Automated detection and forecasting of covid-19 using deep learning techniques: A review
Abstract In March 2020, the World Health Organization (WHO) declared COVID-19 a global
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
Edge-guided recurrent positioning network for salient object detection in optical remote sensing images
Optical remote sensing images (RSIs) have been widely used in many applications, and one
of the interesting issues about optical RSIs is the salient object detection (SOD). However …
of the interesting issues about optical RSIs is the salient object detection (SOD). However …
Multi-modal medical image classification using deep residual network and genetic algorithm
Artificial intelligence (AI) development across the health sector has recently been the most
crucial. Early medical information, identification, diagnosis, classification, then analysis …
crucial. Early medical information, identification, diagnosis, classification, then analysis …
Multi-layer information fusion based on graph convolutional network for knowledge-driven herb recommendation
Y Yang, Y Rao, M Yu, Y Kang - Neural Networks, 2022 - Elsevier
Abstract Prescription of Traditional Chinese Medicine (TCM) is a precious treasure
accumulated in the long-term development of TCM. Artificial intelligence (AI) technology is …
accumulated in the long-term development of TCM. Artificial intelligence (AI) technology is …
An analysis on ensemble learning optimized medical image classification with deep convolutional neural networks
Novel and high-performance medical image classification pipelines are heavily utilizing
ensemble learning strategies. The idea of ensemble learning is to assemble diverse models …
ensemble learning strategies. The idea of ensemble learning is to assemble diverse models …
An imbalanced multifault diagnosis method based on bias weights AdaBoost
X Jiang, Y Xu, W Ke, Y Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Fault diagnosis plays an important role in ensuring process safety. It is noted that imbalance
between fault data and normal data always exists, and multifault obviously outranges a …
between fault data and normal data always exists, and multifault obviously outranges a …
Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing
Pre-processing is widely applied in medical image analysis to remove the interference
information. However, the existing pre-processing solutions mainly encounter two …
information. However, the existing pre-processing solutions mainly encounter two …
Conv-ervfl: Convolutional neural network based ensemble RVFL classifier for Alzheimer's disease diagnosis
As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the
following decades. Identifying AD at the intermediate stage became challenging, with mild …
following decades. Identifying AD at the intermediate stage became challenging, with mild …
Hybrid resampling and weighted majority voting for multi-class anomaly detection on imbalanced malware and network traffic data
L Xue, T Zhu - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
In a large skewed dataset, the data imbalance is severe and the classifier's accuracy is
biased towards the majority class. Insufficient data makes it challenging for the classifier to …
biased towards the majority class. Insufficient data makes it challenging for the classifier to …
MetaBoost: A novel heterogeneous DCNNs ensemble network with two-stage filtration for SAR ship classification
H Zheng, Z Hu, J Liu, Y Huang… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
Current synthetic aperture radar (SAR) ship classification research mainly focuses on
modifying deep convolutional neural networks (DCNNs) and injecting manual features on …
modifying deep convolutional neural networks (DCNNs) and injecting manual features on …