With the advancements of artificial intelligence technology, ChatGPT, a new practice of
artificial intelligence, holds immense potential across multiple fields. Its user-friendly human …

Recent developments in deep learning have enabled data-driven algorithms that can reach
human-level performance and beyond. The development and deployment of medical image …

Lightweight deep convolutional neural networks (LDCNNs) are vital components of mobile
intelligence, particularly in mobile vision. Although various heavy networks with increasingly …

Background and objectives Non-alcoholic fatty liver disease (NAFLD) is a common liver
disease with a rapidly growing incidence worldwide. For prognostication and therapeutic …

Recently, there has been a substantial interest in on-device Machine Learning (ML) models
to provide intelligence for the Internet of Things (IoT) applications such as image …

Tiny machine learning (TinyML) has become an emerging field according to the rapid
growth in the area of the internet of things (IoT). However, most deep learning algorithms are …

Abstract Knowledge distillation is the technique of compressing a larger neural network,
known as the teacher, into a smaller neural network, known as the student, while still trying …

Simple Summary Intelligent monitoring of endangered and rare wildlife using deep learning
is important for biodiversity conservation. The present study aims to train deep learning …

Mechanical intelligence diagnostic models based on deep learning have increased
diagnostic accuracy. However, the industrial application of deep-learning models is …

Fire hazards cause huge ecological, social and economical losses in day to day life. Due to
the rapid increase in the prevalence of fire accidents, it has become vital to equip the assets …