Abstract Due to the challenges posed by background noise and the limited information available for small targets in remote sensing images, the detection performance for such targets remains unsatisfactory. To address these issues and enhance detection accuracy, we propose an improved algorithm based on RTDETR, named Adaptive Selective Transformer. Firstly, in the feature extraction network, we introduce an adaptive convolutional feature enhancement module to improve the multi-scale feature extraction capability in low-resolution remote sensing images. Secondly, we design a multi-scale enhancement structure to extract detailed information from small target images through enhanced multi-scale representation learning, thereby generating target features with stronger discriminative power. Finally, we propose a hierarchical frequency attention mechanism to achieve localized enhancement of contextual awareness, effectively capturing high-frequency local feature information of small targets. Experimental results demonstrate that the Adaptive Selective Transformer achieves superior small target detection performance, validating the effectiveness of our modifications to the original RTDETR model.

Abstract Click-through rate (CTR) prediction has become a crucial task in online advertising and other fields. Many researchers focus on improving CTR prediction models by exploring feature interactions. One popular model, Deep Factorization Machine (DeepFM), addresses both high-order and low-order feature interactions, but it overlooks the variability of feature representation in different contexts and lacks a comprehensive explanation of high-order feature interactions. In this paper, we propose a CTR prediction model called DeepFM-GA, which is based on improved feature refinement generation and attention enhancement representation. Firstly, we incorporate an attention convolutional generation module into $\text{DeepFM}_{\text{FRNet}}$, which enriches the feature space by generating complementary features through convolutional neural networks while maintaining context-aware feature representation. Secondly, we utilize a multi-head self-attention layer for feature-enhanced representation, enhancing the model’s ability to select important features. Finally, experiments are conducted on four real-world datasets, and the results show that DeepFM-GA has a better performance compared to other mainstream CTR models.

ABSTRACT In this study, a traffic flow prediction framework based on multigraph structural modeling and dynamic sensing of key nodes is proposed to address the significant differences in traffic patterns between holidays and workdays. Firstly, the original data are categorized using calendar labels to generate holiday‐indication tags for each sample. Then, a sliding‐window sample entropy method is employed to identify highly dynamic key nodes from the workday and holiday datasets to construct the key‐node set. A dynamic masking mechanism based on the calendar labels is introduced during model training to enhance the transformer's attention to core areas. To fully capture the connectivity variations of the traffic system under different contexts, this study adopts a dual adjacency matrix design and dynamically switches the graph structure based on sample labels during forward propagation, enabling context‐aware spatial modeling. Finally, traffic prediction accuracy is effectively improved by integrating a graph convolutional network (GCN) to extract spatial features with an enhanced transformer to dynamically model temporal dependencies. Experimental results demonstrate that this method significantly outperforms traditional single‐graph structure models across various calendar scenarios, validating the effectiveness and potential of multigraph dynamic modeling with a key‐node mechanism for traffic prediction tasks. In addition, ablation experiments are also conducted to compare the effects of different key‐node selection and processing strategies.

The Aircraft Landing Problem (ALP) involves optimizing the scheduling of flight arrivals and departures while simultaneously managing airport resources to maximize flight utilization, minimize delays, reduce costs, and enhance overall operational efficiency. However, existing algorithms for solving ALP often face challenges in converging optimally and effectively handling penalty values associated with timing violations. To address these issues, this paper proposes a novel hybrid heuristic algorithm, AGWOA, designed to enhance convergence rates and effectively manage penalties in ALP. AGWOA integrates Artificial Bee Colony (ABC) and Genetic Algorithm (GA) techniques into the Whale Optimization Algorithm (WOA), leveraging their complementary strengths to strengthen global search efficiency and refine local constraint handling. This integration accelerates convergence and significantly mitigates penalty costs. Moreover, AGWOA incorporates an adaptive coefficient to facilitate improved convergence, along with a Fast Convergence Update Mechanism (FCUM) to guide the algorithm toward optimal solutions more efficiently. Experimental results conducted on public datasets demonstrate that AGWOA outperforms existing advanced algorithms in both convergence speed and penalty minimization. Specifically, AGWOA achieves a 4.05% decrease in penalty costs compared to baseline algorithms. These results underscore AGWOA’s effectiveness in overcoming the challenge of slow convergence and its competitive advantage over other methods in optimizing ALP. The proposed algorithm offers a promising solution for real-world ALP applications, significantly enhancing airline operational efficiency and optimizing resource management.

