Nicosia / Ojha / Giesselbach Machine Learning, Optimization, and Data Science

10th International Conference, LOD 2024, Castiglione della Pescaia, Italy, September 22–25, 2024, Revised Selected Papers, Part III
Erscheinungsjahr 2025
ISBN: 978-3-031-82487-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 15510, 294 Seiten, eBook

Reihe: Lecture Notes in Computer Science

The three-volume set LNAI 15508-15510 constitutes the refereed proceedings of the 10th International Conference on Machine Learning, Optimization, and Data Science, LOD 2024, held in Castiglione della Pescaia, Italy, during September 22–25, 2024.This year, in the LOD Proceedings decided to also include the papers of the fourth edition of the Symposium on Artificial Intelligence and Neuroscience (ACAIN 2024).The 79 full papers included in this book were carefully reviewed and selected from 127 submissions. The LOD 2024 proceedings focus on machine learning, deep learning, AI, computational optimization, neuroscience and big data that includes invited talks, tutorial talks, special sessions, industrial tracks, demonstrations and oral and poster presentations of refereed papers.

Zielgruppe

Research

Autoren/Hrsg.

Nicosia, Giuseppe

Ojha, Varun

Giesselbach, Sven

Pardalos, M. Panos

Umeton, Renato

Inhaltsverzeichnis

.- Combining EEG oscillation analysis and explainable artificial intelligence for characterizing visuospatial attention.
.- Fully automatic meningioma segmentation with nnUNet using T1-weighted contrast-enhanced MR images by leveraging publicly available data and different types of annotations.
.- Multimodal Shannon Game with Images.
.- On the Role of Activation Functions in EEG-To-Text Decoder.
.- INVISIONS: Innovative Neuromorphic Vision Sensors in real-scenarios.
.- Path-weight-based Pruning and SHAP-based Explanations of an ANN with fMRI Data.
.- Deep learning in a bilateral brain with hemispheric specialisation.
.- A compact convolutional neural network for decoding EEG functional connectivity: application to motor imagery.
.- Inference of Abstraction for Human-like Probabilistic Reasoning.
.- Conformal Prediction for Uncertainty Quantification in Brain Age Estimation using Random Forests Quantile Regression on MRI Features of the HCP Young Adult.
.- Emotional Reactions To AI-generated Images: A Pilot Study Using Neuro physiological Measures.
.- Exploring Deep Learning Models for EEG Neural Decoding.
.- Transfer Learning for the Cognitive Staging Prediction in Alzheimer’s Disease.
.- Inference of Abstraction for Human-like Logical Reasoning.
.- Sequence Learning with Analog Neuromorphic Multi-Compartment Neurons and On-Chip Structural STDP.
.- Understanding Sleep Dynamics Gathered from Wearable Devices with Ex plainable Recurrent Neural Networks.
.- Brain morphometry differences across sexes revealed through Explainable Artificial Intelligence: a Human Connectome Project Young Adult study.
.- Left/Right brain, human motor control and the implications for robotics.
.- Predicting Psychological Well-being in HCP Young Adult Cohort using Ran dom Forests Regression and SHAP with NIHTB Emotion Battery.