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سی امین کنفرانس بین المللی مهندسی برق
Compare of Machine Learning and Deep Learning Approaches for Human Activity Recognition
نویسندگان :
Babak Moradi
1
Mohammad Aghapour
2
Afshin Shirbandi
3
1- دانشگاه آزاد اسلامی واحد کرمانشاه
2- دانشگاه امیرکبیر
3- دانشگاه امیرکبیر
کلمات کلیدی :
Human activity recognition،Logistic Regression،SVM with RBF kernel،CNN،LSTM،Bi-Directional LSTM،CNN-LSTM
چکیده :
Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. This research aimed to find the best algorithm for human activity recognition. We used Logistic Regression, SVM with RBF kernel; CNN, LSTM, Bi-Directional LSTM, and CNN-LSTM algorithms for analyzing the data. The data analysis measured and compared the accuracy and training time. The most accuracy belonged to the CNN-LSTM and Bi-Directional LSTM, and the least training time belonged to the SVM with RBF kernel
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