Article
Green & Sustainable Science & Technology
M. Alimohammadlou, Z. Khoshsepehr
Summary: Sustainable development is a process of meeting the needs of the present generation without harming the resources for future generations, a study proposes a multi-criteria decision-making method to assess sustainable development at companies, emphasizing the importance of environmental impacts in the main criteria, and provides practical guidance, validating its effectiveness through sensitivity analysis and comparative analysis.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2022)
Article
Mathematics
Jeevaraj Selvaraj, Abhijit Majumdar
Summary: The paper introduces a new ranking principle for comparing IVIFNs by defining a score function based on the non-membership value. It discusses the limitations of existing ranking methods and proposes a new non-membership score. The superiority of the proposed score function in ranking arbitrary IVIFNs is demonstrated.
Article
Computer Science, Information Systems
Kamal Kumar, Shyi-Ming Chen
Summary: This paper proposes a new group decision making approach using interval-valued intuitionistic fuzzy values (IVIFVs). The paper introduces a new score function for IVIFVs that overcomes the drawbacks of existing methods and presents its properties. Then, the paper proposes advanced aggregation operators for IVIFVs and a new GDM approach based on these operators and the score function. The proposed approach is applied to a real-world cloud service selection problem, and it can overcome the limitations of existing methods in distinguishing ranking orders of alternatives in certain situations, providing a useful way to deal with GDM problems in the IVIFV environments.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Theory & Methods
Michal Boczek, LeSheng Jin, Marek Kaluszka
Summary: This paper proposes a novel mapping method, which maps interval-valued functions defined on an arbitrary set to closed subintervals of nonnegative extended real numbers. The method can be used to aggregate interval-valued data and has wide application prospects.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Information Systems
Michal Boczek, LeSheng Jin, Marek Kaluszka
Summary: The study explores the structures and properties of interval-valued fuzzy operators and introduces the concept of interval-valued seminormed fuzzy operator (ISFO). The research reveals related properties of admissible orders and cones, leading to a fundamental structural analysis of ISFO.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Shuai Li, Jie Yang, Guoyin Wang, Taihua Xu
Summary: This paper investigates the interval-valued intuitionistic fuzzy (IvIF) set, proposes a pair of IvIF granular structure distances, defines a more generalized coarseness/fineness relation, formulates two complementary uncertainty measures, and introduces a new attribute reduction algorithm. Experimental results show that the algorithm has a shorter reduction and guarantees high classification accuracy. Finally, a case study demonstrates the feasibility of using the proposed method to solve large linguistic group decision-making problems.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Deeba R. Naqvi, Rajkumar Verma, Abha Aggarwal, Geeta Sachdev
Summary: Game theory has wide applications in various fields, and researchers have extended traditional game theory to handle uncertainty and ambiguity. Qualitative information is crucial in describing the payoffs of game problems, and experts prefer expressing their perspective in natural language. This paper uses linguistic interval-valued intuitionistic fuzzy numbers to describe the payoffs, and solves the matrix games through linear or nonlinear programming problems.
Article
Physics, Multidisciplinary
Chengli Fan, Qiang Fu, Yafei Song, Yingqi Lu, Wei Li, Xiaowen Zhu
Summary: This article proposes a dynamic multi-time fusion target threat assessment method that effectively improves the accuracy and reliability of target threat assessment in missile defense by introducing new computational models and operators, as well as an improved distance measurement model.
Article
Computer Science, Artificial Intelligence
Jiu-Ying Dong, Shu-Ping Wan
Summary: This paper introduces a new interval-valued intuitionistic fuzzy (IVIF) BWM method with additive consistency, which distinguishes the best and worst criteria by defined score out-degree and accuracy out-degree, and achieves additive consistency by introducing the concept of IVIF reference comparisons and using left and right intuitionistic fuzzy preference relations. A linear goal programming model is constructed to generate the IVIF priority weights, and the consistency of IVIF reference comparisons is checked by defining a consistency ratio.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Mehmet Unver, Ezgi Turkarslan, Nuri Celik, Murat Olgun, Jun Ye
Summary: This paper introduces the characteristics and applications of single-valued neutrosophic multi-sets, which are very useful in multi-criteria group decision making. By using sequences of intuitionistic fuzzy values to define the concept of intuitionistic fuzzy-valued neutrosophic multi-sets, more powerful information can be provided. The paper also presents set theoretic operations, algebraic operations, weighted aggregation operators, and a similarity measure for intuitionistic fuzzy-valued neutrosophic multi-values.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Sina Salimian, Seyed Meysam Mousavi, Jurgita Antucheviciene
Summary: This paper proposes a new integrated MCDM model for assessing sustainable healthcare device suppliers in organ transplantation networks. The model combines E-VIKOR and MARCOS methods and utilizes interval-valued intuitionistic fuzzy sets. An IVIF-similarity measure is introduced to compute the weights of decision-makers, and the IVIF-E-VIKOR and IVIF-MARCOS methods are used to calculate rankings. The effectiveness of the proposed approach is validated through an illustrative example and comparative analysis, along with sensitivity analysis for key parameters of the model.
