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Learning Models for Concept Extraction From Images With Drug Labels for a Unified Knowledge Base Utilizing NLP and IoT Tasks

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  • Sukumar Rajendran

    (Vellore Institute of Technology, Vellore, India)

  • Prabhu J.

    (Vellore Institute of Technology, Vellore, India)

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.

Suggested Citation

  • Sukumar Rajendran & Prabhu J., 2020. "Learning Models for Concept Extraction From Images With Drug Labels for a Unified Knowledge Base Utilizing NLP and IoT Tasks," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 15(3), pages 18-33, July.
    • Handle: RePEc:igg:jitwe0:v:15:y:2020:i:3:p:18-33
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