Emerging Fuzzy Intelligent Systems for Smart Healthcare Management
Applications of Disc q-Rung Orthopair Fuzzy Sets
- 1st Edition - April 10, 2025
- Imprint: Academic Press
- Authors: Shahzaib Ashraf, Chiranjibe Jana, Valentina Emilia Balas, Witold Pedrycz
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 9 9 7 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 9 9 8 - 1
Emerging Fuzzy Intelligent Systems for Smart Healthcare Management: Applications of Disc q-Rung Orthopair Fuzzy Sets presents comprehensive methodological frameworks and the latest… Read more
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Request a sales quoteEmerging Fuzzy Intelligent Systems for Smart Healthcare Management: Applications of Disc q-Rung Orthopair Fuzzy Sets presents comprehensive methodological frameworks and the latest empirical research findings concerning disc q-rung orthopair fuzzy operators, with a specific focus on their applications in smart technologies for healthcare management. The book solves a crucial problem by offering readers an invaluable opportunity to conduct a comparative analysis, contrasting the proposed methods with their existing knowledge base. Disc q-Rung Orthopair sets, being the generalization of q-Rung Orthopair fuzzy sets, which are, in turn, the generalization of Pythagorean fuzzy sets, extend the capabilities of handling uncertainty beyond conventional fuzzy sets.
The authors strive to narrow the knowledge gap by clarifying the practical applications of disc q-rung orthopair fuzzy logic. In addition, it explores an enhanced version of q-Rung Orthopair Fuzzy Sets, specifically focusing on Disc q-Rung Orthopair Fuzzy Sets, introducing various types of operators. These operators play a crucial role in solving decision-making and optimization problems. A notable contribution is the development of a hybrid operator, termed as the Disc q-Rung Orthopair Fuzzy Hybrid Weighted Averaging/Geometric (D-qROFHWA/G) operator.
The authors strive to narrow the knowledge gap by clarifying the practical applications of disc q-rung orthopair fuzzy logic. In addition, it explores an enhanced version of q-Rung Orthopair Fuzzy Sets, specifically focusing on Disc q-Rung Orthopair Fuzzy Sets, introducing various types of operators. These operators play a crucial role in solving decision-making and optimization problems. A notable contribution is the development of a hybrid operator, termed as the Disc q-Rung Orthopair Fuzzy Hybrid Weighted Averaging/Geometric (D-qROFHWA/G) operator.
- Proposes a novel enhancement in the realm of q-rung orthopair fuzzy operators
- Provides insights for both practitioners and academia, focusing on debated aspects in the field
- Covers Advanced Aggregation Operators and MADM Methods for Smart Healthcare Management
- Discusses comparative analysis and integration with established mathematical results
Biomedical engineers, computer scientists, as well as researchers in Artificial Intelligence, Machine Learning, and smart technologies in healthcare management. The primary market also includes scientific researchers and engineers across a range of topics who are interested in a cutting-edge exploration of disc q-rung orthopair fuzzy operators and their real-world implementations as a progressive extension of traditional fuzzy sets
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Chapter 1: Introduction to Disc q-Rung Orthopair Fuzzy Sets and their aggregation operators
- 1.1. Introduction
- 1.2. Preliminaries
- 1.3. Exploring Disc q-Rung Orthopair Fuzzy Sets
- 1.3.1. Crafting Disc q-Rung Orthopair Fuzzy Sets
- 1.4. Comparison rules for Disc q-Rung Orthopair Fuzzy Sets
- 1.5. Fundamental Disc q-Rung Orthopair Fuzzy Set operations
- 1.6. Operational excellence: exploring basic operations with algebraic norms
- 1.7. Aggregation operators for Disc q-Rung Orthopair Fuzzy Sets
- 1.7.1. Exploring Disc q-Rung Orthopair Fuzzy Set averaging aggregation operators
- 1.7.2. Exploring Disc q-Rung Orthopair Fuzzy Set geometric aggregation operators
