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Bayesian analysis of complex mutations in HBV, HCV, and HIV studies
This article provides a review of the Bayesian-inference-based methods applied to Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) studies with a focus on the detection of the viral mutations and various problems which are correlated to these mutations. The authors also provide a summary of the Bayesian methods' applications toward these viruses' studies, where several important and useful results have been discovered.
Date:
April 4, 2019
Creator:
Guo, Xuan; Liu, Bing; Feng, Shishi & Zhang, Jing
System:
The UNT Digital Library
Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network
This article develops a deep-learning algorithm for an on-loom fabric defect inspection system by combining the techniques of image pre-processing, fabric motif determination, candidate defect map generation, and convolutional neural networks (CNNs).
Date:
April 29, 2019
Creator:
Ouyang, Wenbin; Xu, Bugao; Hou, Jue & Yuan, Xiaohui
System:
The UNT Digital Library