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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.
IDFusion: Joint Angle Measurement Method through Fusion of Inertial Measurement Sensor and Depth Camera
Juyeon Park, Mingyu Park, Gyumin Park, Hyun Lee
http://doi.org/10.5626/JOK.2025.52.3.208
Recent advancements in human and object recognition technologies are increasingly applied across various fields, particularly in motion detection research utilizing inertial measurement sensors and depth cameras in areas such as gaming, healthcare, and security. However, challenges such as cumulative errors and variable measurement accuracies depending on the environment persist. This study proposed IDFusion, a method that could integrate inertial measurement sensors and depth cameras for joint angle measurement, distinguishing itself through data transformation and joint angle conversion stages before fusion. Comparative analysis against using inertial measurement sensors and depth cameras individually demonstrated a superior performance of IDFusion. This technique holds promise for applications in healthcare, sports science, and human-computer interaction.
Optimizing Throughput Prediction Models Based on Feature Category Contribution in 4G/5G Network Environments
http://doi.org/10.5626/JOK.2024.51.11.961
The acceleration in 5G technology adoption due to increased network data consumption and limitations of 4G has led to the establishment of a heterogeneous network environment comprising both 4G and limited 5G. Consequently, this highlights the importance of throughput prediction for network service quality (QoS) and resource optimization. Traditional throughput prediction research mainly relies on the use of single attributes or extraction of attributes through correlation analysis. However, these approaches have limitations, including potential exclusion of variables with nonlinear relationships with arbitrariness and inconsistency of correlation coefficient thresholds. To overcome these limitations, this paper proposed a new approach based on Feature Importance. This method could calculate the relative importance of features used in the network and assign contribution scores to attribute categories. By utilizing these scores, throughput prediction was enhanced. This approach was applied and tested on four open network datasets. Experiments demonstrated that the proposed method successfully derived an optimal category combination for throughput prediction, reduced model complexity, and improved prediction accuracy compared to using all categories.
Analysis of Vulnerabilities in Autonomous Driving Environments through Physical Adversarial Attacks Incorporating Natural Elements
Kyuchan Cho, Woosang Im, Sooyong Jeong, Hyunil Kim, Changho Seo
http://doi.org/10.5626/JOK.2024.51.10.935
Advancements in artificial intelligence technology have significantly impacted the field of computer vision. Concurrently, numerous vulnerabilities related to adversarial attacks, which are techniques designed to force models into misclassification, have been discovered. In particular, adversarial attacks such as physical adversarial attacks in the real world, pose a serious threat to autonomous vehicle systems. These attacks include artificially created attacks such as adversarial patches and attacks that exploit natural elements to cause misclassification. A common scenario in autonomous driving environments involves obstruction of traffic signs by natural elements such as fallen leaves or snow. These elements do not remain stationary. They can cause misclassification even in fleeting moments, highlighting a critical vulnerability. Therefore, this study investigated adversarial patch attacks based on natural elements, proposing fallen leaves as a natural adversarial element. Specifically, it reviewed current trends in adversarial attack research, presented an experimental environment based on natural elements, and analyzed experimental results to assess vulnerabilities posed by fallen leaves in physical environments to autonomous vehicles.
Adaptation of A Hierarchical Cumulative Prompting with Generative Large-scale Language Models in the Legal Domain
Yeenheui Yeen, HaeIn Jung, MinJu Kim, Jeong Yang, Minhye Kim, Hyunji Jang, Myoung-Wan Koo
http://doi.org/10.5626/JOK.2024.51.7.592
This study introduces a stepwise hierarchical prompting method suitable for large-scale generative language models in complex legal reasoning tasks. Complex logical problems are decomposed into multiple steps, accumulating results from each step to set prompts for subsequent ones. It was confirmed that when this method was applied to the evaluation process of the Korean bar exam's essay-type questions, it achieved better results than fine-tuning with original data. Notably, in the final evaluation by legal experts, both tasks showed a human precision of over 0.70, indicating its capability to produce interpretations based on accurate evidence. This prompting technique suggests a solution to the hallucination issue in large language models and demonstrates its effective application. Future research will consider the introduction of a specialized retriever to reflect more accurate legal knowledge in the large language model, aiming to incorporate more precise evidence into prompts. While the current research applied the prompting method only to the legal field, it is expected to be applicable to other complex logical reasoning tasks that rely on specialized knowledge.
