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Detecting CCTV Traffic Accidents and Automating Emergency Rescue Based on Deep Learning
Changhoon Park, Jihyeon Kim, Inhee Cho, Sunho Jang, Kihag Kwon
http://doi.org/10.5626/JOK.2024.51.12.1061
This paper presents a novel approach to real-time detection of traffic accidents using CCTV footage and provision of immediate information about nearby hospitals. By ensemble Densenet121 and YOLOv8 models, the proposed system effectively identified the occurrence and type of traffic accidents. Based on accident location, the system searched for the nearest available emergency rooms and confirmed their capacity in real time. This enabled a prompt delivery of accident details and hospital information to the user, addressing issues of delayed reporting and inefficient allocation of emergency room resources. This approach aims to reduce initial response time during traffic accidents, thereby maximizing the efficiency of emergency medical services and ultimately minimizing accident-related harm. Specifically, Densenet121's deep neural network architecture effectively classified accident scenes in the footage, while YOLOv8's object detection algorithm identified accident types in real-time.
Breast Cancer Subtype Classification Using Multi-omics Data Integration Based on Neural Network
Joungmin Choi, Jiyoung Lee, Jieun Kim, Jihyun Kim, Heejoon Chae
http://doi.org/10.5626/JOK.2020.47.9.835
Breast cancer is one of the highly heterogeneous diseases comprising multiple biological factors, causing multiple subtypes. Early diagnosis and accurate subtype prediction of breast cancer play a critical role in the prognosis of cancer and are crucial to providing appropriate treatment for each patient with different subtypes. To identify significant patterns from enormous volumes of genetic and epigenetic data, machine learning-based methods have been adopted to the breast cancer subtype classification. Recently, multi-omics data integration has attracted much attention as a promising approach in recognizing complex molecular mechanisms and providing a comprehensive view of patients. However, because of the characteristics of high dimensionality, multi-omics based approaches are limited in prediction accuracy. In this paper, we propose a neural network-based breast cancer subtype classification model using multi-omics data integration. The gene expression, DNA methylation, and miRNA omics dataset were integrated after preprocessing and the classification model was trained based on the neural network using the dataset. Our performance evaluation results showed that the proposed model outperforms all other methods, providing the highest classification accuracy of 90.45%. We expect this model to be useful in predicting the subtypes of breast cancer and improving patients’ prognosis.
C++ based General-purpose Open Source Deep Learning Framework, WICWIU
Chunmyong Park, Jeewoong Kim, Yunho Kee, Jihyeon Kim, Seonggyeol Yoon, Eunseo Choi, Injung Kim
http://doi.org/10.5626/JOK.2019.46.3.253
In this paper, we introduce WICWIU, the first open source deep learning framework among Korean universities. WICWIU provides a variety of operators and modules together with a network structure that can represent an arbitrary general computational graph. The WICWIU features are sufficient to compose widely used deep learning models such as Inception, ResNet, and DenseNet. WICWIU also supports GPU-based massive parallel computing which significantly accelerates the training of neural networks. It is also easily accessible for C++ developers because the whole API is provided in C++. WICWIU has an advantage over Python-based frameworks in memory and performance optimization based on the C++ environment. This eases the customizability of WICWIU for environments with limited resources. WICWIU is readable and extensible because it is composed of C++ codes coupled with consistent APIs. With Korean documentation, it is particularly suitable for Korean developers. WICWIU applies the Apache 2.0 license which is available for any research or commercial purposes for free.
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