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Data collection using deep reinforcement learning for serious games
Mild Cognitive Impairment (MCI) often occurs prior to the more serious condition of dementia and early detection of MCI is an important but challenging task because of its indistinct symptoms. Work has been done developing ...
Unsupervised deep anomaly detection in a recirculating aquaculture system
An unsupervised deep anomaly detection system is implemented to augment the water quality monitoring system used at a recirculating aquaculture system (RAS) facility. Its purpose is to increase the system’s anomaly detection ...
Using machine learning methods for wind turbine power curve modeling
Wind turbine power curve modeling plays an important role in wind energy management. Accurate estimation of power curves can help reducing power systems maintenance costs. In this thesis, we use machine learning techniques ...
Parametric inversion of experimental electromagnetic imaging data using synthetically-trained neural networks
Electromagnetic imaging is a method that aims to reconstruct a spatial map of the material properties of a target through non-invasive measurements, allowing an observer to see inside objects, such as the human body, ...
Cognitive vector quantization for malware detection
In today’s world, detection of malware is a prevalent challenge due to the evolving nature of malware designs and techniques. Many machine learning algorithms, such as ANNs, use supervised learning, which relies on a labeled ...