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Mostrando ítems 11-18 de 18
Artículo
Analysis of the evolution of the Spanish labour market through unsupervised learning
(Institute of Electrical and Electronics Engineers (IEEE), 2019)
Unemployment in Spain is one of the biggest concerns of its inhabitants. Its unemployment rate is the second highest in the European Union, and in the second quarter of 2018 there is a 15.2% unemployment rate, some 3.4 ...
Ponencia
Aproximación al índice externo de validación de clustering basado en chi cuadrado
(Asociación Española para la Inteligencia Artificial (AEPIA), 2018)
El clustering es una de las técnicas más utilizadas en minería de datos. Tiene como objetivo principal agrupar datos en clusters de manera que los objetos que pertenecen al mismo clúster sean más similares que los que ...
Ponencia
An Approach to Silhouette and Dunn Clustering Indices Applied to Big Data in Spark
(Springer, 2016)
K-Means and Bisecting K-Means clustering algorithms need the optimal number into which the dataset may be divided. Spark implementations of these algorithms include a method that is used to calculate this number. Unfortunately, ...
Artículo
¿Cómo transformar información en ahorro para el consumidor doméstico? El caso del contador eléctrico inteligente en España
(Publicaciones Dyna, 2018)
El cliente doméstico era el gran olvidado del sistema eléctrico. A pesar de su peso en el consumo total, hasta hace pocos años su poder de intervenir en el mercado era extremadamente limitado. La aparición de las nuevas ...
Artículo
Short-term solar irradiance forecasting in streaming with deep learning
(Elsevier, 2023)
Solar energy is one of the most common and promising sources of renewable energy. In photovoltaic (PV) systems, operators can benefit from future solar irradiance predictions for efficient load balancing and grid stability. ...
Artículo
Temporal convolutional networks applied to energy-related time series forecasting
(MDPI, 2020)
Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting ...
Artículo
A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation
(Elsevier, 2017)
Breast cancer is the most common cause of cancer death in women. Today, post-transcriptional protein products of the genes involved in breast cancer can be identified by immunohistochemistry. However, this method has ...
Artículo
Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance
(Elsevier, 2020)
Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often ...