International Journal of Uncertainty Fuzziness and Knowledge-Based Systems最新文献

{"title":"An OWA Based MCDM Framework for Analyzing Multidimensional Twitter Data: A Case Study on the Citizen-Government Engagement During COVID-19","authors":"Ankit Gupta, Sarabjeet Singh, Harmesh Rana, Vinay Kumar Prashar, Rajan Yadav","doi":"10.1142/s0218488524500144","DOIUrl":"https://doi.org/10.1142/s0218488524500144","url":null,"abstract":"<p>In this global pandemic caused by coronavirus, the role of social media is found to be vital for spreading awareness and faultless news about various aspects of the pandemic. Governments across the world are constantly using available social media platforms to communicate crisis information efficiently to the public, which ultimately making citizens aware about the prevailing conditions. This study systematically investigates how Indian government agencies used social media platform-Twitter to disseminate the relevant information, and to reach out to the citizens during COVID 19 crisis. Spread across various parameters over many days, the twitter data was scrapped from the official Twitter accounts of different government officials. To aggregate and summarize this multi-dimensional data and to process it further, a novel multi-criteria decision making based framework that makes the use of Clustering and Ordered weighted operators is being introduced in this study. Many OWA operators have been introduced in the recent past after their introduction in late 90s, and are used successfully for aggregation purposes in many domains. The summarized values received as an output of the framework are then used to analyze the government response towards COVID 19 situation across various parameters.</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"29 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Homogenous Ensembles of Neuro-Fuzzy Classifiers using Hyperparameter Tuning for Medical Data","authors":"Hafsaa Ouifak, Zaineb Afkhkhar, Alain Thierry Iliho Manzi, Ali Idri","doi":"10.1142/s0218488524500119","DOIUrl":"https://doi.org/10.1142/s0218488524500119","url":null,"abstract":"<p>Neuro-fuzzy techniques have been widely used in many applications due to their ability to generate interpretable fuzzy rules. Ensemble learning, on the other hand, is an emerging paradigm in artificial intelligence used to improve performance results by combining multiple single learners. This paper aims to develop and evaluate a set of homogeneous ensembles over four medical datasets using hyperparameter tuning of four neuro-fuzzy systems: adaptive neuro-fuzzy inference system (ANFIS), Dynamic evolving neuro-fuzzy system (DENFIS), Hybrid fuzzy inference system (HyFIS), and neuro-fuzzy classifier (NEFCLASS). To address the interpretability challenges and to reduce the complexity of high-dimensional data, the information gain filter was used to identify the most relevant features. After that, the performance of the neuro-fuzzy single learners and ensembles was evaluated using four performance metrics: accuracy, precision, recall, and f1 score. To decide which single learners/ensembles perform better, the Scott-Knott and Borda count techniques were used. The Scott-Knott first groups the models based on the accuracy to find the classifiers appearing in the best cluster, while the Borda count ranks the models based on all the four performance metrics without favoring any of the metrics. Results showed that: (1) The number of the combined single learners positively impacts the performance of the ensembles, (2) Single neuro-fuzzy classifiers demonstrate better or similar performance to the ensembles, but the ensembles still provide better stability of predictions, and (3) Among the ensembles of different models, ANFIS provided the best ensemble results.</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"6 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Structure-Enhanced Heterogeneous Graph Representation Learning with Attention-Supplemented Embedding Fusion","authors":"Phu Pham","doi":"10.1142/s0218488524500132","DOIUrl":"https://doi.org/10.1142/s0218488524500132","url":null,"abstract":"<p>In recent years, heterogeneous network/graph representation learning/embedding (HNE) has drawn tremendous attentions from research communities in multiple disciplines. HNE has shown its outstanding performances in various networked data analysis and mining tasks. In fact, most of real-world information networks in multiple fields can be modelled as the heterogeneous information networks (HIN). Thus, the HNE-based techniques can sufficiently capture rich-structured and semantic latent features from the given information network in order to facilitate for different task-driven learning tasks. This is considered as fundamental success of HNE-based approach in comparing with previous traditional homogeneous network/graph based embedding techniques. However, there are recent studies have also demonstrated that the heterogeneous network/graph modelling and embedding through graph neural network (GNN) is not usually reliable. This challenge is original come from the fact that most of real-world heterogeneous networks are considered as incomplete and normally contain a large number of feature noises. Therefore, multiple attempts have proposed recently to overcome this limitation. Within this approach, the meta-path-based heterogeneous graph-structured latent features and GNN-based parameters are jointly learnt and optimized during the embedding process. However, this integrated GNN and heterogeneous graph structure (HGS) learning approach still suffered a challenge of effectively parameterizing and fusing different graph-structured latent features from both GNN- and HGS-based sides into better task-driven friendly and noise-reduced embedding spaces. Therefore, in this paper we proposed a novel attention-supplemented heterogeneous graph structure embedding approach, called as: AGSE. Our proposed AGSE model supports to not only achieve the combined rich heterogeneous structural and GNN-based aggregated node representations but also transform achieved node embeddings into noise-reduced and task-driven friendly embedding space. Extensive experiments in benchmark heterogeneous networked datasets for node classification task showed the effectiveness of our proposed AGSE model in comparing with state-of-the-art network embedding baselines.