Data-Driven Modelling with Fuzzy Sets : A Neutrosophic Perspective

Fuzzy sets have long been employed to handle imprecise and uncertain information in the real world, but their limitations in dealing with incomplete and inconsistent data led to the emergence of neutrosophic sets.

In this thought-provoking book, titled Data-Driven Modelling with Fuzzy Sets: A Neutrosophic Perspective, the authors delve into the theories and extensive applications of neutrosophic sets, ranging from neutrosophic graphs to single-valued trapezoidal neutrosophic sets, and their practical implications in knowledge management, including student learning assessment, academic performance evaluation, and technical article screening.

This comprehensive resource is intended to benefit mathematicians, physicists, computer experts, engineers, scholars, practitioners, and students seeking to deepen their understanding of neutrosophic sets and their practical applications in diverse fields.

This book comprises 11 chapters that provide a thorough examination of neutrosophic set theory and its extensions.

Each chapter presents valuable insights into various aspects of data-driven modeling with neutrosophic sets and explores their applications in different domains.

The book covers a wide range of topics. The specific topics covered in the book include neutrosophic submodules, applications of neutrosophic sets, solutions to differential equations with neutrosophic uncertainty, cardinalities of neutrosophic sets, neutrosophic cylindrical coordinates, applications to graphs and climatic analysis, neutrosophic differential equation approaches to growth models, neutrosophic aggregation operators for decision making, and similarity measures for Fermatean neutrosophic sets.

The diverse contributions from experts in the field, coupled with the constructive feedback from reviewers, ensure the book's high quality and relevance. This book· presents a qualitative assessment of big data in the education sector using linguistic quadripartitioned single-valued neutrosophic soft sets. · showcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity index. · covers scientific evaluation of student academic performance using single-valued neutrosophic Markov chain. · illustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessment. · examines estimation of distribution algorithms based on multiple-attribute group decision-making to evaluate teaching quality.

With its wealth of knowledge, this book aims to inspire further research and innovation in the field of neutrosophic sets and their extensions, providing a valuable resource for scholars, practitioners, and students alike.