This paper proposes a method called autoencoder with probabilistic LightGBM (AED-LGB) for detecting credit card frauds. This deep learning-based AED-LGB algorithm first extracts low-dimensional feature data from high-dimensional bank credit card feature data using the characteristics of an autoencoder which has a symmetrical network structure, enhancing the ability of feature representation learning. The credit card fraud dataset comes from a real dataset anonymized by a bank and is highly imbalanced, with normal data far greater than fraud data. For this situation, the smote algorithm is used to resample the data before putting the extracted feature data into LightGBM, making the amount of fraud data and non-fraud data equal. After comparing the resampled and non-resampled data, it was found that the performance of the AED-LGB algorithm was not improved after resampling, and it was concluded that the AED-LGB algorithm is more suitable for imbalanced data. Finally, the AED-LGB algorithm is comparable with other commonly used machine learning algorithms, such as KNN and LightGBM, and it has an overall improvement of 2% in terms of the ACC index compared to LightGBM and KNN. When the threshold is set to 0.2, the MCC index of AED-LGB is 4% higher than that of the second-highest LightGBM algorithm and 30% higher than that of KNN. It shows that the AED-LGB algorithm has higher performance in accuracy, true positive rate, true negative rate, and Matthew’s correlation coefficient.

Rejoining oracle bone fragments plays an import role in studying the history and culture of the Shang dynasty by its characters. However, current computer vision technology has a low accuracy in judging whether the texture of oracle bone fragment image pairs can be put back together. When rejoining fragment images, the coordinate sequence and texture features of edge pixels from original and target fragment images form a continuous symmetrical structure, so we put forward an internal similarity network (ISN) to rejoin the fragment image automatically. Firstly, an edge equidistant matching (EEM) algorithm was given to search similar coordinate sequences of edge segment pairs on the fragment image contours and to locally match the edge coordinate sequence of an oracle bone fragment image. Then, a target mask-based method was designed in order to put two images into a whole and to cut a local region image by the local matching edge. Next, we calculated a convolution feature gradient map (CFGM) of the local region image texture, and an internal similarity pooling (ISP) layer was proposed to compute the internal similarity of the convolution feature gradient map. Finally, ISN was contributed in order to evaluate a similarity score of a local region image texture and to determine whether two fragment images are a coherent whole. The experiments show that the correct judgement probability of ISN is higher than 90% in actual rejoining work and that our method searched 37 pairs of correctly rejoined oracle bone fragment images that have not been discovered by archaeologists.

In recent years, high-fidelity three-dimensional (3D) oracle bone models (3D-OBMs) have received extensive attention from oracle bone experts due to their unparalleled reducibility to real oracle bone. In the research process of 3D-OBMs, the first procedure is to extract oracle bone inscriptions (OBIs) from the model to form individual oracle bone characters (OBCs). However, the manual extraction of OBIs is a time-consuming and labor-intensive task that relies heavily on oracle bone knowledge. To address these problems, we propose a texture-mapping-based OBI extractor (tm-OBIE), which leverages the symmetrical characteristics of the texture mapping process and is able to extract 3D-OBIs from 3D-OBMs saved as a wavefront file. The OBIs in the texture file were first located using a trained 2D object detector. After that, the 3D mesh area, where the OBIs are located, was obtained using an inverse texture mapping method. Thirdly, a specific 2D plane was fitted using the centroid of triangular faces in the flat regions of the mesh via a singular value decomposition (SVD) method. Finally, by measuring the distances between the triangle meshes and the fitted plane, the meshes of the 3D-OBIs were obtained. This paper verifies the feasibility of this method via experiments and analyzes the possibility of using the algorithm framework for extracting other ancient characters from their corresponding 3D models.