POSITION
RAIS-WMOVE-ESR12
TITLE
Real Time Anomaly Detection
HOST INSTITUTION
Royal Institute of Technology (KTH), Sweden
SUPERVISORY COMMITTEE
Prof. Šarūnas Girdzijauskas/Prof. Christer Norström / Dr. Mohammed El-Beltagy
START DATE
2019-08-19
KEYWORDS
Feature Selection, Anomaly Detection Classification, Personalization, Uncertainity Quantification, Machine Learning
SHORT BIO
a)Bsc. Electronic Engineering at Polytechinic University of Tirana, Albania.
b)Msc. Smart System Integration at Heriot-Watt University, Edinburgh, UK.
RESEARCH INTERESTS
Machine Learning, Image/Signal Processing
RESEARCH
Machine learning is undoubtedly a revolutionary technology in closing the gap between personalized services and health care. At the moment, the research activity undertaken at the RAIS program is developing advanced (deep) machine learning systems capable of early prediction of potential deterioration of leg (knee) status. This continuous risk prediction of upcoming injuries, especially in athletes who have undergone in the past some knee surgery, could mitigate any re-occurrence of adverse events at the same time comforts the recovery period.
The current roadmap for this issue contains a combination of recurrent neural networks and model-based reinforcement learning. A follow-up within the scope of injury prevention is also a personalized guidance system able to provide continuous recommendations for potentially risky situations.
PUBLICATIONS
N/A