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Research Article

JCR. 2020; 7(19): 4595-4600


G. Aparna, V. Ravi Kishore, Dr. Ch.V.Raghavendran, SVVSR Kumar Pullela.

The species understanding is integral for ensuring biodiversity. Locating bird species is a challenging task often resulting in difficult levels. It is a difficult problem in identifying both humans and computers. To provide a handy tool for bird identification, we develop a deep learning platform and a tensor flow framework for pointing out. To develop such a system a bird data to set is required to classify an image. Winged animal pictures were ordered by a convolution neural system (CNN) to distinguish includes in the picture. The algorithm takes input image, assigns importance to various objects, and differentiates one from the other. In CNN, the Sigmoid function is used to obtain the probability of the image. Image converted to grayscale format and divided into certain pixels where more feature extraction takes place. After, the algorithm is trained good accuracy developed is the score sheet is obtained from it. The outcome acquired was of high effectiveness as the framework could undoubtedly distinguish flying creature animal groups from a picture transferred by the client.

Key words: Image recognition, convolution neural network, deep learning, web Application.

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