Article
Computer Science, Artificial Intelligence
Swarup Kr Ghosh, Anupam Ghosh, Siddhartha Bhattacharyya
Summary: Gene expression analysis plays a crucial role in microarray research. This article introduces a novel feature extraction method based on Intuitionistic fuzzy set for identifying cancer-related human biomarkers. The experimental results demonstrate the effectiveness of this method on microarray datasets.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Zhinan Hao, Zeshui Xu, Hua Zhao, Ren Zhang
Summary: This paper proposes a context-based distance measure for intuitionistic fuzzy sets and defines a new similarity measure to enhance discrimination capability. The effectiveness of these methods is validated through a practical case study, demonstrating their fine discrimination ability and effectiveness.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Bornali Saikia, Palash Dutta, Pranjal Talukdar
Summary: This article discusses the importance of uncertainty in decision-making processes and explores various methods for uncertainty modeling, including fuzzy sets, intuitionistic fuzzy sets, and Pythagorean fuzzy sets. The authors propose an advanced similarity measure for Pythagorean fuzzy sets and apply it to solve transportation problems. The significance of the proposed method is demonstrated through comparisons with other existing methods and statistical tests.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Automation & Control Systems
Lipeng Pan, Yong Deng
Summary: Intuitionistic fuzzy set (IFS) similarity measure is a crucial concept affecting key parameters in fuzzy decision systems. The new similarity measure method fully considers the impact of hesitancy on similarity measurement, proving the rationality of the proposed method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Shuvasree Karmakar, Mijanur Rahaman Seikh, Oscar Castillo
Summary: This study aims to develop matrix games using Type-2 Intuitionistic Fuzzy Sets (T2IFS) as an extension to bridge the gap in portraying decision-makers' fuzzy preferences under different parameters, considering both acceptance and non-acceptance. The proposed methodology includes defining Hamacher aggregation operators in T2IFS environment, proposing Minkowski distance of T2IFS based on the Hausdorff metric, forming a similarity measure of T2IFS, and solving matrix games using the proposed distance measure with the biogas-plant implementation problem to validate applicability and validity.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Wen Sheng Du
Summary: This paper investigates subtraction and division operations on intuitionistic fuzzy values/sets, derived from the Hamming distance, ensuring completeness of the operations. Fundamental properties of the modified arithmetic operations are extensively explored, and continuity and derivatives for intuitionistic fuzzy functions are introduced, providing groundwork for intuitionistic fuzzy differential calculus.
INFORMATION SCIENCES
(2021)
Article
Mathematics
Chunfeng Suo, Xuanchen Li, Yongming Li
Summary: The paper focuses on the knowledge measure or uncertainty measure for constructing interval-valued intuitionistic fuzzy sets. It proposes a new knowledge measure function in compliance with the distance function combined with TOPSIS and investigates its properties through mathematical analysis and numerical examples. Moreover, it applies the proposed entropy measure to the multi-attribute group decision-making problem, demonstrating its effectiveness.
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
Mathematics, Applied
Muhammad Saqlain, Muhammad Riaz, Raiha Imran, Fahd Jarad
Summary: Decision-making in vague and imprecise environments is a crucial issue. This article proposes a method that combines intuitionistic fuzzy sets with hypersoft sets to address multi-attributive decision-making problems. The study develops similarity and distance measures, proves new results and theorems, and discusses a real-life problem.
Article
Computer Science, Artificial Intelligence
Xinxing Wu, Zhiyi Zhu, Chuan Chen, Guanrong Chen, Peide Liu
Summary: All intuitionistic fuzzy TOPSIS methods contain two key elements: the order structure and the distance/similarity measure. This paper proves that there is no score function that can strictly distinguish different intuitionistic fuzzy values and preserve the natural partial order. It also shows that classical similarity measures and intuitionistic fuzzy TOPSIS methods do not ensure monotonicity with linear orders. To overcome this limitation, a novel intuitionistic fuzzy TOPSIS method is proposed and its monotonicity with linear orders is mathematically proved.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Harish Garg, Dimple Rani
Summary: The paper presents a novel distance/similarity measure among IFSs based on transformation techniques and characteristics. It proposes new distance and similarity measures based on right-angled triangles over a unit square area, and an algorithm for decision-making problems, demonstrating improved performance compared to existing measures. The reliability of the developed measure is verified through clustering and pattern recognition problems, showing successful classification results where existing measures fail.
