Using text analytics to assess state-of-the-art machine learning in manufacturing

Juxihong Julaiti, Seifu Chonde, Soundar Kumara

Research output: Contribution to conferencePaperpeer-review

Abstract

Literature review is crucial for any study because researchers can discover what knowledge, problems and methodologies exist related to the topic. However, it is time consuming to summarize and find helpful articles to conduct the research because of the large amount of articles and search results may not always be good enough. A method is proposed to score and rank articles, so that potentially influential articles can be found in a shorter time. Using scores of articles and author-provided keywords (APKs), a weighted network can be built to visualize methods and problems. Given a problem, the weighted APKs network highlights suitable benchmarking methods, and vice versa. We implemented this methodology to analyze applications of machine learning in manufacturing. Popular techniques and problems and a list of recommended articles are provided as results of experimentation. The results also show that neural networks and support vector machine are most commonly used, and there is not article used random forest.

Original languageEnglish (US)
Pages519-524
Number of pages6
StatePublished - 2020
Event2016 Industrial and Systems Engineering Research Conference, ISERC 2016 - Anaheim, United States
Duration: May 21 2016May 24 2016

Conference

Conference2016 Industrial and Systems Engineering Research Conference, ISERC 2016
Country/TerritoryUnited States
CityAnaheim
Period5/21/165/24/16

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Fingerprint

Dive into the research topics of 'Using text analytics to assess state-of-the-art machine learning in manufacturing'. Together they form a unique fingerprint.

Cite this