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, robotics, as well as process control and automation.
Our methodological research concerns: metaheuristics for learning, data driven learning in optimization, real-time and continuous 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
- Smart and resillient digital twins for distributed systems
- Cyber attack identification and mitigation
DAT (Dec 2024) Ali Asghar Sharifi, Ali Zoljodi , Masoud Daneshtalab Sensors (MDPI Sensors)
TrajectoryNAS (Sep 2024) Ali Asghar Sharifi, Ali Zoljodi , Masoud Daneshtalab Sensors (SENSC9)
Contrastive Learning for Lane Detection via cross-similarity (Sep 2024) Ali Zoljodi , Sadegh Abadijou , Mina Alibeigi , Masoud Daneshtalab Pattern Recognition (PR)
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 Networks (IJCNN 22)
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)
