Learning and Optimisation
Focus:
The group aims to explore new methods and algorithms for collaborative learning and optimization to achieve synergistic effects. We also seek to promote seamless integration of learning and optimization techniques with real-time systems, cyber-physical systems, autonomous robotics, as well as process control and automation.
Our methodological research concerns: metaheuristics for learning, data driven learning in optimization, real-time learning, distributed learning, data reduction and feature mining, as well as reasoning under uncertainty.
We are also actively engaged in practical applications, to apply the new developed methods and algorithms to solve challenging problems in industrial and medical domains. The interesting application areas include (yet are not limited to) the following:
- Machine learning and optimization in power devices and power systems
- Real-time process monitoring (both in industry and health care) and anomaly detection
- Complex data analysis in biofeedback systems
- Process automation and networked control systems
- Behavior learning and control for autonomous robots
- Cyber security and safety analysis
| First Name | Last Name | Title |
|---|---|---|
| Francisco | Herrera | |
| Joaquin | Ballesteros | Post Doc |
| Johan | Hjorth | Doctoral student |
| Johan | Holmberg | Doctoral student |
| Johannes | Deivard | Research Assistant |
| Miguel | Leon Ortiz | Senior Lecturer |
| Ning | Xiong | Professor |
| Ruifang | Huo | Visiting Teacher |
| Sarala | Mohan | Industrial Doctoral Student |
| Sharmin Sultana | Sheuly | Doctoral student |
Comparative Evaluation of Machine Learning Algorithms for Network Intrusion Detection and Attack Classification (Jul 2022) Miguel Leon Ortiz, Tijana Markovic, Sasikumar Punnekkat International Conference on Neural Network (IJCNN)
Feature encoding with autoencoder and differential evolution for network intrusion detection using machine learning (Jul 2022) Miguel Leon Ortiz, Tijana Markovic, Sasikumar Punnekkat Genetic and Evolutionary Computation Conference (GECCO 2022)
Random Forest Based on Federated Learning for Intrusion Detection (Jun 2022) Tijana Markovic, Miguel Leon Ortiz, David Buffoni , Sasikumar Punnekkat International Converence on Artificial Intelligence Applications and innovations (AIAI 2022)
Federated fuzzy learning with imbalanced data (Dec 2021) Lukas Dust, Marina López Murcia, Petter Nordin , Andreas Mäkilä , Ning Xiong, Francisco Herrera IEEE Conference on Machine Learning and Applications 2021 (ICMLA'21)
BELIEF: A distance-based redundancy-proof feature selection method for Big Data (Feb 2021) D. López, Sergio Ramírez-Gallego, Salvador García, Ning Xiong, Francisco Herrera Information Sciences (INS21)
Interval Number-Based Safety Reasoning Method for Verification of Decentralized Power Systems in High-Speed Trains (Jan 2021) Peng Wu, Ning Xiong, Jinzhao Wu Mathematical problems in engineering (MPE21)
