About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
IoT 24(1):

Editorial

Personalized recognition system in online shopping by using deep learning

Download86 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eetiot.4810,
        author={Manjula Devarakonda Venkata and Prashanth Donda and N. Bindu Madhavi and Pavitar Parkash Singh and A. Azhagu Jaisudhan Pazhani and Shaik Rehana Banu},
        title={Personalized recognition system in online shopping by using deep learning},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={IOT},
        year={2024},
        month={1},
        keywords={Customer emotions, conventional analysis, customer experience, Deep learning},
        doi={10.4108/eetiot.4810}
    }
    
  • Manjula Devarakonda Venkata
    Prashanth Donda
    N. Bindu Madhavi
    Pavitar Parkash Singh
    A. Azhagu Jaisudhan Pazhani
    Shaik Rehana Banu
    Year: 2024
    Personalized recognition system in online shopping by using deep learning
    IOT
    EAI
    DOI: 10.4108/eetiot.4810
Manjula Devarakonda Venkata1,*, Prashanth Donda2, N. Bindu Madhavi3, Pavitar Parkash Singh4, A. Azhagu Jaisudhan Pazhani5, Shaik Rehana Banu6
  • 1: Pragati Engineering College
  • 2: CVR College of Engineering
  • 3: Foundation University
  • 4: Lovely Professional University
  • 5: Ramco Institute of Technology
  • 6: Vasavi Institute of Management and Computer Science
*Contact email: dv.manjula@pragati.ac.in

Abstract

This study presents an effective monitoring system to watch the Buying Experience across multiple shop interactions based on the refinement of the information derived from physiological data and facial expressions. The system's efficacy in recognizing consumers' emotions and avoiding bias based on age, race, and evaluation gender in a pilot study. The system's data has been compared to the outcomes of conventional video analysis. The study's conclusions indicate that the suggested approach can aid in the analysis of consumer experience in a store setting.

Keywords
Customer emotions, conventional analysis, customer experience, Deep learning
Received
2023-10-31
Accepted
2024-01-02
Published
2024-01-10
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.4810

Copyright © 2024 M. D. Venkata et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL