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Effective Embedding Techniques for Misbehavior Classification in Vehicular Ad-Hoc Networks
http://doi.org/10.5626/JOK.2024.51.11.970
Vehicular Ad-Hoc Networks (VANET) is a network technology enabling data transmission between vehicles that includes V2X communication, which facilitates the exchange of both external and internal vehicle information based on communication between vehicles, infrastructure, and pedestrians. However, broadcasting data containing faults or attack information within the network can lead to critical issues, making Misbehavior Detection (MBD) systems an essential technology in VANET. While recent studies have increasingly employed machine learning for MBD, the patterns of misbehavior types in VANET often resemble normal behavior, posing challenges for comprehensive and accurate classification. Existing research has suggested a hierarchical classification system to categorize misbehaviors based on different types of attacks and faults. This study proposed an embedding representation method for constructing a hierarchical classification system to improve the accuracy of misbehavior classification models. By extracting embedding vectors for multivariate time-series data through a pre-trained LSTM model, this study compressed core data related to misbehavior types and employed hierarchical clustering to group various attack types into broader categories.
Deep Learning-Based Abnormal Event Recognition Method for Detecting Pedestrian Abnormal Events in CCTV Video
Jinha Song, Youngjoon Hwang, Jongho Nang
http://doi.org/10.5626/JOK.2024.51.9.771
With increasing CCTV installations, the workload for monitoring has significantly increased. However, a growing workforce has reached its limits in addressing this issue. To overcome this problem, intelligent CCTV technology has been developed. However, this technology experiences performance degradation in various situations. This paper proposes a robust and versatile method for integrated abnormal behavior recognition in CCTV footage that could be applied in multiple situations. This method could extract frame images from videos to use raw images and heatmap representation images as inputs. It could remove feature vectors through merging methods at both image and feature vector levels. Based on these vectors, we proposed an abnormal behavior recognition method utilizing 2D CNN models, 3D CNN models, LSTM, and Average Pooling. We defined minor classes for performance validation and generated 1,957 abnormal behavior video clips for testing. The proposed method is expected to improve the accuracy of abnormal behavior recognition through CCTV footage, thereby enhancing the efficiency of security and surveillance systems.
A Study on Sales Prediction Model Based on BiLSTM-GAT Using Credit Card Transaction Data
Wonseok Jung, Dohyung Kim, Young Ik Eom
http://doi.org/10.5626/JOK.2024.51.9.807
Sales prediction using credit card transaction data is essential for understanding consumer buying patterns and market trends. However, traditional statistical and machine learning models have limitations when it comes to analyzing temporal features and the relationships between different variables, such as geographical data and sales information by service types, population, and transaction times. This paper proposes two models that can simultaneously analyze the relationships based on commercial district features and sales time-series features. To evaluate the performance of these models, we constructed graphs based on the distances and sales similarity of features between commercial districts. We then compared the performance of the proposed models with traditional time-series models, namely LSTM and BiLSTM. The results of the experiment showed that the GAT-BiLSTM model improved prediction accuracy by approximately 15% compared to the BiLSTM model, while the BiLSTM-GAT model improved it by about 29% over the BiLSTM model, as measured by RMSE.
ConTL: Improving the Performance of EEG-based Emotion Recognition via the Incorporation of CNN, Transformer and LSTM
Hyunwook Kang, Byung Hyung Kim
http://doi.org/10.5626/JOK.2024.51.5.454
This paper proposes a hybrid-network called ConTL, which is composed of a convolutional neural network (CNN), Transformer, and long short-term memory (LSTM) for EEG-based emotion recognition. Firstly, CNN is exploited to learn local features from the input EEG signals. Then, the Transformer learns global temporal dependencies from the output features. To further learn sequential dependencies of the time domain, the output features from the Transformer are fed to the bi-directional LSTM. To verify the effects of the proposed model, we compared the classification accuracies with five state-of-the-art models. There was an 0.73% improvement on SEED-IV compared to CCNN, and improvements of 0.97% and 0.63% were observed compared to DGCNN for valence and arousal of DEAP, respectively.
A Deep Learning Model for Fire Anomaly Detection in Underground Utility Tunnel based on ConvLSTM Variational AutoEncoder
http://doi.org/10.5626/JOK.2024.51.4.333
As the failure of fire detection not only leads to an escalation in disaster management costs but also inflicts significant damages and disruptions to citizens" lives and industries, accurate detection of fire anomalies is of paramount importance. There have been several studies on monitoring and managing catastrophic events using AI, IoT and digital twin technologies. However, the challenges arise from the telecommunications environment and the level of sensor maintenance, making it difficult for IoT sensors to collect data without experiencing loss or noise. This paper proposes a hybrid deep learning model called ConvLSTM-VAE that can detect anomalies by considering spatial and temporal information simultaneously, demonstrating robust results even in the presence of noise or data loss. A virtual environment modeled after the underground utility tunnel located in Ochang, Chungcheongbuk-do is constructed to collect fire data using Fire Dynamics Simulator (FDS) software. In the experiment we compared the proposed model to other time-series anomaly detection models and evalutated its predictive performance. The results show that the precision, recall, accuracy, and F1-score of ConvLSTM-VAE are 0.881579, 0.99505, 0.930693, and 0.934884, respectively, and far superior to other models in terms of its predictive performance.