</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PSO Based Constraint Optimization of Intuitionistic Fuzzy Shortest Path Problem in an Undirected Network","authors":"Chanchal Dudeja","doi":"10.1142/s0218488524500120","DOIUrl":"https://doi.org/10.1142/s0218488524500120","url":null,"abstract":"<p>Shortest Path Problem (SPP) is mainly used in network optimization; also, it has a wide range of applications such as routing, scheduling, communication and transportation. The main objective of this work is to find the shortest path between two specified nodes by satisfying certain constraints. This modified version of SP is called Constraint Shortest Path (CSP), which establishes a certain limit on selected constraints for the path. The limit for constraint values is precisely specified in traditional CSP problems. But, the precise data may vary due to environmental conditions, traffic and payload. To resolve this, the proposed CSP uses intuitionistic fuzzy numbers to deal with imprecise data. Also, finding an optimal solution in the complex search space of an undirected network is difficult. Hence, Particle Swarm Optimization (PSO) is used in the proposed work to obtain the optimal global solution within feasible regions. A numerical example and the implementation of the proposed work in Matlab 2016a working environment are also illustrated. The simulation analysis shows that the proposed PSO algorithm takes 1.8<span><math altimg=\"eq-00001.gif\" display=\"inline\"><mspace width=\".17em\"></mspace></math></span><span></span>s to find the CSP in a specified undirected network graph, which is comparatively lower than the existing Genetic Algorithm (2.4<span><math altimg=\"eq-00002.gif\" display=\"inline\"><mspace width=\".17em\"></mspace></math></span><span></span>s) and without optimization (5.6<span><math altimg=\"eq-00003.gif\" display=\"inline\"><mspace width=\".17em\"></mspace></math></span><span></span>s).</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"42 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Classifier Based on the Generalized Heronian Mean Operator and Fuzzy Robust PCA Algorithms","authors":"Onesfole Kurama","doi":"10.1142/s0218488524500077","DOIUrl":"https://doi.org/10.1142/s0218488524500077","url":null,"abstract":"<p>We present a new classifier that uses a generalized Heronian mean (GHM) operator, and fuzzy robust principal component analysis (FRPCA) algorithms. The similarity classifier was earlier studied with other aggregation operators, including: the ordered weighted averaging (OWA), generalized mean, arithmetic mean among others. Parameters in the GHM operator makes the new classifier suitable for handling a variety of modeling problems involving parameter settings. Motivated by the nature of the GHM operator, we examine which FRPCA algorithm is suitable for use to achieve optimal performance of the new classifier. The effects of dimensionality reduction and fuzziness variable on classification accuracy are examined. The performance of the new classifier is tested on three real-world datasets: fertility, horse-colic, and Haberman’s survival. Compared with previously studied similarity classifiers, the new method achieved improved classification accuracy for the tested datasets. In fertility dataset, the new classifier achieved improvements in accuracy of <span><math altimg=\"eq-00001.gif\" display=\"inline\" overflow=\"scroll\"><mn>1</mn><mn>4</mn><mo>.</mo><mn>6</mn><mn>0</mn><mi>%</mi><mo>,</mo><mn>1</mn><mn>9</mn><mo>.</mo><mn>7</mn><mn>3</mn><mi>%</mi></math></span><span></span>, and <span><math altimg=\"eq-00002.gif\" display=\"inline\" overflow=\"scroll\"><mn>2</mn><mn>3</mn><mo>.</mo><mn>0</mn><mn>0</mn><mi>%</mi></math></span><span></span> compared with the OWA, generalized mean, and arithmetic mean based classifiers respectively. The new classifier is simpler to implement since it does not require any weight generation criteria as the case is for the OWA based classifier.</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"29 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140630497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solution of Uncertain Constrained Multi-Objective Travelling Salesman Problem with Aspiration Level Based Multi Objective Quasi Oppositional Jaya Algorithm","authors":"Aaishwarya Bajaj, Jayesh Dhodiya","doi":"10.1142/s0218488524500090","DOIUrl":"https://doi.org/10.1142/s0218488524500090","url":null,"abstract":"<p>Multi-Objective Travelling Salesman Problem (MOTSP) is one of the most crucial problems in realistic scenarios, and it is difficult to solve by classical methods. However, it can be solved by evolutionary methods. This paper investigates the Constrained Multi-Objective Travelling Salesman Problem (CMOTSP) and the Constrained Multi-Objective Solid Travelling Salesman Problem (CMOSTSP) under an uncertain environment with zigzag uncertain variables. To solve CMOTSP and CMOSTSP models under uncertain environment, the expected value and optimistic value models are developed using two different ranking criteria of uncertainty theory. The models are transformed to their deterministic forms using the fundamentals of uncertainty. The Models are solved using two solution methodologies Aspiration level-based Multi-Objective Quasi Oppositional Jaya Algorithm (AL-based MOQO Jaya) and Fuzzy Programming Technique (FPT) with linear membership function. Further, the numerical illustration is solved using both methodologies to demonstrate its application. The sensitivity of the OVM model’s objective functions regarding confidence levels is also investigated to look at the variation in the objective function. The paper concludes that the developed approach has solved CMOTSP and CMOSTSP efficiently with an effective output and provides alternative solutions for decision-making to DM.