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5975-5984, 2024
Authors: Simin, Wang | Yifei, Kang | Yixuan, Xu | Chunmiao, Ma | Jinyu, Wang | Weiguo, Wu
Article Type: Research Article
Abstract: Task scheduling based on temperature perception is beneficial for avoiding hotspots and optimizing the internal temperature distribution of data centers. However, the accuracy of task scheduling largely depends on the accuracy of temperature prediction. There are many features that affect the accuracy of temperature prediction in data centers, and the variation periods of these features vary greatly. Traditional machine learning models are difficult to accurately fit them. Therefore, this article proposes a step-by-step temperature prediction algorithm based on Gated Recurrent Unit (GRU). This algorithm establishes prediction models for important parameters such as CPU utilization and air conditioning temperature that affect …temperature prediction, and uses the outputs of these two models as inputs for the server temperature prediction model to better fit the changes of feature values. The model combines the principle of thermal locality and integrates the temperature of upper and lower servers for joint modeling. Experiments show that our prediction model can accurately predict the inlet temperature evolution of the server with dynamic workload. RSME reaches 0.278 and the average prediction temperature difference is 0.633, which is much higher than the traditional model. In addition, this article also propose a minimum temperature difference scheduling algorithm based on temperature prediction model, which can effectively reduce the number of servers running at high temperature and low temperature in the data center, make the temperature of the data center more balanced and achieve better energy-saving compared with other baseline algorithms. Show more
Keywords: Machine learning, GRU, data center, energy saving, temperature prediction, thermal-aware, task scheduling
DOI: 10.3233/JIFS-231320
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5985-5999, 2024
Authors: Tong, Mingjia
Article Type: Research Article
Abstract: How to explore the potential value of landscape, realize the organic combination of tourism landscape, enrich landscape elements and enhance tourism experience has become an important topic of tourism landscape planning and design, which is also a practical problem that needs to be solved urgently in the process of tourism landscape development and planning in different regions of China. The tourism landscape planning design scheme evaluation based on the virtual reality technology a typical multi-attribute group decision-making (MAGDM) problem. With the complexity of economic activities, uncertain information has an increasing impact on production activities. However, due to the ambiguity and …uncertainty of human cognition, the factors affecting the risk of things cannot be accurately expressed. Therefore, selecting spherical fuzzy sets (SFSs) can make the expression of information more accurate and complete. On basis of the TODIM method and the PROMETHEE method, in this study, spherical fuzzy number TOMIM-PROMETHEE (SFN-TOMIM-PROMETHEE) method is implemented to solve the MAGDM problem under SFSs. Furthermore, CRITIC method under SFSs is implemented to determine relative weights. Then a numerical example for tourism landscape planning design scheme evaluation based on the virtual reality technology is selected to illustrate the effectiveness and practicality of the method. Finally, the comparative analysis shows that the SFN-TOMIM-PROMETHEE method under SFSs is an effective method to deal with MAGDM problems. The main contribution of this paper is managed: (1) the TODIM and PROMETHEE technique was extended to SFSs; (2) CRITIC technique is employed to manage the weight values under SFSs. (3) the SFN-TOMIM-PROMETHEE technique is founded to manage the MAGDM under IVPFSs; (4) a numerical example for tourism landscape planning design scheme evaluation based on the virtual reality technology and comparison analysis are constructed to verify the feasibility and effectiveness of the SFN-TOMIM-PROMETHEE technique. Show more
Keywords: Multi-attribute group decision-making (MAGDM), TODIM-PROMETHEE method, spherical fuzzy sets, CRITIC method, tourism landscape planning design scheme
DOI: 10.3233/JIFS-233401
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6001-6017, 2024
Authors: Tyagi, Pooja | Singh, Jaspreeti | Gosain, Anjana
Article Type: Research Article
Abstract: The contemporary real-world datasets often suffer from the problem of class imbalance as well as high dimensionality. For combating class imbalance, data resampling is a commonly used approach whereas for tackling high dimensionality feature selection is used. The aforesaid problems have been studied extensively as independent problems in the literature but the possible synergy between them is still not clear. This paper studies the effects of addressing both the issues in conjunction by using a combination of resampling and feature selection techniques on binary-class imbalance classification. In particular, the primary goal of this study is to prioritize the sequence or …pipeline of using these techniques and to analyze the performance of the two opposite pipelines that apply feature selection before or after resampling techniques i.e., F + S or S + F. For this, a comprehensive empirical study is carried out by conducting a total of 34,560 tests on 30 publicly available datasets using a combination of 12 resampling techniques for class imbalance and 12 feature selection methods, evaluating the performance on 4 different classifiers. Through the experiments we conclude that there is no specific pipeline that proves better than the other and both the pipelines should be considered for obtaining the best classification results on high dimensional imbalanced data. Additionally, while using Decision Tree (DT) or Random Forest (RF) as base learner the predominance of S + F over F + S is observed whereas in case of Support Vector Machine (SVM) and Logistic Regression (LR), F + S outperforms S + F in most cases. According to the mean ranking obtained from Friedman test the best combination of resampling and feature selection techniques for DT, SVM, LR and RF are SMOTE + RFE (Synthetic Minority Oversampling Technique and Recursive Feature Elimination), Least Absolute Shrinkage and Selection Operator (LASSO) + SMOTE, SMOTE + Embedded feature selection using RF and SMOTE + RFE respectively. Show more
Keywords: Imbalanced data, feature selection, machine learning, oversampling, undersampling
DOI: 10.3233/JIFS-233511
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6019-6040, 2024
Authors: Yuan, Songlin
Article Type: Research Article
Abstract: Since the dawn of the digital web era, web-based learning resources have become more and more significant in the field of education. To a certain extent, the visual communication design of these resources influences how well students learn. In view of this, the study proposes a deep learning-based approach to visual communication design. Convolutional neural networks are introduced to automatically construct the visual communication interface, a recommendation algorithm is used to develop the system’s recommendation function, and machine translation is used to translate the language description text. The study method’s efficacy was evaluated. According to the experimental results, the research …method’s runtime in a color environment was only about 37.7 seconds at 4k resolution; in a non-color environment, the method’s F1 value was 0.87 at a recommended list length of 35, which was higher than that of other methods; and when it came to the interface solutions in real terms, the research method produced 526 at 30 buttons. The aforementioned findings demonstrate that the suggested approach can successfully increase the visual communication’s design speed and performance in online learning materials and offer a suitable answer to the needs of real-world applications. Show more
Keywords: Visual communication design, convolutional neural networks, transformer, learning resources, teacher forcing
DOI: 10.3233/JIFS-233944
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6041-6052, 2024
Authors: Gou, Hongyuan | Zhang, Xianyong
Article Type: Research Article
Abstract: Multi-granularity rough sets facilitate knowledge-based granular computing, and their compromised models (called CMGRSs) outperform classical optimistic and pessimistic models with extremity. Three-level CMGRSs with statistic-optimization-location effectively process hierarchical granularities with attribute enlargements, and they are worth generalizing for general granularities with arbitrary feature subsets. Thus, three-level CMGRSs on knowledge, approximation, and accuracy are established for arbitrary granularities by using three-way decision (3WD). Corresponding 3WD-CMGRSs adopt statistic-optimization-3WD by adding optimistic and pessimistic bounds to the representative location, so they resort to optimal index sets to acquire the multi-granularity equilibrium and decision systematicness. As a result, multiple CMGRSs emerge within the three-level …and three-way framework, they improve the classical MGRSs and enrich 3WD as well as three-level analysis, and exhibit the good simulation, extension, effectiveness, improvement, and generalization. Firstly at the knowledge level, cardinality statistic-optimization improves previous label statistic-optimization for equilibrium realization, so CMGRSs are improved for hierarchical granularities while 3WD-CMGRSs are proposed for arbitrary granularities. Then at the approximation and accuracy levels, measure statistic-optimization determines optimal index sets, so 3WD-CMGRSs are similarly proposed to complete the simulation and extension. Furthermore, mathematical properties and computational algorithms of relevant models are investigated. Finally, three-level 3WD-CMGRSs are illustrated by table examples and are validated by data experiments. Show more
Keywords: Multi-granularity rough sets, compromised models, statistic-optimal equilibrium, three-way decision, three-level analysis
DOI: 10.3233/JIFS-236063
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6053-6081, 2024
Authors: Zhang, Hao | Sheng, Yuhong
Article Type: Research Article
