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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM38596

SURVEY ON AI BASED DOG BREED PREDICTION MODEL AND VIRTUAL CANINE HUB

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM38596

Volume:

8 |

11 |

Nov 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM38596

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PJB

Prof. Jaitee Bankar

AN

Atharva Nalawade

SW

Shriyas Wali

HM

Harshad Matlani

AR

Ankita Rane

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Abstract

Abstract—This research focuses on the difficulties that dog enthusiasts face when trying to accurately and efficiently identify different canine breeds. Recognizing the challenges of obtaining breed information di- rectly from dog owners, a new solution is proposed: an automated system that can be accessed through a user-friendly website. This system utilizes image recognition technology, allowing users to easily upload a photo of a dog and accurately determine its breed. In addition to breed identifica- tion, the platform offers comprehensive information about temperament, care requirements, and other unique characteristics of each breed, pro- viding a holistic understanding. The objective is to empower potential dog owners to make well-informed decisions that align with their lifestyle and preferences. Specifically tailored for the United States market and available in English, this resource aims to assist dog lovers nationwide in selecting and caring for their pets by providing detailed and easily – accessible information.

KEYWORDS

User-friendly Temperament Holistic

Cite This Article

Prof. Jaitee Bankar, Atharva Nalawade, Shriyas Wali, Harshad Matlani, Ankita Rane, "SURVEY ON AI BASED DOG BREED PREDICTION MODEL AND VIRTUAL CANINE HUB," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 11, Nov 2024. https://doi.org/10.55041/IJSREM38596

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Publication Date

11/10/2024

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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