Article
Automation & Control Systems
Peide Liu, Hui Gao
Summary: This paper proposes a green supplier selection method based on multi-criteria decision-making, which combines the advantages of prioritized aggregation, Choquet integral, Bonferroni mean, and interval type-2 fuzzy set, allowing for the consideration of interactions, interrelationships, and prioritizations over the criteria simultaneously.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Green & Sustainable Science & Technology
Cui Haiyun, Huang Zhixiong, Serhat Yuksel, Hasan Dincer
Summary: This study aims to analyze innovation strategies for green supply chain management through a multidimensional approach using the QFD method. The research reveals that understanding customer expectations and managing customer relationships are the most important innovation strategies in the energy industry, while benchmarking the competitive market environment ranks relatively low. Therefore, it is recommended that energy companies should enhance customer relationship management and conduct detailed analysis of their products and services based on customer expectations.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Computer Science, Artificial Intelligence
Muhammad Sajjad Ali Khan, Saeed Ullah Jan, Rahim Jan, Tapan Senapati, Sarbast Moslem
Summary: This paper focuses on the study of new operators for complex interval-valued intuitionistic fuzzy sets based on Aczel-Alsina t-norm and t-conorm. The operators developed include complex interval-valued intuitionistic Aczel-Alsina weighted average, complex interval-valued intuitionistic Aczel-Alsina weighted geometric, complex interval-valued intuitionistic Aczel-Alsina ordered weighted average, and complex interval-valued intuitionistic Aczel-Alsina ordered weighted geometric. These operators are more adaptable and give more accurate results than existing ones. A group decision-making method and a multi-criteria decision-making technique are also developed based on the proposed operators.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuchu Qin, Qunfen Qi, Peizhi Shi, Paul J. Scott, Xiangqian Jiang
Summary: In this paper, a weighted averaging operator of linguistic interval-valued intuitionistic fuzzy numbers (LIVIFNs) based on Dempster-Shafer evidence theory is proposed for solving cognitively inspired decision-making problems. The developed operational rules of LIVIFNs are proven to be always invariant and persistent, and the constructed aggregation operator is proven to be always monotone. The effectiveness and advantage of the presented method are demonstrated through quantitative comparisons with several existing methods.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Fang Zhou, Ting-Yu Chen
Summary: Blockchain technology has gained global attention and is widely used to improve efficiency, fairness, and security. This article introduces a combined decision-making technique that integrates the analytic hierarchy process and TODIM method to select an appropriate blockchain technology provider considering decision-makers' uncertainties and psychological behaviors.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Ting-Yu Chen
Summary: This study constructs a likelihood-based consensus ranking model using point operator-oriented likelihood measures for conducting multiple criteria decision making with Pythagorean fuzzy sets. The model includes useful point operators and penalty weights for comprehensive disagreement and dominated relations characterization. An effective likelihood measure is used to determine outranking relations of Pythagorean fuzzy information.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Ting-Yu Chen
Summary: In this study, a T-SF REGIME method is proposed for multiple criteria choice analysis in uncertain T-spherical fuzzy sets. The method utilizes dominance analysis and prioritization analysis, and it is well-suited for qualitative information processing and complex scenarios. The paper introduces a new Minkowski-type distance measure and an evolved Gaussian preference function to enhance the REGIME methodology. The effectiveness of the method is demonstrated through a realistic application in the field of solar plant.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Materials Science, Textiles
Jing Ye, Ting-Yu Chen
Summary: This research proposes a technique using TOPSIS approach under Pythagorean fuzzy sets context to select the best cotton fabric, and conducts a sensitivity analysis to investigate the ranking effects of different parameter settings. Comparative analysis is done between different distance measurements and aggregation operators to provide reliable results applicable to other textile areas.
JOURNAL OF NATURAL FIBERS
(2022)
Article
Computer Science, Artificial Intelligence
Chueh-Yung Tsao, Ting-Yu Chen
Summary: This research introduces a parametric likelihood measure based on the beta distribution and develops a likelihood-oriented methodology for solving multiple criteria decision analysis problems with Pythagorean fuzzy sets. The method proposed in this study shows a great adaptability and superiority in solving complex real-world problems.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Materials Science, Textiles
Jing Ye, Ting-Yu Chen
Summary: In this study, the PROMETHEE approach is used to rank cotton fabrics, and a comparative analysis of different score functions is performed. The proposed PF-PROMETHEE method shows good ranking accuracy and better ability to handle uncertainty, making it applicable to multicriteria decision-making problems in the textile industry.