Abstract: In this study, an innovative approach that combines least square support vector regression (LSSVR) with uncertainty theory to enhance its performance in dealing with low-quality or imprecise data from real-world be proposed. The resulting model, called uncertain least square support vector regression (ULSSVR), incorporates chance constraints and simplified parameter selection, which are critical to handle imprecise observations. A numerical algorithm called the conjugate residual method (CR) is introduced to reduce the computational complexity of the model solution. The experimental results using both small and medium-sized datasets demonstrate the superior performance of ULSSVR in terms of prediction accuracy and generalization ability …compared to other models such as uncertain support vector regression (USVR), uncertain linear lodel, uncertain polynomial model, and uncertain growth models. ULSSVR not only improves prediction accuracy by at least 28.49% but also demonstrates faster computational speed. Overall, ULSSVR presents a promising solution for data science and internet applications where dealing with imprecise and low-quality data is a common challenge. Show more
Keywords: Least square support vector regression, uncertainty theory, conjugate residual method, chance constraint
DOI: 10.3233/JIFS-236849
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6083-6092, 2024
Authors: Shi, Lin
Article Type: Research Article
Abstract: With the improvement of the public’s aesthetic level, product appearance has become an important influencing factor for consumers to make purchasing decisions. Product styling design is based on this market demand, combining the aesthetic and functional aspects of the product to create a personalized product appearance, in order to better attract consumers, improve the competitiveness and added value of the product. Usually, product styling design involves multiple elements such as product form, color, proportion, etc. The quality evaluation of product styling design is a MAGDM problems. Recently, the TODIM and EDAS technique has been employed to manage MAGDM issues. The …interval-valued Pythagorean fuzzy sets (IVPFSs) are employed as a tool for characterizing uncertain information during the quality evaluation of product styling design. In this paper, the interval-valued Pythagorean fuzzy TODIM-EDAS (IVPF-TODIM-EDAS) technique is construct to manage the MAGDM under IVPFSs. Finally, a numerical case study for quality evaluation of product styling design is employed to validate the proposed technique. The main contribution of this paper is managed: (1) the TODIM and EDAS technique was extended to IVPFSs; (2) Entropy technique is employed to manage the weight values under IVPFSs. (3) the IVPF-TODIM-EDAS technique is founded to manage the MAGDM under IVPFSs; (4) Algorithm analysis for quality evaluation of product styling design and comparison analysis are constructed based on one numerical example to verify the feasibility and effectiveness of the IVPF-TODIM-EDAS technique. Show more
Keywords: Multiple-attribute group decision-making (MAGDM), Interval-valued Pythagorean fuzzy sets (IVPFSs), TODIM technique, EDAS technique, product styling design
DOI: 10.3233/JIFS-236947
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6093-6108, 2024
Authors: Weng, Shizhou | Huang, Zhengwei | Lv, Yuejin
Article Type: Research Article
Abstract: In the face of increasingly complex data forms and decision-making problems, the uncertainty of information poses a major challenge to multi-attribute decision-making methods. How to effectively organize information and serve realistic decision-making problems has attracted extensive attention in the academic circles. In view of this, based on the distribution law of random variables, we put forward the basic concept of probability numbers and construct a general framework, including the concepts of type, order, item, isomorphism and isomerism, same domain and same distribution of probability numbers. On this basis, we further define the expectation and variance formula of probability numbers, and …its operation rules are defined for the same type of probability numbers. To compare the dominance and inferiority of probability numbers further accurately, we put forward the concepts of dominance degree and comparability degree of probability numbers, so that decision makers can realize the ranking of probability numbers by calculating the comprehensive dominance degree. In view of the related concepts of probability numbers, we summarize the properties and theorems of probability numbers and prove them. In addition, a probability numbers-based multi-attribute decision-making framework model is proposed to solve the multi-attribute decision-making problem. Decision makers can select appropriate sub-models to construct personalized multi-attribute decision-making methods according to actual needs. At the end of the paper, we apply the method to the multi-attribute decision case of campus express stations evaluation and verify the scientificity and rationality of the evaluation method. The concept of probability numbers and its decision model proposed in this paper extend the concept category of numbers, enrich the multi-attribute decision-making method based on probability numbers, and have certain reference significance for further research of uncertain decision theory and method. Show more
Keywords: Probability numbers, calculation rule, dominance degree, ranking method, multi-attribute decision-making
DOI: 10.3233/JIFS-223565
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6109-6132, 2024
Authors: Jiang, Yanping | Tang, Zhenpeng | Song, Xinchao | Shao, Xinran
Article Type: Research Article
Abstract: There has been widespread and growing concern about parking. This paper attempts to provide decision support for a shared parking system to reduce parking difficulty. We study a many-to-many matching problem between shared private idle parking spaces and their demanders. A novelty is that the demanders are allowed to use different parking spaces successively in parking relocation service support. This can further reintegrate the idle time of the parking spaces and improve their utilization rate. A multi-objective optimization model is constructed to maximize the number of matched demanders, the total priority of the parking spaces, and the total priority of …the demanders. More importantly, the priorities of the parking spaces and the demanders are innovatively considered. Each of the parking spaces and the demanders is given a priority for the matching and the priority of a parking space or a demander will be increased if the parking space or demander rarely gets matched successfully. This helps reduce the withdrawal of parking spaces and the demanders from the parking platform. In addition, an NSGA-II algorithm is designed to solve the model efficiently. Finally, the feasibility of the proposed method is illustrated via an example. Show more
Keywords: Sharing economy, shared private idle parking space, many-to-many matching, parking priority, improved NSGA-II algorithm
DOI: 10.3233/JIFS-223789
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6133-6148, 2024
Authors: Wang, Daiwen | Sun, Jie | Wei, Cuiping
Article Type: Research Article
Abstract: Spent lithium-ion battery (LIB) recycling can create great pollution to the environment. Understanding the safety, environment, technique, and regulation factors’ impact on the recycling process is crucial. Due to the complexity of the relevant factors, and there is a certain degree of correlation and dependence between the factors, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyze the factors’ degree of impact in this study. As the experts are ambiguous about some relations between the factors, it is impossible to conduct integrated evaluation. The improved DEMATEL method is proposed in this study to make up the …missing relations. Further, the weights of the factors will be calculated. In the improved DEMATEL method, the numerical scale of a linguistic term set is introduced. Therefore, the numerical scale used by experts can not only be uniform and symmetrical, but can also be non-uniform symmetric, non-uniform asymmetric, etc. Finally, both reusing and recycling companies are included in this study and their factors’ importance weights were analyzed with the fuzzy comprehensive evaluation method. Show more
Keywords: Automotive waste lithium-ion battery recycling, numerical scale function, DEMATEL method, fuzzy comprehensive evaluation
DOI: 10.3233/JIFS-224124
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6149-6169, 2024
Authors: Mohan Raj, K.R. | Katiravan, Jeevaa
Article Type: Research Article
Abstract: Recently, security has been necessary in this computer world due to the fast development of technology and enormous user strength. The different kinds of security mechanisms including the Intrusion Detection System (IDS) were developed by many researchers to confirm the security of the data in the communication process. In general, the IDS are used to detect anomalous nodes, and attacks and increase the security level. Even though, the various disadvantages are available to ensure the data reliability on different kinds of applications. For this purpose, this work proposes a cross-layer IDS that is a combination of the trust-based secure routing …method, attribute selection and classification algorithms. This study introduces a novel attribute selection approach known as the Weighted Genetic Feature Selection Algorithm (WGFSA). This method is designed to identify and prioritize valuable attributes within the context of network, physical, and data link layers. And introduce a deep classifier called the Hyperparameter-Tuned Fuzzy Temporal Convolutional Neural Network (HFT-CNN) for efficient categorization. Additionally, we propose a pioneering secure routing algorithm known as the Fuzzy Logic and Time-Constrained Dynamic Trusted Cross-Layer-Based Secure Routing Algorithm (FCSRA) to ensure the secure transmission of data packets. The effectiveness of the newly developed system is proved by conducting experiments with the network, standard Aegean Wi-Fi intrusion dataset (AWID) and proved superior to other systems in delay, energy consumption, packet delivery rate, and prediction accuracy. Show more
Keywords: Fuzzy temporal logic, intrusion detection systems, trust score, cross-layer, deep learning, attribute selection, secure routing
DOI: 10.3233/JIFS-233275
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6171-6183, 2024
Authors: Wei, Ying | Gong, Kaixin | Chen, Chunfang | Zhu, Xianghong
Article Type: Research Article
