A human activity recognition framework for grossly labeled smartphone sensing data through combining genetic algorithm with multiple instance multiple label learning-Reference-Cited by-同舟云学术

A human activity recognition framework for grossly labeled smartphone sensing data through combining genetic algorithm with multiple instance multiple label learning

Published:2022-03-22 Issue:17 Volume:81 Page:24887-24911
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Rajak Sajan,Bose Debasish,Saha Anindita,Chowdhury Chandreyee^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-022-12261-z.pdf

Reference44 articles.

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2. Batool M, Jalal A, Kim K (2019) Sensors technologies for human activity analysis based on svm optimized by pso algorithm. In: 2019 International Conference on Applied and Engineering Mathematics (ICAEM). IEEE, pp 145–150

3. Bouaguel W (2016) A new approach for wrapper feature selection using genetic algorithm for big data. In: Intelligent and Evolutionary Systems. Springer, pp 75–83

4. Chen J, Sun Y, Sun S (2021) Improving human activity recognition performance by data fusion and feature engineering. Sensors 21(3):692

5. Chen L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42 (6):790–808

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