Machine Learning-Based Automatic Tag Generation Using CNN Algorithm

Manishkumar Sharma, Deepak Mane
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Abstract

CNNs attempt to capture this pattern using a conceptual network, which represents the knowledge as nodes connected in automatic tag generation. The main advantage of using CNN over other traditional methods is its ability to learn from examples rather than being given a predefined set of rules. The introduction of automatic tag generation using CNN algorithms is described here. The materials that have been used for image detection and tag generation is proceeding with the multiple processes of image detection. YOLO algorithms is used for the experiment, which is critically analyzed. Result describe the experiment outcome of the tagged image, which helps to describe the conclusion.
基于机器学习的CNN算法自动标签生成
cnn试图使用概念网络捕捉这种模式,该网络将知识表示为自动标签生成中连接的节点。与其他传统方法相比,使用CNN的主要优势在于它能够从示例中学习,而不是给定一组预定义的规则。本文介绍了使用CNN算法自动生成标签的方法。已经用于图像检测和标签生成的材料正在进行图像检测的多个过程。实验中使用了YOLO算法,并对其进行了批判性分析。结果描述了标记图像的实验结果,有助于描述结论。
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