Article utilizing active learning and support vector machine (SVM) algorithms to classify facial action units (AU) for human facial expression recognition. Experimental results show that the proposed algorithm can effectively suppress correlated noise and achieve higher recognition rates than principal component analysis and a human observer on seven different facial expressions.

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.