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Article
Computer Science, Artificial Intelligence
Chao Zhang et al.
ACM Transactions on Asian and Low-Resource Language Information Processing
(2023)
Article
Mathematics
M. A. El-Gayar et al.
Summary: This paper suggests new topologically-inspired approximations for rough set approximation. Four distinct techniques are proposed based on a binary relation to define four neighborhoods. The approaches are generalizations of previous works and their characteristics and connections are investigated. The paper provides examples to compare the proposed techniques with existing ones.
JOURNAL OF MATHEMATICS
(2023)
Article
Computer Science, Cybernetics
Chao Zhang et al.
Summary: The objective of this article is to explore a fuzzy intelligence learning approach based on bounded rationality in IoMT systems for biomedical data analysis. The approach utilizes adjustable multigranulation rough sets and interactive multicriteria decision-making to detect freezing of gait in Parkinson's disease. Experimental analyses on a UCI dataset demonstrate the effectiveness of this approach in diagnosing freezing of gait.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Mathematics, Applied
Mostafa K. El-Bably et al.
Summary: One of the challenges in medical diagnosis is accurately determining the nature of an injury due to similar symptoms of different diseases. This study focuses on proposing new mathematical methodologies using soft rough sets to improve precise decision-making in diagnosing Chikungunya virus disease. The proposed approach utilizes soft sets to approximate any set and demonstrates its superiority over previous works. The study also provides important medical applications using soft delta-rough sets and presents algorithms for diagnosis.
Article
Computer Science, Artificial Intelligence
Pengfei Zhang et al.
Summary: The paper introduces an Adaptive Multi-Granulation Decision-Theoretic Rough Sets (AMG-DTRS) model, which adaptively obtains probabilistic thresholds by setting a compensation coefficient. Three types of mean AMG-DTRS models are studied, offering a new perspective on information fusion. The advantages and generalization of the AMG-DTRS model are demonstrated by analyzing its connections and differences with existing MGRS models.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Artificial Intelligence
Muhammad Irfan Ali et al.
Summary: Approximation space is crucial for the accuracy of approximations on a subset of the universal set. This paper aims to develop new soft rough sets models using near open sets, enhancing the accuracy of approximations significantly. The concepts of near soft rough approximations and their properties are proposed, and comparisons with previous methods are made. An algorithm is provided for decision-making problems, and tested on hypothetical data for comparison with existing methods.
Article
Automation & Control Systems
Anhui Tan et al.
Summary: This article examines the application of granular structures of intuitionistic fuzzy information in data mining and information processing. It defines partial-order relations at different hierarchical levels to reveal the granularity of the structures, characterizes the granularity invariance between different structures using relational mappings, and generalizes Shannon's entropies to IF entropies. The significance of intuitionistic attributes using the information measures is introduced, and an information-preserving algorithm for data reduction of IF information systems is constructed. Numerical experiments confirm the performance of the proposed technique by inducing substantial IF relations from public datasets considering the similarity/diversity between samples from the same/different classes.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Multidisciplinary Sciences
Radwan Abu-Gdairi et al.
Summary: The rough set principle is an efficient method for dealing with the uncertainty of data in information systems. This paper introduces new topological approaches as a generalization of Pawlak's theory, elucidating the relationships between different types of approximations through examples. By comparing these approaches with previous ones, a more affirmative solution for decision-making problems can be obtained.
Article
Mathematics, Applied
Ashraf S. Nawar et al.
Summary: The main objective of this work is to enhance the application aspects of Pawlak rough sets. Different methods for generalizing Pawlak rough sets are proposed and compared, along with new generalizations of Pawlak's models. The importance of decision-making methods is demonstrated through an application to real-life problems.
Article
Computer Science, Artificial Intelligence
Mostafa K. El-Bably et al.
Summary: This article proposes three new methods for generalizing Pawlak rough sets based on topological structures, and proves that these methods are more accurate than other methods. Furthermore, a practical example in economic decision-making is used to demonstrate the application of these methods.
Article
Mathematics, Interdisciplinary Applications
Mostafa A. El-Gayar et al.
