To analyze the research hotspots on the application of machine learning methods in the field
of ergonomics, we collected 1141 articles related to machine learning methods in the field of …

Deep neural networks (DNNs), which learn a hierarchical representation of features, have
shown remarkable performance in big data analytics of remote sensing. However, previous …

Objective To investigate the accuracy of automated identification of cephalometric
landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms …

Multi-source remote sensing imagery has become widely accessible owing to the
development of data acquisition systems. In this paper, we address the challenging task of …

Accurate urban green space (UGS) measurement has become crucial for landscape
analysis. This paper reviews the recent technological breakthroughs in deep learning (DL) …

Wave runup observations are key data for understanding coastal response to storms. Lidar
scanners are capable of collecting swash elevation data at high spatial and temporal …

Semantic segmentation on high-resolution aerial images plays a significant role in many
remote sensing applications. Although the Deep Convolutional Neural Network (DCNN) has …

Semantic segmentation of high-resolution aerial images is a concerning issue of remote
sensing applications. To address the issues of intra-class heterogeneity and inter-class …

Semantic segmentation is an important issue in intelligent interpretation of remote sensing,
playing an important role in applications such as Earth observation and land data update …

Objective To investigate the accuracy of artificial intelligence‐assisted growth prediction
using a convolutional neural network (CNN) algorithm and longitudinal lateral …