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CO-WOA:用于深度学习的新型优化方法 鱼类图像的分类
Rabia Musheer Aziz1, Rajul Mahto2, Aryan Das2
1Mathematics division, School of Advanced Sciences and Languages, VIT Bhopal University, Kothrikalan, Sehore, 466116, M.P., India.
Chemistry & biodiversity
|July 2, 2023
概括
这项研究引入了一种先进的深度学习模型,用于准确的鱼类图像分类,达到100%的准确性. 这种方法超越了传统技术和其他领先的模型,用于识别鱼类和帮助检测疾病.
科学领域:
- 鱼类学和计算机视觉
- 人工智能在生物科学中的应用
背景情况:
- 准确的鱼类物种识别对于监测海鲜疾病和腐烂至关重要,因为症状在物种之间有很大差异.
- 传统的鱼类分类方法往往是缓慢和繁的,需要更高效的自动化方法.
- 了解鱼群分布和地理模式对于推进渔业科学至关重要.
研究的目的:
- 用先进的计算机视觉,数据挖掘和优化算法来确定鱼类图像分类的最佳策略.
- 开发和验证一种新的深度学习模型,用于高精度的鱼类物种识别.
- 将拟议模型的性能与已建立的深度学习架构进行比较.
主要方法:
- 利用混乱的对立基于鱼优化算法 (CO-WOA) 结合数据挖掘技术进行特征提取.
- 开发了一种用于图像分类的深度学习模型.
- 他将拟议的深度学习模型与卷积神经网络 (CNN),VGG-19,ResNet150V2,DenseNet,Inception V3和Xception进行了比较.
主要成果:
- 拟议的深度学习模型在鱼类图像分类中实现了100%的完美准确率.
- 拟议的方法显著优于其他最先进的模型,其准确度从98.48%到99.63%不等.
- 使用人工神经网络进行的经验验证证证实了拟议的深度学习模型的优势.
结论:
- 开发的拟议深度学习模型代表了对鱼类图像分类的高度有效和准确的解决方案.
- 这种人工智能驱动的方法比传统方法有了显著的改进,使得识别更快,更精确.
- 该模型的高精度对渔业管理,疾病监测和生物研究产生了重大影响.
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