Journal of Intelligent & Fuzzy Systems

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ISSN / EISSN : 1064-1246 / 1875-8967
Published by: IOS Press (10.3233)
Total articles ≅ 1,358
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Xiaoguang Zhou, Xin He, Xiaoxia Huang
Journal of Intelligent & Fuzzy Systems, pp 1-18; https://doi.org/10.3233/jifs-211766

Abstract:
Traditionally, the return on investment has been described as either a random variable or a fuzzy variable, while this paper discusses the uncertain portfolio selection in which each security return is assumed to be an uncertain variable. To better optimize the return and risk of a portfolio, we propose two models: uncertain minimax mean-variance (UM-EV) model and uncertain minimax mean-semivariance (UM-SVE) model. The crisp equivalents of the UM-EV model that regard the security return as a normal and linear uncertain variable are derived, and the optimization problem is solved using linear programming. For the UM-SVE model, the crisp equivalent of a zigzag uncertain variable is introduced, and the optimization solution is calculated using hybrid intelligent algorithm. Finally, the effectiveness of the proposed models is illustrated using numerical examples.
Yanping He, Taiben Nan, Haidong Zhang
Journal of Intelligent & Fuzzy Systems, pp 1-16; https://doi.org/10.3233/jifs-211994

Abstract:
This paper is devoted to discussing the reverse triple I method based on the Pythagorean fuzzy set (PFS). We first propose the concepts of Pythagorean t-norm, Pythagorean t-conorm, residual Pythagorean fuzzy implication operator (RPFIO) and Pythagorean fuzzy biresiduum. The reverse triple I methods for Pythagorean fuzzy modus ponens (PFMP) and Pythagorean fuzzy modus tollens (PFMT) are also established. In addition, some interesting properties of the reverse triple I method of PFMP and PFMT inference models are analysed, including the robustness, continuity and reversibility. Finally, a practical problem is provided to illustrate the effectiveness of the reverse triple I method for PFMP in decision-making problems. The advantages of the new method over existing methods are also expounded. Overall, compared with the existing methods, the proposed methods are based on logical reasoning rather than using aggregation operators, so the novel methods are more logical, can better deal with the uncertain problems in complex decision-making environments and can completely reflect the decision-making opinions of decision-makers.
Hongping Liu, Qian Ge, Ruiju Wei
Journal of Intelligent & Fuzzy Systems, pp 1-13; https://doi.org/10.3233/jifs-212027

Abstract:
This paper aims to further study the new kind of ordered fuzzy group named ordered L-group, which is put forward in literature [20]. Some algebraic properties of ordered L-groups, such as the relationship between elements, the equivalent characterizations and the products of these groups are discussed. Following that, the properties of substructures including characterization theorems, the convexity, the products of (normal) subgroups maintain the original substructure, along with the properties of ordered L-group homomorphisms are explored. The discussion of ordered fuzzy groups in this paper is from the perspective of fuzzy binary operation, which is different from the commonly method that just discuss the fuzzification of substructures in the research of fuzzy algebra. It can better reflect the essence of fuzzy groups logically just like that of classical groups.
Arti Saxena, Y.M. Dubey, Manish Kumar
Journal of Intelligent & Fuzzy Systems, pp 1-14; https://doi.org/10.3233/jifs-212566