COGNITIVE COMPUTATION
(2021)
Article
Mathematics, Applied
Changlin Xu, Yaqing Wen
Summary: This paper firstly defines a new distance measure for circular intuitionistic fuzzy sets based on the theory of circular intuitionistic fuzzy sets, considering the information of four aspects: membership degree, non-membership degree, radius and the assignment of hesitation degree, and proves that the new distance satisfies the distance measure conditions. Secondly, by constructing a manual testing framework, the new distance is analyzed in comparison with the existing distance metric to show the rationality of the new method. Finally, the method is applied to fuzzy multi-criteria decision making to further demonstrate the effectiveness and practicality of the method.
Article
Computer Science, Artificial Intelligence
Surender Singh, Sonam Sharma
Summary: The text discusses the concept of fuzziness and the development of intuitionistic fuzzy theory. It proposes flexible measure of intuitionistic fuzzy entropy and generalized IF-dissimilarity measure, showcasing their superiority. The text also introduces an improved decision-making method and investigates the performance of proposed IF-dissimilarity in a pattern recognition problem, obtaining encouraging results.
Article
Computer Science, Information Systems
Jie Yang, Xiaodan Qin, Guoyin Wang, Xiaoxia Zhang, Baoli Wang
Summary: This paper studies the relative knowledge distance of intuitionistic fuzzy concept (IFC) and proposes a micro-knowledge distance (md) based on information entropy to measure the difference between intuitionistic fuzzy information granules. A macro-knowledge distance (MD) with strong distinguishing ability is further constructed based on md, and the rule that MD is monotonic with the granularity being finer in multi-granularity spaces is revealed. Furthermore, the relative MD is proposed to analyze the relative differences between different granular spaces from multiple perspectives.
Article
Computer Science, Artificial Intelligence
Pankhuri Jain, Anoop Kumar Tiwari, Tanmoy Som
Summary: This study introduces a novel approach to enhance the prediction of anti-tubercular peptides by extracting sequence features, selecting optimal features, and utilizing different machine learning techniques. The proposed method outperforms previous methods and achieves high accuracy and sensitivity rates.
Article
Computer Science, Information Systems
Wen Sheng Du, Bao Qing Hu
INFORMATION SCIENCES
(2016)
Article
Computer Science, Information Systems
Wen Sheng Du, Bao Qing Hu
INFORMATION SCIENCES
(2016)
Article
Management
Wen Sheng Du, Bao Qing Hu
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2017)
Article
Management
Wen Sheng Du, Bao Qing Hu
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2018)
Article
Computer Science, Artificial Intelligence
Wen Sheng Du
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Wen Sheng Du
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Wen Sheng Du
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Wen Sheng Du
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Wen Sheng Du
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Wen Sheng Du
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Wen Sheng Du
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2020)
Article
Computer Science, Information Systems
Wen Sheng Du
Summary: This paper investigates subtraction and division operations on intuitionistic fuzzy values/sets, derived from the Hamming distance, ensuring completeness of the operations. Fundamental properties of the modified arithmetic operations are extensively explored, and continuity and derivatives for intuitionistic fuzzy functions are introduced, providing groundwork for intuitionistic fuzzy differential calculus.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Wen Sheng Du
Summary: This paper establishes a novel theoretical framework for measuring the information granularity of knowledge structures, introducing two new relations and proposing an axiomatic definition of information granularity. It presents a general form of information granularity and develops an attribute significance measure as an application of the proposed measure.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Wen Sheng Du
Summary: This paper investigates the operations and aggregation operators for q-rung orthopair fuzzy values based on the Einstein operational laws, comparing them with those built on algebraic operations. The properties of the developed operators and their application in multiattribute decision making are discussed, with an example illustrating the feasibility and effectiveness of the proposed methods in selecting a design scheme for a blockchain-based agricultural product traceability system.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
