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The idea came from the need for their families to compress and send large documents via email. [5] The initial version of the site featured a standalone PDF compression tool. [6] Since then, the online platform has introduced over 16 PDF tools to convert, compress and edit PDF documents. [7]
By operation of the pigeonhole principle, no lossless compression algorithm can shrink the size of all possible data: Some data will get longer by at least one symbol or bit. Compression algorithms are usually effective for human- and machine-readable documents and cannot shrink the size of random data that contain no redundancy. Different ...
The most widely used lossy compression algorithm is the discrete cosine transform (DCT), first published by Nasir Ahmed, T. Natarajan and K. R. Rao in 1974. Lossy compression is most commonly used to compress multimedia data (audio, video, and images), especially in applications such as streaming media and internet telephony. By contrast ...
Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points.
According to Lindo Bacon, in Health at Every Size (2008), the basic premise of HAES is that "well-being and healthy habits are more important than any number on the scale." [7] Emily Nagoski, in her book Come as You Are (2015), promoted the idea of Health at Every Size for improving women's self-confidence and sexual well-being. [8] [page needed]
There are many forms of PHI, with the most common being physical storage in the form of paper-based personal health records (PHR). Other types of PHI include electronic health records, wearable technology, and mobile applications. In recent years, there has been a growing number of concerns regarding the safety and privacy of PHI.
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