Skyline Computation for Low-Latency Image-Activated Cell Identification

Because of breakthroughs in the field of deep learning, the accuracy of image classification and multimedia recognition in Artificial Intelligence (AI) research has improved rapidly. In this study, the objective is the classification of high-dimensional data, and, in particular, the screening of very rare entries from a large population. In general, the initial Figure 2: Examples of skylines in multidimensional spaces set of vectors is divided into several groups by using a (left: two dimensions and right: three dimensions); the yellow clustering method, and an outlier detection method identifies and blue points denote the skyline and non-skyline distinct entries such as noise. Our focus is on a new cognitive points, respectively. Serendipiter (Guo et al. 2017) is a fast cell sorter that discovers Points that are not dominated by the other points are called very rare cells with atypical ability from an enormous skyline or pareto-optimal points.