In the process of rice production, rice pests are one of the main factors that cause rice yield reduction. To implement prevention and control measures, it is necessary to accurately identify the types of rice pests and diseases. However, the application of image recognition technologies focused on the agricultural field, especially in the field of rice disease and pest identification, is relatively limited. Existing research on rice diseases and pests has problems such as single data types, low data volume, and low recognition accuracy. Therefore, we constructed the rice pest and disease dataset (RPDD), which was expanded through data enhancement methods. Then, based on the ResNet structure and the convolutional attention mechanism module, we proposed a Lightweight Multi-scale Feature Extraction Network (LMN) to extract multi-scale features at a finer granularity. The proposed LMN model achieved an average classification accuracy of 95.38% and an F1-Score of 94.5% on the RPDD. The parameter size of the model is 1.4 M, and the FLOPs is 1.65 G. The results suggest that the LMN model performs rice disease and pest classification tasks more effectively than the baseline ResNet model by significantly reducing the model size and improving accuracy.

Knowledge Graphs (KGs) serve as valuable auxiliary information to improve the accuracy of recommendation systems. Previous methods have leveraged the knowledge graph to enhance item representation and thus achieve excellent performance. However, these approaches heavily rely on high-quality knowledge graphs and learn enhanced representations with the assistance of carefully designed triplets. Furthermore, the emergence of knowledge graphs has led to models that ignore the inherent relationships between items and entities. To address these challenges, we propose a Self-Derived Knowledge Graph Contrastive Learning framework (CL-SDKG) to enhance recommendation systems. Specifically, we employ the variational graph reconstruction technique to estimate the Gaussian distribution of user-item nodes corresponding to the graph neural network aggregation layer. This process generates multiple KGs, referred to as self-derived KGs. The self-derived KG acquires more robust perceptual representations through the consistency of the estimated structure. Besides, the self-derived KG allows models to focus on user-item interactions and reduce the negative impact of miscellaneous dependencies introduced by conventional KGs. Finally, we apply contrastive learning to the self-derived KG to further improve the robustness of CL-SDKG through the traditional KG contrast-enhanced process. We conducted comprehensive experiments on three public datasets, and the results demonstrate that our CL-SDKG outperforms state-of-the-art baselines.

Electronic shopping has become an important way of shopping in our daily life. A good recommendation system can greatly improve the consumer shopping experience through alleviating consumer and product information overload problem. In the real scenario, each consumer has a wide variety of interests, and consumers interests are constantly changing. Many recent models usually give an overall embedding for consumer behavior sequence or only capture features of customers and characteristics of products from single perspective. However, these methods can hardly model consumers multiple interests and evolving of interests completely. Thus, in this study, we consider both the diversity and dynamics of consumer interests, and model them in a unified model. In light of this, we propose a novel model named Deep Self-Attention Mulit-Interest Networks (DSMN), which captures latent multiple interests by using capsule networks. It can perceive evolving of interests from consumer behavior sequences by using attention mechanism. We evaluate our model on three real-world datasets. In the experiments, our DSMN is 4.934% higher than the average of competitors in terms of AUC. Compared to our original silver medal method on the Kaggle competition, our DSMN is 18.978% higher in terms of MAP@12.

In the medical field, text classification based on natural language process (NLP) has shown good results and has great practical application prospects such as clinical medical value, but most existing research focuses on English electronic medical record data, and there is less research on the natural language processing task for Chinese electronic medical records. Most of the current Chinese electronic medical records are non-institutionalized texts, which generally have low utilization rates and inconsistent terminology, often mingling patients’ symptoms, medications, diagnoses, and other essential information. In this paper, we propose a Capsule network model for electronic medical record classification, which combines LSTM and GRU models and relies on a unique routing structure to extract complex Chinese medical text features. The experimental results show that this model outperforms several other baseline models and achieves excellent results with an F1 value of 73.51% on the Chinese electronic medical record dataset, at least 4.1% better than other baseline models.