Article
Computer Science, Information Systems
Salvador Linares-Mustaros, Joan Carles Ferrer-Comalat, Dolors Corominas-Coll, Jose M. Merigo
Summary: Experton theory is a generalization of probabilistic set theory that aims to improve expert opinion processing and analysis, producing mathematical objects like expertons; a new aggregative method of several expertons is presented in this paper, providing a new tool for dealing with group decision-making problems and introducing some properties of the new object.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Asmat Hadi, Waqar Khan, Asghar Khan
Summary: The article introduces the intuitionistic fuzzy set, Pythagorean fuzzy set, and Fermatean fuzzy set as tools for expressing uncertain and incomplete information, discussing their applications in decision-making. New operations on Fermatean fuzzy information are devised using Hamacher norms, proposing Fermatean fuzzy Hamacher arithmetic and geometric aggregation operators. A direct application in cyclone disaster assessment is used to demonstrate the efficacy of the model and comparison analysis is conducted for authenticity.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Aerospace
Muhammet Ozturk, Ibrahim Ozkol
Summary: The study introduces the interval type-2 adaptive network-fuzzy inference system (ANFIS) structure for the first time, showing better results compared to previously presented methods. By modifying the Karnik-Mendel algorithm, uncertainties of ANFIS parameters are transformed into known ones, simplifying the system for effective training.
AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
F. Abbasi, T. Allahviranloo
Summary: Recent studies have focused on solving fuzzy linear systems, most of which use an extension method that may lead to inaccurate or incorrect results. The existing methods may involve lengthy procedures and lack computational efficiency. Therefore, a new approach using transmission-average-based fuzzy operations has been proposed to solve fully fuzzy linear systems, along with a computational procedure to address the shortcomings of previous techniques.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Majid Moradi Zirkohi, Tsung-Chih Lin
Summary: IT2FLS has better abilities to handle uncertainties, but the high computational cost of the KM algorithm hinders practical applications. The novel MZL TR method simplifies the implementation of IT2FLS, outperforming the KM TR method in terms of computational burden and achieving closer results in accuracy.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Mathematics, Applied
Yang Chen, Jiaxiu Yang, Chenxi Li
Summary: This paper introduces type-reduction algorithms for interval type-2 fuzzy logic systems (IT2 FLSs), with a focus on the initialization and weighting methods of Karnik-Mendel algorithms. The experimental results show that the proposed RIWEKM algorithms outperform EKM and RIEKM algorithms in terms of absolute error and convergence speed.
Article
Mathematics, Interdisciplinary Applications
Sarmad A. Altaie, Nidal Anakira, Ali Jameel, Osama Ababneh, Ahmad Qazza, Abdel Kareem Alomari
Summary: This article extends the analytical scheme of the homotopy analysis method to develop an approximate analytical solution for Fuzzy Partial Differential Equations. By utilizing powerful tools and concepts, it ensures high precision in fuzzy environments and demonstrates efficiency in linear and nonlinear cases of Fuzzy Reaction-Diffusion Equation and Fuzzy Wave Equation.
FRACTAL AND FRACTIONAL
(2022)
Article
Mathematics, Applied
O. Pavlacka, M. Pavlackova
Summary: The paper investigates whether the properties of the weighted average operator can be observed in its fuzzy extension, with results showing strict monotonicity in case of positive fuzzy weights, and symmetry in case of equal fuzzy weights, but not coinciding with the fuzzy arithmetic mean operator.
IRANIAN JOURNAL OF FUZZY SYSTEMS
(2021)
Article
Computer Science, Theory & Methods
Marko Palangetic, Chris Cornelis, Salvatore Greco, Roman Slowinski
Summary: This paper discusses the importance of granular representations of crisp and fuzzy sets in rule induction algorithms based on rough set theory. It demonstrates that the OWA-based fuzzy rough set model, which has been successfully applied in various machine learning tasks, allows for a granular representation. The practical implications of this result for rule induction from fuzzy rough approximations are highlighted.
FUZZY SETS AND SYSTEMS
(2022)
Article
Mathematics
Jose M. Brotons-Martinez, Manuel E. Sansalvador-Selles
Summary: This paper examines the impact of the COVID-19 pandemic on the treatment of other diseases and provides a financial model to analyze the cost overrun resulting from worsening illnesses and deaths. By classifying deaths by causes and estimating the worsening condition of patients, a fuzzy relation is established, and experts' opinions are used to estimate cost overrun. A correction coefficient is determined through fuzzy logic to optimize the implementation of the proposed model. Ultimately, the paper presents a new tool for improving healthcare system efficiency to the scientific community and public administration entities.
Article
Mathematics, Interdisciplinary Applications
Mamta Kapoor, Nasser Bin Turki, Nehad Ali Shah
Summary: In this paper, a hybrid methodology is used to calculate the solutions of fuzzy Volterra integral equations. The combination of the Elzaki transform and Adomian decomposition method leads to the development of a novel regime. The reliability, efficacy, and application of the established scheme are demonstrated by solving three instances of the considered equations. The results have a significant impact on the theory of fuzzy analytical dynamic equations.
