Citations and socials
Prof. David Gutiérrez Avilés, Ph.D. is a Computer Science Engineer (University of Seville, 2010), Master in Software Engineering and Technology (University of Seville, 2013), Ph.D. (University of Seville, 2015). He is an Assistant Professor in the Languages and Systems Department of the University of Seville.
His main scientific achievement is the TrLab methodology for mining and evaluating behavior patterns from large time-dependent datasets. This novel method extracts patterns from 3D large data using triclustering and genetic algorithms techniques. Through this research, several research productions and goals have been obtained: five JCR papers published, six conferences, a stay abroad in the University of Chile, belongings to one R&D team, a Regional project, and a National project; His Ph.D. thesis and intellectual property for the TrLab application.
The research lines of Prof. David Gutiérrez Avilés, Ph.D. are focused on: Electricity fraud detection in Big Data environments, On-line machine learning from Big data streaming, Analysis of Internet of Things protocols, and sensor data analysis.
Teaching
TUTORIALS:
Please request an appointment.
- Wednesday from 10:45h to 14:45h
- Friday from 10:45h to 12:45h
Publications
2022 |
A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture Journal Article Neurocomputing, in press , 2022. |
2021 |
Deformation forecasting of a hydropower dam by hybridizing a Long Short-Term Memory deep learning network with the Coronavirus Optimization Algorithm Journal Article Computer-Aided Civil and Infrastructure Engineering, in press , 2021. |
Discovering three-dimensional patterns in real-time from data streams: An online triclustering approach Journal Article Information Sciences, 558 , pp. 174-193, 2021. |
Nearest neighbours-based forecasting for electricity demand time series in streaming Conference Conference of the Spanish Association for Artificial Intelligence (CAEPIA'21), Lecture Notes in Artificial Intelligence 2021. |
2020 |
Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering Conference SOCO 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, Advances in Intelligent Systems and Computing 2020. |
Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model Journal Article Big Data, 8 (4), pp. 308-322, 2020. |
Automated Deployment of a Spark Cluster with Machine Learning Algorithm Integration Journal Article Big Data Research, 19-20 , pp. 100135, 2020. |
High-content screening images streaming analysis using the STriGen methodology Conference SAC 35th Annual ACM Symposium on Applied Computing, 2020. |
2019 |
Random Hyper-Parameter Search-Based Deep Neural Network for Power Consumption Forecasting Conference IWANN 15th International Work-Conference on Artificial Neural Networks, 11506 , Lecture Notes in Computer Science 2019. |
Real-Time Big Data Analytics in Smart Cities from LoRa-based IoT Networks Conference SOCO 14th International Conference on Soft Computing Models in Industrial and Environmental Applications, Advances in Intelligent Systems and Computing 2019. |
2018 |
TRIQ: a new method to evaluate triclusters Journal Article BioData Mining, 11 (1), pp. 15, 2018. |
SmartFD: A real big data application for electrical fraud detection Conference HAIS 13th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2018. |
2016 |
TRIQ: A Comprehensive Evaluation Measure for Triclustering Algorithms Conference Hybrid Artificial Intelligent Systems: 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016, Proceedings, Lecture Notes in Computer Science 2016. |
2015 |
MSL: A Measure to Evaluate Three-dimensional Patterns in Gene Expression Data Journal Article Evolutionary Bioinformatics, 11 , pp. 121—135, 2015. |
A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula Journal Article Entropy, 17 (7), pp. 5000-5021, 2015. |