Vincent C. S. Lee

TL;DR: This paper compares the new fraud detection method (meta-learning approach) against C4.5 trained using undersampling, oversamplings, and SMOTEing without partitioning, and shows that, given a fixed decision threshold and cost matrix, the partitioning and multiple algorithms approach achieves marginally higher cost savings than varying the entire training data set with different class distributions.

TL;DR: A structured framework to harvest civilian sentiment and response on Twitter during terrorism scenarios, based on observations of Twitter’s role in civilian response during the recent 2009 Jakarta and Mumbai terrorist attacks is proposed.

TL;DR: In this article, the information of pavement cracks is extracted from pavement crosstalk and the information is used to determine the cause of pavement cracking. But, this is difficult to be done accurately.

TL;DR: It is demonstrated that faster R‐CNN can automatically locate crack from raw images, even with the presence of handwriting scripts, as well as compared with You Only Look Once v2 detection technique.

TL;DR: The findings reveal a pattern behind trends on Twitter, enabling us to see how it 'ticks' and evolves though visualization methods, and enable us to understand the underlying characteristics behind the 'trend setters', providing a new perspective on the contributors of a trend.