Conduct an internet search and select 10 different healthcare information systems (you choose). Create a table/chart that lists the systems and categorize them as either Clinical, Administrative, Management, or Financial information systems. Also, you must include in the table a column to identify the purpose of the system, the different functions/applications of the systems, and the healthcare setting that the system is utilized in. Finally, include a column that provides a justification of why the systems fall into the selected category.
An area of heavy debate within video summarization and recommendation literature is the tradeoff between low-level features and high-level features, the former expressing semantic properties of media content that are obtained from meta-information (e.g., plot, genre, director, actors), and the latter being extracted directly from the media file itself, typically representing design aspects of a movie (such as lighting, colors, and motion). This tradeoff naturally forms a semantic gap problem that has been discussed heavily in the literature. Much of the video summarization and recommendation literature is guided by the assumption that user preferences are influenced by high-level features to a greater extent than low-level features. For instance, 2.3.1 Low level features Recent literature on RSs suggest that consumer preferences when choosing an item are influenced in a greater deal by visual aspects of items and less by their semantic features. Deldjoo, Elahi, Quadrana, and Cremonesi (2018) use low-level visual features extracted using the MPEG-7 standard and a deep neural network (DNN). The MPEG-7 standard extracts visual descriptors of images as color descriptors and texture descriptors. Alternatively, the authors used the activation values of inner neurons of the GoogLeNet DNN as visual features for each key frame. Whereas MPEG-7 features capture stylistic descriptors (i.e., color and texture), DNN features capture semantic content (e.g, objects, people, etc.). In this study, MPEG-7 features generated more accurate recommendations than semantic featur>GET ANSWER