FRACTAL AND FRACTIONAL
(2023)
Article
Computer Science, Artificial Intelligence
Mijanur Rahaman Seikh, Shibaji Dutta
Summary: This paper explores the use of single-valued trapezoidal neutrosophic numbers (SVTNNs) in matrix games and proposes two different solution methodologies. By converting the neutrosophic mathematical programming problems and utilizing a new ranking order relation for SVTNNs, the paper successfully solves the games and obtains values for both players in SVTNN forms. Additionally, the concept of a-weighted possibility mean value is used to transform the models into crisp ones, showing the validity and applicability of the approaches through market share problems and numerical examples.
Article
Automation & Control Systems
Weizhong Wang, Xiao Han, Weiping Ding, Qun Wu, Xiaoqing Chen, Muhammet Deveci
Summary: This study develops a hybrid Fine-Kinney-based occupational risk evaluation framework with an extended Fermatean fuzzy MARCOS method. The framework can handle the occupational risk analysis problem with Fermatean fuzzy data and effectively handle uncertain risk rating information from decision-makers by establishing Fermatean fuzzy numbers-based risk rating scales.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Mojtaba Borza, Azmin Sham Rambely
Summary: In this paper, an approach to solving the multi-objective linear fractional programming problem is presented. The fuzzy problem is transformed into a linear programming problem to obtain a solution that is at least weakly epsilon-efficient.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Toly Chen, Hsin-Chieh Wu
Summary: The study proposed a fuzzy collaborative intelligence fuzzy analytic hierarchy process (FAHP) approach for assessing the performance of a 3D printer, ensuring consensus among decision makers. Experiment results showed the superior accuracy of the proposed methodology compared to existing methods.
Article
Computer Science, Artificial Intelligence
Dongrui Wu, Ye Yuan, Yihua Tan
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2020)
Article
Engineering, Biomedical
He He, Dongrui Wu
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2020)
Article
Computer Science, Artificial Intelligence
Parham Mohsenzadeh Kebria, Abbas Khosravi, Saeid Nahavandi, Dongrui Wu, Fernando Bello
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Dongrui Wu, Jerry M. Mendel
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2020)
Article
Engineering, Electrical & Electronic
Jian Huang, Junzhe Wang, Yihua Tan, Dongrui Wu, Yu Cao
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2020)
Article
Engineering, Biomedical
Zhenhua Shi, Xiaomo Chen, Changming Zhao, He He, Veit Stuphorn, Dongrui Wu
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2020)
Article
Automation & Control Systems
Gui-Ping Ren, Zhiyong Chen, Hai-Tao Zhang, Yue Wu, Haofei Meng, Dongrui Wu, Han Ding
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2020)
Article
Computer Science, Artificial Intelligence
Xin Song, Pengjiang Qian, Jiamin Zheng, Yizhang Jiang, Kaijian Xia, Bryan Traughber, Dongrui Wu, Raymond F. Muzic
PATTERN RECOGNITION LETTERS
(2020)
Article
Computer Science, Artificial Intelligence
Yuqi Cui, Dongrui Wu, Jian Huang
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2020)
Article
Engineering, Civil
Yizhang Jiang, Yuanpeng Zhang, Chuang Lin, Dongrui Wu, Chin-Teng Lin
Summary: This paper proposes an online multi-view and transfer TSK fuzzy system for driver drowsiness estimation, which has higher interpretability and can better utilize pattern information from different views.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Chemistry, Analytical
Zhongzheng Fu, Xinrun He, Enkai Wang, Jun Huo, Jian Huang, Dongrui Wu
Summary: Human activity recognition based on wearable devices has gained more attention from researchers, with a focus on personalized recognition and high accuracy while maintaining model generalization. A new transfer learning algorithm with improved pseudo-labels was proposed to address personalized recognition challenges and achieved a high average recognition accuracy of 93.2% for different subjects.
Article
Biochemical Research Methods
Xiaotong Gu, Zehong Cao, Alireza Jolfaei, Peng Xu, Dongrui Wu, Tzyy-Ping Jung, Chin-Teng Lin
Summary: BCIs enhance human brain activities to interact with the environment, with recent advancements in technology and machine learning attracting interest in EEG-based BCI applications. Current research focuses on improving signal sensing technologies and computational intelligence techniques to monitor cognitive states and task performance, with potential applications in healthcare and other research areas.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Yurui Ming, Dongrui Wu, Yu-Kai Wang, Yuhui Shi, Chin-Teng Lin
Summary: This paper proposes using deep Q-learning to study the correlation between drowsiness and driving performance, based on analyzing EEG data and designing a deep Q network to indirectly estimate drowsiness. The results demonstrate that this method performs well in tracking variations in mental state, outperforming supervised learning and confirming the feasibility and practicality of this new computational paradigm.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2021)
Proceedings Paper
Automation & Control Systems
Lei Wang, Jian Huang, Dongrui Wu, Tao Duan, Rui Zong, Shicong Jiang
2020 5TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2020)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Dongrui Wu, Chenfeng Guo, Feifei Liu, Chengyu Liu
2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2020)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
