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A Graph Neural Network Approach for Predicting the Lung Carcinogenicity of Single Molecular Compounds
http://doi.org/10.5626/JOK.2025.52.6.482
Cancer is one of the major diseases causing millions of deaths worldwide every year, and lung cancer has been recorded as the leading cause of cancer-related deaths in Korea in 2022. Therefore, research on lung cancer-causing compounds is essential, and this study proposes and evaluates a novel approach to predict lung cancer-causing potential using graph neural networks to overcome the limitations of existing machine learning and deep learning methods. Based on SMILES(Simplified Molecular Input Line Entry System) information from the compound carcinogenicity databases CPDB, CCRIS, IRIS and T3DB, the structure and chemical properties of molecules were converted into graph data for training, and the proposed model showed superior prediction performance compared to other models. This demonstrates the potential of graph neural networks as an effective tool for lung cancer prediction and suggests that they can make important contributions to future cancer research and treatment development.
Drug Toxicity Prediction Using Integrated Graph Neural Networks and Attention-Based Random Walk Algorithm
Jong-Hoon Park, Jae-Woo Chu, Young-Rae Cho
http://doi.org/10.5626/JOK.2025.52.3.234
The traditional drug development process is often burdened by high costs and lengthy timelines, leading to increasing interest in AI-based drug development. In particular, the importance of AI models for preemptively evaluating drug toxicity is being emphasized. In this study, we propose a novel drug toxicity prediction model, named Integrated GNNs and Attention Randon Walk (IG-ARW). The proposed method integrates various Graph Neural Network (GNN) models and uses attention mechanisms to compute random walk transition probabilities, extracting graph features precisely. The model then conducts random walks to extract node features and graph features, ultimately predicting drug toxicity. IG-ARW was evaluated on three different datasets, demonstrating strong performances with AUC scores of 0.8315, 0.8894, and 0.7476, respectively. Notably, the model was proven to be highly effective not only in toxicity prediction, but also in predicting other drug characteristics.
Reference Image-Based Contrastive Attention Mechanism for Printed Circuit Board Defect Classification
http://doi.org/10.5626/JOK.2025.52.1.70
Effective classification of defects in Printed Circuit Boards (PCBs) is critical for ensuring product quality. Traditional approaches to PCB defect detection have primarily relied on single-image analysis or failed to adequately address alignment issues between reference and test images, leading to reduced reliability and precision in defect detection. To overcome these limitations, this study aimed to introduce a novel deep image comparison method that could incorporate contrastive loss functions to improve image alignment with a contrastive attention mechanism to focus the model on areas with a higher likelihood of defects. Experiments conducted on actual PCB data demonstrated that the proposed method achieved superior classification performance, even with limited data, highlighting its potential to significantly enhance the reliability of PCB defect detection and address existing challenges in the field.
Task-Oriented Dialogue System Using a Fusion Module between Knowledge Graphs
Jinyoung Kim, Hyunmook Cha, Youngjoong Ko
http://doi.org/10.5626/JOK.2024.51.10.882
The field of Task-Oriented Dialogue Systems focuses on using natural language processing to assist users in achieving specific tasks through conversation. Recently, transformer-based pre-trained language models have been employed to enhance performances of task-oriented dialogue systems. This paper proposes a response generation model based on Graph Attention Networks (GAT) to integrate external knowledge data into transformer-based language models for more specialized responses in dialogue systems. Additionally, we extend this research to incorporate information from multiple graphs, leveraging information from more than two graphs. We also collected and refined dialogue data based on music domain knowledge base to evaluate the proposed model. The collected dialogue dataset consisted of 2,076 dialogues and 226,823 triples. In experiments, the proposed model showed a performance improvement of 13.83%p in ROUGE-1, 8.26%p in ROUGE-2, and 13.5%p in ROUGE-L compared to the baseline KoBART model on the proposed dialogue dataset.
A Hybrid Deep Learning Model for Generating Time-series Fire Data in Underground Utility Tunnel based on Convolutional Attention TimeGAN
http://doi.org/10.5626/JOK.2024.51.6.490
Underground utility tunnels (UUTs) play a crucial role in urban operation and management. Fires are the most common disasters in the facilities, and there is a growing demand for fire management systems using artificial intelligence (AI). However, due to the difficulty of collecting fire data for AI training, utilizing data generation models reflecting the key characteristics of real fires can be an alternative. In this paper, we propose an approach for generating AI training data based on the fire data generation model CA-TimeGAN. To collect fire simulation data for training the proposed model, we constructed a UUT in Chungbuk Ochang within the fire dynamic simulator (FDS) virtual environment. In the experiments, we compared data generated by TimeGAN and CA-TimeGAN, verifying the data quality and effectiveness. Discriminative score converged to 0.5 for both CA-TimeGAN and TimeGAN. Predictive scores improved by 66.1% compared to models trained only on simulated data and by 22.9% compared to models incorporating TimeGAN-generated data. PCA and t-SNE analyses showed that the distribution of generated data was similar to that of simulated data.
