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José C. Riquelme

José C. Riquelme

D-Index & Metrics

Computer Science

D-Index
36
Citations
5528
World Ranking
11263
National Ranking
189

Overview

José C. Riquelme is affiliated with the University of Seville in Spain. Their research spans across the fields of Computer Science and Engineering, with a particular concentration on Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing, Management Science and Operations Research, and Computer Vision and Pattern Recognition.

Their scientific output covers a broad range of topics including:

  • Energy Load and Power Forecasting
  • Time Series Analysis and Forecasting
  • Data Stream Mining Techniques
  • Solar Radiation and Photovoltaics
  • Anomaly Detection Techniques and Applications
  • Stock Market Forecasting Methods
  • Advanced Neural Network Applications

José C. Riquelme has contributed to several recent papers, among which the following are notable:

  • "Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting," 2020, Applied Sciences
  • "Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting," 2020, Preprints.org
  • "Asynchronous dual-pipeline deep learning framework for online data stream classification," 2020, Integrated Computer-Aided Engineering
  • "Enhancing object detection for autonomous driving by optimizing anchor generation and addressing class imbalance," 2021, Neurocomputing
  • "Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance," 2020, Artificial Intelligence in Medicine

Frequent co-authors collaborating with José C. Riquelme include:

  • Manuel Carranza-García
  • José María Luna-Romera
  • Pedro Lara-Benítez
  • Jorge García-Gutiérrez
  • M. Martínez-Ballesteros

Their work has appeared repeatedly in certain publication venues, which include:

  • Applied Sciences
  • Integrated Computer-Aided Engineering
  • Neurocomputing
  • Heliyon
  • Paste/\x98P\x9caste

Best Publications

  • An Experimental Review on Deep Learning Architectures for Time Series Forecasting.

    Pedro Lara-Benítez;Manuel Carranza-García;José C. Riquelme

  • Incremental wrapper-based gene selection from microarray data for cancer classification

    Roberto Ruiz;José C. Riquelme;Jesús S. Aguilar-Ruiz

  • Energy Time Series Forecasting Based on Pattern Sequence Similarity

    Francisco Martinez Alvarez;A Troncoso;J C Riquelme;Jesus S Aguilar Ruiz

  • Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

    Pedro Lara-Benítez;Manuel Carranza-García;José M. Luna-Romera;José C. Riquelme

  • A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks

    Manuel Carranza-García;Jorge García-Gutiérrez;José C. Riquelme

  • A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

    Francisco Martínez-Álvarez;Alicia Troncoso;Gualberto Asencio-Cortés;José C. Riquelme

  • Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model.

    Francisco Martínez-Álvarez;Gualberto Asencio-Cortés;José F. Torres;David Gutiérrez-Avilés

  • Minería de Datos: Conceptos y Tendencias

    José C. Riquelme;Roberto Ruiz;Karina Gilbert;Área de Lenguajes

  • Preliminary comparison of techniques for dealing with imbalance in software defect prediction

    Daniel Rodriguez;Israel Herraiz;Rachel Harrison;Javier Dolado

  • Evolutionary learning of hierarchical decision rules

    J.S. Aguilar-Ruiz;J.C. Riquelme;M. Toro

  • An evolutionary algorithm to discover numeric association rules

    J. Mata;J. L. Alvarez;J. C. Riquelme

  • Advances in Artificial Intelligence — IBERAMIA 2002

    Francisco J. Garijo;José C. Riquelme;Miguel Toro

  • An evolutionary approach to estimating software development projects

    Jesús S. Aguilar-Ruiz;Isabel Ramos;José C. Riquelme;Miguel Toro

  • Finding representative patterns with ordered projections

    José C. Riquelme;Jesús S. Aguilar-Ruiz;Miguel Toro

  • Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities

    Rubén Pérez-Chacón;José M. Luna-Romera;Alicia Troncoso;Francisco Martínez-Álvarez

  • A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables

    J. García-Gutiérrez;F. Martínez-Álvarez;A. Troncoso;J.C. Riquelme

  • Local models-based regression trees for very short-term wind speed prediction

    A. Troncoso;S. Salcedo-Sanz;C. Casanova-Mateo;J.C. Riquelme

  • Mining Numeric Association Rules with Genetic Algorithms

    J. Mata;J. L. Alvarez;J. C. Riquelme

  • Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

    Francisco Fernández-Navarro;César Hervás-Martínez;Roberto Ruiz;Jose C. Riquelme

  • Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution

    M. Martínez-Ballesteros;A. Troncoso;F. Martínez-Álvarez;J. C. Riquelme

Frequent Co-Authors

Francisco Martínez-Álvarez
Francisco Martínez-Álvarez Pablo de Olavide University
Alicia Troncoso
Alicia Troncoso Pablo de Olavide University
Miguel A. Toro
Miguel A. Toro Technical University of Madrid
César Hervás-Martínez
César Hervás-Martínez University of Córdoba
Eduardo F. Camacho
Eduardo F. Camacho University of Seville
Sancho Salcedo-Sanz
Sancho Salcedo-Sanz University of Alcalá
Mario Piattini
Mario Piattini University of Castilla-La Mancha
Paul R. Cohen
Paul R. Cohen University of Pittsburgh
Jaume Bacardit
Jaume Bacardit Newcastle University
Francisco Herrera
Francisco Herrera University of Granada

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