Article
Computer Science, Artificial Intelligence
Ashish Garg, J. Maiti, Akhilesh Kumar
Summary: The paper introduces a novel scoring method and aggregation operator for ordering and aggregating granulized Z-numbers while addressing the possibilistic and probabilistic information contained within them. A new fuzzy risk assessment scheme is developed based on these methods, providing the system-level failure probability in a reliable format.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Aurang Zeb, Asghar Khan, Muhammad Izhar, Kostaq Hila
Summary: This paper investigates the multiple attribute decision making problems using fuzzy bi-polar soft set based information and develops some aggregation operators based on this information. These operators are then used to develop approaches for solving fuzzy bi-polar soft multiple attribute decision making problems, with a practical example provided for verification.
JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Jesus Serrano-Guerrero, Francisco P. Romero, Jose A. Olivas
Summary: This study introduces a fuzzy framework for computing user mood based on SenticNet and sentic patterns, guiding an ordered weighted averaging operator to provide insights on why certain aspects are rated more or less in a overall rating. The promising framework shows potential for application in tools like customized recommender systems or decision support systems.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Information Systems
Rana Muhammad Zulqarnain, Imran Siddique, Rifaqat Ali, Jan Awrejcewicz, Hanen Karamti, Dariusz Grzelczyk, Aiyared Iampan, Muhammad Asif
Summary: This paper introduces the application of the Pythagorean fuzzy hypersoft set in decision-making and extends the Einstein-weighted ordered aggregation operators. A numerical example in the agricultural field is used to validate the superiority and applicability of the proposed method.
Article
Computer Science, Artificial Intelligence
Sumera Naz, Muhammad Akram, Mamoona Muzammal
Summary: In this research, a multi-attribute group decision-making method is proposed for selecting an optimal data mining strategy in a T-spherical fuzzy environment. The proposed aggregation operators are able to capture diverse human opinions effectively, leading to reliable decision outcomes.
Article
Computer Science, Artificial Intelligence
Bo Li, Junqi Ding, Zhengqing Yin, Kaiyu Li, Xue Zhao, Lingxian Zhang
Summary: In this paper, an optimized neural network combined model based on the induced ordered weighted averaging operator is proposed for vegetable price forecasting. The framework integrates the fruit fly algorithm (FOA) with the induced ordered weighted averaging (IWOA) operator. Results show that our model outperforms traditional forecasting models in terms of prediction accuracy and parameter optimization.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
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
Abazar Keikha
Summary: This article discusses various theories related to uncertainty and their applications in practical problems, introduces concepts and algorithms related to hesitant fuzzy numbers, and explores the application of aggregation operators of hesitant fuzzy numbers in multi-attribute group decision making.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
LeSheng Jin, Zhen-Song Chen, Ronald R. Yager, Tapan Senapati, Radko Mesiar, Diego Garcia Zamora, Bapi Dutta, Luis Martinez
Summary: This study introduces a novel approach to establish OWA operators for basic uncertain information, using problem factorization and integration techniques similar to the Choquet integral. It also discusses various methodologies for determining specific weights to define these operators.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Theory & Methods
Jacek M. Leski
Summary: A new method called Fuzzy Double Ordered C-Regression Models (FDOCRM) is introduced in this paper, which incorporates ordering and fuzzy S-regression estimator to improve method robustness. Large-scale simulations demonstrate the competitiveness and usefulness of the proposed method.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Information Systems
Kamal Kumar, Shyi-Ming Chen
Summary: This paper proposes an improved intuitionistic fuzzy Einstein weighted averaging operator to overcome the drawbacks of existing operators, and introduces a new multiattribute decision making method based on it.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Wenyi Zeng, Yue Xi, Qian Yin, Ping Guo
Summary: This paper introduces the concepts of weighted dual hesitant fuzzy set and element, explores their basic operations and aggregation operators, and proposes an approach to group decision making based on weighted dual hesitant fuzzy elements.
