Titlebook: Intelligent Data Engineering and Automated Learning – IDEAL 2024; 25th International C Vicente Julian,David Camacho,Antonio Tallón-Balles C

STERN

Colonnade

compassion

MetaLIRS: Meta-learning for Imputation and Regression Selectiont handle missing data directly, imputation methods are used in data pre-processing. Finding the best imputation method is non-trivial, however. Moreover, our results show that an independent choice for a best imputation method does not always result in the best predictive performance in the end; the

肿块

Pipeline for Semantic Segmentation of Large Railway Point Cloudsipeline adapts DINOv2 [.] for labeling point clouds, with tailored self-distillation pre-training and fine-tuning. The adopted transformer architecture successfully generalizes to multiple railway datasets, with a lightweight pipeline that outperforms manual labeling speed by a factor of 6, despite

无礼回复

Preliminary Investigation on Machine Learning and Deep Learning Models for Change of Direction Classwidely used to assess athlete performance. Automating the process of COD classification during live games or training can provide real-time feedback. In this study, we evaluated Machine Learning (ML) and Deep Learning (DL) models for the classification of COD using accelerometers and gyroscope senso

wangle

Efficient Radar Scheduling Using Genetic Algorithms and Stochastic Heuristic Initializationnaging operational constraints. This study addresses the complex Radar System Scheduling Problem, which involves efficiently allocating various radars across multiple sessions to maximize coverage while minimize the number of radars across all sessions. We propose a specialized Genetic Algorithm wit

石墨

DAUNT

领先

María Valero-Redondo,Javier Huertas-Tato,Sergio D’Antonio Maceiras,Alejandro Martín,David Camacho

谦虚的人

Georgian Anghelescu,Camelia Chira,Kristoffer N. T. Månsson