A deep learning model for mining object-energy correlations using social media image data

Matthew Dering, Chonghan Lee, Kenneth M. Hopkinson, Conrad S. Tucker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The authors of this work present a method that mines big media data streams from large Social Media Networks in order to discover novel correlations between objects appearing in images and electricity utilization patterns. The hypothesis of this work is that there exist correlations between what users take pictures of, and electricity utilization patterns. This work employs a Convolutional Neural Network to detect objects in 578,232 images gathered from over 15,000,000 tweets sent in the San Diego area. These objects were considered in the context of concurrent power use, on a monthly and hourly basis. The results reveal both positive and negative correlations between power use and specific objects, such as lamps(.053 hourly), dogs(-.011 hourly), horses(.422 monthly) and motorcycles(-.415, monthly).

Original languageEnglish (US)
Title of host publication38th Computers and Information in Engineering Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791851739
DOIs
StatePublished - Jan 1 2018
EventASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018 - Quebec City, Canada
Duration: Aug 26 2018Aug 29 2018

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume1B-2018

Other

OtherASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018
CountryCanada
CityQuebec City
Period8/26/188/29/18

Fingerprint

Social Media
Mining
Electricity
Motorcycles
Energy
Electric lamps
Neural networks
Data Streams
Model
Concurrent
Neural Networks
Object
Learning
Deep learning

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

Cite this

Dering, M., Lee, C., Hopkinson, K. M., & Tucker, C. S. (2018). A deep learning model for mining object-energy correlations using social media image data. In 38th Computers and Information in Engineering Conference (Proceedings of the ASME Design Engineering Technical Conference; Vol. 1B-2018). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC201885417
Dering, Matthew ; Lee, Chonghan ; Hopkinson, Kenneth M. ; Tucker, Conrad S. / A deep learning model for mining object-energy correlations using social media image data. 38th Computers and Information in Engineering Conference. American Society of Mechanical Engineers (ASME), 2018. (Proceedings of the ASME Design Engineering Technical Conference).
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Dering, M, Lee, C, Hopkinson, KM & Tucker, CS 2018, A deep learning model for mining object-energy correlations using social media image data. in 38th Computers and Information in Engineering Conference. Proceedings of the ASME Design Engineering Technical Conference, vol. 1B-2018, American Society of Mechanical Engineers (ASME), ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018, Quebec City, Canada, 8/26/18. https://doi.org/10.1115/DETC201885417

A deep learning model for mining object-energy correlations using social media image data. / Dering, Matthew; Lee, Chonghan; Hopkinson, Kenneth M.; Tucker, Conrad S.

38th Computers and Information in Engineering Conference. American Society of Mechanical Engineers (ASME), 2018. (Proceedings of the ASME Design Engineering Technical Conference; Vol. 1B-2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Dering M, Lee C, Hopkinson KM, Tucker CS. A deep learning model for mining object-energy correlations using social media image data. In 38th Computers and Information in Engineering Conference. American Society of Mechanical Engineers (ASME). 2018. (Proceedings of the ASME Design Engineering Technical Conference). https://doi.org/10.1115/DETC201885417