Prediction of the taxonomical classification of the Ranunculaceae family using a machine learning method

• Affiliation of Author(s):西南交通大学
• Journal:Machine Learning, Multi Agent and Cyber Physical Systems: Proceedings of the 15th International FLINS Conference (FLINS 2022)
• Place of Publication:Tianjin, China
• Key Words:Action recognition; transformer; deep learning
• Abstract:Human action recognition (HAR) has received extensive attention in artificial intelligence today. In view of the huge advantages of transformers in capturing global context information and extracting effective features compared with traditional deep neural networks, in this paper, we propose to utilize transformer to solve HAR problems and to our knowledge, this is the first time to solve HAR problems by transformer in sensor datasets. Experiments on 12 real sensor datasets with three evaluation metrics demonstrate the superiority of using transformer against four classic or state-of-the-art models.
• Co-author:Jian Wu,Yueying Li,Ran Hao
• First Author:Kehan Wu
• Indexed by:Vol.
• Correspondence Author:Hongjun Wang
• Discipline:Engineering
• Document Type:C
• Page Number:450-456
• Translation or Not:no