Laboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA): Envíos recentes
Mostrando ítems 16-20 de 90
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On the scalability of feature selection methods on high-dimensional data
(Springer, 2018)[Abstract]: Lately, derived from the explosion of high dimensionality, researchers in machine learning became interested not only in accuracy, but also in scalability. Although scalability of learning methods is a trending ... -
Feature selection with limited bit depth mutual information for portable embedded systems
(Elsevier, 2020-06)[Abstract]: Since wearable computing systems have grown in importance in the last years, there is an increased interest in implementing machine learning algorithms with reduced precision parameters/computations. Not only ... -
A novel intelligent approach for man-in-the-middle attacks detection over internet of things environments based on message queuing telemetry transport
(Wiley, 2024)[Abstract]: One of the most common attacks is man-in-the-middle (MitM) which, due to its complex behaviour, is difficult to detect by traditional cyber-attack detection systems. MitM attacks on internet of things systems ... -
CUDA acceleration of MI-based feature selection methods
(Elsevier, 2024-08)[Abstract]: Feature selection algorithms are necessary nowadays for machine learning as they are capable of removing irrelevant and redundant information to reduce the dimensionality of the data and improve the quality of ... -
A novel framework for generic Spark workload characterization and similar pattern recognition using machine learning
(Elsevier, 2024-07)[Abstract]: Comprehensive workload characterization plays a pivotal role in comprehending Spark applications, as it enables the analysis of diverse aspects and behaviors. This understanding is indispensable for devising ...