With the rapid increase in vehicle use during the fourth Industrial Revolution, road resources have reached their supply limit. Active studies have therefore been conducted on intelligent transportation systems (ITSs) to realize traffic management systems utilizing fewer resources. As part of an ITS, real-time traffic services are provided to improve user convenience. Such services are applied to prevent traffic congestion and disperse existing traffic. Therefore, these services focus on immediacy at the expense of accuracy. As these services typically rely on measured data, the accuracy of the models are contingent on the data collection. Therefore, this study proposes a long short-term memory (LSTM)-based traffic congestion prediction approach based on the correction of missing temporal and spatial values. Before making predictions, the proposed prediction method applies pre-processing that consists of outlier removal using the median absolute deviation of the traffic data and the correction of temporal and spatial values using temporal and spatial trends and pattern data. In previous studies, data with time-series features have not been appropriately learned. To address this problem, the proposed prediction method uses an LSTM model for time-series data learning. To evaluate the performance of the proposed method, the mean absolute percentage error (MAPE) was calculated for comparison with other models. The MAPE of the proposed method was found to be the best of the compared models, at approximately 5%.

Arrhythmia detection through deep learning is mainly classified through supervised learning. Supervised learning progresses through the labeled data. However, in the medical field, it is challenging to collect ECG data of patients with arrhythmia than ECG data of healthy people, and thus data bias occurs. Therefore, if you use a supervised learning model, there are problems with lack of data and imbalance between labels that arise during learning. Accordingly, this study proposes the decision boundary-based Anomaly detection model using improved AnoGAN from ECG data. In this study, at the time of learning, the loss of the Generator does not reduce, but the loss of a Discriminator lowers. Even if the Generator and Discriminator were designed to have the same learning count, the learning competency of Generator was judged to be lowered. In repeated experiments, it was found that the best loss balance was achieved when the learning count of Discriminator was 1 and that of Generator was 4. Another problem is that the decision boundary of AnoGAN is subjective. Accordingly, the repeated experiments based on F-measure are conducted to determine a decision boundary. For performance evaluation, the accuracy of the model is evaluated on the basis of Epoch, and the goodness-of-fit of the model is evaluated on the basis of AUC and F-measure. According to the evaluation of F-measure, the model has the best performance when the decision boundary is 200. In terms of Epoch, the model has the highest accuracy when the Epoch is 10. In addition, the proposed model has better goodness-of-fit than AnoGAN.