Recently, security monitoring facilities have mainly adopted artificial intelligence (AI) technology to provide both increased security and improved performance. However, there are technical challenges in the pursuit of elevating system performance, automation, and security efficiency. In this paper, we proposed intelligent anomaly detection and classification based on deep learning (DL) using multi-modal fusion. To verify the method, we combined two DL-based schemes, such as (i) the 3D Convolutional AutoEncoder (3D-AE) for anomaly detection and (ii) the SlowFast neural network for anomaly classification. The 3D-AE can detect occurrence points of abnormal events and generate regions of interest (ROI) by the points. The SlowFast model can classify abnormal events using the ROI. These multi-modal approaches can complement weaknesses and leverage strengths in the existing security system. To enhance anomaly learning effectiveness, we also attempted to create a new dataset using the virtual environment in Grand Theft Auto 5 (GTA5). The dataset consists of 400 abnormal-state data and 78 normal-state data with clip sizes in the 8-20 s range. Virtual data collection can also supplement the original dataset, as replicating abnormal states in the real world is challenging. Consequently, the proposed method can achieve a classification accuracy of 85%, which is higher compared to the 77.5% accuracy achieved when only employing the single classification model. Furthermore, we validated the trained model with the GTA dataset by using a real-world assault class dataset, consisting of 1300 instances that we reproduced. As a result, 1100 data as the assault were classified and achieved 83.5% accuracy. This also shows that the proposed method can provide high performance in real-world environments.

Fabry disease is a lysosomal storage disorder characterized by the lysosomal accumulations of glycosphingolipids in a variety of cytotypes, which include endothelial cells. The disease is inherited and originates from an error in glycosphingolipid catabolism caused by insufficient α-galactosidase A activity, which causes uncontrolled progressive storage of intracellular globotriaosylceramide (Gb3) in the vasculature and extracellular accumulation of lyso-Gb3 (a deacetylated soluble form of Gb3). Necrosis can lead to inflammation, which exacerbates necrosis and creates a positive feedback loop that triggers necroinflammation. However, the role played by necroptosis, a form of programmed necrotic cell death, in the cell-to-cell inflammatory reaction between epithelial and endothelial cells is unclear. Thus, the present study was undertaken to determine whether lyso-Gb3 induces necroptosis and whether necroptosis inhibition protects endothelial dysfunction against lyso-Gb3 inflamed retinal pigment epithelial cells. We found lyso-Gb3 induced necroptosis of a retinal pigment epithelial cell line (ARPE-19) in an autophagy-dependent manner and that conditioned media (CM) from ARPE-19 cells treated with lyso-Gb3 induced the necroptosis, inflammation, and senescence of human umbilical vein endothelial cells. In addition, a pharmacological study showed CM from lyso-Gb3 treated ARPE-19 cells induced endothelial necroptosis, inflammation, and senescence were significantly inhibited by an autophagy inhibitor (3-MA) and by two necroptosis inhibitors (necrostatin and GSK-872), respectively. These results demonstrate lyso-Gb3 induces necroptosis via autophagy and suggest that lyso-Gb3 inflamed retinal pigment epithelial cells trigger endothelial dysfunction via the autophagy-dependent necroptosis pathway. This study suggests the involvement of a novel autophagy-dependent necroptosis pathway in the regulation of endothelial dysfunction in Fabry disease.