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- 검색키워드
- 저자 : Kenji Takakura (검색결과 2 건)
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A Clustering Method for Improving Performance of Anomaly-Based Intrusion Detection System
SONG, Jungsuk,OHIRA, Kenji,TAKAKURA, Hiroki,OKABE, Yasuo,KWON, Yongjin The Institute of Electronics, Information and Comm 2008 IEICE transactions on information and systems Vol.91 No.5
<P>Intrusion detection system (IDS) has played a central role as an appliance to effectively defend our crucial computer systems or networks against attackers on the Internet. The most widely deployed and commercially available methods for intrusion detection employ signature-based detection. However, they cannot detect unknown intrusions intrinsically which are not matched to the signatures, and their methods consume huge amounts of cost and time to acquire the signatures. In order to cope with the problems, many researchers have proposed various kinds of methods that are based on unsupervised learning techniques. Although they enable one to construct intrusion detection model with low cost and effort, and have capability to detect unforeseen attacks, they still have mainly two problems in intrusion detection: <I>a low detection rate and a high false positive rate</I>. In this paper, we present a new clustering method to improve the detection rate while maintaining a low false positive rate. We evaluated our method using KDD Cup 1999 data set. Evaluation results show that superiority of our approach to other existing algorithms reported in the literature.</P>
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Serum lactate dehydrogenase is a possible predictor of platinum resistance in ovarian cancer
Asami Ikeda,Ken Yamaguchi,Hajime Yamakage,Kaoru Abiko,Noriko Satoh-Asahara,Kenji Takakura,Ikuo Konishi 대한산부인과학회 2020 Obstetrics & Gynecology Science Vol.63 No.6
ObjectiveThe need for tailoring ovarian cancer treatments to individual patients is increasing. This study aimed to evaluate theprognostic value of pretreatment laboratory test data for predicting the response and survival outcomes of platinumbasedchemotherapy in ovarian cancer. MethodsWe enrolled 270 patients with ovarian cancer diagnosed at the Kyoto Medical Center (n=120; group A) and KyotoUniversity (n=150; group B). Data on 9 blood parameters (neutrophil to lymphocyte ratio [NLR], platelet to lymphocyterate [PLR], C-reactive protein, lactate dehydrogenase [LDH], glucose, total cholesterol, high-density lipoprotein [HDL],low-density lipoprotein, and triglyceride levels), cancer pathology, cancer stage, cytoreduction outcomes, serumcancer antigen 125 levels, platinum-free interval (PFI), disease-free survival (DFS), and overall survival were assessedretrospectively. ResultsNLR, PLR, LDH, and HDL were significantly different in advanced stage patients (P<0.001, <0.001, 0.029, and <0.001,respectively). The Kaplan-Meier curves revealed that high LDH level (≥250 U/L) was associated with reduced PFI(P=0.037 and 0.012) and DFS (P=0.007 and 0.002) in groups A and B, respectively. High NLR (≥4) was associated withreduced DFS in both groups (P=0.036 and 0.005, respectively). LDH showed higher area under the curve (AUC) valuesin predicting platinum resistance with a PFI of less than 6 months and 12 months (AUC=0.606 and 0.646, respectively)than NLR. In the multivariate analysis, LDH remained significant (P=0.019) after adjusting for the 9 blood parameters. ConclusionSerum LDH level may possibly predict platinum resistance and prognosis in ovarian cancer and may be useful whendeveloping precision medicine for individual patients.
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