SASRec vs. BERT4Rec: Performance Analysis of Transformer-based Sequential Recommendation Models
Hye-young Kim, Mincheol Yoon, Jongwuk Lee
http://doi.org/10.5626/JOK.2024.51.4.352
Sequential recommender systems extract interests from user logs and use them to recommend items the user might like next. SASRec and BERT4Rec are widely used as representative sequential recommendation models. Existing studies have utilized these two models as baselines in various studies, but their performance is not consistent due to differences in experimental environments. This research compares and analyzes the performance of SASRec and BERT4Rec on six representative sequential recommendation datasets. The experimental result shows that the number of user-item interactions has the largest impact on BERT4Rec training, which in turn leads to the performance difference between the two models. Furthermore, this research finds that the two learning methods, which are widely utilized in sequential recommendation settings, can also have different effects depending on the popularity bias and sequence length. This shows that considering dataset characteristics is essential for improving recommendation performance.
Explainable Artificial Intelligence in Molecular Graph Classification
Yeongyeong Son, Yewon Shin, Sunyoung Kwon
http://doi.org/10.5626/JOK.2024.51.2.157
With the advancement of artificial intelligence (AI), there is a growing need for explainable artificial intelligence (XAI). Recently, Graph neural network-based XAI research has been actively conducted, but it mainly focuses on generic graphs. Due to the distinctive characteristics relying on the chemical properties of molecular graphs, we emphasize the necessity for research to investigate whether existing XAI techniques can provide interpretability in molecular graphs. In this paper, we employ existing XAI techniques to molecular graphs and assess them quantitatively and qualitatively to see their interpretability. Furthermore, we examine the outcomes after standardizing the significance ratio of essential features, highlighting the significance of sparsity as one of the XAI evaluation metrics.
Exploring Neural Network Models for Road Classification in Personal Mobility Assistants: A Comparative Study on Accuracy and Computational Efficiency
Gwanghee Lee, Sangjun Moon, Kyoungson Jhang
http://doi.org/10.5626/JOK.2023.50.12.1083
With the increasing use of personal mobility devices, the frequency of traffic accidents has also risen, with most accidents resulting from collisions with cars or pedestrians. Notably, the compliance rate of the traffic rules on the roads is low. Auxiliary systems that recognize and provide information about roads could help reduce the number of accidents. Since road images have distinct material characteristics, models studied in the field of image classification are suitable for application. In this study, we compared the performance of various road image classification models with parameter counts ranging from 2 million to 30 million, enabling the selection of the appropriate model based on the situation. The majority of the models achieved an accuracy of over 95%, with most models surpassing 99% in the top-2 accuracy. Of the models, MobileNet v2 had the fewest parameters while still exhibiting excellent performance and EfficientNet had stable accuracy across all classes, surpassing 90% accuracy.
Multidimensional Subset-based Systems for Bias Elimination Within Binary Classification Datasets
KyeongSu Byun, Goo Kim, Joonho Kwon
http://doi.org/10.5626/JOK.2023.50.5.383
As artificial intelligence technology develops, artificial intelligence-related fairness issues are drawing attention. As a result, many related studies have been conducted on this issue, but most of the research has focused on developing models and training methods. Research on removing bias existing in data used for learning, which is a fundamental cause, is still insufficient. Therefore, in this paper, we designed and implemented a system that divides the biases existing within the data into label biases and subgroup biases and removes the biases to generate datasets with improved fairness. The proposed system consists of two steps: (1) subset generation and (2) bias removal. First, the subset generator divides the existing data into subsets on formed by a combination of values in an datasets. Subsequently, the subset is divided into dominant and weak groups based on the fairness indicator values obtained by validating the existing datasets based on the validation datasets. Next, the bias remover reduces the bias shown in the subset by repeating the process of sequentially extracting and verifying the dominant group of each subset to reduce the difference from the weak group. Afterwards, the biased subsets are merged and a fair data set is returned. The fairness indicators used for the verification use the F1 score and the equalized odd. Comprehensive experiments with real-world Census incoming data, COMPAS data, and bank marketing data as verification data demonstrated that our proposed system outperformed the existing technique by yielding a better fairness improvement rate and providing more accuracy in most machine learning algorithms.
Improving the Performance of Knowledge Tracing Models using Quantized Correctness Embeddings
Yoonjin Im, Jaewan Moon, Eunseong Choi, Jongwuk Lee
http://doi.org/10.5626/JOK.2023.50.4.329
Knowledge tracing is a task of monitoring the proficiency of knowledge based on learners" interaction records. Despite the flexible usage of deep neural network-based models for this task, the existing methods disregard the difficulty of each question and result in poor performance for learners who get the easy question wrong or the hard question correct. In this paper, we propose quantizing the learners’ response information based on the question difficulty so that the knowledge tracing models can learn both the response and the difficulty of the question in order to improve the performance. We design a method that can effectively discriminate between negative samples with a high percentage of correct answer rate and positive samples with a low percentage of correct answer rate. Toward this end, we use sinusoidal positional encoding (SPE) that can maximize the distance difference between embedding representations in the latent space. Experiments show that the AUC value is improved to a maximum of 17.89% in the target section compared to the existing method.
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