Machine Learning, Optimization, and Data Science c - Nicosia, Giuseppe; Ojha, Varun; La Malfa, Emanuele; La Malfa, Gabriele; Jansen, Giorgio; Pardalos, Panos M.; Giuffrida, Giovanni; Umeton, Renato; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2022)
This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021. The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
Deep Learning -- Machine Learning -- Reinforcement Learning -- Neural Networks -- Deep Reinforcement Learning -- Optimization -- Global Optimization -- Multi-Objective Optimization -- Computational Optimization -- Data Science -- Big Data -- Data Analytics -- Artificial Intelligence.
Keywords: Artificial intelligence; Algorithms; Application software; Numerical analysis; 9; Social sciences -- Data processing; Artificial Intelligence; Design and Analysis of Algorithms; Computer and Information Systems Applications; Numerical Analysis; Computer Communication Networks; Computer Application in Social and Behavioral Sciences