Abstract: This research proposes a new method to solve group decision-making(GDM) problems with intuitionistic fuzzy preference relations(IFPRs). First, a new definition of multiplicative consistency of IFPR is presented to address the defects of the existing consistency definitions. Then, two programming models are established to obtain the most optimistic and pessimistic consistent IFPRs and corresponding intuitionistic fuzzy priority weights. Also, in order to improve the accuracy of aggregate information, a new method to determine the weights of decision-makers(DMs) is offered by considering the interaction among DMs. Subsequently, by combining the vagueness and non-vagueness of the aggregated information, a multiplicative consistency definition of …the collective IFPR is provided. Moreover, to simplify the GDM process, a programming model for solving the priority weight is established, which effectively avoids the consistency test and correction of IFPRs. Finally, the values of the proposed method are illustrated by comparative analysis. Show more
Keywords: Group decision-making, interaction, risk preference, intuitionistic fuzzy preference relation, consistency
DOI: 10.3233/JIFS-233543
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6185-6199, 2024
Authors: Hou, Xianyu | Chen, Yumin | Wu, Keshou | Zhou, Ying | Lu, Junwen | Weng, Xuan
Article Type: Research Article
Abstract: Neighborhood granulation is a classical granulation method. Although it is adequate for clustering and classification tasks, its granules are more complex, and the data representation is binary. This paper proposes a new granulation method based on the neighborhood granulation. Firstly, a detailed definition of the granular form is given with fuzzy rough set theory. Then, a modified fuzzy rough discriminant function is proposed based on neighborhood systems. The samples are globally granulated on single features to construct granules and on multiple features to construct granular vectors. Also, a feature selection technique based on the Chi-square, which strikingly reduces the complexity …of the fuzzy rough granular vectors, is introduced to address the disadvantage of the fuzzy rough granular vectors. An ensemble model structure is also proposed in the paper for the mixed nature of fuzzy rough granular vectors. The paper makes a detailed comparison between the fuzzy rough granulation and the neighborhood granulation. The results show that fuzzy rough granulation has higher computational efficiency and classification performance. Finally, a detailed comparison is made between the fuzzy rough granular ensemble model and various classical ensemble algorithms. The final results show that the fuzzy rough granular ensemble model has better robustness and generalization. Show more
Keywords: Granular computing, fuzzy rough granulation, neighborhood granulation, granular ensemble learning, granular selection
DOI: 10.3233/JIFS-234510
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6201-6217, 2024
Authors: Sun, Hong | Zhang, Xianyong
Article Type: Research Article
Abstract: Z-numbers contain fuzzy restrictions, credibility measures, and probability distributions to effectively represent uncertain information. Converting Z-numbers to fuzzy numbers facilitates extensive applications (such as multi-attribute decision-making (MADM)), thus becoming valuable for research purposes. Regarding Z-number conversions, the original method never considers the association probability, while probabilistic strategies offer better informatization. Recently, a probability-driven conversion starts with a linear transformation of the centroid difference between the fuzzy restriction and probabilistic distribution. However, it has the invalidation weakness of edge information due to underlying non-normalization. To improve this probability-linear conversion, a Z-number conversion is proposed by using underlying probability-exponential descriptions, and this …new method is further applied to MADM. At first, the current probability-linear conversion is analyzed based on the initial non-probabilistic conversion, and its intrinsic weakness and correctional improvement are revealed. Then, the novel probability-exponential conversion resorts to an exponential characterization of centroid difference between the restriction and distribution, and it gains information enrichment due to underlying normalization. The refined method preserves the inherent characteristics of Z-numbers more effectively, facilitating their application in subsequent engineering practices. This is especially pertinent in decision-making systems based on expert input and initial value problems. The proposed method for converting Z-numbers aims to minimize information loss in transitions between Z-numbers and classical fuzzy numbers. This approach will be further explored in future research. Furthermore, the probability-exponential conversion induces an ExpTODIM algorithm for MADM, called PE-ExpTODIM. Three Z-number conversions (i.e., the non-probabilistic, probability-linear, and probability-exponential types) and three decision algorithms (i.e., ExpTODIM, EDAS, MOORA) are combined to establish a 3 × 3 framework of Z-number-driven MADM. Finally, the systematical 9 algorithms are applied to the problem of site selection of carbon storage. They are validated by criss-cross contrast analyses and statistical significance tests. Thus, PE-ExpTODIM exhibits the desired optimization. The last technology of statistical testing is original, ingenious, and valuable for MADM. Show more
Keywords: Z-numbers, fuzzy numbers, probability-exponential conversion, multi-attribute decision making, ExpTODIM/EDAS/MOORA
DOI: 10.3233/JIFS-235304
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6219-6233, 2024
Authors: Kavitha, D. | Radha, V.
Article Type: Research Article
Abstract: This work proposes an unique hardware design for a multi-line Refreshable Braille Display (RBD) device using Imprint Punch Head Technology with Optical Character Recognition (OCR) capabilities for learning and reading text in Braille codes. The device uses a microprocessor board to seamlessly integrate stepper/servo motors with OCR algorithms. A thin flexible metal sheet coated with rubber is used as a display surface on which the raised points for Braille codes are repeatedly formed and deformed. The device is designed in such a way that the material used for its construction are low cost which makes them economical and affordable. The …device was evaluated in the lab setup and showed promising results, and had prospects of becoming a vital Assistive Technology for vision impaired people. Show more
Keywords: OCR, refreshable braille display, raspberry pi, text detection
DOI: 10.3233/JIFS-236527
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6235-6248, 2024
Authors: Li, Heng
Article Type: Research Article
Abstract: The financial performance of enterprises has always been the key to their survival and development, especially for high-tech enterprises. Evaluating the financial performance of high-tech enterprises is beneficial for the management department to accurately understand the financial situation of the enterprise, timely identify financial problems, and study solutions based on this; On the other hand, the scientific evaluation of enterprise performance also provides useful assistance for other stakeholders such as the government, creditors, and enterprise employees to exercise their rights. With the development of the times and the progress of society, high-tech enterprises have developed rapidly. Studying the financial performance …of high-tech enterprises has important theoretical and practical significance. The financial performance evaluation of high-tech enterprises is a classical MAGDM problems. Recently, the TODIM (TODIM) and (grey relational analysis) GRA technique has been employed to cope with MAGDM issues. The interval neutrosophic sets (INSs) are employed as a tool for characterizing uncertain information during the financial performance evaluation of high-tech enterprises. In this manuscript, the interval neutrosophic number TODIM-GRA (INN-TODIM-GRA) technique is implemented to solve the MAGDM under INSs. In the end, a numerical case study for financial performance evaluation of high-tech enterprises is employed to validate the proposed technique. Show more
Keywords: Multiple-attribute group decision-making (MAGDM), Interval neutrosophic sets (INSs), TODIM, GRA, financial performance evaluation
DOI: 10.3233/JIFS-237196
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6249-6263, 2024
Authors: Li, Bo | Lu, TongWei | Min, Feng
Article Type: Research Article
Abstract: 3D point cloud has irregularity and disorder, which pose challenges for point cloud analysis. In the past, the projection or point cloud voxelization methods often used were insufficient in accuracy and speed. In recent years, the methods using Transformer in the NLP field or ResNet in the deep learning field have shown promising results. This article expands these ideas and introduces a novel approach. This paper designs a model AaDR-PointCloud that combines self-attention blocks and deep residual point blocks and operates iteratively to extract point cloud information. The self-attention blocks used in the model are particularly suitable for point cloud …processing because of their order independence. The deep residual point blocks used provide the expression of depth features. The model performs point cloud classification and segmentation tests on two shape classification datasets and an object part segmentation dataset, achieving higher accuracy on these benchmarks. Show more
Keywords: PointCloud, transformer, ResNet, point cloud classification, point cloud part segmentation
DOI: 10.3233/JIFS-231997
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6265-6277, 2024
Authors: Yu, Huan
Article Type: Research Article
Abstract: With the acceleration of urbanization and the significant improvement of people’s living standards, the motorization of urban transportation in China has developed rapidly, and the number of urban motor vehicles has sharply increased. This has also caused a series of problems such as increasingly severe urban road traffic congestion, increased traffic energy consumption, and atmospheric environmental pollution. Unprecedented social and environmental pressures have put forward higher requirements for the development model of urban transportation. Against the backdrop of increasingly severe conflicts between urban transportation and resource environment in China, green transportation with the goal of “meeting maximum demand with minimum …consumption” has gradually received widespread attention from the academic community. The urban green transportation development level evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional geometric BM (GBM) operator and generalized weighted GBM (GWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy generalized GBM (TPFGGBM) operator and triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for urban green transportation development level evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity. Show more