- 1.8. Disc q-Rung Orthopair Fuzzy Set decision matrix construction
- 1.9. Case study
- 1.10. Conclusion
- Chapter 2: Strategic resource allocation for smart healthcare management using Disc q-Rung Orthopair Fuzzy hybrid aggregation information
- 2.1. Introduction
- 2.2. Preliminaries
- 2.3. Exploring Disc q-Rung Orthopair Fuzzy Sets
- 2.3.1. Comparison rules for D-qROFS
- 2.4. D-qROFS operations
- 2.5. Basic operations with algebraic norms
- 2.6. Average aggregation operations
- 2.6.1. Ordered aggregation operations
- 2.6.2. Hybrid aggregation operations
- 2.7. Geometric aggregation operations
- 2.7.1. Ordered aggregation operations
- 2.7.2. Hybrid aggregation operations
- 2.8. Disc q-ROF MAGDM method
- 2.9. Case study: strategic resource allocation for smart healthcare management
- 2.10. Conclusion
- Chapter 3: Disc q-Rung Orthopair Fuzzy Einstein aggregation operators and its application in precision drug delivery for cancer treatment
- 3.1. Introduction
- 3.2. Preliminaries
- 3.3. Disc q-Rung Orthopair Fuzzy Set (D-qROFS)
- 3.3.1. Crafting Disc q-Rung Orthopair Fuzzy Sets
- 3.3.2. Comparison rules for D-qROFS
- 3.4. Einstein operation aggregation operators based on Disc q-Rung Orthopair Fuzzy Set
- 3.5. Disc q-Rung Orthopair Fuzzy Operators for aggregation
- 3.5.1. Weighted average aggregation operators of Disc q-Rung Orthopair Fuzzy Einstein (D-qROFEWA)
- 3.5.2. D-qROF Einstein Ordered Weighted Average operations (D-qROFEOWA)
- 3.5.3. D-qROF Einstein Weighted Geometric (D-qROFEWG) operator
- 3.5.4. D-qROF Einstein Ordered Weighted Geometric operator (D-qROFEOWG)
- 3.6. Integrating the D-qROF Einstein operator in MAGDM: a new approach
- 3.7. Numerical problem
- 3.8. Conclusion
- Chapter 4: Decision aid technique for optimal design in wearable health monitoring devices through Disc q-Rung Orthopair Fuzzy Dombi aggregation information
- 4.1. Introduction
- 4.2. Preliminaries
- 4.3. Disc q-Rung Orthopair Fuzzy Sets
- 4.3.1. Crafting disc picture fuzzy
- 4.3.2. Comparison rules for DqROFS
- 4.4. Dombi operation
- 4.5. Disc qROFS aggregation operators
- 4.5.1. Weighted average aggregation operators of Disc q-Rung Orthopair Fuzzy Dombi (D-qROFDWA)
- 4.5.2. Ordered weighted average aggregation operators based on Disc q-Rung Orthopair Fuzzy Dombi information (D-qROFDOWA)
- 4.5.3. Weighted geometric aggregation operators of Disc q-Rung Orthopair Fuzzy Dombi information (D-qROFDWG)
- 4.5.4. Ordered weighted geometric aggregation operators of Disc q-Rung Orthopair Fuzzy Dombi information (D-qROFDOWG)
- 4.6. D-qROF MADM construction
- 4.7. Case study: wearable health monitoring devices
- 4.8. Conclusions
- Chapter 5: A minimally invasive robot empowered by Aczel–Alsina aggregation operators within a Disc-q-Rung Orthopair Fuzzy environment
- 5.1. Introduction
- 5.2. Preliminaries
- 5.3. Disc q-Rung Orthopair Fuzzy Set (D-qROFS)
- 5.3.1. Crafting Disc q-Rung Orthopair Fuzzy Sets
- 5.3.2. Comparison rules for D-qROFS
- 5.4. Aczel–Alsina basic operations based on D-qROFS
- 5.5. Aggregation operators of D−qROFS by using Aczel–Alsina aggregation operator
- 5.5.1. q-Rung Orthopair Fuzzy Weighted Arithmetic Aczel–Alsina operator (D−qROFSWAAA)
- 5.5.2. D-qROF Aczel–Alsina Ordered Weighted Averaging aggregation operator (D−qROFSOWAAA)
- 5.5.3. D-qROFS Weighted Geometric Aczel–Alsina operator (D−qROFSWGAA)
- 5.5.4. D-qROFS Aczel–Alsina Ordered Weighted Geometric aggregation operator (D−qROFSOWGAA)
- 5.6. D−qROFAA operator for multi-attributes group decision-making
- 5.7. Numerical problem
- 5.8. Conclusion
- Chapter 6: Disc q-Rung Orthopair Fuzzy Sugeno–Weber aggregation operators and their application in emergency healthcare decision-making
- 6.1. Introduction
- 6.2. Preliminaries
- 6.3. Exploring disc q-Rung Orthopair Fuzzy Sets
- 6.3.1. Comparison rules for D-qROFS
- 6.4. Basic operations with Sugeno–Weber norms
- 6.5. Aggregation operators for D-qROFSs
- 6.5.1. Sugeno–Weber D-qROFS averaging operators
- 6.5.2. Ordered aggregation operations
- 6.5.3. D-qROFS geometric aggregation operators
