Information Processing And Management Of Uncertainty In Knowledge Based Systems Theory And Foundations
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Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations
This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cádiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).
Information Processing and Management of Uncertainty in Knowledge-Based Systems
This two-volume set (CCIS 1601-1602) constitutes the proceedings of the 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2021, held in Milan, Italy, in July 2022. The 124 papers were carefully reviewed and selected from 188 submissions. The papers are organized in topical sections as follows: aggregation theory beyond the unit interval; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy mathematical analysis and its applications; generalized sets and operators; information fusion techniques based on aggregation functions, pre-aggregation functions, and their generalizations; interval uncertainty; knowledge acquisition, representation and reasoning; logical structures of opposition and logical syllogisms; mathematical fuzzy logics; theoretical and applied aspects of imprecise probabilities; data science and machine learning; decision making modeling and applications; e-health; fuzzy methods in data mining and knowledge discovery; soft computing and artificia intelligence techniques in image processing; soft methods in statistics and data analysis; uncertainty, heterogeneity, reliability and explainability in AI; weak and cautious supervised learning.
Information Processing and Management of Uncertainty in Knowledge-Based Systems
Author: Marie-Jeanne Lesot
language: en
Publisher: Springer Nature
Release Date: 2025-04-30
This book is a collection of papers focused on techniques for managing uncertainty and aggregation. It provides a forum for exchanging ideas between theoreticians and practitioners in these and related areas. The papers are part of the 20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, which will occur in Lisbon, Portugal, from July 22 to 26, 2024. The collection describes the latest findings on topics such as advances in fuzzy systems and data analysis, optimization, scheduling via modeling uncertainty, explainability, decision-making, implications, data aggregation, and aggregation operators. A special chapter is dedicated to the memory of Michio Sugeno. The book is a valuable resource for practitioners, researchers, and graduate students who want to apply fuzzy-based techniques to real-world data analysis and management processes involving imprecision and uncertainty.