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Data Driven Techniques for Organizing Scientific Articles Relevant to Biomimicry

Published: 27 December 2018 Publication History

Abstract

Life on earth presents elegant solutions to many of the challenges innovators and entrepreneurs across disciplines face every day. To facilitate innovations inspired by nature, there is an emerging need for systems that bring relevant biological information to this application-oriented market. In this paper, we discuss our approach to assembling a system that uses machine learning techniques to assess a scientific article's potential usefulness to innovators, and classifies these articles in a way that helps innovators find information relevant to the challenges they are attempting to solve.

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  • (2024)Combining BioTRIZ and Multi-Factor Coupling for Bionic Mechatronic System DesignApplied Sciences10.3390/app1414602114:14(6021)Online publication date: 10-Jul-2024
  • (2020) A co‐training ‐based approach for the hierarchical multi‐label classification of research papers Expert Systems10.1111/exsy.1261338:4Online publication date: 24-Aug-2020

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    cover image ACM Conferences
    AIES '18: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
    December 2018
    406 pages
    ISBN:9781450360128
    DOI:10.1145/3278721
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    Published: 27 December 2018

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    Author Tags

    1. biomimicry
    2. machine learning

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    AIES '18: AAAI/ACM Conference on AI, Ethics, and Society
    February 2 - 3, 2018
    LA, New Orleans, USA

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    AIES '18 Paper Acceptance Rate 61 of 162 submissions, 38%;
    Overall Acceptance Rate 61 of 162 submissions, 38%

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    • (2024)Combining BioTRIZ and Multi-Factor Coupling for Bionic Mechatronic System DesignApplied Sciences10.3390/app1414602114:14(6021)Online publication date: 10-Jul-2024
    • (2020) A co‐training ‐based approach for the hierarchical multi‐label classification of research papers Expert Systems10.1111/exsy.1261338:4Online publication date: 24-Aug-2020

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