Article
Computer Science, Artificial Intelligence
Ziwei Shu, Ramon Alberto Carrasco Gonzalez, Javier Portela Garcia-Miguel, Manuel Sanchez-Montanes
Summary: With the growth of online tourism, it is important to analyze customer reviews to improve a hotel's online reputation. This paper proposes a new approach using the OWA operator, 2-tuple linguistic model, and K-means clustering to classify hotels based on online reviews.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Rui Cheng, Jianping Fan, Meiqin Wu
Summary: This study proposes an R-number multi-attributive real-ideal comparative analysis method for multi-attribute group decision-making. By using R-numbers to eliminate uncertainty in data, the feasibility and applicability of the proposed method are demonstrated.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Mathematics, Applied
Kaiyan Yang, Lan Shu, Guowu Yang
Summary: Compared with CFS and IFS, CIFS can handle two-dimensional and uncertain information simultaneously, and its importance in capturing useful information is considered. The CIFOWD measure provides a parameterized family of aggregation distance measures, including special types like CIFOWGD, CIFOWHD, and CIFOWED. A multiple criteria group decision-making approach is presented under CIFSs environment, and its effectiveness is demonstrated through an illustrative example of coronavirus vaccine selection.
COMPUTATIONAL & APPLIED MATHEMATICS
(2022)
Article
Computer Science, Information Systems
Sidong Xian, Jiahui Chai, Tangjin Li, Jie Huang
Summary: The paper introduces the concept of Z-mixture numbers and a weighting method for Z-multi-attribute decision making based on correlation coefficients. It also proposes two new operators to address MADM problems involving both continuous and discrete attributes.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Sidong Xian, Yue Cheng, Kaiyuan Chen
Summary: This paper proposes a spatial T-spherical fuzzy c-means model with bias correction to improve the effect of antinoise and image edge recognition in image segmentation. By utilizing novel technology and algorithms, it successfully enhances the accuracy and efficiency of the segmentation process.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Sidong Xian, Yue Cheng, Zhou Liu
Summary: The study redefined operational laws for PFLS by introducing linguistic scale functions and defined new score and accuracy functions. Furthermore, the MM operator was extended to the Picture fuzzy linguistic context and a new Picture fuzzy linguistic weighted MM operator was proposed. These developments were applied in solving multiple attribute decision-making problems with a practical example of Mobike sharing bike design.
Article
Computer Science, Artificial Intelligence
Sidong Xian, Renping Liu, Zhijun Yang, Xin Li
Summary: This paper introduces the concept of the intuitionistic principal value Z-linguistic set and proposes the intuitionistic principal value Z-linguistic hybrid geometric operator to effectively aggregate linguistic evaluation information. An example of targeted poverty alleviation is used to demonstrate the effectiveness and superiority of these methods.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Cybernetics
Sidong Xian, Renping Liu, Ke Qing, Shengxia Tu
Summary: The Rural Revitalization Strategy is crucial for China's development. However, the uncertainty of information and neglect of credibility lead to inaccurate decision-making. To address this issue, the concept of interval-valued intuitionistic Z-linguistic set considering uncertainty and credibility is proposed, along with corresponding operation rules and a ranking method.
CYBERNETICS AND SYSTEMS
(2022)
Article
Mathematics, Applied
Sidong Xian, Wenhua Wan, Huilan Pan, Xin Li
Summary: This study proposes the concept of 2-tuple linguistic hesitant Pythagorean fuzzy sets (2TLHPSs) and introduces the operational laws and comparison rules. Additionally, a meta-synthesis approach is proposed to consider the preference of decision points and improve the classical MULTIMOORA method.
COMPUTATIONAL & APPLIED MATHEMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Sidong Xian, Kaiyuan Chen, Yue Cheng
Summary: This paper proposes a new method for predicting fuzzy time series using the improved seagull optimization algorithm and XGBoost. By partitioning the domain of discourse and forecasting the change of fuzzy membership, the accuracy of the prediction model is enhanced, and superior performance is achieved in predicting daily confirmed COVID-19 cases.
ADVANCES IN ENGINEERING SOFTWARE
(2022)
Article
Computer Science, Artificial Intelligence
Sidong Xian, Hualiang Lei, Kaiyuan Chen, Zhengyan Li
Summary: This paper proposes a novel fuzzy time series model (NFTSM) based on an improved sparrow search algorithm (ISSA) and complete ensemble empirical mode decomposition with adaptive noises (CEEMDAN) to optimize the performance of the fuzzy time series model when dealing with time series with trends and disturbances. The simulation experiments demonstrate that NFTSM has good performance.