Keywords: Multiple attribute decision making (MADM), Triangular Pythagorean fuzzy set, geometric BM (GBM) operator, Triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM), green transportation development level
DOI: 10.3233/JIFS-232579
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6279-6297, 2024
Authors: Bolourchi, Pouya | Gholami, Mohammadreza
Article Type: Research Article
Abstract: Alzheimer’s disease (AD) is the most prevalent brain disorder which affects millions of people worldwide. Early detection is crucial for possible treatment. In this regard, machine learning (ML) approaches are widely utilized for AD detection. In this paper, we propose an ML-based method that drastically reduces the dimensionality of features while maintaining the relevant features and boosting the overall performance. To remove irrelevant features, first statistical feature extraction method is applied, and then further reduction among remaining features is applied by utilizing the harmony search method (HSM). The selected features are the most informative features that are fed to the …different classifiers. To test the effectiveness of the proposed method, we deployed three classification techniques including support vector machine (SVM), k -nearest neighbor (k -NN), and decision tree (DT). The experimental results show that the proposed method has a higher performance while decreasing the dimensionality of feature space. To guarantee that the performance of the proposed method is accurate, we applied an ensemble of three classifiers (SVM, KNN, and DT) for classification. The results of the proposed method verify that this method can be successfully deployed for AD detection, due to its high performance and low dimensional features, and can help improve the accuracy and efficiency of Alzheimer’s disease diagnosis. The proposed method demonstrated a significant improvement, achieving high performance in AD/HC classification, with accuracy, sensitivity, specificity, F1 -score, MCC , and Cohen’s Kappa rates reaching 95.5%, 97%, 94%, 95.56%, 0.9104, and 0.9109, respectively. AD/HC classification displayed the highest performance. Additionally, in the more challenging pMCI/sMCI classification, the method achieved an accuracy of 78.50%, sensitivity of 84.00%, specificity of 73.00%, F1 -score of 79.62%, MCC of 0.57, and Cohen’s Kappa of 0.59. Show more
Keywords: Alzheimer’s disease, ensemble of classifiers, harmony search, statistical feature extraction, sMRI
DOI: 10.3233/JIFS-233000
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6299-6312, 2024
Authors: Khenglawt, Vanlalmuansangi | Laskar, Sahinur Rahman | Pakray, Partha | Khan, Ajoy Kumar
Article Type: Research Article
Abstract: Low-resource language in machine translation systems poses multiple complications regarding accuracy in translation due to insufficient incorporation of linguistic information. The difference in the linguistic information between the language pair also significantly impacts the dataset creation for improving translation accuracy. Although neural machine translation achieves a state-of-the-art approach, dealing with low-resource language is challenging since it struggled with limited resources. This paper attempts to address the data scarcity problem using augmentation of synthetic parallel sentences, source-target phrase pairs, and language models at the target side for English-to-Mizo and Mizo-to-English translation via transformer-based neural machine translation. We have attained state-of-the-art results …for both directions of translation. Show more
Keywords: English–Mizo, NMT, transformer, augmentation, language model
DOI: 10.3233/JIFS-235740
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6313-6323, 2024
Authors: Jin, Yongbing | Ran, Teng | Yuan, Liang | Lv, Kai | Wang, Guoliang | Xiao, Wendong
Article Type: Research Article
Abstract: Handwriting robots as an application of Imitation Learning (IL). However, most methods have poor accuracy of trajectory generation under task constraints, and models are less robust to changes in demonstration data. This paper proposes an IL algorithm named Bagging in Hidden Semi-Markov Model (BHSMM). The demonstration data is first divided into several sub-datasets, and each sub-dataset is encoded into several basic learning models by Hidden Semi-Markov Models (HSMM). Then the relationship between the task constraint points and the basic learning models is used to derive the weights. Finally, the trajectories adapted to the task constraints are generated based on the …weights. We conducted experiments on the handwritten dataset LASA and compared the accuracy error with the original HSMM method. The results show that the BHSMM can generate trajectories that satisfy the position and velocity constraints and is more robust to changes in the demonstration data than the HSMM. In addition, satisfactory results are obtained in trajectory generation for real robot handwriting. Show more
Keywords: Imitation learning, human-robot collaboration, handwriting robot, BHSMM
DOI: 10.3233/JIFS-237275
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6325-6335, 2024
Authors: You, Haoyang
Article Type: Research Article
Abstract: Students’ English learning ability depends on the knowledge and practice provided during the teaching sessions. Besides, language usage improves the self-ability to scale up the learning levels for professional communication. Therefore, the appraisal identification and ability estimation are expected to be consistent for different English learning levels. This paper introduces Performance Data-based Appraisal Identification Model (PDAIM) to support such reference. This proposed model is computed using fuzzy logic to identify learning level lags. The lag in performance and retains in scaling-up are identified using different fuzzification levels. The study suggests a fuzzy logic model pinpointing learning level gaps and consistently …evaluating performance across various English learning levels. The PDAIM model gathers high and low degrees of variance in the learning process to give students flexible learning knowledge. Based on the student’s performance and capacity for knowledge retention, it enables scaling up the learning levels for professional communication. The performance measure in the model is adjusted to accommodate the student’s diverse grades within discernible assessment boundaries. This individualized method offers focused education and advancement to students’ unique requirements and skills. The model contains continuous normalization to enhance the fuzzification process by employing prior lags and retentions. Several indicators, including appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time, are used to validate the PDAIM model’s performance. The model may adjust to the various performance levels and offer pertinent feedback using fuzzification. The high and low variation levels in the learning process are accumulated to provide adaptable learning knowledge to the students. Therefore, the performance measure is modified to fit the student’s various grades under distinguishable appraisal limits. If a consistent appraisal level from the fuzzification is observed for continuous sessions, then the learning is scaled up to the next level, failing, which results in retention. This proposed model occupies constant normalization for improving the fuzzification using previous lags and retentions. Hence the performance of this model is validated using appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time. Show more
Keywords: Appraisal model, big data, English learning, fuzzy logic and fuzzification
DOI: 10.3233/JIFS-233414
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6337-6353, 2024
Authors: Men, Rui | FAN, Xiumei | Yan, Jun | Shan, Axida | Fan, Shujia
Article Type: Research Article
Abstract: Vehicle Edge Computing (VEC) is a promising technique to improve the quality of service (QoS) and quality of experience (QoE) in autonomous driving by exploiting the resources at the network edge. However, the high mobility of the vehicles leads to stochastic communication link duration, and the tasks generated by various applications in autonomous driving incur fierce competition for resources. These challenges cause excessive task completion delays. In this paper, we propose a vehicle-to-vehicle (V2V) partial computation offloading scheme that leverages the prediction results of the communication link lifetime between vehicles. A History track, Current interactions and Future planning trajectory-aware Gated …Recurrent Units (HCF-GRU) network is built to capture the essential factors to improve the prediction accuracy. Then, we design a GRU-based Proximal Policy Optimization (GRU-PPO) algorithm to obtain an optimal one-to-many offloading decision to minimize the task execution cost. The HCF-GRU prediction algorithm is evaluated on a real world vehicle trajectory dataset, and the performance of the GRU-PPO algorithm is analyzed on extensive numerical simulations. Experimental results demonstrate that our prediction network and offloading decision algorithm outperform the baseline methods in terms of prediction accuracy and task execution cost. Show more
Keywords: Communication link lifetime prediction, partial offloading decision, machine learning, autonomous driving
DOI: 10.3233/JIFS-235954
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6355-6368, 2024
Authors: Amin, Umair | Fahmi, Aliya | Yaqoob, Naveed | Farid, Aqsa | Hassan, Muhammad Arshad Shehzad
Article Type: Research Article
Abstract: The concept of domination in graphs is very ancient. Several types of notions of domination in graphs have been discussed by many researchers. In this work, the concept of domination and some notions of domination sets, minimal dominating sets, independence sets, and maximal independence sets are introduced in bipolar fuzzy soft graphs. Additionally, several properties of dominating sets are discussed and some theorems in bipolar fuzzy soft graphs are proved.
