Aggregation function is an important component in an information aggregation or information fusion system. Interrelationships usually exist between the input arguments (e.g., the criteria in the multicriteria decision making) of an aggregation function. In this paper, we make a comprehensive survey on the aggregation operators (AOs) that consider the argument interrelationships in crisp and fuzzy settings. In particular, we discuss the mechanisms of modeling the argument interrelationships of the Choquet integral (CI), the power average (PA), the Bonferroni mean (BM), the Heronian mean (HM), and the Maclaurin symmetric mean (MSM) operators, and introduce their extended (e.g., generalized or weighted) forms and their applications in different fuzzy sets. In addition, we compare these ve types of operators and summarize their advantages and disadvantages. Furthermore, we discuss the applications of these operators. Finally, we identify some future research directions in the AOs considering the argument interrelationships. The reviewed papers are mainly about the development of the CI, the PA, the BM, the HM, and the MSM in (fuzzy) MCDMs, most of which fall in the period of 20092018.

This book provides recent research on soft computing and fuzzy methodologies in innovation management and sustainability. The uncertainty in the business world is increasing. Significant changes are generated unexpectedly, so using fuzzy logic and soft computing methods allows us to create flexible scenarios adaptable to new realities. Within the book, we will find different applications of fuzzy methodologies that can apply to various topics such as sustainability, innovation, tourism, costs, exports, systems administration, among others. The book's main contribution is the applicability of the various methodologies to specific cases, which allows generating a relationship between theory and practice. In addition, it has some bibliometric studies on various topics that give us a visualization of what has happened and where multiple topics are headed. This book is recommended mainly for students who wish to know how the various fuzzy and soft computing tools can be taken to real situations, allowing a better understanding of these and generating new visions of future applicability.

This book gathers the most recent developments in fuzzy & intelligence systems and real complex systems presented at INFUS 2020, held in Istanbul on July 21–23, 2020. The INFUS conferences are a well-established international research forum to advance the foundations and applications of intelligent and fuzzy systems, computational intelligence, and soft computing, highlighting studies on fuzzy & intelligence systems and real complex systems at universities and international research institutions. Covering a range of topics, including the theory and applications of fuzzy set extensions such as intuitionistic fuzzy sets, hesitant fuzzy sets, spherical fuzzy sets, and fuzzy decision-making; machine learning; risk assessment; heuristics; and clustering, the book is a valuable resource for academics, M.Sc. and Ph.D. students, as well as managers and engineers in industry and the service sectors.

This book covers the latest advances for the development of artificial intelligence systems and their applications in various fields from medicine and technology to education. The proceedings comprise refereed papers presented at the Fourth International Conference of Artificial Intelligence, Medical Engineering, Education (AIMEE2020), held at the Mechanical Engineering Institute of the Russian Academy of Sciences, Moscow, Russia, in 3–4 October 2020. Given the rapid development of artificial intelligence systems, the book emphasizes the need for the intensification of training of a growing number of relevant specialists, in particular, in medical engineering to increase the effectiveness of medical diagnosing and treatment. In digital artificial intelligence systems, scientists endeavor to reproduce the innate intellectual abilities of human and other organisms, and the in-depth study of genetic systems and inherited biological processes can provide new approaches to create more and more effective artificial intelligence methods. Topics of the included papers concern thematic materials in the following spheres: mathematics and biomathematics; medical approaches; technological and educational approaches. The book is a compilation of state-of-the-art papers in the field, covering a comprehensive range of subjects that is relevant to business managers and engineering professionals alike. The breadth and depth of these proceedings make them an excellent resource for asset management practitioners, researchers, and academics, as well as undergraduate and postgraduate students interested in artificial intelligence and bioinformatics systems as well as their growing applications. Intended readership includes specialists, students, and other circles of readers who would like to know where artificial intelligence systems can be applied in the future with great benefit.

This book constitutes the proceedings of the 9th International Conference on Health Information Science, HIS 2020, which took place in Amsterdam, The Netherlands, during October 20-23, 2020. The 11 full papers and 6 short papers presented in this volume were carefully reviewed and selected from 62 submissions. They were organized in topical sections named: mental health; medical record processing; medical information systems; medical diagnosis with machine learning; and health behavior and medication.

This paper proposes a hesitant bipolar-valued neutrosophic set (HBVNS) based on the combination of bipolar neutrosophic sets and hesitant fuzzy sets. The proposed set generalizes the notions of fuzzy set, intuitionistic fuzzy set, hesitant fuzzy set, single-valued neutrosophic set, single-valued neutrosophic hesitant fuzzy set, bipolar fuzzy set and bipolar neutrosophic set. Further, we define the basic operational laws, union, intersection and complement for hesitant bipolar -valued neutrosophic elements (HBVNEs) and study its associated properties. Some relevant examples are also given to provide a better understanding of the proposed concept. Two aggregation operators are developed based HBVNS which are the hesitant bipolar-valued neutrosophic weighted averaging (HBVNWA) and the hesitant bipolar-valued neutrosophic weighted geometric (HBVNWG). A decision making method is developed based on HBVNS and the proposed HBVNWA and HBVNWG operators. Finally, an illustrative example is given to show the applicability of the proposed decision making method and a comparative analysis with the existing methods is also provided.

This book presents a collection of recent research on topics related to Pythagorean fuzzy set, dealing with dynamic and complex decision-making problems. It discusses a wide range of theoretical and practical information to the latest research on Pythagorean fuzzy sets, allowing readers to gain an extensive understanding of both fundamentals and applications. It aims at solving various decision-making problems such as medical diagnosis, pattern recognition, construction problems, technology selection, and more, under the Pythagorean fuzzy environment, making it of much value to students, researchers, and professionals associated with the field.