- 6.5.4. Ordered aggregation operations
- 6.6. D-qROF construction for MADM
- 6.7. Responding to a public health crisis-emergency preparedness and decision-making
- 6.8. Comparative analysis
- 6.8.1. TOPSIS approach
- 6.8.2. Numerical example
- 6.8.3. Comparative assessment
- 6.9. Conclusion
- Chapter 7: Disc q-Rung Orthopair Fuzzy Schweizer–Sklar aggregation operators and their application in adaptive healthcare technologies
- 7.1. Introduction
- 7.2. Preliminaries
- 7.3. Disc q-Rung Orthopair Fuzzy Set (D-qROFS)
- 7.3.1. Crafting Disc q-Rung Orthopair Fuzzy Sets
- 7.3.2. Comparison rules for D-qROFS
- 7.4. Schweizer–Sklar based fundamental operations on D-qROFS
- 7.5. Aggregation operators of D-qROFS by using Schweizer–Sklar weighted aggregation operators
- 7.5.1. Disc q-Rung Orthopair Fuzzy Set Schweizer–Sklar Weighted Averaging aggregation operator (D−qROFWASS)
- 7.5.2. Disc q-Rung Orthopair Fuzzy Schweizer–Sklar ordered weighted averaging aggregation operator (D−qROFOWASS)
- 7.5.3. Disc q-Rung Orthopair Fuzzy Schweizer–Sklar weighted geometric aggregation operator (D−qROFWGSS)
- 7.5.4. Disc q-Rung Orthopair Fuzzy Schweizer–Sklar ordered weighted geometric aggregation operator (D−qROFOWGSS)
- 7.6. D-qROF construction for multi-attributes group decision-making
- 7.7. Numerical problem
- 7.8. Conclusion
- Chapter 8: Methodological exploration: CoCoSo integrated Disc q-Rung Orthopair Fuzzy Sets for multi-criteria decision-making in genomic data analysis for personalized medicine
- 8.1. Introduction
- 8.2. Preliminaries
- 8.3. Disc q-Rung Orthopair Fuzzy Set
- 8.4. Operations and relations
- 8.4.1. Rules of comparison for D-qROFS
- 8.5. D-qROFS aggregation operators
- 8.5.1. Disc q-Rung orthopair fuzzy number weighted averaging aggregation operators
- 8.6. Disc q-Rung orthopair fuzzy MCDM method
- 8.6.1. Method for calculating combined weight
- 8.6.2. Disc q-Rung orthopair fuzzy MCDM based on CoCoSo
- 8.7. Case study
- Discussion
- 8.8. Conclusion
- Chapter 9: Enhanced Disc q-Rung Orthopair Fuzzy TODIM approach for smart medication adherence solutions in chronic patient care
- 9.1. Introduction
- 9.2. Preliminaries
- 9.2.1. Information of IFSs, PyFS, and C-IFSs
- 9.3. Disc q-Rung Orthopair Fuzzy Set
- 9.4. Operations and relations
- 9.4.1. TODIM approach description
- 9.5. TODIM analysis in a Disc q-Rung Orthopair Fuzzy environment
- 9.6. Case study
- 9.6.1. Description
- 9.6.2. Decision-making
- 9.6.3. Analysis
- 9.7. Comparative analysis
- 9.8. Discussion
- 9.9. Conclusions
- Chapter 10: Enhancing health data security in blockchain: ELECTRE method with Disc q-Rung Orthopair Fuzzy Sets
- 10.1. Introduction
- 10.2. Preliminaries
- 10.3. Disc q-Rung Orthopair Fuzzy Set
- 10.4. Operations and relations
- 10.4.1. Comparison rules for D-qROFSs
- 10.5. Construction of D-qROF decision matrix
- 10.6. ELECTRE methods on D-qROFS
- 10.6.1. Concordant and discordant sets
- 10.6.2. D-qROF ELECTRE
- 10.6.3. Algorithm
- 10.7. Numerical example
- 10.8. Case study
- 10.9. Comparative analysis
- 10.10. Conclusion
- Chapter 11: Extension of CODAS technique for hospital patient admission management with Disc q-Rung Orthopair Fuzzy Sets
- 11.1. Introduction
- 11.2. Preliminaries
- 11.3. Disc q-Rung Orthopair Fuzzy Set
- 11.4. Operations and relations
- 11.4.1. Comparison rules for D-qROFSs
- 11.5. CODAS method for Disc q-Rung Orthopair Fuzzy Set
- 11.6. Case study
- 11.6.1. Comparative analysis
- Discussion
- 11.7. Conclusion
- Chapter 12: MEREC-MARCOS method for Disc q-Rung Orthopair Fuzzy Sets and its application in multi-criteria group decision-making
- 12.1. Introduction
- 12.2. Preliminaries
- 12.3. Exploring Disc q-Rung Orthopair Fuzzy Sets
- 12.3.1. Conversion to Disc q-Rung Orthopair Fuzzy
- 12.3.2. Comparison rules for D-qROFS
- 12.4. Basic operations and aggregation operators
- 12.5. Disc q-ROF MAGDM method
- 12.6. Case study—healthcare policy reform by navigating decision-making for optimal outcomes
- 12.7. Comparative analysis
- 12.8. Conclusion
- Index
- Edition: 1
- Published: April 10, 2025
