Modeling Decisions for Artificial Intelligence 21st International Conference, MDAI 2024, Tokyo, Japan, August 27–31, 2024, Proceedings

This book constitutes the refereed proceedings of the 21st International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2024, held in Umeå, Sweden, during August 27-30, 2024.

The 18 full papers were carefully reviewed and selected from 37 submissions. There were organized in topical headings as follows: Fuzzy measures and integrals; uncertainty in AI; clustering; and data science and data privacy.

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.