APPLIED INTELLIGENCE
(2023)
Article
Mathematics, Applied
Sidong Xian, Danni Ma, Hailin Guo, Xu Feng
Summary: In this paper, a novel Pythagorean hesitant fuzzy linguistic term set (PHFLTS) is introduced to describe complex and uncertain travel information, unifying Pythagorean fuzzy linguistic term set (PFLTS) and hesitant fuzzy linguistic term set (HFLTS). The proposed Pythagorean hesitant fuzzy linguistic (PHFL) aggregation operators help travelers consider factors such as time, distance, and economic cost. A PHFL route recommendation dynamic programming model and PHFL Dijkstra's algorithm are developed to find optimal routes and the shortest directed route considering multiple factors. A route intelligent recommendation algorithm (RIRA) is proposed to assist travelers in choosing the most suitable route based on uncertain factor weights. The effectiveness and practicality of the model and algorithm are illustrated through a numerical example.
COMPUTATIONAL & APPLIED MATHEMATICS
(2023)
Article
Management
Sidong Xian, Ke Qing, Ling Tang, Huilan Pan
Summary: This paper proposes a new linguistic set called Z-probabilistic double hierarchy linguistic term set (Z-PDHLTS) based on Z-number, which takes into account both the complexity of linguistic variables and the credibility of evaluation information. A new comparison method based on possibility degree is then introduced to compare the size of two sets. Additionally, possibility degree matrix method and ZPDHL-VIKOR method are proposed for multi-criteria group decision making problem with Z-PDHLTS to rank alternatives. Finally, the effectiveness of the proposed methods is illustrated through an example of green mine selection and comparative analyses.
GROUP DECISION AND NEGOTIATION
(2023)
Article
Computer Science, Artificial Intelligence
Sidong Xian, Ke Qing, Chaozhen Li, Miao Luo, Renping Liu
Summary: Traditional Chinese medicine (TCM) has become essential in maintaining people’s health, especially in treating chronic diseases. However, doctors often have uncertainties and hesitations in understanding and diagnosing diseases, which affect patients' recognition and treatment decisions. To address these issues, the authors propose a PDHLTS model that accurately describes TCM language information and supports decision-making.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Sidong Xian, Xu Feng
Summary: In this paper, a meerkat optimization algorithm (MOA) is proposed by simulating the behavior pattern of meerkats in nature. MOA is mainly inspired by the survival strategies of meerkat populations, and its sentinel mechanism controls meerkats to switch between different behavior patterns. Mathematical properties of meerkat optimization algorithm are proven, and the advantages of MOA are verified with classical optimization test functions. MOA is also applied to solve real-world engineering problems with constraints, demonstrating its effectiveness and superiority.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yue Cheng, Weiwei Xing, Witold Pedrycz, Sidong Xian, Weibin Liu
Summary: This article proposes a long-term time-series forecasting method based on the nonlinear fuzzy information granule series, which improves the long-term performance of predictors. The method represents information granules with nonlinear time-dependent curves, and introduces a temporal window splitting algorithm based on curvature equations and weighted directed graphs. Nonlinear trend fuzzy granulation is used as a data preprocessing module to achieve better long-term forecasting performance. The proposed method achieves superior performance in traffic flow forecasting compared to existing models.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Sidong Xian, Junkang Li, Zhaoyu Yan, Wenhua Wan
Summary: This paper proposes a double hierarchy hesitant fuzzy linguistic multi-attributive border approximation area method (DHHFL-MABAC) based on distance measure and synthetic weight for selecting the appropriate governance company. It addresses the issue of language information loss in distance measurement calculations of DHHFLEs with varying lengths using a double hierarchy hesitant fuzzy linguistic distance measure based on least common multiple (DHHFLDM-LCME). The method is validated using a case study involving a sewage treatment enterprise, demonstrating its consideration for potential loss and its simplicity and stability in calculation.
Article
Energy & Fuels
Sidong Xian, Miaomiao Feng, Yue Cheng
Summary: Carbon trading is a significant tool in international politics and diplomacy, as well as having important economic value. This paper proposes innovative methods, using incremental Gaussian nonlinear trend fuzzy granulation, to predict carbon prices. The study shows that this prediction method has the smallest error in long-term prediction compared with other models, and it is validated using daily closing price datasets of carbon exchanges in Shenzhen and Beijing.
