Neutrosophic Computing and Vague Data Analysis by Smarandache, Florentin; Khan, Zahid;

Short description:

This edited volume provides a comprehensive exploration of neutrosophic methodologies, emphasizing their computational frameworks, theoretical advancements, and practical applications. It aims to make neutrosophic theory more accessible and applicable to a wider audience, including researchers, practitioners, and students. 

Long description:

This edited volume provides a comprehensive exploration of neutrosophic methodologies, emphasizing their computational frameworks, theoretical advancements, and practical applications. Neutrosophic logic, originally developed to address problems involving vague, inconsistent, incomplete, or unclear information, has evolved significantly over the years. Its extensions have found relevance across diverse disciplines, offering innovative solutions to challenges in science, economics, environmental studies, healthcare, and more. 

The book highlights the limitations of traditional mathematical and statistical models in handling the complexity and uncertainty inherent in modern systems. While existing frameworks have contributed to uncertainty modeling, they often fail to treat indeterminacy as a distinct and independent component of information. Neutrosophic theory addresses this gap, providing a robust foundation for analyzing and optimizing systems characterized by indeterminacy. 

Key Features:

• Computational Toolboxes: Practical tools for estimating unknown values, simulating results, and making decisions using neutrosophic models. These computational approaches enable practitioners to analyze systems that are too complex for traditional analytical methods.


• Applications across Disciplines: Detailed examples and case studies demonstrating the use of neutrosophic models in science, engineering, medical diagnostics, environmental modeling, economics, and decision-making under uncertainty.


• Accessibility and Adaptability: By focusing on computational approaches, the book aims to make neutrosophic theory more accessible and applicable to a wider audience, including researchers, practitioners, and students.

This volume serves as a valuable resource for anyone seeking to understand and apply neutrosophic logic to real-world scenarios. It bridges the gap between theoretical advancements and practical applications, empowering readers to tackle complex problems with innovative solutions grounded in neutrosophic methodologies.

Table of Contents:

1. Matrices of Neutrosophic Numbers: An Overview  2. A Computational Study of Neutrosophic Probability, with Focus on Risk Evaluation  3. Optimization Algorithms in Nonstandard Neutrosophic Environments: A Metaheuristic Approach  4. An Algebraic Approach to Interval-Valued Neutrosophic Oversets and Undersets  5. Neutrosophic Graph Rewriting as a Computational Model  6. A Novel Correlation Coefficient Between Single-Valued Neutrosophic Sets and Its Use in Renewable Energy Source Selection  7. Several Similarity Measures of Trigonometric Functions for Refined Neutrosophic Sets and Their Application in Medical Diagnosis  8. Enhancing Antiviral Drug Repurposing for Future Pandemics with a Linguistic Single-Valued Neutrosophic MAGDM Framework  9. Weighted Adjustable Level and Graded Utility Decision-Making Frameworks Based on Neutrosophic Soft Sets  10. Neutrosophic ISOCOV in Smart and Sustainable Material Selection  11. Latin Square Designs under Indeterminacy: Data Analysis and Applications  12. Neutrosophic Programming Approach for Mixed Allocations in Multivariate-Multiobjective Nonlinear Stratified Sampling Problem with Parabolic Fuzzy Numbers  13. A Neutrosophic Control Chart for the Conway-Maxwell-Poisson Distribution Using Multiple Dependent State Sampling  14. A Hierarchical Framework for Assessing Groundwater Quality Using Evidential Neutrosophic Tree Soft Sets   15. An Innovative Trapezoidal Neutrosophic CRADIS Algorithm Using Multiple-Attribute Group Decision-Making for Sharia Bank Branch Location Selection  16. A Novel Approach for the Shortest Path Computation in Networks Using Quadripartitioned Single-Valued Neutrosophic Refined Numbers