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    "title": "BIRD SPECIES IDENTIFICATION USING DEEP LEARNING",
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            "value": "Now a day some bird species are being found rarely and if found classification of bird species prediction is difficult. Naturally, birds present in various scenarios appear in different sizes, shapes, colors, and angles from human perspective. Besides, the images present strong variations to identify the bird species more than audio classification. Also, human ability to recognize the birds through the images is more understandable. So this m ethod uses the Caltech-UCSD Birds 200 [CUB -200-2011] dataset for training as well as testing purpose. By using deep convolutional neural network (DCNN) algorithm an image converted into grey scale format to generate autograph by using tensor flow, where th e multiple nodes of comparison are generated. These different nodes are compared with the testing dataset and score sheet is obtained from it. After analyzing the score sheet it can predicate the required bird species by using highest score. Experimental a nalysis on dataset (i.e. Caltech -UCSD Birds 200 [CUB -2002011]) shows that algorithm achieves an accuracy of bird identification between 80% and 90%.The experimental study is done with the Ubuntu 16.04 operating system using a Tensor flow library",
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                "doi": "10.1155/2007/38637",
                "unstructured": "Fagerlund, S., 2007. Bird species recognition using support vector machines. EURASIP Journal on Applied Signal Processing, 2007(1), pp.64-64"
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                "unstructured": "Xception: Deep Learning with Depthwise Separable Convolutio ns François Chollet Google, Inc. Page | 40"
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