A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging.

Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck.

Contextual Correlation Preserving Multiview Featured Graph Clustering.

Graph clustering, which aims at discovering sets of related vertices in graph-structured data, plays a crucial role in various applications, such as social community detection and biological module discovery. With the huge increase in the volume of data in recent years, graph clustering is used in an increasing number of real-life scenarios. However, the classical and state-of-the-art methods, which consider only single-view features or a single vector concatenating features from different views and neglect the contextual correlation between pairwise features, are insufficient for the task, as features that characterize vertices in a graph are usually from multiple views and the contextual correlation between pairwise features may influence the cluster preference for vertices.

Identifying Key Opinion Leaders in Social Media via Modality-Consistent Harmonized Discriminant Embedding.

The digital age has empowered brands with new and more effective targeted marketing tools in the form of key opinion leaders (KOLs). Because of the KOLs' unique capability to draw specific types of audience and cultivate long-term relationship with them, correctly identifying the most suitable KOLs within a social network is of great importance, and sometimes could govern the success or failure of a brand's online marketing campaigns. However, given the high dimensionality of social media data, conducting effective KOL identification by means of data mining is especially challenging.