This paper compares the performance of various popular convolutional neural network (CNN) architectures for image classification on the CIFAR10 dataset. The comparison includes CNN architectures such as Inception V3, Inception-ResNet-v2, ResNetV1, and V2, ResNeXt, MobileNet, and DenseNet, with the addition of two attention mechanisms - Convolutional Block Attention Module (CBAM), and Squeeze and Excitation (SE). CBAM and SE are believed to improve CNNs' performance, especially for complex images with multiple objects and backgrounds. The models are evaluated using loss and accuracy. The main focus of this study is to identify the most effective CNN architecture for image classification on the CIFAR10 dataset with attention mechanisms. The study aims to compare the accuracy of various CNN architectures with and without attention mechanisms and to identify the critical differences between these architectures in terms of their ability to handle complex images. The findings of this study could have implications for developing advanced CNN architectures that can potentially improve the accuracy of computer vision systems in various applications.

Spam comments pose a significant challenge in maintaining the quality of online platforms, including YouTube. In this paper, we propose a novel approach, the Graph-Enhanced Hierarchical Attention Network (GE-HAN), for spam comment detection. Our approach leverages the content of comments, the hierarchical structure of conversations, and user interactions to accurately classify comments as spam or non-spam. The GE-HAN model combines attention mechanisms to capture important textual features within comments and graph convolutional networks to analyze user interactions and detect coordinated spamming efforts. We trained and evaluated the model using a labeled dataset of spam comments, incorporating user names along with comment content and adjacency matrix representing user interactions. Experimental results demonstrate that the GE-HAN model achieves superior performance in spam detection, outperforming traditional methods. By considering both content and user dynamics, the model effectively identifies spam comments in YouTube, providing a robust solution to combat spam. Our research contributes to the field by showcasing the potential of graph-enhanced attention mechanisms in capturing complex patterns within comments and user interactions.