Kernel Metadata Declaration — on demand

What the DOI identifies

BIRD SPECIES IDENTIFICATION USING DEEP LEARNING

10.46243/jst.2022.v7.i09.pp32-40 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2022-05-11

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 7 · no. 9 · pp. 32–40

Principal agents

• JOHN BENNET JOHN BENNET (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 13 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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    "abstract": {
        "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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            "unstructured": "Tóth, B.P. and Czeba, B., 2016, September. Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment. In CLEF (Working Notes) (pp. 560-568)"
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            "key": "ref2",
            "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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            "doi": "10.1007/978-3-030-17872-7_5",
            "unstructured": "Pradelle, B., Meister, B., Baskaran, M., Springer, J. and Lethin, R., 2017, November. Polyhedral Optimization of TensorFlow Computation Graphs. In 6th Workshop on Extreme scale Programming Tools (ESPT-2017) at The International Conference for High Performance Computing, Networking, Storage and Analysis (SC17)"
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            "doi": "10.1016/j.neunet.2012.02.023",
            "unstructured": "Cireşan, D., Meier, U. and Schmidhuber, J., 2012. Multi-column deep neural networks for image classification. arXiv preprint arXiv:1202.2745"
        },
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            "doi": "10.1109/smc.2013.740",
            "unstructured": "Andr´eia Marini, Jacques Facon and Alessandro L. Koerich Postgraduate Program in Computer Science (PPGIa) Pontifical Catholic University of Paran´a (PUCPR) Curitiba PR, Brazil 80215–901 Bird Species Classification Based on Color Features"
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            "key": "ref6",
            "unstructured": "Image Reco gnition with Deep Learning Techniques ANDREIPETRU BĂRAR, VICTOR-EMIL NEAGOE, NICU SEBE Faculty of Electronics, Telecommunications & Information Technology Polytechnic University of Bucharest"
        },
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            "key": "ref7",
            "doi": "10.1109/cvpr.2017.195",
            "unstructured": "Xception: Deep Learning with Depthwise Separable Convolutio ns François Chollet Google, Inc. Page | 40"
        },
        {
            "key": "ref8",
            "unstructured": "Zagoruyko, S. and Komodakis, N., 2016. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928"
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            "key": "ref9",
            "doi": "10.1609/aaai.v31i1.11231",
            "unstructured": "Inception-v4,Inception-ResNetand the Impact of Residual Connectionson Learning Christian Szegedy, Sergey Ioffe,Vincent Vanhoucke,Alexandrr A.Alemi"
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            "unstructured": "Stefan Kahl, Thomas Wilhelm-Stein, Hussein Hussein, Holger Klinck, Danny Kowerko, Marc Ritter, and Maximilian Eibl Large-Scale Bird Sound Classification using Convolutional Neural Networks"
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            "key": "ref12",
            "doi": "10.1007/s11633-017-1053-3",
            "unstructured": "Bo Zhao, Jias hi Feng Xiao Wu Shuicheng Yan A Survey on Deep Learning-based Fine grained Object Classification and Semantic Segmentation"
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            "key": "ref13",
            "doi": "10.1109/iccv.2011.6126546",
            "unstructured": "Yuning Chai Electrical Engineering Dept. ETH Zurich,Victor Lempitsky Dept. of Engineering Science University of Oxford, Andrew Zisserman Dept. of Engineering Science University of Oxford BiCoS:A BiSegmentation Method for Image Classification"
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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