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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Recognition of Human Activity Using Machine Learning |
| Authors Name: | Dr. S. Aruna , T. Prapulla Naidu , U. Rajasree , G. Satya , T. Divya Chandini,K. Ashisha Vijalani |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_192093
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| Published Paper Id: | IJNRD2304457 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | http://doi.one/10.1729/Journal.33898 |
| Abstract: | The main purpose of this design is to achieve the certain exertion that is looking for by the system called Neural Networks which is like a heart in machine literacy. Device detectors give perceptivity into what persons are doing in real-time (walking, running, driving). Knowing the exertion of druggies allows, for case, to interact with them through an app. Artificial Neural Networks (ANNs) or Simulated Neural Networks are a subset of neural networks. Deep learning algorithms are built on the foundations of machine learning. Their name and structure are derived from the human brain, and they replicate the way organic neurons communicate with one another. The content of mortal exertion Recognition (HAR) is a prominent exploration area content in the field of computer vision and image processing area. It has empowered state- of- art operations in multiple sectors, including surveillance, digital entertainment, and medical healthcare. It is intriguing to observe and interesting to prognosticate similar kinds of movements. Several detector-grounded approaches have also been introduced to study and prognosticate mortal conditioning like accelerometer, gyroscope, etc., and it has their own advantages and disadvantages. In this paper, an intelligent mortal exertion recognition system is developed. Convolution Neural Networks (CNN) with spatiotemporal three-dimensional (3D) kernels are trained on a kinetics data set, which reflects people's actions in their daily lives and at work. As the notions of human activity recognition aid in understanding the concepts and issues of human action comprehension, which greatly aids in medication, management, learning patterns, and many circumstances involving video retrievals. |
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| Cite Article: | "Recognition of Human Activity Using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.e422-e427, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304457.pdf |
| Downloads: | 000118751 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2304457 Registration ID: 192093 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.33898 Page No: e422-e427 Country: Visakhapatnam, Andhra Pradesh, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304457 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304457 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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