Federated Learning (FL) is a decentralized machine learning paradigm that enables
collaborative model training across dispersed nodes without having to force individual …

Glycemic variability remains frequent in patients with type 1 diabetes treated with insulin
pumps. Heterogeneous spreads of insulin infused by pump in the subcutaneous (SC) tissue …

The problem of executing machine learning algorithms over data while complying with data
privacy is highly relevant in many application areas, including medicine in general and …

The increasing number of diabetic patients is a serious issue in society today, which has
significant negative impacts on people's health and the country's financial expenditures …

Glucose prediction can greatly benefit people with diabetes by allowing them to anticipate
and proactively manage changes in their glucose levels. In this paper, we propose a novel …

Newly diagnosed Type 1 Diabetes (T1D) patients often struggle to obtain effective Blood
Glucose (BG) prediction models due to the lack of sufficient BG data from Continuous …

Renewable energy research has become significant in the modern period owing to
escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions …

In the past few years, Federated Learning (FL) has emerged as an effective approach for
training neural networks (NNs) over a computing network while preserving data privacy …

Tato práce s názvem" Predikce glykémmie pro Diabetes Mellitus 1. typu: Kombinace
prediktivních modelů využivajících přímé a interované metody pro předpovídání více kroků …