Imbalanced data is a major challenge in network security applications, particularly in DDoS (Distributed Denial of Service) traffic classification, where detecting minority classes is critical for timely and cost-effective defense. Existing machine learning and deep learning models often fail to accurately classify such underrepresented attack types, leading to significant degradation in performance. In this study, we propose an adaptive sampling strategy that combines oversampling and undersampling techniques to address the class imbalance problem at the data level. We evaluated our approach using benchmark DDoS traffic datasets, where it demonstrated improved classification performance across key metrics, including accuracy, recall, and F1-score, compared to baseline models and conventional sampling methods. The results indicate that the proposed adaptive sampling approach improved minority class detection performance under the tested conditions, thereby improving the reliability of sensor-driven security systems. This work contributes a robust and adaptable method for imbalanced data classification, with potential applications across simulated sensor environments where anomaly detection is essential.

To build an automated system detecting toxic chemicals from Raman spectra, we have to obtain sufficient data of toxic chemicals. However, it usually costs high to gather Raman spectra of toxic chemicals in diverse situations. Tackling this problem, we develop methods to generate synthetic Raman spectra of DMMP and 2-CEES without actual experiments. First, we propose certain mathematical transforms to augment few original Raman spectra. Then, we train deep generative models to generate more realistic and diverse data. Analyzing synthetic Raman spectra of toxic chemicals generated by our methods through visualization, we qualitatively verify that the data are sufficiently similar to original data and diverse. For conclusion, we obtain a synthetic dataset of DMMP and 2-CEES with the proposed algorithm.

The combination of ENB101-LNP, which inhibits E6/E7 of HPV 16, with cisplatin, demonstrated significant anticancer activity in the xenograft mouse model of cervical cancer.

Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single model for a dataset. By contrast, the proposed algorithm first determines a mathematical cover of the dataset via unsupervised clustering and subsequently constructs multiple models, each of which is trained with the data in one cover. For each data point in the test dataset, an optimal model is selected for regression. Results of empirical experiments show that the proposed method improves the obsolescence prediction accuracy and accelerates the training procedure. A novelty of this study is that it demonstrates the effectiveness of unsupervised clustering methods for improving supervised regression algorithms.

Product obsolescence occurs in every production line in the industry as better-performance or cost-effective products become available. A proactive strategy for obsolescence allows firms to prepare for such events and reduces the manufacturing loss, which eventually leads to positive customer satisfaction. We propose a machine learning-based algorithm to forecast the obsolescence date of electronic diodes, which has a limitation on the amount of data available. The proposed algorithm overcomes these limitations in two ways. First, an unsupervised clustering algorithm is applied to group the data based on their similarity and build independent machine-learning models specialized for each group. Second, a hybrid method including several reliable techniques is constructed to improve the prediction accuracy and overcome the limitation of the lack of data. It is empirically confirmed that the prediction accuracy of the obsolescence date for the electrical component data is improved through the proposed clustering-based hybrid method.

This study deals with one of the most important issues for understanding financial markets, future asset fluctuations. Predicting the direction of asset fluctuations accurately is very difficult due to the uncertainty of the stock market, the influence of various economic indicators, and the sentiment of investors, etc. In this study, we present a new method to improve the effectiveness of machine learning by selecting appropriate training data using an adaptive method. The application to various sector data of the S&P 500 and many machine learning methods shows that the proposed adaptive data selection algorithm improves the prediction accuracy of the stock price direction. In addition, it can be seen that the adaptive data selection method increases the return on the asset investment.