The diagnosis and quantification of Multiple Sclerosis (MS) have typically depended on
skilled doctors recognizing visual patterns, such as Magnetic Resonance Imaging (MRI) and …

The current disease risk prediction model with many parameters is complex to run smoothly
on mobile terminals such as tablets and mobile phones in imaginative elderly care …

Drug addiction (DA) is a long-term and relapsing brain disorder with limited effective
treatments. Electroencephalography (EEG) is a highly promising tool for investigating DA …

Abstract Conversational Artificial Intelligence (AI) has emerged as a promising technology in
the healthcare domain, facilitating interactive and personalized interactions between …

Wearable technologies and self-tracking healthcare apps are becoming increasingly
popular among people all over the world. Moreover, with the rapid increase in technological …

Abstract Convolutional Neural Networks have been widely applied in medical image
segmentation. However, the existence of local inductive bias in convolutional operations …

In the domain of medical image segmentation, traditional diffusion probabilistic models are
hindered by local inductive biases stemming from convolutional operations, constraining …

The development of a stroke prediction system using machine learning algorithms offers a
novel approach to identifying individuals at risk for stroke. By analyzing large datasets, it is …