As one of the state-of-the-art object detection algorithms, YOLOv5 relies heavily on the quality of the training dataset. In order to improve the detection accuracy and performance of YOLOv5 and to reduce its false positive and false negative rates, we propose to improve the Segment Anything Model (SAM) used for data augmentation. The feature maps and mask predictions generated by the SAM are used as auxiliary inputs for the Mask-to-Mask (M2M) module. The experimental results show that after processing the dataset with the improved Segment Anything Model, the detection performance of YOLOv5 is improved with 99.9% precision and 99.1% recall. The improved YOLOv5 model has a higher license plate recognition accuracy than the original detection model under strong snowfall conditions, and the incidence of false-negative and false-positive is greatly reduced. The enhanced model can meet the requirement of accurate real-time recognition of license plates under strong snowfall weather conditions.

There are various biases present in recommendation systems, and recommendation results that do not consider these biases are unfair to users, items, and platforms. To address the problem of selection bias in recommendation systems, in this study, the propensity score was utilized to mitigate this bias. A selection bias propensity score estimation method (SPE) was developed, which takes into account both user and item information. This method accurately estimates the user’s choice tendency by calculating the degree of difference between the user’s selection rate and the selected preference of the item. Subsequently, the SPE method was combined with the traditional matrix decomposition-based recommendation algorithms, such as the latent semantic model (LFM) and the bias singular value model (BiasSVD). The propensity score was then inversely weighted into the loss function, creating a recommendation model that effectively eliminated selection bias. The experiments were carried out on the public dataset MovieLens, and root mean square error (RMSE) and mean absolute error (MAE) were selected as evaluation indicators and compared with two baseline models and three models with other propensity score estimation methods. Overall, the experimental results demonstrate that the model combined with SPE achieves a minimum increase of 2.00% in RMSE and 2.97% in MAE compared to its baseline model. Moreover, in comparison to other propensity score estimation methods, the SPE method effectively eliminates selection bias in the scoring data, thereby enhancing the performance of the recommendation model.

With the rapid development of space exploration technology, the detection of extraterrestrial bodies has become increasingly important. Among these, path planning and target recognition and positioning technologies are particularly critical for applications in intelligent agents with low computational power operating in complex environments. This paper introduces an A* path planning algorithm with an adaptive heuristic function, which demonstrates improved robustness in low-resolution maps and can plan paths that stay as far away from obstacles as possible, even when the accuracy of the prior map is limited. Additionally, this study proposes a Dynamic Environment Target Identification and Localization (DETIL) algorithm, which includes the identification of unknown obstacles and the spatio-temporal dimension clustering to locate points of interest. Simulation results of the mixed control scheme using both algorithms indicate that the improved A* algorithm reduces the maximum elevation difference by 55% and the maximum cumulative elevation difference by 68% compared to the traditional A* algorithm. The unknown obstacle identification component of the DETIL algorithm can recognize all obstacles along the path, while the spatio-temporal dimension clustering section improves the average number of target discoveries by 152% over the conventional DBSCAN clustering approach.

A model framework for the prediction of defects in strip steel is proposed with the objective of enhancing the accuracy of defect detection. Initially, the data are balanced through the utilisation of the Improved Synthetic Minority Oversampling Technique (ISmote), which is based on clustering techniques. Subsequently, further enhancements are made to the inertia weights and learning factors of the immune particle swarm optimisation (IPSO), with additional optimisations in speed updates and population diversity. These enhancements are designed to address the issue of premature convergence at the early stages of the process and local optima at the later stages. Finally, a prediction model is then constructed based on stacking, with its hyperparameters optimised through the improved immune particle swarm optimisation (IIPSO). The results of the experimental trials demonstrate that the IIPSO-ISmote-Stacking model framework exhibits superior prediction performance when compared to other models. The Macro_Precision, Macro_Recall, and Macro_F1 values for this framework are 93.3%, 93.6%, and 92.2%, respectively.

Federated learning, unlike centralized training, enables multiple clients to train a global model without exposing raw data, making it useful for applications such as medical diagnosis in smart healthcare and mobile edge networks. This setting is vulnerable to dynamic collusive attacks, where colluding clients coordinate abnormal parameter updates and adjust their strategies across training rounds. Existing studies address this problem with isolated mechanisms that focus on a single defense stage, but such mechanisms are insufficient for attacks that evolve across rounds. To address this challenge, we propose reputation-aware Stackelberg incentive aggregation (RSIA) to mitigate the influence of dynamic collusive attacks while balancing model utility in federated learning. Specifically, we first use incentive rules before local training to make repeated collusive behavior less beneficial. We then update client reputation after each round, so earlier unreliable behavior affects later incentives. Finally, we use reputation and update reliability to weight client updates, reducing the influence of abnormal updates on global model training. Experiments on three datasets show that RSIA reduces the average effective attack success rate by 84.9% compared with representative state-of-the-art (SOTA) methods. RSIA also maintains competitive test accuracy, providing a favorable trade-off between model utility and attack suppression.

With the rapid development of space exploration technology, the detection of extraterrestrial bodies has become increasingly important. Among these, path planning and target recognition and positioning technologies are particularly critical for applications in intelligent agents with low computational power operating in complex environments. This paper presents a novel approach to path planning on low-resolution lunar surface maps by introducing an improved A* algorithm with an adaptive heuristic function. This innovation enhances robustness in environments with limited map accuracy and enables paths that maintain maximum distance from obstacles. Additionally, we innovatively propose the Dynamic Environment Target Identification and Localization (DETIL) algorithm, which identifies unknown obstacles and employs spatiotemporal clustering to locate points of interest. Our main contributions provide valuable references for the aerospace industry, particularly in lunar exploration missions. The simulation results demonstrate that the improved A* algorithm reduces the maximum elevation difference by 55% and the maximum cumulative elevation difference by 68% compared to the traditional A* algorithm. Furthermore, the DETIL algorithm’s obstacle identification component successfully recognizes all the obstacles along the path, and its spatiotemporal clustering improves the average number of target discoveries by 152% over the conventional DBSCAN clustering approach.