Ji-Won Baek

KSII Transactions on Internet and Information Systems TIIS Paper Details

In order to minimize damage in the event of a fire, the ignition point must be detected and dealt with before the fire spreads. However, the method of detecting fire by heat or fire is more damaging because it can be detected after the fire has spread. Therefore, this study proposes a Swin Transformer-based object detection model using explainable meta-learning mining. The proposed method merges the Swin Transformer and YOLOv3 model and applies meta-learning so as to build an explainable object detection model. In order for efficient learning with small data in the course of learning, it applies Few-Shot Learning. To find the causes of the object detection results, Grad-CAM as an explainable visualization method is used. It detects small objects of smoke in the fire image data and classifies them according to the color of the smoke generated when a fire breaks out. Accordingly, it is possible to predict and classify the risk of fire occurrence to minimize damage caused by fire. In this study, with the use of Mean Average Precision (mAP), performance evaluation is carried out in two ways. First, the performance of the proposed object detection model is evaluated. Secondly, the performance of the proposed method is compared with a conventional object detection method’s performance. In addition, the accuracy comparison using the confusion matrix and the suitability of real-time object detection using FPS are judged. Given the results of the evaluation, the proposed method supports accurate and real-time monitoring and analysis.

Patients with similar diseases are able to have similar treatments, care, symptoms, and causes. Based on these relations, it is possible to predict latent risks. Therefore, this study proposes Graph Neural Network-based Multi-Context mining for predicting emerging health risks. The proposed method collects and pre-processes chronic disease patients’ disease information, behavioral pattern information, and mental health information. After that, it performs context mining. This is a multivariate regression analysis for predicting multiple dependent variables, it extracts a regression model and generates a feature map. Then, the initial graph is created by defining the number of clusters as nodes and constructing edges through correlation. By expanding the graph according to the results of context mining, it is possible to predict that a user has a similar chronic disorder and similar symptoms through users’ connection relations. For performance evaluation, the validity of the regression analysis of context mining used in the proposed method, and the suitability of the clustering technique are evaluated.

Human pose estimation has long been researched as a significant topic in computer vision. However, studies via deep learning models are insufficient because of the lack of 2D and 3D skeleton data in various domains. An augmentation technique is applied to solve the problem of data scarcity. Data augmentation techniques can improve the performance of an analytical model by increasing the amount of data. However, the model’s performance is degraded if the augmented results differ significantly from the actual distribution. Therefore, it is necessary to implement an optimized augmentation policy for image datasets. This study proposed a dimensional expansion and time-series data augmentation policy for pose estimation based on skeletons. The proposed method improves the model’s performance by using 3D skeleton data. The 3D skeleton data were preprocessed through an affine transformation. The data were augmented for dimensional expansion from 2D to 3D data. In addition, sampling was applied to the data in consideration of time-series features, and thus, the number of frames per unit time was redefined. Subsequently, part of the information was lost by cutout, and thus, the data size, rather than the data shape, was changed. Through the image and video augmentation policies and cutout expansion, search candidates for 3D time-series data augmentation policies were extracted from the number of cases generated through the combination of 16 skeleton augmentation policies, 11 probabilities, and ten intensities. Finally, 20 candidates were extracted, and the five best-performing policies were applied.

It is difficult to provide information to patients because the cause of Alzheimer’s disease is not accurately identified. Therefore, there are difficulties in management and prevention. However, if one can manage the basic influencing factors, one can maintain a healthy brain. Therefore, this study proposes a prediction support model for dementia based on regression analysis using an image style transfer. The proposed method collects images of factors extracted from text information about Alzheimer’s disease, images of a normal brain, and images of a brain with Alzheimer’s disease to provide precautions for the factors affecting Alzheimer’s disease. Accordingly, it transforms the brain’s style by transferring image features of the factors affecting it onto the normal brain image. The transformed results allow for discovery of the factors that affect Alzheimer’s disease, compared to the brain with Alzheimer’s disease, and allow the medical team or the patients themselves to prevent and manage it. In addition, performance evaluation compares the similarities in style transmission results for factors affecting it according to each stage of the dementia condition. A comparison of similarities shows that a brain with cerebral hemorrhage and the brain of an alcoholic have the highest similarities to all stages of dementia.

KSII Transactions on Internet and Information Systems TIIS Paper Details

Traffic accidents are emerging as a serious social problem in modern society but if the severity of an accident is quickly grasped, countermeasures can be organized efficiently. To solve this problem, the method proposed in this paper derives the MDG (Mean Decrease Gini) coefficient between variables to assess the severity of traffic accidents. Single models are designed to use coefficient, independent variables to determine and predict accident severity. The generated single models are fused using a weighted-voting-based bagging method ensemble to consider various characteristics and avoid overfitting. The variables used for predicting accidents are classified as dependent or independent and the variables that affect the severity of traffic accidents are predicted using the characteristics of causal relationships. Independent variables are classified as categorical and numerical variables. For this reason, a problem arises when the variation among dependent variables is imbalanced. Therefore, a harmonic average is applied to the weights to maintain the variables’ balance and determine the average rate of change. Through this, it is possible to establish objective criteria for determining the severity of traffic accidents, thereby improving reliability.

Video content data have a variety of objects that could be associated with each other. Although content data contains similar objects or themes, their associations can become ambiguous. Accordingly, if associations between video content data are found in general association rules, their accuracy and confidence are low. Therefore, this study proposes the associative knowledge graph using fuzzy clustering and min-max normalization in video contents. With the use of the objects of video content, the proposed method finds clear and accurate associations between video content data and generates a knowledge graph. In the first step, the streaming video content data massively generated are collected, and objects in each image video are classified by an object detection algorithm. In the second step, normalization is executed in consideration of the different length of each video content and generate transaction data based on object frequency. In this way, it is possible to consider all the collected video content in the same condition. Additionally, it is possible to find unnecessary objects and significant objects in video content. Lastly, the degree of ambiguity is analyzed through fuzzy clustering using a probability that each object is involved in a group. Associations between fuzzy-clustered objects are extracted and an association knowledge graph is created. In this way, the accuracy and confidence of associations between different video content data are improved. As for performance, the association knowledge graph generated in the proposed method was better than a conventional association rule method in terms of the count of generated rules, support, and confidence.