Abstract:
On the everlasting demand for better accuracy, high speed, and the inevitable approach for the high-quality surface finish as the basic requirements in the process industry, there felt the requirement to develop models which are reliable for predicting surface roughness (SR) as it is having a crucial role in the process industries. In this paper, SBCNC-60 of HMT make used to study the purpose of machining, while cutting speed (CS), feed rate (FR), and the depth of cut (DoC) were considered as parameters for machining of P8 material. Turning experiments data is studied by keeping two parameters constant at the mid-level out of three parameters. An artificial intelligence technique named fuzzy was engaged in working out for surface roughness and material removal rate (MRR) to design the models of reliable nature for the predictions. The accurate prediction performance of the fuzzy logic model was then better analyzed by calculating MAPE, RMSE, MAD, and correlation coefficient between experimental values and fuzzy logic predictions. MAPE, RMSE, MAD, and correlation coefficient calculated 2.66%, 8.20, 6.44, and 0.98 for MRR and 4.19%,1.16, 0.86 and 0.90 for SR, respectively. Hence, the proposed fuzzy logic rules efficiently predict the SR and MRR on P8 material with higher accuracy and computational cost.
Manju Lata Joshi, Namita Mittal, Nisheeth Joshi
Journal of Intelligent & Fuzzy Systems, pp 1-18; https://doi.org/10.3233/jifs-212603

Abstract:
In this study, a Fuzzy Semantic Graph-based approach is proposed to extract keywords and generate extractive text summaries from Hindi text documents. Hindi Wordnet is used as a knowledge source to construct the semantic graph. As the semantic relations defined in Hindi Wordnet are crisp, they do not capture the semantic relationship as a matter of degree. Due to that, many terms are represented as not being related, while these can share some meaningful relationship as per real-life scenarios. To overcome this curb of Hindi Wordnet, the paper presents several fuzzy semantic associations between such terms by assigning a value ranging from 0 to 1 to such relations. While constructing the semantic graph to represent documents using Hindi Wordnet semantic relations, the terms sharing fuzzy semantic relations are also added to enhance the quality of the graph. The experiments are done to extract potential keywords and to generate a good content summary. It is observed that such semantics generate a more accurate summary and produce prospective keywords for the document. The performance of the proposed approach fuzzy-based semantic graph is compared to semantic graph-based approach for keyword extraction and text summarization. The keywords extracted and the summary generated by the proposed approach is match up to human extracted keywords and human-generated text summary. The proposed approach results are evaluated using precision, recall, and f-measure. Different outcomes of generated text summaries are evaluated using the ROUGE matrix. The results of the proposed approach are pretty encouraging.
Yifan Zhao
Journal of Intelligent & Fuzzy Systems, pp 1-15; https://doi.org/10.3233/jifs-211477

Abstract:
Interval fuzzy implications play an important role in both theoretical and applied communities of interval-valued fuzzy sets and have been widely studied. Recently, Dimuro et al. analyzed the law of O-conditionality for fuzzy implications in general. However, there is no corresponding researches about the interval extension. To fill the gap, in this paper, we introduce the generalized law of O-conditionality 𝕆 ( X , 𝕀 ( X , Y ) ) ≤ Y (GOC), where 𝕀 is an interval fuzzy implication and 𝕆 is an interval overlap function. Meanwhile, we discuss the advantages one may get using it. Moreover, we consider the conditional antecedent boundary condition (CABC) for interval fuzzy implications derived from interval overlap and grouping functions, including, interval R 𝕆 - , ( 𝔾 , ℕ ) - , ( 𝕆 , 𝔾 , ℕ ) - and ( 𝔾 , 𝕆 , ℕ ) - implications. Finally, we further analyze the generalized law of O-conditionality for these four classes of interval fuzzy implications.
Meishe Liang, Jusheng Mi, Tao Feng, Chenxia Jin
Journal of Intelligent & Fuzzy Systems, pp 1-13; https://doi.org/10.3233/jifs-202719

