论文标题
具有灰狼优化（GWO）的有效模式挖掘卷积神经网络（CNN）算法
An Efficient Pattern Mining Convolution Neural Network (CNN) algorithm with Grey Wolf Optimization (GWO)
论文作者
论文摘要
动态图像框架数据集中特征分析的自动化涉及正常和异常类的强度映射的复杂性。基于阈值的数据聚类和特征分析需要迭代模型，以了解多模式中的图像框架的组件，以了解不同的图像框架数据类型。本文提出了一种基于小波变换（CPWT）特征向量的曲折模式的新型特征分析方法，该模型通过灰狼优化（GWO）算法进行了优化。最初，图像框架通过将中位过滤器应用于降低噪声并在其上施加平滑的图像框架来归一化。由此，边缘信息代表图像框架中亮点的边界区域。基于神经网络的图像框架分类对功能进行重复学习，并对数据集的最小培训进行群集像素聚类。基于小波转换方法的复杂模型，以不同的特征提取模型模式分析了过滤后的图像框架的特征。这些功能代表了它的空间和纹理模式中不同类别的图像框架。卷积神经网络（CNN）分类器支持分析特征并对图像框架数据集进行操作标签进行分类。此过程通过最少数量的培训数据集增强了分类。可以通过与传统的最先进方法进行比较来验证此提出的方法的性能。
Automation of feature analysis in the dynamic image frame dataset deals with complexity of intensity mapping with normal and abnormal class. The threshold-based data clustering and feature analysis requires iterative model to learn the component of image frame in multi-pattern for different image frame data type. This paper proposed a novel model of feature analysis method with the CNN based on Convoluted Pattern of Wavelet Transform (CPWT) feature vectors that are optimized by Grey Wolf Optimization (GWO) algorithm. Initially, the image frame gets normalized by applying median filter to the image frame that reduce the noise and apply smoothening on it. From that, the edge information represents the boundary region of bright spot in the image frame. Neural network-based image frame classification performs repeated learning of the feature with minimum training of dataset to cluster the image frame pixels. Features of the filtered image frame was analyzed in different pattern of feature extraction model based on the convoluted model of wavelet transformation method. These features represent the different class of image frame in spatial and textural pattern of it. Convolutional Neural Network (CNN) classifier supports to analyze the features and classify the action label for the image frame dataset. This process enhances the classification with minimum number of training dataset. The performance of this proposed method can be validated by comparing with traditional state-of-art methods.
