Titel |
Modeling Decisions for Artificial Intelligence : 21st International Conference, MDAI 2024, Tokyo, Japan, August 27–31, 2024, Proceedings / edited by Vicenç Torra, Yasuo Narukawa, Hiroaki Kikuchi |
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Beteiligt |
Vicenç Torra (Herausgeber) |
Erschienen |
Cham: Springer Nature Switzerland, Imprint: Springer |
Ausgabe |
1st ed. 2024 |
Umfang |
Online-Ressource, XIII, 252 p. 76 illus., 64 illus. in color. : online resource. |
Enthält |
Invited paper -- Taste Media Innovative Technology Transforms the Eating Experience -- Fuzzy measures and integrals -- An axiomatic definition of non discrete Mbius transform -- Fuzzy Rough Choquet Distances -- Uncertainty in AI -- Entropies from f divergences -- Comparative Study of Methods for Estimating Interval Priority Weights Focusing on the Accuracy in Selecting the Best Alternative -- Clustering -- Sequential Cluster Extraction by Noise Clustering Based on Local Outlier Factor -- On Objective Based Clustering from the Perspective of Transportation Problem -- Data science and data privacy -- Decision Tree Based Inference of Lightning Network Client Implementations -- nuggets Data Pattern Extraction Framework in R -- User centred Argumentation Analysis of Local Explanations in Explainable AI -- Revised Margin-Maximization Method for Fuzzy Nearest Prototype Classification -- Bistochastically private release of data streams with delay -- Differentially Private Extreme Learning Machine -- Studying the impact of edge privacy on link prediction in temporal graphs -- Dissimilar Similarities Comparing Human and Statistical Similarity Evaluation in Medical AI -- On the necessity of counterfeits and deletions for continuous data publishing -- A Poisoning-Resilient LDP schema leveraging Oblivious Transfer with the Hadamard Transform -- Experimental Evaluation for Risk Assessment of Privacy Preserving Synthetic Data -- Transforming Stock Price Forecasting Deep Learning Architectures and Strategic Feature Engineering |
ISBN |
978-3-031-68208-7 |
Sprache |
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Land |
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Themengebiet |
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Thema |
Artificial Intelligence. |
DDC-Notation |
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Reihe |
Lecture Notes in Artificial Intelligence ; 14986 |
Persistent Identifier |
urn:nbn:de:101:1-2408161318017.675287349133 (URN) |
Datensatz-ID |
1339205955 |
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