- Imprint: Academic Press
- No. of pages: 250
- Language: English
- Paperback ISBN: 9780443339974
- eBook ISBN: 9780443339981
SA
Shahzaib Ashraf
Dr. Shahzaib Ashraf received the B.S. degree in mathematics from the University of Sargodha, the M.S. degree in (fuzzy) mathematics from International Islamic University, Islamabad, and the Ph.D. degree from Abdul Wali Khan University, Mardan, Pakistan. He is an Assistant Professor in Mathematics at Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan. He also served the Bacha Khan University, Charsadda, Pakistan, as an Assistant Professor. He has published 100 research articles with more than 3000 citations. He currently supervises four M.S. and four Ph.D. theses and has supervised four M.S. theses. Dr. Ashraf has published in high impact journals, including International Journal of Intelligent Systems, Soft Computing, and International Journal of Fuzzy Systems. Dr. Ashraf’s specialization is in fuzzy mathematics, spherical fuzzy sets, fuzzy decision support systems, computational intelligence, and soft computing.
Affiliations and expertise
Institute of Mathematics, Khwaja Fareed University of Engineering and Information Technology, PakistanCJ
Chiranjibe Jana
Dr. Chiranjibe Jana is a researcher in the Department of Applied Mathematics with Oceanology and Computer Programming at Vidyasagar University, India. Dr. Jana’s research interests include fuzzy logic, fuzzy set, aggregation operators, BCK-algebra, topological space, fuzzy number, soft set, and congruence relation. He has published on a wide range of these research topics in journals such as International Journal of Intelligent Systems, Computational and Applied Mathematics, Expert Systems, Granular Computing, IEEE Access, and Journal of Ambient Intelligence and Humanized Computing.
Affiliations and expertise
Researcher, Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore, West Bengal, IndiaVB
Valentina Emilia Balas
Valentina E. Balas is a Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a PhD Cum Laude, in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers. Her research interests are in intelligent systems, fuzzy control, soft computing, smart sensors, information fusion, modeling, and simulation. She is a member of the editorial boards of several national and international journals and evaluator expert for national, international projects, and PhD thesis. Dr. Balas is the Director of Intelligent Systems Research Centre and Director of the Department of International Relations, Programs, and Projects in Aurel Vlaicu University of Arad. She is the recipient of the "Tudor Tanasescu" Prize from the Romanian Academy for contributions in the field of soft computing methods (2019).
Affiliations and expertise
Professor, Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania, and Academy of Romanian Scientists, RomaniaWP
Witold Pedrycz
Dr. Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in computational intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. In 2012 he was elected a fellow of the Royal Society of Canada. His main research directions involve computational intelligence, fuzzy modeling and granular computing, knowledge discovery and data science, pattern recognition, data science, knowledge-based neural networks, and control engineering. He is also an author of 18 research monographs and edited volumes covering various aspects of computational intelligence, data mining, and software engineering. Dr. Pedrycz is vigorously involved in editorial activities. He is the editor-in-chief of Information Sciences, editor-in-chief of WIREs Data Mining and Knowledge Discovery, and co-editor-in-chief of International Journal of Granular Computing, and Journal of Data Information and Management. He serves on the advisory board of IEEE Transactions on Fuzzy Systems.
Affiliations and expertise
Professor, Department of Electrical and Computer Engineering, University of Alberta, CanadaRead Emerging Fuzzy Intelligent Systems for Smart Healthcare Management on ScienceDirect