IEEE15d
Unsupervised Anomaly Detection in Hyperspectral Imaging: Integrating Tensor Robust Principal Component Analysis With Autoencoding Adversarial Networks
This paper proposes a novel approach for robust anomaly detection by integrating tensor robust principal component analysis (TRPCA) with autoencoding adversarial networks (AEAN). Our method utilizes ...
Frontiers18d
Multimodal medical image fusion using convolutional neural network and extreme learning machine
Experimental results indicate that, the proposed method is not only helpful to enhance the accuracy of the lesion detection and localization ... maximum choosing, principal component analysis (PCA) ...
dw1mon
New AI method improves early breast cancer detection
Conventional methods of cancer detection are based on detecting markers of a cancerous tumor itself. X-ray mammograms might be used to identify changes in breast tissue that indicate cancer ...
C&EN1mon
A Robust and Sensitive New Peak Detection and Identification Method for Mass Spectrometry-Based Differential Analysis in Biologics Characterization
Protein Analytical Chemistry, Genentech, A Member of the Roche Group, 1 DNA Way, South San Francisco, California 94080, United States ...
GitHub2mon
weed-detection
A curated collection of 45 high-quality RGB image datasets for computer vision in agriculture. Features datasets for weed detection, disease identification, and crop monitoring, focusing on natural ...
Frontiers7y
A Comparative Study on the Detection of Covert Attention in Event-Related EEG and MEG Signals to Control a BCI
In brain-computer interface (BCI) applications the detection of neural processing as ... An investigation of P300 components using spatial PCA has been published by Spencer et al. (2001). The methods ...