Keywords: Domination in bipolar fuzzy soft graphs, minimal domination set in bipolar fuzzy soft graphs, maximal independence set in bipolar fuzzy soft graphs
DOI: 10.3233/JIFS-236485
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6369-6382, 2024
Authors: Xu, Fang
Article Type: Research Article
Abstract: In the context of globalization, cross-border e-commerce platforms have become the main way for enterprises to achieve international trade transformation and overseas investment. From this, it can be seen that cross-border e-commerce platforms are of great importance to the development of enterprises, and the development of cross-border e-commerce platforms is also a necessary choice for the development of the times. In the new era, in order to make cross-border e-commerce platforms better serve enterprises and bring economic benefits to their development. The sustainable development capability evaluation of third-party cross-border e-commerce (TPCBEC) platform is a MAGDM. Recently, the Exponential TODIM (ExpTODIM) …technique and Evaluation Based on Distance from Average Solution (EDAS) technique has been employed to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are employed as a tool for portraying uncertain information during the sustainable development capability evaluation of TPCBEC platform. In this paper, the 2-tuple linguistic neutrosophic number Exponential TODIM-EDAS (2TLNN-ExpTODIM-EDAS) technique is implemented to manage the MAGDM under 2TLNSs. Finally, a numerical study for sustainable development capability evaluation of TPCBEC platform is constructed to validate the implemented technique. Thus, the main advantages of the proposed 2TLNN-ExpTODIM-EDAS technique are outlined: (1) the proposed 2TLNN-ExpTODIM-EDAS technique not only handles the distances information from the 2TLNNAS, but also portrays the DMs’ psychological behavior during the sustainable development capability evaluation of TPCBEC platform. (2) the proposed 2TLNN-ExpTODIM-EDAS technique analyze the behavior of the TODIM technique and EDAS technique as MADM techniques when they are hybridized. Show more
Keywords: Multiple-attribute group decision-making (MAGDM), 2-tuple linguistic neutrosophic sets (2TLNSs), Exponential TODIM (ExpTODIM) technique, EDAS technique, sustainable development capability evaluation
DOI: 10.3233/JIFS-237170
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6383-6398, 2024
Authors: Gao, Jun | Peng, Zhiyuan | Cao, Qiang | Zhang, Jie
Article Type: Research Article
Abstract: The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge …was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study. Show more
Keywords: Energy management strategy, Markov chain, minimum principle, optimal control
DOI: 10.3233/JIFS-238713
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6399-6409, 2024
Authors: Zhang, Nan | Yin, Jiayi | Zhang, Ning | Sun, Tongtong | Yin, Shi | Wan, Lijun
Article Type: Research Article
Abstract: Digital technologies, such as big data, the Internet, and artificial intelligence, are rapidly advancing. Photovoltaic building materials enterprises (PBMEs) have been leveraging digital transformation to enhance their technological innovation capabilities and gain a competitive edge. In the global context of transitioning towards a low-carbon economy, the deep integration of digital technology offers a new solution for the green transformation of PBMEs. The synergy between green traction digitalization and digitalization enables green practices, making collaborative integration crucial for the far-reaching development of PBMEs. Within the framework of China’s “double carbon” policy, domestic PBMEs are experiencing exponential growth, where digital green innovation …(DGI) has become their primary objective. In this DGI context, selecting the right partners is the first step that significantly impacts the efficiency and effectiveness of DGI implementation. Therefore, the purpose of this study is to assist PBMEs in selecting high-quality partners, promoting the DGI process, enhancing technological innovation capabilities, and gaining a competitive advantage. To achieve this, the paper proposes constructing a theoretical framework for evaluating the DGI cooperation ability of PBMEs using the theory of ecological reciprocity. Based on this framework, an evaluation index system is established to assess the DGI cooperation ability of potential partners The interval intuitionistic fuzzy evaluation method, combined with a double combination weighting approach, is employed to evaluate the DGI ability of selected partners. Furthermore, by applying field theory, a dynamic selection model for strategic alliance partners is developed to aid PBMEs in selecting high-quality partners for DGI and facilitating the DGI process. The research findings indicate that: i) The evaluation standard framework for DGI cooperation ability of PBMEs encompasses “symbiosis,” “mutualism,” and “regeneration,” along with the crucial environmental element of mutual trust. ii) The evaluation method based on double combination weighting effectively assesses the comprehensive DGI capabilities of selected PBME partners. The application of field theory enables scientific and effective dynamic partner selection for PBMEs through resource complementarity. iii) The proposed framework and partner selection model can be employed in real partner selection scenarios for PBMEs, allowing them to choose high-quality partners, enhance their DGI capabilities, and attain practical selection outcomes. This paper presents novel partner selection model that integrates decision rules and resource complementarity, enabling PBMEs to efficiently select DGI partners from a pool of potential candidates and improve their innovation efficiency. The utilization of the double combination weighting method and field theory in the partner selection paradigm of D extends the theoretical foundation, while the establishment of the DGI capability evaluation index system for PBME partners contributes to empirical applications. Show more
Keywords: Photovoltaic building materials enterprises, digital green innovation, partner selection, double combination weighting, field theory
DOI: 10.3233/JIFS-234838
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6411-6437, 2024
Authors: You, Fang | Li, Yaru | Fu, Qianwen | Zhang, Jun
Article Type: Research Article
Abstract: With the increasing levels of intelligence and automation, the relationship between humans and vehicles has evolved from a utilitarian perspective to a partnership. Among the crucial factors for enhancing user experiences are the analysis of driving tasks, the construction of user needs models, and the design of intelligent interfaces. Based on this background, this paper proposes a cognitive task analysis model using intelligent steering wheel information interaction design as the vehicle. The model aims to extract key design elements to assist designers in making design decisions, thereby improving the human-machine cooperation performance of intelligent automobiles and enhancing user perceptual experiences. …Firstly, within the context of human-machine cooperation systems, a cognitive task analysis method integrating the SRK model is proposed. By analyzing the behavioral decision characteristics between the vehicle and the user, a framework for the human-machine interface (HMI) logic of the steering wheel and a dynamic layout prototype are established. Secondly, the design of the steering wheel’s HMI interaction is based on an analysis of users’ affective needs and rational physiological characteristics. This paper integrates the analysis of users’ affective needs to identify design elements that align with a high level of user satisfaction. Lastly, the design methodology model is applied to a navigation scenario, resulting in the creation of a steering wheel HMI prototype within a human-machine cooperation system. The prototype is then subjected to a combined subjective and objective experimental analysis, thereby validating the superiority of the steering wheel HMI’s detection indicators over those of the central control HMI and establishing the design pattern for the steering wheel HMI. Show more
Keywords: Intelligent cockpit, steering wheel, cognitive task analysis, human-machine interaction interface
DOI: 10.3233/JIFS-233500
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6439-6464, 2024
Authors: Zhao, Dongping
Article Type: Research Article
Abstract: Chinese Language learning grows ever more essential to develop the students’ personalities and values as the curriculum, thereby improving teaching strategies based on students’ learning preferences are more crucial. Students’ participation in learning the Chinese Language is generally minimal and typically operates in a passive learning mode. The development of the Chinese Language instruction in these higher educational settings will be impacted by the absence of an organized strategy for teaching the Chinese Language. An algorithm is called the Fuzzy Pattern-driven Personalized Teaching (FPPT) has been proposed to identify the association between the students learning patterns and interests in the …Chinese Language in the higher education for providing the personalized teaching to solve these challenges. Fuzzy sets are incorporated into FP-Growth for personalized the Chinese Language learning to improve the suggestions by considering the ambiguous preferences and the proficiency levels. The fuzzy pattern is unrevealed by implementing the Frequent Pattern (FP) growth algorithm to find patterns in the students learning activity and preferences so that personalized the teaching methods can be developed to meet the needs of each student and maximize their motivation for the language learning. Using the support and the confidence measures, these identified Fuzzy association relationships of student learning interest results in personalized the Chinese Language teaching in the higher education. The experimental results showed that the proposed FPPT system significantly improved each student’s learning outcome, communication effectiveness, learning motivation, and the Language proficiency level. Show more
Keywords: Personalized Chinese language, fuzzy set, frequent pattern growth, frequent item set, learning preferences, teaching strategies
DOI: 10.3233/JIFS-235734
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6465-6478, 2024
Authors: Ouyang, Zhiyuan | Wan, Yanling | Zhang, Tao | Wu, Wen-Ze
Article Type: Research Article
