Modeling decisions for artificial in...
MDAI (Conference) (2024 :)

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  • Modeling decisions for artificial intelligence = 21st International Conference, MDAI 2024, Tokyo, Japan, August 27-31, 2024 : proceedings /
  • Record Type: Electronic resources : Monograph/item
    Title/Author: Modeling decisions for artificial intelligence/ edited by Vicenç Torra, Yasuo Narukawa, Hiroaki Kikuchi.
    Reminder of title: 21st International Conference, MDAI 2024, Tokyo, Japan, August 27-31, 2024 : proceedings /
    remainder title: MDAI 2024
    other author: Torra, Vicenç.
    corporate name: MDAI (Conference)
    Published: Cham :Springer Nature Switzerland : : 2024.,
    Description: xiii, 252 p. :ill. (some col.), digital ;24 cm.
    [NT 15003449]: 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.
    Contained By: Springer Nature eBook
    Subject: Artificial intelligence - Congresses. - Mathematical models -
    Online resource: https://doi.org/10.1007/978-3-031-68208-7
    ISBN: 9783031682087
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