Jing Ying

Classifying sports videos is complex due to their dynamic nature. Traditional methods, like optical flow and the Histogram of Oriented Gradient (HOG), are limited by their need for expertise and lack of universality. Deep learning, particularly Convolutional Neural Networks (CNNs), offers more effective feature recognition in sports videos, but standard CNNs struggle with fast-paced or low-resolution sports videos. Our novel neural network model addresses these challenges. It begins by selecting important frames from sports footage and applying a fuzzy noise reduction algorithm to enhance video quality. The model then uses a bifurcated neural network to extract detailed features, leading to a densely connected neural network with a specific activation function for categorizing videos. We tested our model on a High-Definition Sports Video Dataset covering over 20 sports and a low-resolution dataset. Our model outperformed established classifiers like DenseNet, VggNet, Inception v3, and ResNet-50. It achieved high precision (0.9718), accuracy (0.9804), F-score (0.9761), and recall (0.9723) on the high-resolution dataset, and significantly better precision (0.8725) on the low-resolution dataset. Correspondingly, the highest values on the matrix of four traditional models are: precision (0.9690), accuracy (0.9781), F-score (0.9670), recall (0.9681) on the high-resolution dataset, and precision (0.8627) on the low-resolution dataset. This demonstrates our model’s superior performance in sports video classification under various conditions, including rapid motion and low resolution. It marks a significant step forward in sports data analytics and content categorization.

Cross-lingual entity alignment in knowledge graphs is a crucial task in knowledge fusion. This task involves learning low-dimensional embeddings for nodes in different knowledge graphs and identifying equivalent entities across them by measuring the distances between their representation vectors. Existing alignment models use neural network modules and the nearest neighbors algorithm to find suitable entity pairs. However, these models often ignore the importance of local structural features of entities during the alignment stage, which may lead to reduced matching accuracy. Specifically, nodes that are poorly represented may not benefit from their surrounding context. In this article, we propose a novel alignment model called SSR, which leverages the node embedding algorithm in graphs to select candidate entities and then rearranges them by local structural similarity in the source and target knowledge graphs. Our approach improves the performance of existing approaches and is compatible with them. We demonstrate the effectiveness of our approach on the DBP15k dataset, showing that it outperforms existing methods while requiring less time.

The field of Chinese medical natural language processing faces a significant challenge in training accurate entity recognition models due to the limited availability of high-quality labeled data. In response, we propose a joint training model, MCBERT-GCN-CRF, which achieves high performance in identifying medical-related entities in Chinese electronic medical records. Additionally, we introduce CM-NER, a 5-step framework that effectively mitigates the effects of noise in weakly labeled data and establishes a principled connection between supervised and weakly supervised named entity recognition. We demonstrate significant improvements in recall rate and accuracy. Our approach outperforms traditional fully supervised pre-training models and other state-of-the-art methods by suppressing noise in weakly labeled data. Our proposed framework achieves an F1 score of 86.29% on the CCKS-2019 dataset, significantly higher than pre-trained model baselines ranging from 74.17 to 83.06%, and higher than the top-performing named entity recognition supervised learning models in the CCKS-2019 competition. Our results demonstrate the effectiveness of our proposed framework and highlight the potential of leveraging unlabeled data to train accurate models for named entity recognition in Chinese medical natural language processing. This research has significant implications for advancing natural language processing techniques in the medical domain and improving patient care. Graphical Abstract