Article
Mathematics, Applied
Daniel Yi-Fong Lin
Summary: This study focuses on tuning weight functions of existing similarity measures for pattern recognition problems. Analytic derivations are provided to explain the influence of weights for both discrete and continuous cases, supporting the claims with mathematical foundations. The findings from this study can be used for sensitivity analysis of weights and decision-making in future applications.
Article
Computer Science, Artificial Intelligence
Xiaodi Liu, Yukun Sun, Harish Garg, Shitao Zhang
Summary: This paper defines the distance between intuitionistic fuzzy sets (IFSs) using line integral and redefines the distances between IFSs based on the analysis of geometric importance of line integral. The accuracy function is introduced to evaluate the accuracy of distance by applying the physical meaning of line integral. The numerical examples demonstrate the superiority of the proposed approach.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Xiang Li, Zhe Liu, Xue Han, Nan Liu, Weihua Yuan
Summary: In this paper, a new distance measure called D-IFS is proposed to measure the similarities or differences between intuitionistic fuzzy sets (IFSs). Numerical examples show that D-IFS can obtain more reasonable and superior results. Moreover, a new decision-making method based on D-IFS is developed and its performance is evaluated in two applications.
Article
Computer Science, Artificial Intelligence
Sahar Cherif, Nesrine Baklouti, Hani Hagras, Adel M. Alimi
Summary: This article proposes three new interval type-2 fuzzy similarity measures and proves their common properties. The experiments show that these measures are resilient to high levels of uncertainty noise and can overcome the drawbacks of existing similarity measures. The application of these measures in clustering algorithms also achieves good results.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Brindaban Gohain, Rituparna Chutia, Palash Dutta, Surabhi Gogoi
Summary: This paper introduces two new tools for decision-making problems, namely similarity measures between intuitionistic fuzzy sets. By incorporating parameters such as the difference of membership degrees, the difference of nonmembership degrees, the hesitancy factor, and the difference in the minimum and maximum of cross-evaluation factors, the proposed methods outperform existing ones and overcome their limitations.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Muhammad Irfan Ali, Jianming Zhan, Muhammad Jabir Khan, Tahir Mahmood, Haider Faizan
Summary: This paper discusses the distribution and relationship of uncertainties in Atanassov intuitionistic fuzzy sets, defines knowledge measures as a function of entropy and uncertainty index with specific properties, and establishes the existence of such measures. It further demonstrates how these knowledge measures are useful in multi-criteria group decision-making problems.
Article
Automation & Control Systems
Mohd Shoaib Khan, Q. M. Danish Lohani
Summary: In this paper, we discussed the importance of distance measures for solving classification and clustering problems. We proposed a method to address the limitations of existing distance measures by conducting a topological analysis and introducing a new type 2 distance measure. Experimental results demonstrate that the proposed type 2 distance measure overcomes some drawbacks of the type 1 distance measure.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Paul Augustine Ejegwa, Yuming Feng, Shuyu Tang, Johnson Mobolaji Agbetayo, Xiangguang Dai
Summary: Pythagorean fuzzy set is a broader concept with higher application prospects compared to IFS. This paper proposes a new distance measure method under Pythagorean fuzzy environment, which outperforms existing measures in terms of performance indexes. The proposed method takes into account the three parameters of PFSs and avoids error due to exclusion. The applications in pattern classification and disease diagnosis demonstrate the superiority of the proposed Pythagorean fuzzy distance measures.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Mathematics
Mailing Zhao, Jun Ye
Summary: This study proposes an orthopair Z-number (OZN) set to depict the truth and falsehood values of fuzzy values and their reliability levels. The established operators and multiattribute decision-making model demonstrate the superiority of the proposed model in reliability and flexibility of decision results.
JOURNAL OF MATHEMATICS
(2021)
Article
Automation & Control Systems
Fuyuan Xiao
Summary: This article introduces a new distance measure between Intuitionistic Fuzzy Sets (IFSs) based on the Jensen-Shannon divergence, which not only meets the axiomatic definition of distance measure, but also possesses nonlinear characteristics, allowing for better discrimination between IFSs and producing more reasonable results compared to other existing methods. This new distance measure is illustrated through numerical examples and is further applied to pattern classification, offering a promising solution for solving inference problems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Mathematics
Majed Albaity, Tahir Mahmood
Summary: Pattern recognition is a computerized technique used to identify shapes, designs, and reliabilities in information. It has various applications in different fields such as information compression, machine learning, statistical analysis, and bioinformatics. Medical diagnosis, on the other hand, involves the process of identifying diseases or disorders based on symptoms and signs. The theory of generalized dice similarity measures has played a significant role in understanding the relationship between intuitionistic hesitant fuzzy information and has practical applications in medical diagnosis and pattern recognition problems.
Article
Computer Science, Artificial Intelligence
Lei Zhou, Kun Gao
Summary: Three novel pseudometrics on the set of intuitionistic fuzzy numbers are proposed in this paper, and a unified method to calculate the distances between different types of intuitionistic fuzzy sets based on these pseudometrics is introduced, along with corresponding proofs.
Article
Mathematics
Rana Muhammad Zulqarnain, Harish Garg, Imran Siddique, Rifaqat Ali, Abdelaziz Alsubie, Nawaf N. Hamadneh, Ilyas Khan
Summary: This paper introduces a generalized version of multipolar neutrosophic soft set and defines operators and information measures related to it. Later, a decision-making algorithm based on these measures is proposed to handle uncertain and vague information, with its effectiveness demonstrated through a case study.
JOURNAL OF MATHEMATICS
(2021)
Article
Mathematics, Applied
Samet Memis, Burak Arslan, Tugce Aydin, Serdar Enginoglu, Cetin Camci
Summary: This study aims to define metrics and similarities on ifpifs-matrices, develop a new classifier IFPIFSC, and apply it to data classification. The results show that IFPIFSC outperforms other classifiers and is a convenient method for data classification.
Article
Engineering, Multidisciplinary
Tahir Mahmood, Wajid Ali, Zeeshan Ali, Ronnason Chinram
Summary: Intuitionistic hesitant fuzzy set (IHFS) is a combination of intuitionistic fuzzy set (IFS) and hesitant fuzzy set (HFS), used to deal with uncertain information. IHFS is defined with consideration of existing issues, incorporating various operators to propose new concepts.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Qianghua Liu, Yu Tian, Tianshu Zhou, Kewei Lyu, Ran Xin, Yong Shang, Ying Liu, Jingjing Ren, Jingsong Li
Summary: This study proposes a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML). It significantly improves the diagnostic process in primary health care and helps general practitioners diagnose few-shot diseases more accurately.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Balazs Borsos, Corinne G. Allaart, Aart van Halteren
Summary: The study demonstrates the feasibility of predicting functional outcomes for ischemic stroke patients and the usability of multimodal deep learning architectures for this purpose.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Abdelmoniem Helmy, Radwa Nassar, Nagy Ramdan
Summary: This study utilizes machine learning models to detect depression symptoms in Arabic and English texts, and provides manually and automatically annotated tweet corpora. The study also develops an application that can detect tweets with depression symptoms and predict depression trends.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
