The rapid spread of mobile banking and e-commerce has coincided with a dramatic increase in fraudulent online payments in recent years. Although machine learning and deep learning are widely used in credit card fraud detection, the typical credit card transaction data set is unbalanced, and the fraud data is much less than the normal transaction data, limiting the effectiveness of traditional binary classification algorithms. To overcome this issue, researchers oversample minority class data and utilize ensemble learning classification algorithms. However, oversampling still has disadvantages. Hence, we improve the generator part of the Variational Autoencoder Generative Adversarial Network (VAEGAN) and propose a new oversampling method that generates convincing and diverse minority class data. The training set is enhanced by generating minority class fraud data to train the ensemble learning classification model. The method is tested on an open credit card dataset, with the experimental results demonstrating that the oversampling method utilizing the improved VAEGAN is superior to the oversampling method of Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Synthetic Minority Oversampling Technique (SMOTE) in terms of Precision, F1_score, and other indicators. The oversampling method based on the improved VAEGAN effectively deals with the classification problem of imbalanced data.

In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low cost and fast search speed is an important problem. However, in the complex three-dimensional (3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). A 3D mission space model and a flight path cost function are first established to transform the path planning problem into a multidimensional function optimization problem. HHO is then improved for path planning, where the Cauchy mutation strategy and adaptive weight are introduced in the exploration process in order to increase the population diversity, expand the search space and improve the search ability. In addition, in order to reduce the possibility of falling into local extremum, the Sine-cosine Algorithm (SCA) is used and its oscillation characteristics are considered to gradually converge to the optimal solution. The simulation results show that the proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can generate a more optimized path planning result for UAVs.

The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning algorithms perform well in avoiding static obstacles but poor in dynamic ones. A new dynamic path planning algorithm based on obstacles’ position prediction and modified artificial potential field - HOAP is proposed in this paper. The Markov prediction model is employed to predict the obstacles’ future position with an obstacle grid map. And to resolve the local minima of the typical APF algorithm, a new virtual obstacle method is put forward. What’s more, the attractive force gain coefficient gradient increase method is proposed to solve local oscillation. Simulation results show that the UAV can fly a safe path with high accuracy under static or uncertain dynamic obstacles environments.

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for “measuring carbon through electricity”, in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals.

Path planning, which is needed to obtain collision-free optimal paths in complex environments, is one key step within unmanned aerial vehicle (UAV) systems with various applications, such as agricultural production, target tracking, and environmental monitoring. A new hybrid gray wolf optimization algorithm—SSGWO—is proposed to plan paths for UAVs under three-dimensional agricultural environments in this paper. A nonlinear convergence factor based on trigonometric functions is used to balance local search and global search. A new relative-distance fitness adaptation strategy is created to increase the convergence speed of the SSGWO. Integrating the simulated annealing (SA) algorithm, an alternative position update strategy based on SA is proposed to improve the search process with diverse capabilities. Finally, a B-spline curve is introduced into a smooth path to ensure the path’s feasibility. The simulation results show that the SSGWO algorithm has better convergence accuracy and stability, and can obtain higher-quality paths in a three-dimensional environment, compared with GWO, MGWO, IGWO, and SOGWO.

Currently, weed control robots that can accurately identify weeds and carry out removal work are gradually replacing traditional chemical weed control techniques. However, the computational and storage resources of the core processing equipment of weeding robots are limited. Aiming at the current problems of high computation and the high number of model parameters in weeding robots, this paper proposes a lightweight weed target detection model based on the improved YOLOv8 (You Only Look Once Version 8), called RVDR-YOLOv8 (Reversible Column Dilation-wise Residual). First, the backbone network is reconstructed based on RevCol (Reversible Column Networks). The unique reversible columnar structure of the new backbone network not only reduces the computational volume but also improves the model generalisation ability. Second, the C2fDWR module is designed using Dilation-wise Residual and integrated with the reconstructed backbone network, which improves the adaptive ability of the new backbone network RVDR and enhances the model’s recognition accuracy for occluded targets. Again, GSConv is introduced at the neck end instead of traditional convolution to reduce the complexity of computation and network structure while ensuring the model recognition accuracy. Finally, InnerMPDIoU is designed by combining MPDIoU with InnerIoU to improve the prediction accuracy of the model. The experimental results show that the computational complexity of the new model is reduced by 35.8%, the number of parameters is reduced by 35.4% and the model size is reduced by 30.2%, while the mAP₅₀ and mAP_50-95 values are improved by 1.7% and 1.1%, respectively, compared to YOLOv8. The overall performance of the new model is improved compared to models such as Faster R-CNN, SSD and RetinaNet. The new model proposed in this paper can achieve the accurate identification of weeds in farmland under the condition of limited hardware resources, which provides theoretical and technical support for the effective control of weeds in farmland.