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- W4377700014 abstract "With several applications in the disciplines of healthcare, human-computer interaction, assistive learning, and many others, Human Actions Recognition, or HAR, is a trending research area. Despite the fact that this field has seen a lot of development over the past ten years, there is still a need to develop strong hybrid machine learning models for human actions recognition that must meet the objectives of the application, must have solid prediction and high recognition rate. A little work has been done in anomaly detection while performing an action. This paper presents a detailed literature survey in this area. Future directions are also presented based on the literature which shows that there is still a need of hybrid optimization technique to enhance the model's functionality." @default.
- W4377700014 created "2023-05-24" @default.
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- W4377700014 date "2023-04-07" @default.
- W4377700014 modified "2023-10-17" @default.
- W4377700014 title "Human Activity Recognition and Prediction: Overview and Research Gaps" @default.
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- W4377700014 doi "https://doi.org/10.1109/i2ct57861.2023.10126458" @default.
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