The evolution of low-cost embedded systems is growing exponentially; likewise, their use in
robotics applications aims to achieve critical task execution by implementing sophisticated …

In recent years, there has been a growing demand for affordable and user-friendly medical
diagnostic devices due to the rise in global diseases. This study focuses on the development …

Modern methods in artificial intelligence perform very well on many healthcare datasets, at
times outperforming trained doctors. However, many assumptions made in model training …

Cardiovascular disease (CVD) continues to be a major global health concern, underscoring
the need for advancements in medical care. The use of electrocardiograms (ECGs) is crucial …

In this study, an ensemble model of U-Net and artificial neural network (ANN) is developed
for electrocardiogram (ECG)-based human identification. This study specifically presents the …

The risk-informed safety margin characterization (RISMC) method utilizes a probabilistic
safety margin (PSM) comparison between a load and capacity distribution, rather than a …

Federated learning is a framework in machine learning that allows for training a model while
maintaining data privacy. Moreover, it allows clients with their own data to collaborate in …

The emerging modern wireless communication networks and systems such as mobile
networks and Internet of Things (IoT) results in generating big data and enabling …

We utilized comprehensive databases containing various arrhythmia classes and heart
disease types to develop and evaluate the system. By leveraging the power of Machine …

In this paper, our team's Autonomous Surface Vehicle (ASV) strategy and research results
are described for the completion of six missions in the International Roboboat Competition …