Summary: The basic methodology of rough set theory relies on an equivalence relation induced from the generated partition by object classification. This study presents closure operators based on neighborhood adhesion, extending previous methods. Extended rough sets are proposed as an extension of Pawlak's models, and a topological reduction strategy for COVID-19 information system is implemented using these techniques. The importance of the offered methodologies in discovering important factors for COVID-19 infection is highlighted through a medical application, which aligns with findings from the World Health Organization. Lastly, an algorithm is created to implement the recommended decision-making approaches.
Article
Computer Science, Artificial Intelligence
Zhehuang Huang et al.
Summary: This paper proposes a new robust rough set model by combining various rough set methods to address the limitations of the traditional fuzzy beta covering method in real data. By reconstructing the upper and lower approximations of the target concept, introducing a fuzzy dependence function to evaluate the classification ability, and utilizing a feature selection algorithm for dimensionality reduction, the proposed model demonstrates good robustness on datasets contaminated with noise and outperforms some state-of-the-art feature learning algorithms in terms of classification accuracy and the size of the selected feature subset.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Zhehuang Huang et al.
Summary: The article introduces fuzzy beta covering to evaluate the uncertainty of datasets and proposes a discernibility measure to describe the distinguishing ability of fuzzy covering families. Various variant measures are presented to reflect changes in distinguishing ability caused by different fuzzy covering families. Finally, an algorithm is designed to reduce redundant fuzzy coverings and achieve knowledge reduction.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Mathematics, Applied
El-Sayed A. Abo-Tabl et al.
Summary: This study introduces the structure of rough topological space based on the reflexive relation and investigates the relationships between approximation operators, closure operators, and interior operators, as well as the relationship between topological space and rough sets induced by reflexive relations. Furthermore, a medical application for addressing the issue of dengue fever is presented.
Article
Computer Science, Artificial Intelligence
Mostafa K. El-Bably et al.
Summary: The main goal of this paper is to integrate the relationships between rough set theory and topology. Different closure operators and new methods for generating topologies are introduced, and the suggested structures are investigated and compared. The proposed methods emphasize the ties between rough sets, granular computing, information discovery, and data mining.
GRANULAR COMPUTING
(2022)
Article
Mathematics, Interdisciplinary Applications
Mostafa A. El-Gayar et al.
Summary: The basic methodology of rough set theory relies on an equivalence relation induced from the classification of objects, but the requirements of the equivalence relation limit its applications; the paper presents closure operators based on right and left adhesion neighborhoods by any binary relation and demonstrates that these techniques are extensions of existing methods; the topological techniques proposed extend rough sets and provide a strategy for topological reduction of an information system for COVID-19, highlighting the importance of these methods in decision-making and alignment with World Health Organization findings.
Article
Computer Science, Artificial Intelligence
Mohammed Atef et al.
Summary: This article introduces covering-based multigranulation fuzzy rough sets models and four different types of variants, discussing the characteristics of these models and comparing them with previous models. Finally, the proposed models are applied with an algorithm on certain drug forms to assist experts in medical decision making.
Article
Computer Science, Artificial Intelligence
Mohammed Atef et al.
Summary: This paper introduces a new type of fuzzy topological graphs, investigates their properties and an edge calculation method, and explores the concept of homeomorphic between fuzzy topological graphs. It also proposes an algorithm for constructing fuzzy topological graphs and provides a new method for explaining homeomorphic between fuzzy topological graphs, to be applied in smart cities.
Article
Computer Science, Artificial Intelligence
Hanchuan Lu et al.
Summary: The article introduces the concept of generalized picture fuzzy soft sets and explains their operations, establishing several theoretical operations. The applicability of generalized picture fuzzy soft sets in fuzzy decision-making is demonstrated with a numerical example, and a comparison is made with picture fuzzy soft set theory.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Mathematics
Mostafa K. El-Bably et al.
Summary: This study focuses on addressing vagueness and ambiguity through soft rough sets and topological soft rough sets. The research answers important questions about the probability of defining subsets and enhances existing techniques. The proposed technique has shown improvement in accuracy and has been applied to diagnosing heart failure in decision-making problems.
JOURNAL OF MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
M. K. El-Bably et al.
Summary: This study introduces new styles of rough sets based on different neighborhoods generated from a general binary relation, improving accuracy of approximations and extending the concept of nano-topology to any binary relation. The importance of the proposed methods is demonstrated in diagnosing lung cancer diseases, with algorithms provided for decision-making problems.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Mathematical & Computational Biology
Mostafa K. El-Bably et al.