A Token Selection Method for Effective Token Pruning in Vision Transformers
http://doi.org/10.5626/JOK.2024.51.6.567
The self-attention-based models, vision transformers, have recently been employed in the field of computer vision. While achieving excellent performance in a variety of tasks, the computation costs increase in proportion to the number of tokens during inference, which causes a degradation in inference speed. Especially when deploying the model in real-world scenarios, many limitations could be encountered. To address this issue, we propose a new token importance measurement, which can be obtained by modifying the structure of multi-head self-attention in vision transformers. By pruning less important tokens through our method during inference, we can improve inference speed while preserving performance. Furthermore, our proposed method, which requires no additional parameters, exhibits better robustness without fine-tuning and demonstrates that it can maximize performance when integrated with existing token pruning methods.
Domain Generalized Fashion Object Detection using Style Augmentation and Attention
http://doi.org/10.5626/JOK.2023.50.10.845
With the combination of fashion and computer vision, fashion object detection using deep learning has gained much interest. However, due to the nature of supervision, the performance of the model drops when images with different characteristics are used. We define the dataset with different characteristics and the characteristic of the domain as ‘domain’ and ‘style’, respectively, and propose a new augmentation method that mixes up the existing domain’s style to make a new style. We also use an attention method to extract important features from the images. Using a stylized fashion detection dataset, style deepfashion2, we show that the proposed method enhances performance within all domains.
TwinAMFNet : Twin Attention-based Multi-modal Fusion Network for 3D Semantic Segmentation
Jaegeun Yoon, Jiyeon Jeon, Kwangho Song
http://doi.org/10.5626/JOK.2023.50.9.784
Recently, with the increase in the number of accidents due to misrecognition in autonomous driving, interest in 3D semantic segmentation based on sensor fusion using multi-modal sensors has increased. Accordingly, this study introduces TwinAMFNet, a novel 3D semantic segmentation neural network through sensor fusion of RGB cameras and LiDAR. The proposed neural network includes a twin neural network that processes RGB images and point cloud projection images projected on a 2D coordinate plane and through an attention-based fusion module for feature step fusion in the encoder and decoder. The proposed method shows improvement of further extended object and boundary classification. As a result, the proposed neural network recorded approximately 68% performance in 3D semantic segmentation based on mIoU, and showed approximately 4.5% improved performance compared to the ones reported in the existing studies.
Non-autoregressive Korean Morphological Analysis with Word Segment Information
http://doi.org/10.5626/JOK.2023.50.8.653
This paper introduces a non-autoregressive Korean morphological analyzer. The proposed morphological analyzer utilizes a transformer encoder to encode a given sentence and employs two non-autoregressive decoders for morphological analysis. Each decoder generates a morpheme sequence and a corresponding POS tag sequence, which are then combined to produce the final morphological analysis. Additionally, this paper leverages word segment information within the sentence to predict the target sequence length, mitigating performance degradation resulting from incorrect target sequence length predictions. Experimental results show that the proposed non-autoregressive Korean morphological analyzer outperforms all non-autoregressive baselines. It achieves comparable accuracy to an autoregressive Korean morphological analyzer while it performs nearly 14.76 times faster than the autoregressive Korean morphological analyzer.
Automatic Classification of Pneumonia Based on Ensemble Deep Learning Model Using Intensity Normalization and Multiscale Lung-Focused Patches on Chest X-Ray Images
Yoon Jo Kim, Jinseo An, Helen Hong
http://doi.org/10.5626/JOK.2022.49.9.677
It is difficult to classify normal and pneumonia in pediatric chest X-ray (CXR) images due to irregular intensity values. In addition, deep learning model has a limitation in that it can misclassify CXR by incorrectly focusing on the outer part of the lung. This study proposed an automatic classification of pneumonia based on ensemble deep learning model using three intensity normalizations and multiscale lung-focused patches on CXR images. First, to correct for irregular intensity values in internal lungs, three intensity normalization methods were performed respectively. Second, to focus on internal lungs, regions of interest were extracted by segmenting lung regions. Third, multiscale lung-focused patches were extracted to train the characterization of pneumonia. Finally, ensemble modeling with attention module was performed to improve the classification performance. In the experiment, the method using large patches of CLAHE images showed an accuracy of 92%, which was 5% higher than that of original images. Furthermore, the proposed method using an ensemble of large and middle patches showed the best performance with an accuracy of 93%.
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