Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

Heart disease is one of the significant challenges in today's world and one of the leading
causes of many deaths worldwide. Recent advancement of machine learning (ML) …

Recently, much effort has been put into solving arrhythmia classification problems with
machine learning-based methods. However, inter-heartbeat dependencies have been …

Cardiovascular disease is a major illness that causes human death, especially in the elderly.
Timely and accurate diagnosis of arrhythmia types is the key to early prevention and …

Drug–target interaction (DTI) prediction reduces the cost and time of drug development, and
plays a vital role in drug discovery. However, most of research does not fully explore the …

In today's world, heart disease is the leading cause of death globally. Researchers have
proposed various methods aimed at improving the accuracy and efficiency of the clinical …

Over the last decade, cardiovascular diseases (CVD's) are the leading cause of death
globally. Early prediction of CVD's can help in reducing the complications of high-risk …

Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including
healthcare, the adoption of the Transformers neural network architecture is rapidly changing …

Electrocardiogram (ECG) has been widely used for emotion recognition. This paper
presents a deep neural network based on convolutional layers and a transformer …