• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.534-539
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• 2010

In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future observations. There are three inherent steps to build classifiers: a significant gene selection, model selection and prediction assessment. In the paper, with a focus on prediction assessment, we normalize microarray data with quantile-normalization methods that adjust quartile of all slide equally and then design a system comparing several methods to estimate 'true' prediction error of a prediction model in the presence of feature selection and compare and analyze a prediction error of them. LOOCV generally performs very well with small MSE and bias, the split sample method and 2-fold CV perform with small sample size very pooly. For computationally burdensome analyses, 10-fold CV may be preferable to LOOCV.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.94-97
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• 2003

In this Paper, propose for MLP(multilayer perception) neural network that uses optimization recognition training scheme for the wavelet transform and the numeral image add to noise, and apply this system in Numeral Recognition. As important part of original image information preserves maximum using the wavelet transform, node number of neural network and the loaming convergence time did size of input vector so that decrease. Apply in training vector, examine about change of the recognition rate as optimization recognition training scheme raises noise of data gradually. We used original image and original image added 0, 10, 20, 30, 40, 50㏈ noise (or the increase of numeral recognition rate. In case of test image added 30∼50㏈, numeral recognition rate between the original image and image added noise for training Is a little But, in case of test image added 0∼20㏈ noise, the image added 0, 10, 20, 30, 40 , 50㏈ noise is used training. Then numeral recognition rate improved 9 percent.

• The Korean Journal of Physiology and Pharmacology
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• v.10 no.2
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• pp.85-94
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• 2006

A splanchic artery occlusion for 90 min followed by reperfusion of the mesenteric circulation resulted in a severe form of circulatory shock, characterized by endothelial dysfunction, severe hypotension, marked intestinal tissue injury, and a high mortality rate. The effect of defibrotide, a complex of single-stranded polydeoxyribonucleotides having antithrombotic effect, was investigated in a model of splanchnic artery occlusion (SAO) shock in urethane anesthetized rats. Occlusion of the superior mesenteric artery for 90 min produced a severe shock state, resulting in a fatal outcome within 120 min of reperfusion in many rats. Defibrotide (10 mg/kg body weight) 10 min prior to reperfusion significantly improved mean arterial blood pressure in comparison to vehicle treated rats (p＜0.05). Defibrotide treatment also significantly attenuated in the increase of plasma amino nitrogen concentration, intestinal myeloperoxidase activity, intestinal lipid peroxidation, infiltration of neutrophils in intestine and thrombin induced adherence of neutrophils to superior mesentric artery segments. Superoxide anion and hydrogen peroxide production in $1{\mu}M$ formylmethionylleucylphenylalanine (fMLP)-activated PMNs was inhibited by defibrotide in a dose-dependent fashion. Defibrotide effectively scavenged hydrogen peroxide, but not hydroxyl radical. Treatment of SAO rats with defibrotide inhibited tumor necrosis factor-${\alpha}$, and interleukin-1${\beta}$ productions in blood in comparison with untreated rats. These results suggest that defibrotide partly provides beneficial effects by preserving endothelial function, attenuating neutrophil accumulation, and antioxidant in the ischemic reperfused splanchnic circulation

• The Korean Journal of Physiology and Pharmacology
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• v.20 no.5
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• pp.449-457
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• 2016