Abstract: The introduction of fractional order accumulation has played a crucial role in the development of grey forecasting methods. However, accurately identifying a single fractional order accumulation for modeling diverse sequences is challenging due to the dependence of different fractional order accumulations on data structure over time. To address this issue, we propose a novel fractional grey model abbreviated as FGMMA, incorporating a model averaging method. The new model combines existing fractional grey models by using four judgment criteria, including Akaike information criteria, Bayesian information criteria, Mallows criteria, and Jackknife criteria. Meanwhile, the cutting-edge algorithm named breed particle swarm optimization is …employed to search the optimal fractional order for each candidate model to enhance the effectiveness of the designed model. Subsequently, we conduct a Monte Carlo simulation for verification and validation purposes. Finally, empirical analysis based on energy consumption in three countries is conducted to verify the applicability of the proposed model. Compared with other benchmark models, we can conclude that the proposed model outperforms the other competitive models. Show more
Keywords: Grey forecasting model, fractional order accumulation, model averaging, breed particle swarm optimization
DOI: 10.3233/JIFS-237479
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6479-6490, 2024
Authors: Zhang, Yihao | Wang, Yuhao | Lan, Pengxiang | Xiang, Haoran | Zhu, Junlin | Yuan, Meng
Article Type: Research Article
Abstract: Conversational recommender systems use natural language conversations to elicit user preferences and recommend items proactively. Existing methods based on graph neural networks have been proven to be effective in exploiting knowledge graphs. However, node positions are often treated as constants, which leads to the neglect of graph connectivity due to fuzzy processing. In addition, although the transformer has significant advantages in understanding the text, its secondary computational complexity may be incapable when dealing with long texts. In order to solve these problems, we propose an additive positional conversational recommender model called APCR. This model converts the pair product of transformer …into a linear operation, and uses the Laplacian eigenvector to build a location graph. The extended graph neural network captures the topology structure of the location knowledge graph. Specifically, we design an encoder based on additive attention to break through the bottleneck of long text. Furthermore, we develop a recommendation model based on a positional graph neural network to match items with dialogue context, thereby capturing the graph topology. Extensive experiments on the REDIAL dataset show significant improvements in our proposed model over the state-of-the-art methods in recommendation and dialogue generation evaluations. Show more
Keywords: Interactive recommender systems, graph neural networks, knowledge graphs, additive attention
DOI: 10.3233/JIFS-230905
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6491-6503, 2024
Authors: Qian, Jin | Wang, Taotao | Lu, Yuehua | Yu, Ying
Article Type: Research Article
Abstract: Multi-granularity hesitant fuzzy linguistic terms set is an effective expression of linguistic information, which can utilize some fuzzy linguistic terms to evaluate various common qualitative information and plays an important role when experts provide linguistic information to express hesitancy. Since the alternative description in the decision-making information system is characterized by multi-granularity, uncertainty, and vagueness, this paper proposes a multi-granularity hesitant fuzzy linguistic decision-making VIKOR method based on entropy weight and information transformation. Specifically, this paper firstly adopts fuzzy information entropy to obtain the weights of different attributes and introduces a multi-granularity hesitant fuzzy linguistic term set conversion method to …realize the semantic information conversion between different granularities. Then for the converted affiliation linguistic decision matrix, the entropy weighting method is used to obtain the weights of different affiliation granularity layers, and a weight optimization VIKOR method based on the affiliation linguistic decision matrix is further proposed to rank the alternatives. Finally, the feasibility of the proposed method verified by arithmetic examples, experimental analysis is carried out in terms of parameter sensitivity analysis and comparison with other methods. The experimental results prove the rationality and effectiveness of the proposed method. Show more
Keywords: Multi-granularity hesitant fuzzy term set, affiliation degree, information transformation, VIKOR method
DOI: 10.3233/JIFS-237951
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6505-6516, 2024
Authors: Prasath, J.S. | Shyja, V. Irine | Chandrakanth, P. | Kumar, Boddepalli Kiran | Raja Basha, Adam
Article Type: Research Article
Abstract: Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of Internet users over the past two decades has increased the need for cyber security. Users have provided new opportunities for attackers to do harm. Limited security budgets leave IoT devices vulnerable and easily hacked to launch distributed denial-of-service (DDoS) attacks, with disastrous results. Unfortunately, due to the unique nature of the Internet of Things environment, most security solutions and intrusion detection systems (IDS) cannot be directly adapted to the …IoT with acceptable security performance and are vulnerable to various attacks that do not benefit. In this paper we propose an optimal secure defense mechanism for DDoS in IoT network using feature optimization and intrusion detection system (OSD-IDS). In OSD-IDS mechanism, first we introduce an enhanced ResNet architecture for feature extraction which extracts more deep features from given traffic traces. An improved quantum query optimization (IQQO) algorithm for is used feature selection to selects optimal best among multiple features which reduces the data dimensionality issues. The selected features have given to the detection and classification module to classify the traffic traces are affected by intrusion or not. For this, we design a fast and accurate intrusion detection mechanism, named as hybrid deep learning technique which combines convolutional neural network (CNN) and diagonal XG boosting (CNN-DigXG) for the fast and accurate intrusion detection in IoT network. Finally, we validate the performance of proposed technique by using different benchmark datasets are BoNeSi-SlowHTTPtest and CIC-DDoS2019. The simulation results of proposed IDS mechanism are compared with the existing state-of-art IDS mechanism and analyze the performance with respects to different statistical measures. The results show that the DDoS detection accuracy of proposed OSD-IDS mechanism is high as 99.476% and 99.078% for BoNeSi-SlowHTTPtest, CICDDoS2019, respectively. Show more
Keywords: Defense mechanism, DDoS intrusion, intrusion detection system, feature selection, IoT
DOI: 10.3233/JIFS-235529
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6517-6534, 2024
Authors: Ye, Qing | Song, Zihan | Zhao, Yuqi | Zhang, Yongmei
Article Type: Research Article
Abstract: Video anomaly detection refers to the automatic identification of abnormal behaviors, objects, or events in videos. However, current methods for anomaly detection based on original frames lack a comprehensive understanding of the importance of foreground information, making it challenging to efficiently address video anomaly detection in the presence of complex background interference. In this paper, we propose a video anomaly detection algorithm based on Background Separation Network (BSN) to address this issue. Firstly, we utilize a video stabilization algorithm to reduce video jitter and enhance the quality of input video frames. Secondly, BSN shifts the focus from the entire frame …to the foreground region with higher anomaly detection value. BSN utilizes the motion pixel distribution of the video as the basis for foreground extraction, enabling pixel-level background separation to obtain more accurate and complete foreground targets. Lastly, a certain proportion of foreground targets in the foreground image are masked as background, reducing the interference caused by redundant targets on the detection results. The proposed method achieves an accuracy of 96.2% on the UCSD ped2 dataset, demonstrating its effectiveness. This method contributes to accurately detecting abnormal behaviors in real-world surveillance videos to protect the safety of public lives and assets. Show more
Keywords: Video anomaly detection, auto encoder, background separation network, video jitter elimination
DOI: 10.3233/JIFS-235717
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6535-6551, 2024
Authors: Sheikh, Ansar Isak | Sadish Sendil, M. | Sridhar, P. | Thariq Hussan, M.I. | Abidin, Shafiqul | Kumar, Ravi | Irshad, Reyazur Rashid | Muniyandy, Elangovan | Phani Kumar, Solleti
Article Type: Research Article
Abstract: Effective data management has arisen as a major concern in today’s era of ubiquitous data generation from a plethora of intelligent gadgets. While data proliferation promises unparalleled benefits, it imposes significant storage and computing constraints, particularly on end-users with limited capabilities. To solve these difficulties, this article investigates the confluence of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing. Because of their low-cost data storage and processing capabilities, cloud computing services have grown in popularity, leading customers to embrace data outsourcing to reduce local administrative overhead. This study digs into a novel paradigm for ensuring the integrity …and security of data stored in cloud environments using blockchain technology. Integrating public auditing systems enables visible and verifiable data audits, ensuring consumers of data trustworthiness. A reputation system is also included to build trust among cloud service providers and users, improving the overall trustworthiness of the ecosystem. The suggested system also includes dynamic auditing, which allows for real-time changes and data verification, reacting to the changing nature of cloud-stored information. This study provides a thorough examination of the architectural components, techniques, and protocols used in this novel approach. We illustrate the feasibility and usefulness of our approach in ensuring data integrity, security, and reliability in cloud storage systems through empirical analysis and case studies. The findings show the potential benefits of this integrated strategy to solving the issues posed by the modern digital landscape’s tremendous proliferation of data. Through the synergistic integration of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing, this research provides a holistic solution for managing data in the cloud while ensuring data integrity, security, and trust. This comprehensive strategy lays the way for a more robust and dependable cloud data management ecosystem, increasing user trust in cloud-based services. Show more
Keywords: Low-cost data storage, blockchain technology, data integrity, security, cloud storage
DOI: 10.3233/JIFS-237474
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6553-6564, 2024
Authors: Runkler, Thomas A.
