International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 2 March-April 2024 Submit your research before last 3 days of April to publish your research paper in the issue of March-April.

Analysing Deep Learning and Machine learning Model for Cattle Recognition

Author(s) Amanpreet Kaur
Country India
Abstract In machine learning, feature extraction is a very important step in the construction of any pattern classification that extracts relevant features to identify the class from group of images. To recognizing object, accuracy depends upon the quality of features extracted from an image. Unique feature extraction has high accuracy in recognizing classes. In Deep learning image recognition is based on such as strong feature extraction ability and high recognition accuracy. In this paper we have discussed all the approaches used to recognize cattle from traditional to deep learning approaches, we have also analyzed the comparison of machine and deep learning approaches. We tried to explore few models of deep learning and its architecture that may result with high accuracy.
Keywords Alex NET, LeNET, YOLO, Machine Learning, Deep Learning
Field Computer Applications
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-06
Cite This Analysing Deep Learning and Machine learning Model for Cattle Recognition - Amanpreet Kaur - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.14325
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