Uncertainty-aware Deep Learning in digital healthcare
In this thesis, the objective is to investigate the use of Monte Carlo Dropout, a Bayesian Deep Learning technique, to make uncertainty prediction on Neural Network outputs when …
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In this thesis, the objective is to investigate the use of Monte Carlo Dropout, a Bayesian Deep Learning technique, to make uncertainty prediction on Neural Network outputs when …
Wearable devices, combined with Artificial Intelligence (AI) methods, can bring significant and sustainable improvements to our lives – from improved patient monitoring and …
Wearables facilitate continuous data collection to monitor diverse human behaviors, covering activity, health, and stress. This personal data, including electrocardiogram, …
Federated learning and its combination with differential privacy is the latest technique for building privacy-aware machine-learning models [1]. Its primary assumption – no data …
Federated learning (FL) is a state-of-the-art machine-learning technique developed by Google, where the users’ privacy is guaranteed by implementing one simple rule: “No personal …
Federated learning and its combination with differential privacy is the latest technique for building privacy-aware machine-learning models [1]. Its primary assumption – no data …
The rapid advancement of pervasive systems and wearables has paved the way for personal informatics systems, e.g., personal assistants and chatbots. These systems can be deployed …
The rapid advancement of pervasive systems and wearables has paved the way for personal informatics systems, e.g., personal assistants and chatbots. Such systems gear towards …
This thesis addresses the impact of lateralization on Electrodermal Activity (EDA) sensors in wearable devices. Lateralization, influenced by brain hemisphere activation, affects …