Malware is any malicious program that can attack the security of other computer systems for various purposes. The threat of malware has significantly increased in recent years. To protect our computer systems, we need to analyze an executable file to decide whether it is malicious or not. In this paper, we propose two malware classification methods: malware classification using Simhash and PCA (MCSP), and malware classification using Simhash and linear transform (MCSLT). PCA uses the symmetrical covariance matrix. The former method combines Simhash encoding and PCA, and the latter combines Simhash encoding and linear transform layer. To verify the performance of our methods, we compared them with basic malware classification using Simhash and CNN (MCSC) using tanh and relu activation. We used a highly imbalanced dataset with 10,736 samples. As a result, our MCSP method showed the best performance with a maximum accuracy of 98.74% and an average accuracy of 98.59%. It showed an average F1 score of 99.2%. In addition, the MCSLT method showed better performance than MCSC in accuracy and F1 score.

Breast cancer is a highly contagious disease that has killed many people all over the world. It can be fully recovered from early detection. To enable the early detection of the breast cancer, it is very important to classify accurately whether it is breast cancer or not. Recently, the deep learning approach method on the medical images such as these histopathologic images of the breast cancer is showing higher level of accuracy and efficiency compared to the conventional methods. In this paper, the breast cancer histopathological image that is difficult to be distinguished was analyzed visually. And among the deep learning algorithms, the CNN(Convolutional Neural Network) specialized for the image was used to perform comparative analysis on whether it is breast cancer or not. Among the CNN algorithms, VGG16 and InceptionV3 were used, and transfer learning was used for the effective application of these algorithms.The data used in this paper is breast cancer histopathological image dataset classifying the benign and malignant of BreakHis. In the 2-class classification task, InceptionV3 achieved 98% accuracy. It is expected that this deep learning approach method will support the development of disease diagnosis through medical images.

Although the effects of age, period, and cohort (APC) on suicide are important, previous work in this area may have been invalid because of an identification problem. We analyzed these effects under three different scenarios to identify vulnerable groups and thus overcame the identification problem. We extracted the annual numbers of suicides from the National Death Register of Korea (1992–2015) and estimated the APC effects. The annual average suicide rates in 1992–2015 were 31.5 and 14.7 per 100,000 males and females, respectively. The APC effects on suicide were similar in both sexes. The age effect was clearly higher in older subjects, in contrast to the minimal changes apparent during earlier adulthood. The birth cohort effect showed an inverted U shape; a higher cohort effect was evident in females born in the early 1980s when period drift was larger than 3.7%/year. Period effect increased sharply during the early 1990s and 2000s. We found that elderly and young females may be at a particularly high risk of suicide in Korea.