Jellyfish Recognition by Using Convolutional Neural Networks and Support Vector Machine

• Author(s): Aanchal Manikchandra Yadav ; Mithilesh Vishwakarma ; Riya Ganesh Sevekar
• Paper ID: 1705453
• Page: 10-16
• Published Date: 01-02-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 8 February-2024

Abstract

This research presents a comprehensive approach to jellyfish recognition employing Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). The study utilizes a CNN for deep learning-based feature extraction from images, leveraging transfer learning with the MobileNetV2 architecture. The CNN is trained on a diverse dataset of jellyfish species, using an Image Data Generator for data augmentation. Simultaneously, a traditional machine learning model, SVM, is employed to evaluate image features extracted via resizing and flattening. The SVM model is trained on a dataset comprising three distinct jellyfish species. Experimental results demonstrate the effectiveness of both models, with the CNN achieving high accuracy on the training dataset and the SVM demonstrating robust performance on a separate test set. Furthermore, a comparative analysis between the CNN and SVM models underscores the strengths and limitations of each approach. This integrated methodology offers a versatile solution for jellyfish recognition, combining the interpretability of SVMs with the representational power of CNNs.

Keywords

Jellyfish Recognition, Convolutional Neural Networks, Support Vector Machines, Image Classification, Comparative Analysis

Citations

IRE Journals:
Aanchal Manikchandra Yadav , Mithilesh Vishwakarma , Riya Ganesh Sevekar "Jellyfish Recognition by Using Convolutional Neural Networks and Support Vector Machine" Iconic Research And Engineering Journals Volume 7 Issue 8 2024 Page 10-16

IEEE:
Aanchal Manikchandra Yadav , Mithilesh Vishwakarma , Riya Ganesh Sevekar "Jellyfish Recognition by Using Convolutional Neural Networks and Support Vector Machine" Iconic Research And Engineering Journals, 7(8)