</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"112 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140624502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Fuzzy Backstepping and Backstepping Sliding Mode Controllers Based on ICD Observer: A Comparative Study","authors":"Safa Choueikh, Marwen Kermani, Faouzi M’Sahli","doi":"10.1142/s0218488524500065","DOIUrl":"https://doi.org/10.1142/s0218488524500065","url":null,"abstract":"<p>This paper develops a Fuzzy Adaptive Backstepping Control (FABC) and a Fuzzy Adaptive Backstepping Sliding Mode Control (FABSMC) for Single-Input Single-Output (SISO) nonlinear-systems with unmeasured states. The proposed adaptive schemes are fully compared. Thus, the Fuzzy Type-2 (FT2) concept and the High-Order Integral-Chain Differentiator (HOICD) are used as two universal approximators. Indeed, the first one is employed to approximate the nonlinear system model’s and the second one to estimate the unknown states. Special attention is paid for the used approximators robustness under unmodeled dynamics, parameter variations and process noise.</p><p>It should be noted that the asymptotic stability of both the fuzzy adaptive controls and the observer convergence for each scheme have been proved. In addition, the employed schemes have been simulated on a two-tank coupled nonlinear system. Thus, from simulation results, we can prove that the proposed methods guarantee that all signals for closed loop systems are both regular and bounded. Specifically, it can be shown that the performances of the proposed Fuzzy Interval Type-2 (FIT2) schemes are significantly improved compared with Fuzzy Type-1 (FT1) schemes in presence of external disturbances.</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"44 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140629222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Document-Level Relation Extraction with Deep Gated Graph Reasoning","authors":"Zeyu Liang","doi":"10.1142/s0218488524400063","DOIUrl":"https://doi.org/10.1142/s0218488524400063","url":null,"abstract":"<p>Extracting the relations of two entities on the sentence-level has drawn increasing attention in recent years but remains facing great challenges on document-level, due to the inherent difficulty in recognizing the relations of two entities across multiple sentences. Previous works show that employing the graph convolutional neural network can help the model capture unstructured dependent information of entities. However, they usually employed the non-adaptive weight edges to build the correlation weight matrix which suffered from the problem of information redundancy and gradient disappearance. To solve this problem, we propose a deep gated graph reasoning model for document-level relation extraction, namely, BERT-GGNNs, which employ an improved gated graph neural network with a learnable correlation weight matrix to establish multiple deep gated graph reason layers. The proposed deep gated graph reasoning layers make the model easier to reasoning the relations between entities hidden in the document. Experiments show that the proposed model outperforms most of strong baseline models, and our proposed model is <b>0.3%</b> and <b>0.3%</b> higher than the famous LSR-BERT model on the F1 and Ing F1, respectively.</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140200191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Arithmetic Operations on Generalized Trapezoidal Hesitant Fuzzy Numbers and Their Application to Solving Generalized Trapezoidal Hesitant Fully Fuzzy Equation","authors":"F. Babakordi","doi":"10.1142/s0218488524500041","DOIUrl":"https://doi.org/10.1142/s0218488524500041","url":null,"abstract":"<p>Algebraic operations on generalized hesitant fuzzy numbers are key tools to address the problems with decision uncertainty. In this paper, by studying the arithmetic operations on generalized trapezoidal hesitant fuzzy numbers, modified arithmetic operations are introduced for this class of numbers so that, using these arithmetic operations, the multiplication and division of two generalized trapezoidal hesitant fuzzy numbers are always generalized trapezoidal hesitant fuzzy numbers. Furthermore, a generalized trapezoidal hesitant fuzzy number raised to the power of a real number is a generalized trapezoidal hesitant fuzzy number, and in the defined division, the case where the denominator becomes zero is not considered. Numerical examples are used to show the shortcomings of the previous arithmetic operations as well as the efficiencies of the arithmetic operations proposed in this research for generalized trapezoidal hesitant fuzzy numbers. Finally, the application of the proposed new arithmetic operations to generalized trapezoidal hesitant fuzzy numbers in solving the generalized trapezoidal hesitant fully fuzzy equation is discussed.</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"56 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140200178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing Uninorms on Bounded Lattices Through Closure and Interior Operators","authors":"Gül Deniz Çaylı","doi":"10.1142/s0218488524500053","DOIUrl":"https://doi.org/10.1142/s0218488524500053","url":null,"abstract":"<p>Uninorms combining t-conorms and t-norms on bounded lattices have lately drawn extensive interest. In this article, we propose two ways for constructing uninorms on a bounded lattice with an identity element. They benefit from the appearance of the t-norm (resp. t-conorm) and the closure operator (resp. interior operator) on a bounded lattice. Additionally, we include some illustrative examples to highlight that our procedures differ from others in the literature.</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"44 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140200198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}