Identifying Users based on their Activity Pattern using Machine Learning
Author:
Affiliation:
1. Pattern Processing Lab/School of Computer Science and Engineering, The University of Aizu, Japan
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3592307.3592312
Reference25 articles.
1. Ehatisham-ul-Haq M. Malik M. Azam M. Naeem U. Khalid A. and Ghazanfar M. 2020. Identifying Users with Wearable Sensors based on Activity Patterns. Procedia Computer Science 177 pp.8-15. Ehatisham-ul-Haq M. Malik M. Azam M. Naeem U. Khalid A. and Ghazanfar M. 2020. Identifying Users with Wearable Sensors based on Activity Patterns. Procedia Computer Science 177 pp.8-15.
2. Kim J. Lee K. and Hong S. 2017. Random forest based-biometric identification using smart shoes. 2017 Eleventh International Conference on Sensing Technology (ICST) pp.1-4. Kim J. Lee K. and Hong S. 2017. Random forest based-biometric identification using smart shoes. 2017 Eleventh International Conference on Sensing Technology (ICST) pp.1-4.
3. Ahmad M. Alqarni M. Khan A. Khan A. Hussain Chauhdary S. Mazzara M. Umer T. and Distefano S. 2018. Smartwatch-Based Legitimate User Identification for Cloud-Based Secure Services. Mobile Information Systems 2018 pp.1-14. Ahmad M. Alqarni M. Khan A. Khan A. Hussain Chauhdary S. Mazzara M. Umer T. and Distefano S. 2018. Smartwatch-Based Legitimate User Identification for Cloud-Based Secure Services. Mobile Information Systems 2018 pp.1-14.
4. Kumar P. and Suresh S. 2021. Smartphone User Identification and Authentication Based on Raw Accelerometer Walking Activity Data Using Convolutional Neural Networks. Algorithms for Intelligent Systems pp.35-48. Kumar P. and Suresh S. 2021. Smartphone User Identification and Authentication Based on Raw Accelerometer Walking Activity Data Using Convolutional Neural Networks. Algorithms for Intelligent Systems pp.35-48.
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