N-acetyl-L-cysteine (NAC) and cysteine have been implicated in a number of human neutrophils' functional responses. However, though $Ca^{2+}$ signaling is one of the key signalings contributing to the functional responses of human neutrophils, effects of NAC and cysteine on intracellular calcium concentration ($[Ca^{2+}]_i$) in human neutrophils have not been investigated yet. Thus, this study was carried out with an objective to investigate the effects of NAC and cysteine on $[Ca^{2+}]_i$ in human neutrophils. We observed that NAC ($1{\mu}M{\sim}1mM$) and cysteine ($10{\mu}M{\sim}1mM$) increased $[Ca^{2+}]_i$ in human neutrophils in a concentration-dependent manner. In NAC pre-supplmented buffer, an additive effect on N-formyl-methionine-leucine-phenylalanine (fMLP)-induced increase in $[Ca^{2+}]_i$ in human neutrophils was observed. In $Ca^{2+}$-free buffer, NAC- and cysteine-induced $[Ca^{2+}]_i$ increase in human neutrophils completely disappeared, suggesting that NAC- and cysteine-mediated increase in $[Ca^{2+}]_i$ in human neutrophils occur through $Ca^{2+}$ influx. NAC- and cysteine-induced $[Ca^{2+}]_i$ increase was effectively inhibited by calcium channel inhibitors SKF96365 ($10{\mu}m$) and ruthenium red ($20{\mu}m$). In $Na^+$-free HEPES, both NAC and cysteine induced a marked increase in $[Ca^{2+}]_i$ in human neutrophils, arguing against the possibility that $Na^+$-dependent intracellular uptake of NAC and cysteine is necessary for their $[Ca^{2+}]_i$ increasing activity. Our results show that NAC and cysteine induce $[Ca^{2+}]_i$ increase through $Ca^{2+}$ influx in human neutrophils via SKF96365- and ruthenium red-dependent way.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.57-72
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• 2020

The price of iron ore has continued to fluctuate with high demand and supply from many countries and companies. In this business environment, forecasting the price of iron ore has become important. This study developed the machine learning model forecasting the price of iron ore a one month after the trading events. The forecasting model used distributed lag model and deep learning models such as MLP (Multi-layer perceptron), RNN (Recurrent neural network) and LSTM (Long short-term memory). According to the results of comparing individual models through metrics, LSTM showed the lowest predictive error. Also, as a result of comparing the models using the ensemble technique, the distributed lag and LSTM ensemble model showed the lowest prediction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1924-1929
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• 2007

Nowadays, a lot of related data obtained from these research could be given a new present meaning to accomplish the original purpose of the whole research as a human project. The method of tumor classification based on microarray could contribute to being accurate tumor classification by finding differently expressing gene pattern statistically according to a tumor type. Therefore, the process to select a closely related informative gene with a particular tumor classification to classify tumor using present microarray technology with effect is essential. In this thesis, we used cDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer, constructed accurate tumor classification model by extracting informative gene list through normalization separately and then did performance estimation by analyzing and comparing each of the experiment results. Result classifying Multi-Perceptron classifier for selected genes using Pearson correlation coefficient represented the accuracy of 95.6%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1243-1248
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• 2008

As development in technology of bioinformatics recently mates it possible to operate micro-level experiments, we can observe the expression pattern of total genome through on chip and analyze the interactions of thousands of genes at the same time. In this thesis, we used CDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer. It analyzed and compared performance of each of the experiment result using existing DT, NB, SVM and multi-perceptron neural network classifier combined the similar scale combination method after constructing class classification model by extracting significant gene list with a similar scale combination method proposed in this paper through normalization. Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) represented the accuracy of 98.84%, which show that it improve classification performance than case to experiment using other classifier.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2259-2264
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• 2008

Significant genes are defined as genes in which the expression level characterizes a specific experimental condition. Such genes in which the expression levels differ significantly between different groups are highly informative relevant to the studied phenomenon. In this paper, first the system can detect informative genes by similarity scale combination method being proposed in this paper after normalizing data with methods that are the most widely used among several normalization methods proposed the while. And it compare and analyze a performance of each of normalization methods with multi-perceptron neural network layer. The Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) after Lowess normalization represented the improved classification performance of 98.84%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.315-320
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• 2008

Nowadays, a lot of related data obtained from these research could be given a new present meaning to accomplish the original purpose of the whole research as a human genome project. In such a thread, construction of gene expression analysis system and a basis rank analysis system is being watched newly. Recently, being identified fact that particular sub-class of tumor be related with particular chromosome, microarray started to be used in diagnosis field by doing cancer classification and predication based on gene expression information. In this thesis, we used cDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer, created system that can extract informative gene list through normalization separately and proposed combination method for selecting more significant genes. And possibility of proposed system and method is verified through experiment. That result is that PC-ED combination represent 98.74% accurate and 0.04% MSE, which show that it improve classification performance than case to experiment after generating gene list using single similarity scale.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.