Article Type: Research Article
Abstract: Pairwise fuzzy preference matrices can be constructed using expert ratings. The number of pairwise preference values to be specified by the experts increases quadratically with the number of options. Consistency (transitivity) allows to reduce this quadratic complexity to linear complexity which makes this approach feasible also for large scale applications. Preference values are usually expected to be on a fixed finite interval. Additive preference is defined on such a finite interval. However, completing preference matrices using additive consistency may yield preferences outside this finite interval. Multiplicative preference is defined on an infinite interval and is therefore not suitable here. …To overcome this problem we extend the concept of consistency beyond additive and multiplicative to arbitrary commutative, associative, and invertible operators. Infinitely many of such operators induce infinitely many types of consistency. As one example, we examine Einstein consistency, which is induced by the Einstein sum operator. Completing preference matrices using Einstein consistency always yields preferences inside the finite interval, which yields the first method that allows to construct large scale finite preference matrices using expert ratings. A case study with the real–world car preference data set indicates that Einstein consistency also yields more accurate preference estimates than additive or multiplicative consistency. Show more
Keywords: Fuzzy preference relations, consistent preference, additive preference, multiplicative preference, Einstein sum
DOI: 10.3233/JIFS-224179
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6565-6576, 2024
Authors: Bu, Yanbin | Chen, Ting | Duan, Hongxiu | Liu, Mei | Xue, Yandan
Article Type: Research Article
Abstract: In the modern world, structured and semi-structured knowledge bases hold a considerable amount of data. There-fore, people who are familiar with formal query languages should not be the only ones who can efficiently and clearly query them. Semantic Parsing (SP) is converting natural language utterances into formal meaning representations. The paper suggests a model for SP that uses a novel method of utilizing the Semi-Supervised Generative Adversarial Network (SS-GAN) to enhance the classifier performance. The proposed SS-GAN extends the fine-tuning of word embedding architectures using unlabeled examples in a generative adversarial environment. We provide a regularization strategy for addressing the …mode missing problem and unstable training in SS-GAN. The main viewpoint is to use the extracted feature vectors from the discriminator. Hence, the generator produces outputs by aiding the discriminator’s learned features. A reconstruction loss is added to the loss function of the SS-GAN to drive the genera-tor to reconstruct outputs from the discriminator’s features, hence steering the generator toward actual data configurations. The proposed reconstruction loss improves the performance of SS-GAN, produces high-quality outputs, and may be combined with other regularization loss functions to improve the performance of diverse GANs. We employ BERT word embedding for our model, which can be included in a downstream task and fine-tuned as a model, while the pre-trained BERT model can capture various linguistic properties. We examine the suggested model using the WikiSQL and SparC datasets, and the analysis findings reveal our model outperforms its rivals. The findings from our experiments indicate that the need for labeled samples can be minimized, down to as few as 100 instances, while still achieving commendable classification outcomes. Show more
Keywords: Semantic parsing, generative adversarial network, semi-supervised learning, BERT
DOI: 10.3233/JIFS-233212
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6577-6588, 2024
Authors: Li, Feng | Zhu, Mozhong | Lin, Ling
Article Type: Research Article
Abstract: Once industrial control systems are targeted by cyber-attacks, the consequences can be severe, including asset loss, environmental pollution, and public security risks. Risk assessment is an important way to ensure that industrial control systems operate efficiently, steadily and safely. The purpose of this paper is to develop a risk assessment model for industrial control systems based on asymmetric connection cloud and Choquet integral, which fully takes into account the fact that values of risk indicators are often fuzzy, random, asymmetrically distributed in finite intervals, and there are interactions among different indicators. To do so, we first establish a risk assessment …index system to ensure the full reflection of availability, integrity, and confidentiality in the results of risk assessment for industrial control systems. Then we establish classification standards for each evaluation indicator based on the importance of assets, vulnerabilities, and threats in evaluating the risk of industrial control systems. Next we develop a risk assessment model based on asymmetric connection cloud and Choquet integral to determine the risk level of industrial control systems. In the following, an example is provided to demonstrate the feasibility and reliability of this proposed model. The experimental results have demonstrated a high level of credibility in assessing cyber-attacks by the proposed model, indicating its potential for analyzing the current security and risk posture of industrial control systems. Show more
Keywords: Industrial control systems, risk assessment, asymmetric connection cloud, choquet integral
DOI: 10.3233/JIFS-234686
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6589-6605, 2024
Authors: Ma, Fanglan | Zhu, Changsheng | Liu, Dukui
Article Type: Research Article
Abstract: Knowledge tracing (KT), which aims to trace human knowledge learning process by using machines, has widely applied in online learning systems. It dynamically models student’s knowledge states in relation to different learning factors through their learning interactions. Recently, KT has attracted many researches attention due to its good performance to using deep learning. Although most of KT models have shown outstanding results, they have limitations: either ignore the human cognitive law and learning behavior, or lack the ability to go deeper modeling to trace knowledge state. In this paper, we propose a deeper knowledge tracking model integrating cognitive theory and …learning behavior (CLDKT). It united the advantages of memory network and recurrent neural network of the existing deep learning KT models for modeling student learning. To better implement CLDKT, we add the residual network (ResNet) to realize the deep modeling of learning behaviors. Extensive experiments on three open benchmark datasets to evaluate our model. Experimental results demonstrate that (I) CLDKT outperforms the state-of-the-art KT models on students’ performance prediction. (II) CLDKT can deeper modeling to trace knowledge state owing to the ResNet import. (III) CLDKT has better interpretability and predictability, which proves the effectiveness of the knowledge tracing model integrating cognitive law and learning behavior. Show more
Keywords: Knowledge tracing, cognitive law, learning behavior, ResNet, deep learning
DOI: 10.3233/JIFS-235723
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6607-6617, 2024
Authors: Yang, Shuyi | Li, Lusu | Feng, Libo
Article Type: Research Article
Abstract: Currently, scientific big data management is generally faced with the problems of scattered data resources, inconsistent data standards, and the inability to share and circulate data safely. Research personnel attaches great importance to whether sharing the first-hand property is secure under clear ownership and whether it can contribute to the large society. The isolation of the data management system is the obvious obstacle to collecting and managing across-disciplinary data. To a large extent, sharing and trading scientific big data is the primary purpose to realize the clarity of property rights, secure data sharing, and the value of the data assets …step by step. We propose to construct a public platform for scientific big data management. The system is managed to unify and authorize the on-chain data, on which data sharing and trading is tracked throughout the process. Smart contracts are executed with vital functions and guarantee price matching in data transactions. We design the incentive mechanism which measures the incentive yield of data cost quality based on Evolutionary Game Theory and Data Quality Control Theory (EGQCY), considering how the cost of data quality performs in controlling and impacting the rational release of the incentive yields in the sharing and trading process. The experiments found that the design of incentive yield and incentive coefficients only significantly affected the transition from low-quality data to medium-quality data. Both parameters converged to fixed values as the cost of data quality increased. Show more
Keywords: Scientific big data, blockchain, smart contract, data sharing and transaction, data incentive mechanism, the cost of data quality control
DOI: 10.3233/JIFS-236521
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6619-6635, 2024
Authors: Lin, Shanlang | Lin, Zeyu
Article Type: Research Article
Abstract: The relationship between transportation infrastructure and entrepreneurship has been widely discussed by scholars. However, as an important transportation infrastructure, the impact of subway construction on entrepreneurship has been less studied. Based on the Synthetic Control Method, this paper takes the urban data of China from 2003 to 2017 as the research sample and uses the synthetic control method to study the influence of eight cities with subway service on entrepreneurship. The results show that: (1) The impact of subway openings on entrepreneurship varies across different cities. Specifically, it has a positive effect on entrepreneurship in Hangzhou, Zhengzhou, and Changsha, while …it has a negative impact on entrepreneurship in Harbin and Ningbo. In the cases of Suzhou, Wuxi, and Kunming, the influence on entrepreneurship levels could not be conclusively established. (2) For cities where entrepreneurship activity increased following the opening of subways, further investigation revealed that subway openings did not directly stimulate entrepreneurship within transport-related industries. Instead, they indirectly boosted the entrepreneurial landscape in Hangzhou, Zhengzhou, and Changsha by accelerating the flow of resources and enhancing spillover effects within their respective advantageous industries. This study’s contributions are twofold. Firstly, it introduces innovative perspectives and methodologies for assessing the impact of subway systems on entrepreneurship, highlighting the differentiated effects observed across various cities and industries. Secondly, it emphasizes the importance of considering local advantageous industries in subway construction planning for government authorities, as this can maximize the subway’s potential to drive entrepreneurship in urban areas. Show more
Keywords: Subway system, synthetic control analysis, entrepreneurship, analysis of urban differences
DOI: 10.3233/JIFS-233366
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6637-6655, 2024
Authors: Li, Dongmei | Yang, Lehua | Liu, Shaojun | Tan, Ruipu
Article Type: Research Article
Abstract: Emergency rescue decisions in case of a typhoon disaster can be considered multi-attribute decision-making problems. Considering the need for the timeliness and authenticity of decision-making information sources after such a disaster, this study proposed using learning methods to process real-time online data and interval-valued neutrosophic numbers (NNs) to express the classification results. Using Typhoon Hagupit as an example, a trained text classification model was used to classify real-time data (online comments), following which the classification results were used as weights to convert these data into interval-valued NNs. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) …method was adopted to rank the extent of damage caused by the typhoon in each region; the sorting results were consistent with the official statistical data, proving the effectiveness of the proposed method. A detailed sensitivity analysis was conducted to determine the optimal parameter settings of the classification model. Furthermore, the proposed method was compared with existing methods in terms of data conversion and deep learning efficiency; the results confirmed the superior capabilities of the proposed method. Notably, the proposed method can provide support to disaster management professionals in their post-disaster emergency relief work. Show more