Summary: The paper aims to create new rough set models by using different neighborhoods generated by a binary relation, proposing new approximations that extend Pawlak's rough sets and some generalizations, with significantly improved precision. It also provides comparisons between the proposed methods and previous ones, as well as a medical application on lung cancer disease and an algorithm tested on hypothetical data for comparison with current methods.
INTERNATIONAL JOURNAL OF BIOMATHEMATICS
(2021)
Article
Mathematics
Radwan Abu-Gdairi et al.
Summary: The paper introduces new rough set approximations using a multi-knowledge base, based on a new neighborhood called basic-neighborhood. These proposed methods are a generalization of Pawlak's rough sets and some of their extensions, proving their best accuracy. The main goal is to study multi-information systems and extend the application field of rough set models, with discussions on real-life applications like nutrition modeling and decision-making in the medical field.
Article
Computer Science, Artificial Intelligence
Abd El Fattah El Atik et al.
Summary: In this article, neighborhood systems are used to approximate graphs as finite topological structures. New types of eight neighborhoods, called j-adhesion neighborhoods, are constructed for vertices of any graph. The concepts of Allam et al. and Yao are extended through j-adhesion neighborhoods, and new types of j-lower and j-upper approximations for subgraphs of a given graph are investigated. The accuracy of these approximations is calculated and a comparison between accuracy measures and boundary regions for different types of approximations is discussed. Algorithms are introduced to generate j-adhesion neighborhoods and rough sets on graphs, and a chemical example is used to illustrate the proposed methods.
GRANULAR COMPUTING
(2021)
Article
Mathematics, Applied
M. El Sayed et al.
Summary: The research aims to propose a new neighborhood and generalization to expand the application fields of rough sets theory.
Article
Mathematics
Mostafa K. El-Bably et al.
Summary: Soft rough set theory is utilized as a mathematical model for decision-making with real-life data, with this article introducing modifications and generalizations for soft 0-rough approximations. The study includes comparisons with previous approaches and examples demonstrating the validity of the proposals, particularly in predicting COVID-19 infections.
TURKISH JOURNAL OF MATHEMATICS
(2021)
Article
Computer Science, Information Systems
Chao Zhang et al.
INFORMATION SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Abd El Fattah A. El Atik et al.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2020)
Article
Mathematics, Applied
Abd El Fattah A. El Atik et al.
Article
Computer Science, Information Systems
Dongxiao Chen et al.
Article
Mathematical & Computational Biology
Ashraf S. Nawar et al.
INTERNATIONAL JOURNAL OF BIOMATHEMATICS
(2019)
Article
Computer Science, Artificial Intelligence
Chao Zhang et al.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2018)
Article
Computer Science, Artificial Intelligence
Jianhua Dai et al.
Article
Mathematics, Applied
Shokry Nada et al.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2018)
Article
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Yiyu Yao
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2018)
Article
Mathematical & Computational Biology
M. M. El-Sharkasy
INTERNATIONAL JOURNAL OF BIOMATHEMATICS
(2018)
Article
Mathematics, Applied
Shaochen Liang et al.
Article
Computer Science, Artificial Intelligence
W. S. Amer et al.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2017)
Article
Computer Science, Artificial Intelligence
Chao Zhang et al.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2016)
Article
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Huili Dou et al.
KNOWLEDGE-BASED SYSTEMS
(2016)
Article
Mathematical & Computational Biology
Chao Zhang et al.
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
(2015)
Article
Computer Science, Information Systems
Zuoming Yu et al.
INFORMATION SCIENCES
(2013)
Article
Computer Science, Artificial Intelligence
E. A. Abo-Tabl
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2013)
Article
Computer Science, Information Systems
Yiyu Yao
INFORMATION SCIENCES
(2010)
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Computer Science, Information Systems
Keyun Qin et al.
INFORMATION SCIENCES
(2008)
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Mathematics, Applied
H. H. Hung
TOPOLOGY AND ITS APPLICATIONS
(2008)
Article
Computer Science, Artificial Intelligence
N Zhong et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2003)
Article
Computer Science, Information Systems
WZ Wu et al.
INFORMATION SCIENCES
(2002)
