• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1153-1165
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• 2009

There are many traditional statistical tests for randomness. We can consider recurrence plots as an exploratory graphical tool for evaluating randomness.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.807-819
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• 2013

A recurrence plot can be used as a graphical exploratory data analysis tool before confirmatory time series analysis. With the recurrence plot, we can obtain the structural pattern of the time series and recognize the structural change points in a time series at a glance. Korean stock data shows the usefulness of the recurrence plot as a graphical exploratory data analysis tool for time series data.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1103-1112
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• 2013

Exploratory data analysis focuses mostly on data exploration instead of model fitting. We can use the recurrence plot as a graphical exploratory data analysis tool. With the recurrence plot, we can obtain the structural pattern of the time series and recognize the structural change points in time series at a glance.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1243-1249
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• 2016

Background: Expression of p53, cyclin D1, p21 (WAF1) and Ki-67 (MIB1) was evaluated in oral squamous cell carcinoma (OSCC) to test whether levels of these markers at invasive tumour fronts (ITFs) could predict the development of local recurrence. Materials and Methods: Archived paraffin-embedded specimens from 51 patients with T1/T2 tumours were stained immunohistochemically and analysed quantitatively. Local recurrence-free survival was tested with Kaplan-Meier survival plots (log-rank test) using median values to define low and high expression groups and with a Cox's proportional hazards model in which the expression scores were entered as continuous variables. Results: The assessment of expression of all markers was highly reliable, univariate analysis showing that patients with clear surgical margins, with low cyclin D1 and high p21 expression at the ITF had the best local recurrence-free survival. Multivariate analysis showed that these three parameters were independent prognostic factors but that neither p53 nor MIB1 expression were of prognostic value. Conclusions: Assessment of p53, cyclin D1, p21 (WAF1), and Ki-67 (MIB1) at the ITF could help to predict local recurrence in early stage oral squamous cell carcinoma cases.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.14-21
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• 2020

In order to provide confidential services between two communicating parties, block data encryption using a symmetric secret key is applied. A power analysis attack on a cryptosystem is a side channel-analysis method that can extract a secret key by measuring the power consumption traces of the crypto device. In this paper, we propose an attack model that can recover the secret key using a power analysis attack based on a deep learning convolutional neural network (CNN) algorithm. Considering that the CNN algorithm is suitable for image analysis, we particularly adopt the recurrence plot (RP) signal processing method, which transforms the one-dimensional power trace into two-dimensional data. As a result of executing the proposed CNN attack model on an XMEGA128 experimental board that implemented the AES-128 encryption algorithm, we recovered the secret key with 22.23% accuracy using raw power consumption traces, and obtained 97.93% accuracy using power traces on which we applied the RP processing method.

• Journal of Gastric Cancer
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• v.22 no.2
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• pp.120-134
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• 2022

Purpose: This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods: This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as any-cause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results: The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions: Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC.