The accurate detection and identification of pests and diseases on cucumber leaves is a prerequisite for scientifically controlling such issues. To address the limited detection accuracy of existing models in complex and diverse natural backgrounds, this study proposes an improved deep learning network model based on YOLOv8, named SEDCN-YOLOv8. First, the deformable convolution network DCNv2 (Deformable Convolution Network version 2) is introduced, replacing the original C2f module with an improved C2f_DCNv2 module in the backbone feature extraction network’s final C2f block. This enhances the model’s ability to recognize multi-scale, deformable leaf shapes and disease characteristics. Second, a Separated and Enhancement Attention Module (SEAM) is integrated to construct an improved detection head, Detect_SEAM, which strengthens the learning of critical features in pest and disease channels. This module also captures the relationship between occluded and non-occluded leaves, thereby improving the recognition of diseased leaves that are partially obscured. Finally, the original CIOU loss function of YOLOv8 is replaced with the Focaler-SIOU loss function. The experimental results demonstrate that the SEDCN-YOLOv8 network achieves a mean average precision (mAP) of 75.1% for mAP50 and 53.1% for mAP50-95 on a cucumber pest and disease dataset, representing improvements of 1.8 and 1.5 percentage points, respectively, over the original YOLOv8 model. The new model exhibits superior detection accuracy and generalization capabilities, with a model size of 6 MB and a detection speed of 400 frames per second, fully meeting the requirements for industrial deployment and real-time detection. Therefore, the SEDCN-YOLOv8 network model demonstrates broad applicability and can be effectively used in large-scale real-world scenarios for cucumber leaf pest and disease detection.

Traffic forecasting plays a crucial role in smart city research, significantly influencing urban traffic management and facilitating precise route planning. In recent years, numerous deep learning techniques have gained prominence for their effectiveness in spatiotemporal modeling. However, achieving accurate predictions remains a formidable challenge due to the inherent dynamics and heterogeneity present in spatiotemporal data. To address this challenge, we introduce a novel Heterogeneity-aware Graph Convolutional Recurrent Network (HGCRN) specifically designed for traffic prediction. The HGCRN framework begins by constructing a set of spatial-temporal meta-embeddings that effectively encode temporal context and node heterogeneity. It then utilizes time-varying traffic features to generate dynamic graph learners, integrating dynamic graph convolutional networks with gated recurrent units to proficiently model spatial and temporal dependencies. In the decoding phase, we incorporate temporal embeddings of the target predictions to enhance the forecasting process, thereby improving the model’s capacity to capture trend variations. Extensive experiments conducted on four widely recognized benchmark datasets demonstrate the superiority of the proposed model while highlighting its robust interpretability.

To address the critical challenge of low detection rates for rare anomaly classes in network traffic, a problem exacerbated by severe data imbalance, this paper proposes a deep learning framework for anomaly detection in imbalanced network traffic. Initially, the framework employs the Isolation Forest (iForest) and SMOTE-Tomek techniques for outlier removal and data balancing, respectively, to enhance data quality. The model first undergoes unsupervised pre-training using a symmetrically designed Residual Convolutional Autoencoder (ResCAE) to learn robust feature representations. Subsequently, the pre-trained encoder is integrated with a Bidirectional Gated Recurrent Unit (BiGRU) to capture temporal dependencies within the traffic features. During the fine-tuning phase, a Sharpness-Aware Minimization (SAM) optimizer is employed to enhance the model’s generalization capability. The experimental results on the public CICIDS2017 and UNSW-NB15 datasets reveal the model’s outstanding performance, achieving an accuracy, precision, recall, and F1-score of 99.33%, 99.53%, 99.33%, and 99.41%, respectively. Comparative analysis against baseline models confirms that the proposed method not only surpasses traditional machine learning algorithms but also holds a significant advantage over contemporary deep learning models. The results validate that this framework effectively resolves the issue of low detection rates for rare anomaly classes caused by data imbalance, offering a powerful and robust solution for building high-performance anomaly detection frameworks.