In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

Currently, with the rapid development of deep learning, deep neural networks (DNNs) have
been widely applied in various computer vision tasks. However, in the pursuit of …

With the widespread application of mobile edge computing (MEC), MEC is serving as a
bridge to narrow the gaps between medical staff and patients. Relatedly, MEC is also …

In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …

Human activity recognition (HAR) based on sensor networks is an important research
direction in the fields of pervasive computing and body area network. Existing researches …

The ubiquity of smartphones and the growth of computing resources, such as connectivity,
processing, portability, and power of sensing, have greatly changed people's lives. Today …

Deep Neural Network (DNN) has gained unprecedented performance due to its automated
feature extraction capability. This high order performance leads to significant incorporation …

Abstract Knowledge Distillation (KD) methods are capable of transferring the knowledge
encoded in a large and complex teacher into a smaller and faster student. Early methods …

Wearable sensor based Human Activity Recognition (HAR) has been widely used these
years. This paper proposed a novel deep learning model for HAR using inertial sensors …

Convolutional neural network (CNN)-based fault detection approaches based on
multisource signals have attracted increasing interest from the research community and …