Graph-based semi-supervised learning: A review
Y Chong, Y Ding, Q Yan, S Pan - Neurocomputing, 2020 - Elsevier
Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …
Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network
COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically
affects several countries such as the USA, Brazil, India and Italy. Numerous research units …
affects several countries such as the USA, Brazil, India and Italy. Numerous research units …
Tactile and vision perception for intelligent humanoids
Touch and vision perception are two important functions humans use to interact with the real
world. To mimic human‐like abilities, tactile‐and visual‐sensing‐based intelligent …
world. To mimic human‐like abilities, tactile‐and visual‐sensing‐based intelligent …
Shallow 3D CNN for detecting acute brain hemorrhage from medical imaging sensors
Successive layers in convolutional neural networks (CNN) extract different features from
input images. Applications of CNNs to detect abnormalities in the 2D images or 3D volumes …
input images. Applications of CNNs to detect abnormalities in the 2D images or 3D volumes …
Monitoring and surveillance of urban road traffic using low altitude drone images: a deep learning approach
In the contemporary era, the global explosion of traffic has created many eye-catching
concerns for policymakers. This not only enhances pollution but also leads to several road …
concerns for policymakers. This not only enhances pollution but also leads to several road …
Network anomaly detection using channel boosted and residual learning based deep convolutional neural network
N Chouhan, A Khan - Applied Soft Computing, 2019 - Elsevier
Anomaly detection in a network is one of the prime concerns for network security. In this
work, a novel Channel Boosted and Residual learning based deep Convolutional Neural …
work, a novel Channel Boosted and Residual learning based deep Convolutional Neural …
Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters
Abstract Multispectral LiDAR (Light Detection And Ranging) is characterized of the
completeness and consistency of its spectrum and spatial geometric data, which provides a …
completeness and consistency of its spectrum and spatial geometric data, which provides a …
CASA-based speaker identification using cascaded GMM-CNN classifier in noisy and emotional talking conditions
This work aims at intensifying text-independent speaker identification performance in real
application situations such as noisy and emotional talking conditions. This is achieved by …
application situations such as noisy and emotional talking conditions. This is achieved by …
An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet
G Marques, A Ferreras, I de la Torre-Diez - Multimedia tools and …, 2022 - Springer
Abstract Each year, more than 400,000 people die of malaria. Malaria is a mosquito-borne
transmissible infection that affects humans and other animals. According to World Health …
transmissible infection that affects humans and other animals. According to World Health …
Bearing fault classification based on convolutional neural network in noise environment
Q Jiang, F Chang, B Sheng - IEEE Access, 2019 - ieeexplore.ieee.org
Bearing fault diagnosis is an important technique in industrial production as bearings are
one of the key components in rotating machines. In bearing fault diagnosis, complex …
one of the key components in rotating machines. In bearing fault diagnosis, complex …