Publicacións nas que colabora con Miguel Damas (21)

2023

  1. A distributed and energy-efficient KNN for EEG classification with dynamic money-saving policy in heterogeneous clusters

    Computing, Vol. 105, Núm. 11, pp. 2487-2510

  2. Energy-Aware KNN for EEG Classification: A Case Study in Heterogeneous Platforms

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2022

  1. Ant colony optimization for feature selection via a filter-randomized search heuristic

    GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference

  2. Boosting the convergence of a GA-based wrapper for feature selection problems on high-dimensional data

    GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference

2021

  1. A lexicographic cooperative co-evolutionary approach for feature selection

    Neurocomputing, Vol. 463, pp. 59-76

  2. Energy-Time Profiling for Machine Learning Methods to EEG Classification

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. Performance Study of Ant Colony Optimization for Feature Selection in EEG Classification

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  4. Recurrent Neural Networks and Efficiency in High-Dimensional EEG Classification

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2020

  1. A parallel and distributed multi-population GA with asynchronous migrations: Energy-time analysis for heterogeneous systems

    GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

  2. Deep learning for EEG-based motor imagery classification: Accuracy-cost trade-off

    PLoS ONE, Vol. 15, Núm. 6

2019

  1. Energy-Time Analysis of Convolutional Neural Networks Distributed on Heterogeneous Clusters for EEG Classification

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Energy-aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU-GPU architectures

    Concurrency and Computation: Practice and Experience

  3. Time-energy analysis of multilevel parallelism in heterogeneous clusters: the case of EEG classification in BCI tasks

    Journal of Supercomputing, Vol. 75, Núm. 7, pp. 3397-3425

2018

  1. A power-performance perspective to multiobjective electroencephalogram feature selection on heterogeneous parallel platforms

    Journal of Computational Biology

  2. Multi-objective feature selection for EEG classification with multi-level parallelism on heterogeneous CPU-GPU clusters

    GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

  3. Speedup and energy analysis of EEG classification for BCI tasks on CPU-GPU clusters

    ACM International Conference Proceeding Series

2017

  1. Improving memory accesses for heterogeneous parallel multi-objective feature selection on eeg classification

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Issues on GPU parallel implementation of evolutionary high-dimensional multi-objective feature selection

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. Parallel high-dimensional multi-objective feature selection for EEG classification with dynamic workload balancing on CPU–GPU architectures

    Cluster Computing, Vol. 20, Núm. 3, pp. 1881-1897

  4. Power-performance evaluation of parallel multi-objective EEG feature selection on CPU-GPU platforms

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)