Keywords: Deep learning, interval-valued neutrosophic numbers, multi-attribute decision making, typhoon disaster
DOI: 10.3233/JIFS-235315
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6657-6677, 2024
Authors: Liu, Huilin | Wang, Yansi | Yang, Gaoming | Xu, Huan | Wang, Tao
Article Type: Research Article
Abstract: Photorealistic image style transfer aims to transfer style information while preserving the realistic details of the content image. However, an existing limitation is the inability to effectively balance the relationship between image realism and stylization intensity, resulting in poor image transfer performance. To address this issue, we propose an photorealistic style transfer method that fusing Frequency Separation Channel Attention Mechanism (FSCAM) and Mirror Fluid Pyramid Integration (MFPI). This method achieves superior stylization intensity while improves image realism. Firstly, we propose an improved channel attention mechanism called FSCAM. This mechanism utilizes Discrete Cosine Transform (DCT) to decompose features into different frequency …components and screens out high-valued texture and color features, thereby enhancing the stylization intensity of the generated images. In addition, we designed a MFPI module. The module is able to integrate information from different scales, enhance the preservation of low-level detail features in high-level features, and thus improve the realism of the images. Experimental results demonstrate that our method not only enhances the stylization intensity but also improves the image realism. It achieves satisfactory performance in terms of subjective visual performance and objective evaluation metrics. Show more
Keywords: Generate image, style transfer, discrete cosine transform, channel attention mechanism, feature fusion
DOI: 10.3233/JIFS-235903
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6679-6696, 2024
Authors: Zhan, Linjie | Tang, Zhenpeng
Article Type: Research Article
Abstract: Effective energy futures price prediction is an important work in the energy market. However, the existing research on the application of “decomposition-prediction” framework still has shortcomings in noise processing and signal reconstruction. In view of this, this paper first uses PSO to optimize VMD to improve the effectiveness of single decomposition, and further uses SGMD to capture the remaining key information after extracting low-frequency modal components by using PSO-VMD technology. Further, combined with LSTM to predict each component, a new PSO-VMD-SGMD-LSTM hybrid model is innovatively constructed. The empirical research results based on the real energy market transaction price show that …compared with the benchmark model, the hybrid model proposed in this paper has obvious forecasting advantages in different forecasting scenarios. Show more
Keywords: Energy futures price forecast, secondary decomposition technique, long short term memory
DOI: 10.3233/JIFS-236019
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6697-6713, 2024
Authors: Koam, Ali N.A. | Ahmad, Ali | Azeem, Muhammad | Qahiti, Raed
Article Type: Research Article
Abstract: Let G be a graph and R = {r 1 , r 2 , …, r k } be an ordered subset of vertices of G , if every two vertices of G have different representation r (v |R ) = (d (v , r 1 ) , d (v , r 2 ) , …, d (v , r k )) with respect to R , then R is said to be a metric-based resolving parameter or resolving set of G and its minimum cardinality is called the metric dimension of graph G . Metric dimension …is considered as an important applied concept of graph theory especially in the localization of a network and also in the chemical graph theoretical study of molecular compounds. Therefore, it is hot topic to study for different families of graphs as well. Convex polytopes play an important role both in various branches of mathematics and in applied areas, most notably in linear programming. In this paper, we determine the metric-based resolving parameter of line graph of a convex polytope S n , and conclude that it has constant metric dimension but vary with the parity of n . This article presents a measurement of the line graph of a convex polytope, denoted as ( S n ) . The subsequent section provides the metric dimension of the resulting graph. There are two scenarios pertaining to the metric dimension of a selected graph with respect to the metric dimension. The metric dimension of even cycle-based convex polytopes is three, whereas for other values, the metric dimension is four. Show more
Keywords: Convex polytope, metric dimension, resolving set, constant metric dimension, line graph of convex polytope
DOI: 10.3233/JIFS-236517
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6715-6727, 2024
Authors: Arul, A. | Kathirvelu, M.
Article Type: Research Article
Abstract: In this paper, we present a novel DILTS algorithm that uses a new approach inspired by the energy efficiency of dragonflies. The algorithm optimizes the energy-harvesting mechanisms in IoT devices, inspired by the way dragonflies use wind energy to fly. A sophisticated algorithm optimizes power consumption during task execution, saving energy and speeding up tasks while maintaining the application throughput. The algorithm leverages lazy task scheduling (LTS) to enhance task execution performance. The proposed algorithm evaluates the energy levels of each task and implements an LTS method. This LTS approach improves performance and task management by streamlining scheduling data and …reducing overhead. The LTS model reliably optimizes the energy across microbenchmarks and real-time IoT devices. To assess the efficiency and practicality of our algorithm, we compared it to four alternatives. Our novel algorithm outperformed the others with a chip area of 856 μm2 , performance speed of 7.11 ns, scheduling accuracy of 94%, and response time of 2.61 ns. Our simulations showed that our proposed method reduced energy consumption by up to 10.02% compared to existing methods. We evaluated the performance of the algorithms on a Zynq 7000 FPGA using the Xilinx Vivado platform via simulations. Our novel algorithm can improve the energy efficiency of green data centers. Show more
Keywords: Dragonfly algorithm, lazy task scheduling, VHDL, internet of things, energy-efficiency, Xilinx Vivado
DOI: 10.3233/JIFS-237475
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6729-6746, 2024
Authors: Sun, Shaoye
Article Type: Research Article
Abstract: In recent years, the lack of coordination in cross-border logistics has been one of the challenges and challenges faced by cross-border e-commerce. As the primary link in cross-border logistics, the selection of logistics service providers is an important foundation for promoting the development of cross-border e-commerce, and also a key link in improving the competitiveness of cross-border e-commerce enterprises. How to choose suitable and effective cross-border e-commerce logistics service providers has important theoretical significance and practical application value. The cross-border e-commerce logistics service providers evaluation is a multiple-attributed decision-making (MADM) problem. In this paper, the Type-2 neutrosophic number cross-entropy (T2NN-CE) …technique is designed with help of cross-entropy and Type-2 neutrosophic number (T2NN). Furthermore, Then, T2NN-CE technique is built to solve the MADM. Finally, a numerical example for cross-border e-commerce logistics service providers evaluation is given and some comparisons are conducted to illustrate advantages of the designed T2NN-CE technique. The research contribution of the paper is outlined: (1) The T2NN-CE is managed under T2NNs; (2) the T2NN-CE method is implemented for MADM under T2NNs; (3) the T2NN-CE technique for cross-border e-commerce logistics service providers evaluation is constructed and were compared with some existing techniques; (4) Through the comparison, it is known that T2NN-CE technique for cross-border e-commerce logistics service providers evaluation is effective. Show more
Keywords: Multiple-attributed decision-making (MADM), Type-2 neutrosophic number (T2NN), cross entropy, cross-border e-commerce logistics service providers evaluation
DOI: 10.3233/JIFS-238592
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6747-6762, 2024
Authors: Rao, Fengshuo | Chung, Sung-Pil | Xing, Kailin
Article Type: Research Article
Abstract: With the continuous improvement of modern basketball technology, higher requirements have been put forward for the personal abilities of basketball players. As the core of an organization, the offensive ability of a defender largely determines the team’s performance. Therefore, it is necessary to objectively evaluate the attacking ability of defenders. Traditional techniques cannot objectively reflect the true level of players due to their strong subjectivity. Therefore, establishing a scientific evaluation technique is particularly important. The fuzzy comprehensive evaluation of attack ability of basketball defenders is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic number cross-entropy …(TFNN-CE) technique is designed with help of cross-entropy and triangular fuzzy neutrosophic sets (TFNSs). Furthermore, Then, TFNN-CE technique is addressed to solve the MADM. Finally, a numerical example for fuzzy comprehensive evaluation of attack ability of basketball defenders is given and some comparisons are conducted to r illustrate advantages of the designed technique. The main contribution of this paper is addressed: (1) The TFNN-CE technique is addressed under TFNSs; (2) the TFNN-CE technique is addressed for MADM under TFNSs; (2) the TFNN-CE technique for fuzzy comprehensive evaluation of attack ability of basketball defenders is addressed; (3) Through the several efficient comparisons, it is addressed that TFNN-CE technique is effective for fuzzy comprehensive evaluation of attack ability of basketball defenders. Show more
Keywords: Multiple attribute decision making (MADM), triangular fuzzy neutrosophic sets (TFNSs), cross-entropy technique, TFNN-CE technique, attack ability of basketball defenders
DOI: 10.3233/JIFS-238836
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6763-6780, 2024
Authors: Radhakrishnan, P. | Senthilkumar, G.
Article Type: Research Article
Abstract: Automatic text summarization is the task of creating concise and fluent summaries without human intervention while preserving the meaning of the original text document. To increase the readability of the languages, a summary should be generated. In this paper, a novel Nesterov-accelerated Adaptive Moment Estimation Optimization based on Long Short-Term Memory [NADAM-LSTM] has been proposed to summarize the text. The proposed NADAM-LSTM model involves three stages namely pre-processing, summary generation, and parameter tuning. Initially, the Giga word Corpus dataset is pre-processed using Tokenization, Word Removal, Stemming, Lemmatization, and Normalization for removing irrelevant data. In the summary generation phase, the text …is converted to the word-to-vector method. Further, the text is fed to LSTM to summarize the text. The parameter of the LSTM is then tuned using NADAM Optimization. The performance analysis of the proposed NADAM-LSTM is calculated based on parameters like accuracy, specificity, Recall, Precision, and F1 score. The suggested NADAM-LSTM achieves an accuracy range of 99.5%. The result illustrates that the proposed NADAM-LSTM enhances the overall accuracy better than 12%, 2.5%, and 1.5% in BERT, CNN-LSTM, and RNN respectively. Show more
Keywords: Text summarization, automatic text summarization, Nesterov-accelerated Adaptive Moment Estimation, Long Short-Term Memory
DOI: 10.3233/JIFS-224299
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6781-6793, 2024
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