A Hybrid Deep Learning Model for Real-Time Forecasting Fire Temperature in Underground Utility Tunnel Based on Residual CNN-LSTM
http://doi.org/10.5626/JOK.2024.51.2.131
Underground utility tunnels (UUTs) play major roles in sustaining the life of citizens and industries with regard to carrying electricity, telecommunication, water supply pipes. Fire is one of the most commonly common disasters in underground facilities, which can be prevented through proper management. This paper proposes a hybrid deep learning model named Residual CNN-LSTM to predict fire temperatures. Scenarios of underground facility fire outbreaks were created and fire temperature data was collected using FDS software. In the experiment, we analyzed the appropriate depth of residual learning of the proposed model and compared the performance to other predictive models. The results showed that RMSE, MAE and MAPE of Residual CNN-LSTM are each 0.061529, 0.053851, 6.007076 respectively, making Residual CNN-LSTM far superior to other models in terms of its predictive performance.
Hierarchical Representation and Label Embedding for Semantic Classification of Domestic Research Paper
Heejin Kook, Yeonghwa Kim, Sehui Yoon, Byungha Kang, Youhyun Shin
http://doi.org/10.5626/JOK.2024.51.1.41
The sentence"s meaning in the paper is that it has a hierarchical structure, and there is data imbalance among subcategories. In addition, the meaning of the sentence in the paper is closely related to its position within the paper. Existing flat classification methods mainly consider only subcategories, leading to a decrease in classification accuracy due to data imbalance. In response to this, this study proposes hierarchical representation and label embedding methods to perform hierarchical semantic classification of sentences effectively. In addition, the section names of the paper are actively utilized to represent the positional information of the paper sentences. Through experiments, it is demonstrated that the proposed method, which explicitly considers hierarchical and positional information in the KISTI domestic paper sentence semantic tagging dataset, achieves excellent performance in terms of F1 score.
Predicting Significant Blood Marker Values for Pressure Ulcer Forecasting Utilizing Feature Minimization and Selection
Yeonhee Kim, Hoyoul Jung, Jang-Hwan Choi
http://doi.org/10.5626/JOK.2023.50.12.1054
Pressure ulcers are difficult to treat once they occur, and huge economic costs are incurred during the treatment process. Therefore, predicting the occurrence of pressure ulcers is important in terms of patient suffering and economics. In this study, the correlation between the lab codes (features) and pressure ulcers obtained from blood tests of patients with spinal cord injury was analyzed to provide meaningful characteristic information for the prediction of pressure ulcers. We compare and analyze the correlation coefficients of Pearson, Spearman, and Kendall"s tau, which are mainly used in feature selection methods. In addition, the importance of features is calculated using XGBoost and LightGBM, which are machine learning methods based on gradient boosting. In order to verify the performance of this model, we use the long short-term memory (LSTM) model to predict other features using the features occupying the top-5 in importance. In this way, unnecessary features can be minimized in diagnosing pressure ulcers and guidelines can be provided to medical personnel.
LncRNA-Disease Association Prediction Model Applying Distance-based Data Labeling
Jaein Kim, Seung-Won Yoon, In-Woo Hwang, Kyu-Chul Lee
http://doi.org/10.5626/JOK.2023.50.5.420
lncRNAs are noncoding RNAs of 200 or more nucleotides. For a long time, non-coding RNA has been considered unimportant because it cannot directly produce proteins, but recent studies have reported that non-coding RNA plays a role in regulating protein expression. Abnormal expression of lncRNAs causes various diseases and predicting the associations between lncRNAs and diseases would help diagnose diseases in the early stages or prevent diseases. However, research that predicts the correlation of biological data is time-consuming and costly if it is conducted as a direct experiment. Therefore, it is important to overcome these challenges using computational methods. Therefore, in this study, we propose a lncRNA-disease association prediction model based on Long Short-Term Memory (LSTM). In addition, since negative samples were randomly generated in previous studies, there is uncertainty in the data. So this study also proposes a distance-based data labeling method that solves this uncertainty. Our model achieved the highest AUC (0.97) through the data labeling method and classification model presented in this study.
Improving Performance of Recurrent Neural Network based Recommendations by Utilizing Personal Preferences
Dong Shin Lim, Yong Jun Yang, Shin Cho
http://doi.org/10.5626/JOK.2021.48.11.1211
As the amount of content provided on the platform surged, a recommendation system became an essential element of the platform. The collaborative filtering technique is a widely used recommendation system in academia and industry, but it also has a limitation because it relies on quantitative information from consumers such as ratings and purchase history. To overcome this shortcoming, various studies have been done in a bid to improve its performance by collecting qualitative information such as review data in a model. Recently, some studies that applied recurrent neural networks showed better performance than the existing recommendation system by using time-series behavioral data only, but studies that reflect each customer"s preference in the recommendation model have not yet been made. In this paper, an improved recommendation model was presented by calculating a preference matrix based on customer log data and learning it in a recurrent neural network through an embedding vector. It was confirmed that the prediction performance was improved compared to the existing recurrent neural network recommendation model.
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