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Abstract— Bird species detection plays a major role in the importance of ecology and in to monitor the conservation of bird species. A better and novel approach for bird species classification based on the features extracted from the bird images stored in the database. In the image, the bird might be in different angle, posture and sizes. In existing system, there are many methods that make use of the acoustic measure based on the data type of spectrogram such as Mel-Frequency Cepstral Coefficient. For our project, the proposed idea is to perform edge detection and extract the features required to predict the species of the bird using Gaussian Naive Byes (GNB) Approach. In the proposed system we used both K- Nearest Neighbor (KNN) and GNB to predict the bird species. Using KNN the accuracy was found to be 66%. While with GNB was around 88%. Hence the prediction is carried out using GNB approach
Keywords:
Bird Species, Features, Predict, Accuracy.
Cite Article:
"Bird Species Prediction Using Gaussian Naive Bayes Approach", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.3, Issue 11, page no.43 - 46, November-2018, Available :http://www.ijrti.org/papers/IJRTI1811008.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