Tiny machine learning (TinyML) is a new frontier of machine learning. By squeezing deep
learning models into billions of IoT devices and microcontrollers (MCUs), we expand the …

Edge computing promising a vision of processing data close to its generation point, reducing
latency and bandwidth usage compared with traditional cloud computing architectures, has …

In recent years, the increasing demand to see the status of objects over the Internet leads to
an increase in the number of Internet of Things (IoT) applications. The unique nature of IoT …

This paper discusses the development of an IoT-based Congenital Heart Disease prediction
system to automate the process of predicting cardiovascular diseases. This system is based …

Smart wearable devices enable personalized at-home healthcare by unobtrusively
collecting patient health data and facilitating the development of intelligent platforms to …

The main objective of this paper is to highlight the research directions and explain the main
roles of current Artificial Intelligence (AI)/Machine Learning (ML) frameworks and available …

Edge computing has emerged as a pivotal paradigm shift in the realm of data processing
and analytics, revolutionizing the way organizations handle real-time data. This review …

Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis
or monitoring are based on expert knowledge and rule-centered algorithms. In recent years …

Recently, the application of bio-signals in the fields of health management, human–
computer interaction (HCI), and user authentication has increased. This is because of the …

The healthcare sector produces a large amount of data from disparate sources that have to
be processed and analysed to optimize their use. The fact that there are speedy datasets …