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In recent times, the field of human activity recognition
(HAR) has witnessed significant advancements, particularly
through incorporation pertaining to Machine Learning (ML)
techniques. This survey paper explores the developments in HAR,
focusing on the implementation of Convolutional Neural
Networks (CNN). The usage of CNN in detecting human activities
holds immense potential for various domains, including senior
care, anomalous behavior identification, and surveillance systems.
The paper discusses the evolution from conventional machine
learning techniques to feature engineering and, ultimately, the
automatic feature extraction capabilities of CNN models. The
suggested framework aims to leverage CNN to forecast human
behaviors and identify anomalous activities in real time,
contributing to enhanced public safety.
"Recognition of Human Activity using CNN", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.128 - 133, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401022.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator