Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference, Volume 1 Kahraman, Cengiz. editor. edt http://id.loc.gov/vocabulary/relators/edt Cevik Onar, Sezi. editor. edt http://id.loc.gov/vocabulary/relators/edt Cebi, Selcuk. editor. edt http://id.loc.gov/vocabulary/relators/edt Oztaysi, Basar. editor. edt http://id.loc.gov/vocabulary/relators/edt Tolga, A. Cagrı. editor. edt http://id.loc.gov/vocabulary/relators/edt Ucal Sari, Irem. editor. edt http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

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access XVII, 785 p. 293 illus., 132 illus. in color. online resource. This book presents recent research in intelligent and fuzzy techniques on Intelligent Industrial Informatics and Efficient Networks. This cutting-edge field integrates advanced technologies, such as artificial intelligence, machine learning and data analytics, into industrial processes, revolutionizing the way industries operate. The book presents the examples of the implementation of smart sensors and IoT devices, which facilitate real-time data collection and communication. High-speed, low-latency networks ensure that information flows effortlessly between devices, enabling timely responses and enabling the coordination of complex manufacturing processes. This network architecture supports the integration of edge computing, where data processing occurs closer to the source, reducing latency and enabling faster decision-making. The readers can benefit from this book for maintaining a leadership position among competitors in both manufacturing and service companies. The intended readers are intelligent and fuzzy systems researchers, lecturers, M.Sc. and Ph.D. students studying intelligent and fuzzy techniques. The book covers fuzzy logic theory and applications, heuristics and metaheuristics from optimization to machine learning, from quality management to risk management, making the book an excellent source for researchers. Chapter 1. Mathematical Modelling and Fuzzy Logic -- Chapter 2. Extreme Learning Machine – a New Machine Learning Paradigm -- Chapter 3. A New Mandatory-Optional Bipolar Model of Decision Making in a Fuzzy Environments -- Chapter 4. Games, fuzzy measures, indices, and explainable ML: exploiting the game -- Chapter 5. Fuzzy Performance Measurement: A Literature Review -- Chapter 6. Evaluation of business intelligence tools for thelogistics sector with hesitant fuzzy hybrid MCDM methods -- Chapter 7. Investigating Smart City Applications: A Case Study from İstanbul -- Chapter 8. Fermatean fuzzy TOPSIS method based on prospect theory -- Chapter 9. New Multi Criteria Decision Making Methodology EFEE under Uncertainty on Paris City Micromobility -- Chapter 10. Threshold Aggregation of Fuzzy Data Using Fuzzy Cardinalities of a Set of Fuzzy Estimates -- Chapter 11. A Collaborative Decision-Making Framework in Humanitarian Logistics -- Chapter 12. Integrating Fuzzy AHP and TOPSIS for Optimal Air Fryer Selection: A Consumer-Centric Approach -- Chapter 13. A Comparative Analysis of Classical AHP, Fuzzy AHP, and Z-Fuzzy AHP Methods for Flood Risk Assessment in Büyükçekmece District -- Chapter 14. Fuzzy evaluation of stakeholders' aspects in water resources management -- Chapter 15. Sustainability Performance Evaluation in Faculties: A COPRAS-Based Assessment -- Chapter 16. Evaluating the Coherence and Diversity inAI-Generated and Paraphrased Scientific Abstracts: A Fuzzy Topic Modeling Approach -- Chapter 17. Automatic Target Generation for ElectronicData Interchange using Sequence-to-Sequence Models -- Chapter 18. Artificial Intelligence Enriching Contributionsfrom Multiple Perspectives in Ancient Text Analysis -- Chapter 19. Identifying and Mitigating Bias in AI-Generated Image Datasets for Better Cognitive Understanding -- Chapter 20. Comparative Analysis of Large Language Models in Source Code Analysis...etc. edited by Cengiz Kahraman, Sezi Cevik Onar, Selcuk Cebi, Basar Oztaysi, A. Cagrı Tolga, Irem Ucal Sari. Computational intelligence Engineering Data processing Artificial intelligence Computational Intelligence Data Engineering Artificial Intelligence Q342 9783031700187 https://doi.org/10.1007/978-3-031-70018-7 https://doi.org/10.1007/978-3-031-70018-7 240901 20251030112929.0 978-3-031-70018-7