Abstract:
Knowledge acquisition in intuitionistic fuzzy information systems is of importance because those fuzzy information systems are often encountered in many real-life problems. Formal concept analysis is a simple and effective tool for knowledge acquisition. However, there is still little work on introducing knowledge acquisition methods based on formal concept analysis into intuitionistic fuzzy information systems. This paper mainly extends the formal concept theory into intuitionistic fuzzy information systems. Firstly, two pairs of adjoint mappings are defined in intuitionistic fuzzy formal contexts. It is verified that both pairs of adjoint mappings form Galois connections. Secondly, two types of intuitionistic fuzzy concept lattices are constructed. After that, we also present the main theorems and propositions of the intuitionistic fuzzy concept lattices. Thirdly, we deeply discuss the attribute characteristics for type-1 generalized one-sided intuitionistic fuzzy concept lattice. Furthermore, a discernibility matrix-based algorithm is proposed for attribute reduction and the effectiveness of this algorithm is demonstrated by a practical example. The construction of intuitionistic fuzzy conceptS is meaningful for the complex and fuzzy information in real life.
Junfeng Yang, Yuwen Huang, Yubin Guo, Fuxian Huang, Jing Li
Journal of Intelligent & Fuzzy Systems, pp 1-12; https://doi.org/10.3233/jifs-212086

Abstract:
Although some methods of feature extraction for photoplethysmography (PPG) biometric recognition have been extensively studied, effectiveness of local features, time cost of feature extraction, and robust identification for small-scale data remain challenging. To address these issues, we proposed a feature-extraction method of PPG biometrics combining singular value decomposition with local mean decomposition and time-domain parameters. First, we used the singular-value-decomposition method to de-noise the original PPG data. Second, we extracted the local-mean-decomposition-based and time-domain features, which are fused into a concatenated feature. Finally, we combined the concatenated feature with four classifiers for classification and decision-making. Extensive experiments on the three datasets have shown that the waveform of the PPG signal de-noised by singular value decomposition was smoother and more regular, the concatenated feature had strong inter-subject distinguishability and intra-subject similarity, and the concatenated feature combined with a random-forest classifier was the best and could achieve 99.40%, 99.88%, and 99.56% recognition rates on the respective datasets. The method is competitive with several state-of-the-art methods.
Fuwei Cui, Hui Di, Hui Huang, Kazushige Ouchi, Ze Liu, Jinan Xu
Journal of Intelligent & Fuzzy Systems, pp 1-10; https://doi.org/10.3233/jifs-211886

Abstract:
Hierarchical structures have emerged as a powerful framework for response generation, which can generate fluent responses in multi-turn conversation. However, the generated responses are often generic and bland. Some researchers have adopted latent variables to improve the diversity of responses, but they can not make full use of the information from multi-turn background, leading to meaningless replies with irrelevant topics. In order to fully utilize the background information for generating diverse and informative responses, we propose a Variational Hierarchical Conversation RNNs model with Topic aware latent variables (VHCR-T). The model contain three levels of latent variables: the global level latent variable to represent background information, the topic level latent variable to capture topic-related information, and the sentence level latent variable to increase the response diversity. When modeling the topic information, we design two different topic level latent variables to maintain the dialog coherence and role preference, and to enhance the context sensitiveness, respectively. Experimental results on Cornell Movie Dialog and Ubuntu Dialog Corpus show that our model outperforms the state-of-the-art models for multi-turn conversation generation in terms of diversity and informativeness, verifying the effectiveness of our VHCR-T model.
Shu Gong, Gang Hua
Journal of Intelligent & Fuzzy Systems, pp 1-13; https://doi.org/10.3233/jifs-212551

Abstract:
Graphs and hypergraphs are popular models for data structured representation. For example, traffic data, weather data, and animal skeleton data are all described by graph structures. Interval-valued fuzzy sets change the membership function of general fuzzy sets from single value functions to interval-valued functions, and thus describe the fuzzy attributes of things in terms of fuzzy intervals, which is more in line with the characteristics of fuzzy objectives. This paper aims to define the bipolar interval-valued fuzzy hypergraph to reveal the inner relationship of fuzzy data, and give some characterizations of it. The characteristics of bipolar interval-valued intuitionistic fuzzy hypergraph and bipolar interval-valued Pythagorean fuzzy hypergraph are studied. In addition, we discuss the characteristics of the bipolar interval-valued fuzzy threshold graph. Finally, some instances are presented as the applications of bipolar interval-valued fuzzy hypergraphs.
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