Will R. Thomas, Benjamin Galewsky, Sandeep Puthanveetil Satheesan, Gregory Jansen, Richard Marciano, Shannon Bradley, Jong Lee, Luigi Marini and Kenton McHenry
those which would be available with manual labels alone.
Convolutional Neural Networks (CNNs) ( LeCun, Kavukcuoglu, and Farabet, 2010 ) are modeled after mammalian vision, where invariant features of the visual environment that mark a significant event (such as the movement of a predator or prey across the field of vision) are recognized amid the range of incoming stimuli. Its function is to take a first pass at extracting features. It is attempting to recognize characters of text by recognizing the invariant features characteristic of letters in the words.
.e., patent or literature citation) for each of the retrieved patents were then downloaded.
Step 2. Build a Patent Network Using Text Similarities
This study utilized text-based document analysis to measure similarities among documents. Documents with a significant impact were identified through the following steps: collecting the titles and abstracts of a patent set, extracting the term vectors of each document through lower case conversion, removing numbers and punctuation, singularizing and synonymizing the text, eliminating stop words, and forming l -level
information sensitivity based on general information types, it is more appropriate to measure information sensitivity by taking users’ perception of sensitivity into account. Some scholars define information sensitivity as the perceived intimacy level of information ( Lwin, Wirtz, & Williams, 2007 ). More intimate information is perceived as riskier to disclose because it may lead to potential losses, including psychological (e.g., loss of self-esteem), physical (e.g., loss of health), and material (e.g., loss of property and assets) aspects ( Moon, 2000 ). Therefore, this