Lung cancer is a deadly disease with a high mortality rate. Endobronchial ultrasonography (EBUS) is one of the methods for detecting pulmonary lesions. Computer-aided diagnosis of pulmonary lesions from images can help radiologists to classify lesions; however, most of the existing methods need a large volume of data to give good results. Thus, this paper proposes a novel pulmonary lesion classification framework for EBUS images that works well with small datasets. The proposed framework integrates the statistical results from three classification models using the weighted ensemble classification. The three classification models include the radiomics feature and patient data-based model, the single-image-based model, and the multi-patch-based model. The radiomics features are combined with the patient data to be used as input data for the random forest, whereas the EBUS images are used as input data to the other two CNN models. The performance of the proposed framework was evaluated on a set of 200 EBUS images consisting of 124 malignant lesions and 76 benign lesions. The experimental results show that the accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve are 95.00%, 100%, 86.67%, 92.59%, 100%, and 93.33%, respectively. This framework can significantly improve the pulmonary lesion classification.

Many Western-style paintings by Japanese artists in the early 1900s, though maintaining a unique quality, were greatly inspired by the works of Western artists. In this article, we employ machine learning to identify relationships and classify the works of Japanese and Western artists. The relationships are of significant interest to numerous art historians, as they can reveal how Western art was introduced to Japan. Historically, art historians have manually annotated these correspondences, which is a time-consuming and labor-intensive process. In this article, we introduce a new method for finding correspondences between related artworks by comparing their overall outline information. This technique is based on Siamese neural networks (SNNs) and a self-supervised learning approach. Additionally, we have compiled a dataset of illustrations from Japanese artists such as Seiki Kuroda and Western artists such as Raphaël Collin, complete with correspondence annotations. However, to exhibit the unique quality of works by Japanese artists, we demonstrate that machine learning can classify between artworks created by Japanese artists and those created by Western artists.

Accurately modeling claims data and determining appropriate insurance premiums are vital responsibilities for non-life insurance firms. This article presents...