JOURNAL OF NATURAL FIBERS
(2022)
Article
Automation & Control Systems
Ting-Yu Chen
Summary: This paper introduces a useful Pythagorean fuzzy likelihood function and develops several likelihood-based agreement measurements for enrichment evaluations and priority determination in uncertain multiple criteria analysis. By exploiting the characterization parameters of Pythagorean membership grades and constructing innovative likelihood function and linear programming model, this paper provides a valuable approach for prioritizing competing alternatives.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Materials Science, Textiles
Jing Ye, Ting-Yu Chen
Summary: This article introduces a multi-criteria decision-making method for fabric selection problems and proposes the use of sine trigonometry Pythagorean fuzzy sets and weighted averaging, geometric aggregation operators to solve two different types of fabric selection problems. Comparative analysis shows that this method is more efficient and reliable.
JOURNAL OF NATURAL FIBERS
(2022)
Article
Automation & Control Systems
Ting-Yu Chen
Summary: The study aims to construct an evaluation method based on distances from the average solution (EDAS) using circular intuitionistic fuzzy (C-IF) sets. It proposes a new interpretation of EDAS to handle complicated C-IF information and address decision hesitancy. The method is applied to a site selection issue and confirmed to be suitable, robust, and flexible.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ting-Yu Chen
Summary: With a focus on T-spherical fuzzy (T-SF) sets, this paper aims to create a new appraisal mechanism and a decision analytic method for multiple-criteria assessment and selection in uncertain situations. The T-SF frame uses four facets to elucidate complex uncertainties and reduce information loss. The paper proposes correlation-oriented measurements and an accessible appraisal mechanism for T-SF decision analytic methodology. It showcases the applicability and efficiency of the suggested method through a concrete location selection dilemma.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Materials Science, Textiles
Jing Ye, Ting-Yu Chen
Summary: This study introduces Pythagorean fuzzy sets (PFSs) to handle uncertain information, extends the ELECTRE method to solve multi-criteria decision-making problems, and proposes a correlation-based closeness coefficient to compare Pythagorean fuzzy numbers (PFNs). The proposed PF-ELECTRE approach is applied to evaluate cotton fabrics, and sensitivity analysis and comparative analysis are conducted to demonstrate its superiority and robustness. This research contributes to the advancement of outranking MCDM methods through the novel PF-ELECTRE approach and can be applied in other textile domains.
JOURNAL OF NATURAL FIBERS
(2023)
Article
Materials Science, Textiles
Jing Ye, Ting-Yu Chen
Summary: Selecting the best knitted fabric with various comfort properties is a complex multi-criteria decision-making (MCDM) issue. This research proposes a novel MCDM framework that combines the Bayesian best-worst method (BBWM) with Pythagorean fuzzy TOPSIS (PFTOPSIS). The BBWM determines the criteria weights, and the weighted sine similarity-based PFTOPSIS is utilized to rank alternatives. The proposed approach is demonstrated through a real-world case and comparative analyses, and the results validate its viability and effectiveness.
JOURNAL OF NATURAL FIBERS
(2023)
Article
Computer Science, Information Systems
Ting -Yu
Summary: The theory of T-spherical fuzzy sets and the REGIME method are introduced in this paper, and a multiple-criteria choice analysis approach supported by the REGIME structure for manipulating T-SF uncertainties is proposed. By creating new measurements, this study enhances the decision support capability of the T-SF REGIME methodology.
Article
Computer Science, Information Systems
Jih-Chang Wang, Ting -Yu Chen
Summary: This research introduces a multiple-criteria choice method that utilizes correlation measures to handle uncertain decision-making with T-spherical fuzziness. The method demonstrates the overall desirability of choice options across all performance criteria and has been successfully applied to location selection under T-SF uncertainties.
Article
Computer Science, Artificial Intelligence
Ting-Yu Chen
Summary: This paper proposes a creative T-SF VIKOR methodology for compromise ranking modeling in multiple criteria analysis, aiming to address the challenges in applying the VIKOR mechanism in intricate T-SF environments. By introducing new concepts and analytical framework, the proposed methodology enables effective handling of the complexity arising from Tspherical fuzziness and demonstrates practicality and better application outcomes in real-world decision situations.
ADVANCED ENGINEERING INFORMATICS
(2022)
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)
