International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 4 July-August 2024 Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 4
July-August 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Endangered Bird Species Classification using Machine Learning

Author(s) Samina Hashmi, Ritik Rawat, Pushpendra Singh, Raj Singh Parihar, Om Prakash, Sudhakar Dwivedi
Country India
Abstract The conservation of endangered bird species is a critical aspect of biodiversity preservation. Traditional methods of identifying and classifying these species are often labor-intensive and time-consuming. In recent years, advancements in machine learning have offered promising alternatives for enhancing the accuracy and efficiency of such tasks. This paper explores the application of various machine learning algorithms to the classification of endangered bird species. By leveraging a dataset comprising images and audio recordings of bird calls, we train and evaluate models such as Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for audio classification. Our results demonstrate that these models can achieve high accuracy rates, significantly surpassing traditional methods. Furthermore, we discuss the importance of feature selection, data augmentation, and the integration of multimodal data in improving model performance. The findings underscore the potential of machine learning to revolutionize wildlife conservation efforts, providing a scalable and robust tool for the timely identification and protection of endangered bird species.
Keywords Keywords: Endangered Bird Species, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Biodiversity Conservation, Image Recognition, Audio Classification.
Field Engineering
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-26
Cite This Endangered Bird Species Classification using Machine Learning - Samina Hashmi, Ritik Rawat, Pushpendra Singh, Raj Singh Parihar, Om Prakash, Sudhakar Dwivedi - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.21292
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.21292
Short DOI https://doi.org/gtwmnj
View / Download PDF File

Share this


E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 4 Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com