Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation (eBook, PDF)

Proceedings of the INFUS 2021 Conference, held August 24-26, 2021. Volume 2
Redaktion: Kahraman, Cengiz; Sari, Irem Ucal; Tolga, A. Cagri; Oztaysi, Basar; Cevik Onar, Sezi; Cebi, Selcuk

• Format: PDF

Produktbeschreibung

This book presents recent research in intelligent and fuzzy techniques. Emerging conditions such as pandemic, wars, natural disasters and various high technologies force people for significant changes in business and social life. The adoption of digital technologies to transform services or businesses, through replacing non-digital or manual processes with digital processes or replacing older digital technology with newer digital technologies through intelligent systems is the main scope of this book. It focuses on revealing the reflection of digital transformation in our business and social life under emerging conditions through intelligent and fuzzy systems. The latest intelligent and fuzzy methods and techniques on digital transformation are introduced by theory and applications. The intended readers are intelligent and fuzzy systems researchers, lecturers, M.Sc. and Ph.D. students studying digital transformation. Usage of ordinary fuzzy sets and their extensions, heuristics and metaheuristics from optimization to machine learning, from quality management to risk management makes the book an excellent source for researchers.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 897
• Erscheinungstermin: 23. August 2021
• Englisch
• ISBN-13: 9783030855772
• Artikelnr.: 62486053

Inhaltsangabe

A Comparative Study of FAHP with Type-1 and Interval Type-2 Fuzzy Sets for ICT Implementation in Smart Cities.- A New Cuckoo Search Algorithm Using Interval Type-2 Fuzzy Logic for Dynamic Parameter Adaptation.- Monitoring stability of plant species to harmful urban environment under Z- information.- Critical path for projects with activity durations modelled as Z-fuzzy numbers.- Defuzzification of Intuitionistic Z-Numbers for Fuzzy Multi Criteria Decision Making.- Clustering Z- information based on semantic spaces.