Smart Surveillance System and Prediction of Abnormal Activity in ATM Using Deep Learning-Reference-Cited by-同舟云学术

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Smart Surveillance System and Prediction of Abnormal Activity in ATM Using Deep Learning

Published:2023-11-03 Issue: Volume: Page:143-154
ISSN:2367-3370
Container-title:Data Science and Network Engineering
language:
Short-container-title:

Author:

Gnanavel S.^ORCID,Duraimurugan N.^ORCID,Jaeyalakshmi M.^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-6755-1_11

Reference16 articles.

1. Nazabal A, García-Moreno P, Artes-Rodríguez A, Ghahramani Z (2016) Human activity recognition by combining a small number of classifiers. IEEE J Biomed Health Inform 20

2. Ding C, Hong H, Zou Y, Chu H, Zhu X, Fioranelli F, Le Kernec J (2019) Continuous human motion recognition with a dynamic range-doppler trajectory method based on FMCW radar. IEEE Trans Geosci Remote Sens 57

3. Tao D, Lianwen J, Yuan Y, Xue Y (2016) Ensemble manifold rank preserving for acceleration-based human activity recognition. IEEE Trans Neural Netw Learn Syst 27

4. Rosique F, Losilla F, Navarro PJ (2021) Using artificial vision for measuring the range of motion. IEEE Lat Am Trans 19

5. Poh GS, Gope P; Ning J (2019) PrivHome: privacy-preserving authenticated communication in smart home environment. IEEE Trans Dependable Secur Comput 18

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