In the modern digital age, users are exposed to a vast amount of content and information, and the importance of recommendation systems is increasing accordingly. Traditional recommendation systems mainly use matrix factorization and collaborative filtering methods, but problems with scalability due to an increase in the amount of data and slow learning and inference speeds occur due to an increase in the amount of computation. To overcome these problems, this study focused on optimizing LightGCN, the basic structure of the graph-convolution-network-based recommendation system. To improve this, techniques and structures were proposed. We propose an embedding enhancement method to strengthen the robustness of embedding and a non-combination structure to overcome LightGCN’s weight sum structure through this method. To verify the proposed method, we have demonstrated its effectiveness through experiments using the SELFRec library on various datasets, such as Yelp2018, MovieLens-1M, FilmTrust, and Douban-book. Mainly, significant performance improvements were observed in key indicators, such as Precision, Recall, NDCG, and Hit Ratio in Yelp2018 and Douban-book datasets. These results suggest that the proposed methods effectively improved the recommendation performance and learning efficiency of the LightGCN model, and the improvement of LightGCN, which is most widely used as a backbone network, makes an important contribution to the entire field of GCN-based recommendation systems. Therefore, in this study, we improved the learning method of the existing LightGCN and changed the weight sum structure to surpass the existing accuracy.

Artificial intelligence technologies are being studied to provide scientific evidence in the medical field and developed for use as diagnostic tools. This study focused on deep learning models to classify degenerative arthritis into Kellgren–Lawrence grades. Specifically, degenerative arthritis was assessed by X-ray radiographic images and classified into five classes. Subsequently, the use of various deep learning models was investigated for automating the degenerative arthritis classification process. Although research on the classification of osteoarthritis using deep learning has been conducted in previous studies, only local models have been used, and an ensemble of deep learning models has never been applied to obtain more accurate results. To address this issue, this study compared the classification performance of deep learning models, including VGGNet, DenseNet, ResNet, TinyNet, EfficientNet, MobileNet, Xception, and ViT, on a dataset commonly used for osteoarthritis classification tasks. Our experimental results verified that even without applying a separate methodology, the performance of the ensemble was comparable to that of existing studies that only used the latest deep learning model and changed the learning method. From the trained models, two ensembles were created and evaluated: weight and specialist. The weight ensemble showed an improvement in accuracy of 1%, and the proposed specialist ensemble improved accuracy, precision, recall, and F1 score by 5%, 6%, 6%, and 6%, respectively, compared with the results of prior studies.

의료 영상은 사람의 체내 장기를 관찰하고 질병을 진단하기 위해 영상 분석이 필수적으로 요구되는데 다양한 영역 중 크기가 작고 다른 장기에 겹쳐진 췌장은 의료 이미지에서 정확한 위치를 찾기가 어렵다. 본 논문에서는 기계 학습을 통해 복부 이미지에서 췌장의 위치를 보다 정확하게 분할할 수 있는 다중 연쇄 U-Net (MCU-Net) 모델을 제안한다. 제안하는 모델은 최종 계층의 여러 U-Net 구조를 다양한 크기의 패치와 연쇄 결합한 모델이며, 앙상블, 패치 크기에 따른 다양한 실험을 통해 가장 효과적인 MCU-Net을 제안한다. 선행 연구들과의 비교 실험 결과, 제안하는 다중 연결 U-Net 모델의 정확도는 91.6%로 기존 U-Net 모델보다 정확도가 약 2% 더 높은 것으로 나타났다.

모바일 기기의 발전으로 인해 해당 산업군의 비약적으로 발전했으며 이로 인해 사용자들은 이전에 비해 손쉽게 다양한 추천을 받을 수 있게 되었다. 하지만 잘못된 추천의 경우 오히려 사용자 경험 품질을 저해시키는 문제가 발생한다. 이러한 문제를 극복하기 위해서 본 연구에서는 현재 사용자가 원하는 아이템을 보다 정확히 추천하기 위해 시간 기반의 데이터가 추천시스템에 미치는 영향을 실험을 통해 분석하였다. 실험 결과 F1, Precision@20 점수는 전체 데이터를 활용하는 것보다 최근 데이터만을 이용하는 것이 더 높은 점수를 보였으나, Recall@20 점수의 경우 오히려 낮은 점수를 보였다. 이러한 결과로 유추하였을 때 현재의 사용자가 원하는 아이템의 예측 정확도 높이기 위해서는 데이터의 개수보다, 시간을 기준으로 하여 데이터를 분류하여 학습하는 것이 보다 높은 성능을 낼 수 있음을 확인하였다.