Publications by the researcher in collaboration with José Luis Calvo Rolle (42)


  1. A distributed topology for identifying anomalies in an industrial environment

    Neural Computing and Applications, Vol. 34, Núm. 23, pp. 20463-20476


  1. Outlier Generation and Anomaly Detection Based on Intelligent One-Class Techniques over a Bicomponent Mixing System

    Advances in Intelligent Systems and Computing

  2. Anomaly Detection on Patients Undergoing General Anesthesia

    Advances in Intelligent Systems and Computing

  3. A global classifier implementation for detecting anomalies by using one-class techniques over a laboratory plant

    Advances in Intelligent Systems and Computing

  4. A Hybrid One-Class Topology for Non-convex Sets

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

  5. Machine learning based method for the evaluation of the Analgesia Nociception Index in the assessment of general anesthesia

    Computers in Biology and Medicine, Vol. 118

  6. Lithium iron phosphate power cell fault detection system based on hybrid intelligent system

    Logic Journal of the IGPL, Vol. 28, Núm. 1, pp. 71-82

  7. Hybrid model for the ANI index prediction using Remifentanil drug and EMG signal

    Neural Computing and Applications, Vol. 32, Núm. 5, pp. 1249-1258

  8. Electromyogram prediction during anesthesia by using a hybrid intelligent model

    Journal of Ambient Intelligence and Humanized Computing, Vol. 11, Núm. 11, pp. 4467-4476

  9. Detección de anomalías basada en técnicas inteligentes de una planta de obtención de material bicomponente empleado en la fabricación de palas de aerogenerador

    Revista iberoamericana de automática e informática industrial ( RIAI ), Vol. 17, Núm. 1, pp. 84-93

  10. Comparative Study of One-Class Based Anomaly Detection Techniques for a Bicomponent Mixing Machine Monitoring

    Cybernetics and Systems, Vol. 51, Núm. 7, pp. 649-667

  11. Adaptive drug interaction model to predict depth of anesthesia in the operating room

    Biomedical Signal Processing and Control, Vol. 59