• Soft Computing and Machine Intelligence
• /
• v.1 no.1
• /
• pp.24-30
• /
• 2021

The accelerated growth of the internet and the enormous amount of data availability has become the primary reason for machine learning applications for data analysis and, more specifically, pattern recognition and decision making. In this paper, we focused on the crowdfunding site Kickstarter and collected the comments in order to apply neural networks to classify the projects based on the sentiments of backers. The power of customer reviews and sentiment analysis has motivated us to apply this technique in crowdfunding to find timely indications and identify suspicious activities and mitigate the risk of money loss.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.15
• /
• pp.6239-6241
• /
• 2014

Purpose: To compare the diagnostic accuracy of the neutrophil/lymphocyte ratio (NLR) with the platelet/lymphocyte ratio (PLR) in predicting malignancy of pelvic masses which are pre-operatively malignant suspicious. Materials and Methods: In this retrospective study we evaluated the clinical features of patients with ovarian masses which had pre-operatively been considered suspicious for malignancy. The patients whose intraoperative frozen sections were malign were classified as the study group, while those who had benign masses were the control group. Data recorded were age of the patient, diameter of the mass, pre-operative serum Ca 125 levels, platelet count, neutrophil/lymphocyte ratio and platelet/lymphocyte ratio. Results: There was statistically significantly difference between the groups in terms of age, diameter of the mass, serum Ca 125 levels, platelet number and platelet/lymphocyte ratio. Mean neutrophil/lymphocyte ratios showed no difference between the groups. ROC curve analysis showed that age, serum Ca 125 levels, platelet number and PLR were discriminative markers in predicting malignancy in adnexal masses. Conclusions: According to the current study, serum Ca 125 levels, pre-operative platelet number and PLR may be good prognostic factors, while NLR is an ineffective marker in predicting the malignant characteristics of a pelvic mass.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.114-114
• /
• 2020

Water contamination in a water distribution network (WDN) is harmful since it directly induces the consumer's health problem and suspends water service in a wide area. Actions need to be taken rapidly to countermeasure a contamination event. A contaminant source ident ification (CSI) is an important initial step to mitigate the harmful event. Here, a CSI approach focused on determining the contaminant intrusion possible location and time (PLoT) is introduced. One of the methods to discover the PLoT is an inverse calculation to connect all the paths leading to the report specification of a sensor. A filtering procedure is then applied to narrow down the PLoT using the results from individual sensors. First, we spatially reduce the suspect intrusion points by locating the highly suspicious nodes that have similar intrusion time. Then, we narrow the possible intrusion time by matching the suspicious intrusion time to the reported information. Finally, a likelihood-score is estimated for each suspect. Another important aspect that needs to be considered in CSI is that there are inherent uncertainties, such as the variations in user demand and inaccuracy of sensor data. The uncertainties can lead to overlooking the real intrusion point and time. To reflect the uncertainties in the CSI process, the Monte-Carlo Simulation (MCS) is conducted to explore the ranges of PLoT. By analyzing all the accumulated scores through the random sets, a spread of contaminant intrusion PLoT can then be identified in the network.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.1
• /
• pp.113-120
• /
• 2018

Angiography and CT angiography are used widely for the examination of vascular diseases, but the diagnosis of such diseases is made mostly by the subjective judgment of the inspector. This paper proposes a method for detecting the suspicious regions of stenosis and aneurysm in the inner surfaces of 3D blood vessel models reconstructed from medical images. Initially, the 3D curve-skeletons of the blood vessel models and the contours at the nodes of the curve-skeletons were generated. Next, the 3D curve-skeletons were divided into a set of branches and the areas of normal contours of nodes located in each branch were calculated. The nodes whose contours contain suspicious regions were detected by taking into account the average area, maximum and minimum areas, and the area difference between the adjacent normal contours. The diagnosis of stenosis and aneurysm can be supported by properly visualizing the suspicious regions detected. The suspicious regions of the disease were identified by implementing and testing it using several data sets of human blood vessels, highlighting the usefulness of the proposed method.

