Jianjun Wei

Weijie He, Runyuan Bao, Yiru Cang et al.

This paper delves into the challenges and advancements in the field of medical image segmentation, particularly focusing on breast cancer diagnosis. The authors propose a novel Transformer-based segmentation model that addresses the limitations of traditional convolutional neural networks (CNNs), such as U-Net, in accurately localizing and segmenting small lesions within breast cancer images. The model introduces an axial attention mechanism to enhance the computational efficiency and address the issue of global contextual information that is often overlooked by CNNs. Additionally, the paper discusses improvements tailored to the small dataset challenge, including the incorporation of relative position information and a gated axial attention mechanism to refine the model's focus on relevant features. The proposed model aims to significantly improve the segmentation accuracy of breast cancer images, offering a more efficient and effective tool for computer-aided diagnosis.

Xu Yan, Yaoting Jiang, Wenyi Liu et al.

This paper introduces a novel spatiotemporal feature representation model designed to address the limitations of traditional methods in multidimensional time series (MTS) analysis. The proposed approach converts MTS into one-dimensional sequences of spatially evolving events, preserving the complex coupling relationships between dimensions. By employing a variable-length tuple mining method, key spatiotemporal features are extracted, enhancing the interpretability and accuracy of time series analysis. Unlike conventional models, this unsupervised method does not rely on large training datasets, making it adaptable across different domains. Experimental results from motion sequence classification validate the model's superior performance in capturing intricate patterns within the data. The proposed framework has significant potential for applications across various fields, including backend services for monitoring and optimizing IT infrastructure, medical diagnosis through continuous patient monitoring and health trend analysis, and internet businesses for tracking user behavior and forecasting sales. This work offers a new theoretical foundation and technical support for advancing time series data mining and its practical applications in human behavior recognition and other domains.

Bingyao Liu, Jiajing Chen, Rui Wang et al.

The development of Internet technology has led to a rapid increase in news information. Filtering out valuable content from complex information has become an urgentproblem that needs to be solved. In view of the shortcomings of traditional manual classification methods that are time-consuming and inefficient, this paper proposes an automaticclassification scheme for news texts based on deep learning. This solution achieves efficient classification and management of news texts by introducing advanced machine learning algorithms, especially an optimization model that combines Bi-directional Long Short-Term Memory Network (Bi-LSTM) and Attention Mechanism. Experimental results show that this solution can not only significantly improve the accuracy and timeliness of classification, but also significantly reduce the need for manual intervention. It has important practical significance for improving the information processing capabilities of the news industry and accelerating the speed of information flow. Through comparative analysis of multiple common models, the effectiveness and advancement of the proposed method are proved, laying a solid foundation for future news text classification research.

Jianjun Wei, Yue Liu, Xin Huang et al.

This paper explores the applications and challenges of graph neural networks (GNNs) in processing complex graph data brought about by the rapid development of the Internet. Given the heterogeneity and redundancy problems that graph data often have, traditional GNN methods may be overly dependent on the initial structure and attribute information of the graph, which limits their ability to accurately simulate more complex relationships and patterns in the graph. Therefore, this study proposes a graph neural network model under a self-supervised learning framework, which can flexibly combine different types of additional information of the attribute graph and its nodes, so as to better mine the deep features in the graph data. By introducing a self-supervisory mechanism, it is expected to improve the adaptability of existing models to the diversity and complexity of graph data and improve the overall performance of the model.

Wenyi Liu, Rui Wang, Yuanshuai Luo et al.

With the explosive growth of Internet data, users are facing the problem of information overload, which makes it a challenge to efficiently obtain the required resources. Recommendation systems have emerged in this context. By filtering massive amounts of information, they provide users with content that meets their needs, playing a key role in scenarios such as advertising recommendation and product recommendation. However, traditional click-through rate prediction and TOP-K recommendation mechanisms are gradually unable to meet the recommendations needs in modern life scenarios due to high computational complexity, large memory consumption, long feature selection time, and insufficient feature interaction. This paper proposes a recommendations system model based on a separation embedding cross-network. The model uses an embedding neural network layer to transform sparse feature vectors into dense embedding vectors, and can independently perform feature cross operations on different dimensions, thereby improving the accuracy and depth of feature mining. Experimental results show that the model shows stronger adaptability and higher prediction accuracy in processing complex data sets, effectively solving the problems existing in existing models.

Hao Luo, Jianjun Wei, Shuchen Zhao et al.

This research delves into advanced route optimization for robots in smart logistics, leveraging a fusion of Transformer architectures, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs). The approach utilizes a graph-based representation encompassing geographical data, cargo allocation, and robot dynamics, addressing both spatial and resource limitations to refine route efficiency. Through extensive testing with authentic logistics datasets, the proposed method achieves notable improvements, including a 15% reduction in travel distance, a 20% boost in time efficiency, and a 10% decrease in energy consumption. These findings highlight the algorithm's effectiveness, promoting enhanced performance in intelligent logistics operations.

Jiacheng Hu, Zhen Qi, Jianjun Wei et al.

Deep learning has revolutionized various fields, yet its efficacy is hindered by overfitting and the requirement of extensive annotated data, particularly in few-shot learning scenarios where limited samples are available. This paper introduces a novel approach to few-shot learning by employing a Residual Weight Masking Conditional Generative Adversarial Network (RWM-CGAN) for data augmentation. The proposed model integrates residual units within the generator to enhance network depth and sample quality, coupled with a weight mask regularization technique in the discriminator to improve feature learning from small-sample categories. This method addresses the core issues of robustness and generalization in few-shot learning by providing a controlled and clear augmentation of the sample space. Extensive experiments demonstrate that RWM-CGAN not only expands the sample space effectively but also enriches the diversity and quality of generated samples, leading to significant improvements in detection and classification accuracy on public datasets. The paper contributes to the advancement of few-shot learning by offering a practical solution to the challenges posed by data scarcity and the need for rapid generalization to new tasks or categories.