Vehicle ad hoc network (VANET), with its characteristics of fast mobility and uneven distribution, adds complexity and uncertainty to the network. To ensure reliable routing connectivity in VANET, it is crucial to tackle challenges, such as implementing small-scale and low-precision solutions based on the spanning tree, as well as addressing the absence of effective contingency plans in case of failures. Constructing a large-scale suboptimal spanning tree solution set (LST) becomes the key to solving the aforementioned problems. Previous researchers have utilized swarm intelligence optimization algorithms to address the spanning tree construction problem. However, these methods suffer from drawbacks, such as low precision, poor scalability, lack of diversity, and uneven distribution. To tackle the aforementioned issues, this article proposes a quantum binary artificial bee colony algorithm (QBABC). First, the spanning tree hit ratio (SHR) is introduced to evaluate the probability of acquiring a spanning tree in VANET. Second, a mathematical model and data set are constructed based on the considered Quality-of-Service (QoS) metrics. Then, a quantum random number generator (QRNG) is proposed, which incorporates a fusion of binary encoding strategies. Finally, a multistage search strategy inspired by honeybee behavior is adopted. Through nonparametric statistics and validation with corresponding metrics, the results demonstrate that QBABC exhibits strong competitiveness and provides effective solutions in the event of VANET failures. QBABC’s advantages lie in improving the precision, scalability, diversity, and uniformity of spanning tree construction. This research is of significant importance for enhancing reliable routing connectivity in VANET.

With the advancement of automatic driving and smart city, it is critical to predict traffic information for traffic management, traffic planning, and traffic safety. When predicting traffic information, the spatial structure of the roads will also affect the traffic flow information, such as speed, occupancy rate, etc. The common method either merely focusing on the temporal feature without the considering the spatial structure, or the method of spatial feature extraction is only applicable to Euclidean structure, which does not apply to Non-Euclidean structure. This paper proposes a traffic speed prediction method based on time classification in combination with spatial Graph Convolutional Network. This method employs Gated Recurrent Unit to extract the temporal correlation and Graph Convolutional Network to extract the traffic network’s spatial structure. In consideration of the varying features of traffic speed on weekdays and weekends in the time dimension, time is divided into two types: weekdays and weekends. Since the structure of the road network will not change in the short term in actual process, the same network structure of spatial graph convolution can reasonably be shared in the spatial dimension after which the two sections are fused for training and prediction. Finally, this proposed method is compared to some baseline models to prove the performance. Generally speaking, this strategy produces more accurate prediction results on the PEMS_BAY and METR_LA data sets than the baseline models.

With the rapid development of Internet of Things (IoT) in the field of transportation, the vehicle-to-vehicle (V2V) communication not only becomes available on a large scale, but also will be an indispensable part of the future transportation. License plates are the identification of vehicles, so the license plate detection and recognition in the V2V communication scenario is very important. However, the existing license plate detection and recognition methods are suffering from a low accuracy rate issue. To solve this issue, we propose a hybrid deep learning algorithm as the license plate detection and recognition model by fusing YOLOV3 and CRNN. The proposed model enables the network itself to better utilize the different fine-grained features in the high and low layers to carry out multi-scale detection and recognition. In this model, we utilize the fast and accurate performance of YOLOV3, and the excellent detection ability of CRNN. As a result, this proposed model reaps the benefit of both. Finally, we test this proposed model in difficult scenarios and low-quality license plate images caused by weather, and results show this proposed license plate detection and recognition model can achieve a higher mean average precision, better comprehensive performance, and excellent robustness.