Heart sound auscultation has been applied in clinical usage for early screening of
cardiovascular diseases. Due to the high demand for auscultation expertise, automatic …

Intelligent applications supported by Machine Learning have achieved remarkable
performance rates for a wide range of tasks in many domains. However, understanding why …

Federated learning (FL) has recently emerged as a compelling machine learning paradigm,
prioritizing the protection of privacy for training data. The increasing demand to address …

Efficiently executing inference tasks of deep neural networks on devices with limited
resources poses a significant load in IoT systems. To alleviate the load, one innovative …

AI techniques for cybersecurity are advancing, but AI-based classifiers are suspectable of
adversarial attacks. It is challenging to quantify the efforts required of an adversary to …

Recent advancements in recommender systems have focused on integrating knowledge
graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced …

Currently, convolutional neural networks have demonstrated outstanding efficiency in heart
sound detection and automatic diagnosis of cardiovascular diseases. However, due to the …

In recent years, there has been a notable increase in the deployment of machine learning
(ML) models as services (MLaaS) across diverse production software applications. In …

Heart sound auscultation has been applied in clinical usage for early screening of
cardiovascular diseases. Due to the high demand for auscultation expertise, automatic …

Federated learning (FL) enables privacy-preserving model training but faces growing
demands for unlearning–removing specific training data to enforce privacy rights and …