• Korean Journal of Radiology
• /
• v.22 no.4
• /
• pp.559-567
• /
• 2021

Objective: To evaluate the impact of multiparametric magnetic resonance imaging (mpMRI) before confirmatory prostate biopsy in patients under active surveillance (AS). Materials and Methods: This retrospective study included 170 patients with Gleason grade 6 prostate cancer initially enrolled in an AS program between 2011 and 2019. Prostate mpMRI was performed using a 1.5 tesla (T) magnetic resonance imaging system with a 16-channel phased-array body coil. The protocol included T1-weighted, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced imaging sequences. Uroradiology reports generated by a specialist were based on prostate imaging-reporting and data system (PI-RADS) version 2. Univariate and multivariate analyses were performed based on regression models. Results: The reclassification rate at confirmatory biopsy was higher in patients with suspicious lesions on mpMRI (PI-RADS score ≥ 3) (n = 47) than in patients with non-suspicious mpMRIs (n = 61) and who did not undergo mpMRIs (n = 62) (66%, 26.2%, and 24.2%, respectively; p < 0.001). On multivariate analysis, presence of a suspicious mpMRI finding (PI-RADS score ≥ 3) was associated (adjusted odds ratio: 4.72) with the risk of reclassification at confirmatory biopsy after adjusting for the main variables (age, prostate-specific antigen density, number of positive cores, number of previous biopsies, and clinical stage). Presence of a suspicious mpMRI finding (adjusted hazard ratio: 2.62) was also associated with the risk of progression to active treatment during the follow-up. Conclusion: Inclusion of mpMRI before the confirmatory biopsy is useful to stratify the risk of reclassification during the biopsy as well as to evaluate the risk of progression to active treatment during follow-up.

• Convergence Security Journal
• /
• v.18 no.5_1
• /
• pp.45-51
• /
• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.

• Bioinformatics and Biosystems
• /
• v.2 no.1
• /
• pp.24-27
• /
• 2007

Human cell regulatory mechanism is one of suspicious problems among biologists. Here we tried to uncover the human breast cancer cell regulatory mechanism from gene expression data (Marc J. Van de vijver, et. al., 2002) using a module network algorithm which is suggested by Segal, et. al.(2003) Finally, we derived a module network which consists of 50 modules and 10 tree depths. Moreover, to validate this candidate network, we applied a GO enrichment test and known transcription factor-target relationships from Transfac(R) (V. Matys, et. al, 2006) and HPRD database (Peri, S. et al., 2003).

• Journal of Multimedia Information System
• /
• v.7 no.2
• /
• pp.125-136
• /
• 2020

Nowadays, cloud computing is becoming more popular among companies. However, the characteristics of cloud computing such as a virtualized environment, constantly changing, possible to modify easily and multi-tenancy with a distributed nature, it is difficult to perform attack detection with traditional tools. This work proposes a solution which aims to collect traffic packets data by using Flume and filter them with Spark Streaming so it is possible to only consider suspicious data related to HTTP Slow Rate Denial-of-Service attacks and reduce the data that will be stored in Hadoop Distributed File System for analysis with the FP-Growth algorithm. With the proposed system, we also aim to address the difficulties in attack detection in cloud environment, facilitating the data collection, reducing detection time and enabling an almost real-time attack detection.

• Journal of Information Processing Systems
• /
• v.17 no.4
• /
• pp.675-689
• /
• 2021

Nowadays, cloud computing is being adopted for more organizations. However, since cloud computing has a virtualized, volatile, scalable and multi-tenancy distributed nature, it is challenging task to perform attack detection in the cloud following conventional processes. This work proposes a solution which aims to collect web server logs by using Flume and filter them through Spark Streaming in order to only consider suspicious data or data related to denial-of-service attacks and reduce the data that will be stored in Hadoop Distributed File System for posterior analysis with the frequent pattern (FP)-Growth algorithm. With the proposed system, we can address some of the difficulties in security for cloud environment, facilitating the data collection, reducing detection time and consequently enabling an almost real-time attack detection.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.2
• /
• pp.67-74
• /
• 2018

This study has proposed a methodology to detect suspicious facilities that occupy national and public land by using the cadastral and digital maps. First, we constructed a spatial database of national & public land based on the cadastral maps by linking its management ledger. Using the PNU (Parcel Number) code as a key field, the data managed by different institutions are integrated into a single spatial information DB (database) and then, the use or nonuse state of each parcel is confirmed on the cadastral map. Next, we explored the suspicious facilities that existed in the unused parcel by utilizing the digital topographical map. Then, the proposed methodology was applied for various regions and tested its feasibility. Through this study, it will be possible to improve the utilization of digital maps and to manage the national and public land efficiently and economically.