The emergence of consumer electronic Unmanned Aerial Vehicle (UAV) systems has brought innovation to the field of ship inspection. Traditional ship detection is carried out through traditional target recognition methods, but the efficiency and accuracy cannot meet the requirements. With the major breakthrough in the resolution of remote sensing images, it has become possible to use UAV to capture remote sensing images to detect ships. In this paper, object detection technology based on deep learning is used to improve the current detection methods and achieve accurate ship target detection. We propose an anchor-free detection method based on FCOS to reduce model hyperparameters. Meanwhile, a positive and negative sample selection method is put forth based on attention mechanism feature fusion and self-adaptation to enhance the fusion expression of features and improve the efficiency of sample selection, therefore improving the accuracy of the model. Experiments demonstrate notable progress in the detection accuracy from the proposed method, especially when small ship targets are concerned. Compared with Faster R-CNN and R3Det, the method introduced in this paper needs fewer hyperparameters, while achieving higher detection accuracy, with AP50 reaching 83.90%.

As consumer electronics become more ubiquitous and Generative Artificial Intelligence (GenAI) technologies advance rapidly, processing and analyzing semantic information collected from users to enhance their experiences becomes especially important. Traditionally, monitoring students' educational and psychological health has relied primarily on teachers, a method that is both inefficient and inadequate for comprehensively grasping students' needs. Therefore, the application of artificial intelligence is particularly critical. GenAI has the capability to gather semantic information from consumer electronics commonly used by students, such as smartphones, wearable devices, and tablets, and perform in-depth analysis. By analyzing curriculum schedules, GenAI can personalize learning paths and predict students' psychological states through examining individual mental health and media usage data. This paper explores how GenAI processes this information to intervene in students' education and psychological health, and discusses the analysis of adolescent mental health test data collected in recent years as well. Evidence suggests that students enrolled in high schools suffer from the highest rates of depression and major depressive disorder compared to other stages, at 40% and 12.5% respectively, underscoring the importance of using GenAI for educational and psychological health monitoring. The article summarizes the relevant technologies and models, highlighting the potential of GenAI in enhancing student well-being and academic performance.

UAV technology has been developing rapidly in recent years, and UAV remote sensing image target detection plays an important role in military, agriculture, forestry and marine fields. Currently there is a lack of consideration for the safety aspects of UAV image target detection, so we propose an intelligent interference information fusion method to expand the dataset. By adding interference to the original dataset images and generating the affected dataset images to train the model, the anti-interference ability and robustness of the target detection model are improved. We also propose a highly robust target detection framework (RA-RTDETR) with strong anti-interference capability to improve the detection accuracy of disturbed images. The framework fuses three methods, deep residual contraction network and cascading attention mechanism, and is experimentally compared with other methods (yolov5s, yolov8s, RTDETR) on a dataset. The experimental results demonstrate that the dataset with fused interference information has a significant improvement in model robustness, and the accuracy of our proposed RA-RTDETR achieves the best results.

With the advancement of internet technology, there has been a surge in network communication and applications, accompanied by an increasing prevalence of cybersecurity issues. The detection of encrypted traffic stands as a critical measure for ensuring network security, and its significance is self-evident. While current deep learning-based techniques for encrypted traffic detection have achieved high detection precision, several shortcomings remain to be addressed. First, most approaches rely primarily on packet-level features, neglecting valuable session-level traffic features, which limits detection performance. Second, while a few studies have explored session-level detection, they are often restricted to fixed-length traffic and require extensive preprocessing, reducing practicality and adaptability. In light of these issues, we propose a novel encrypted traffic detection approach, EMLF-ETD, which facilitates the detection of variable-length encrypted traffic by fusing multi-level network traffic features. EMLF-ETD innovatively proposes the concept of a hierarchical information network traffic graph, which enhances the flexibility of data input forms for encrypted traffic, thereby facilitating the representation of intra-packet information at the packet level and inter-packet relationships at the session level. It further incorporates a Traffic Packet Embedding module and a Traffic Session Embedding module, designed to extract and fuse packet- and session-level features from graph-structured data. The proposed architecture effectively overcomes the limitations of prior methods in handling variable-length traffic. To ascertain the effectiveness of the EMLF-ETD approach, we conducted experiments on various public datasets. The results demonstrate that our approach outperforms baseline techniques in terms of detection precision and data adaptability.