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Learning models for concept extraction from images with drug labels for a unified knowledge base utilizing NLP and IoT tasks
S. Rajendran,
Published in IGI Global
2020
Volume: 15
   
Issue: 3
Pages: 18 - 33
Abstract
The evolution of humankind is through the exchange of information and extraction of knowledge from available information. The process of exchange of the information differs by the probability of the medium through which the information is exchanged. The Internet of things (IoT) contains millions of devices with sensors simultaneously transferring real time information to devices as rapid streams of data that need to be processed on the go. This leads to the need for development of effective and efficient approaches for segregating data based on class, relatedness, and differences in the information. The extraction of text from images is performed through tesseract irrespective of the language. SCIBERT models to extract scientific information and evaluating on a suite of tasks specially in classifying drugs based on free data (tweets, images, etc.). The images and text-based semantic similarity analysis provide similar drugs grouped together by composition or manufacturer. © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
About the journal
JournalInternational Journal of Information Technology and Web Engineering
PublisherIGI Global
ISSN15541045