• Journal of the Korea Society for Simulation
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• v.20 no.2
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• pp.77-85
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• 2011

It is believed that the anomalies or diseases of human organs are identified by the analysis of the patterns. This paper proposes a new classification technique for the identification of cancer disease using the proteome patterns obtained from two-dimensional polyacrylamide gel electrophoresis(2-D PAGE). In the new classification method, three different classification methods such as support vector machine(SVM), multi-layer perceptron(MLP) and k-nearest neighbor(k-NN) are extended by multi-boosting method in an array of subclassifiers and the results of each subclassifier are merged by ensemble method. Genetic algorithm was applied to obtain optimal feature set in each subclassifier. We applied our method to empirical data set from cancer research and the method showed the better accuracy and more stable performance than single classifier.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Analytical Science and Technology
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• v.19 no.5
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• pp.441-448
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• 2006

11-nor-9-carboxy-${\Delta}^9$-tetrahydrocannabinol (THCCOOH) is the major metabolite of tetrahydrocannabinol (THC) which is the primary psychoactive component of marijuana. It is also the target analyte for the discrimination marijuana use. A method using solid-phase extraction (SPE) and gas chromatography/mass spectrometry (GC/MS) was developed for the determination of THCCOOH in human urine. Urine samples (3 mL) were extracted by SPE column with a cation exchange cartridge after basic hydrolysis. The eluents were then evaporated, derivatized, and injected into the GC/MS. The limits of detection (LOD) and quantitation (LOQ) were 0.4 and 1.2 ng/mL, respectively. The response was linear with a correlation coefficient of 0.999 within the concentration range of 1.2 (LLE 1.3)~50.0 ng/mL. The precision and accuracy were stable within 1.20% and the recovery was 83.6~90.7%. The recovery of SPE method was lower than that of liquid-liquid extraction (LLE), but there were no apparent differences in LOD, LOQ, precision and accuracy between the two methods. While SPE method is used as a very effective and rapid procedure for sample pretreatment, and clean extracts, LLE method was not suitable for the extraction procedure of THCCOOH in urine. The applicability of the method was proven by analyzing a urine samples from a marijuana abusers.

• Journal of Food Hygiene and Safety
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• v.35 no.4
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• pp.291-303
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• 2020

Marine organism-derived toxins have negative effects not only on human health but also in aquaculture, fisheries, and marine ecosystems. However, traditional analytical methods are insufficient in preventing this threat. In this paper, we reviewed new rapid methods of toxin detection, which have been improved by adopting diverse types of nanomaterials and technologies. Moreover, we herein describe the main strategies for toxin detection and their related sensing performance. Notably, to popularize and commercialize these newly developed technologies, simplifying the process of pre-treating real samples real samples is very important. As part of these efforts, numerous studies have reported pretreatment methods based on the antibody-immobilized magnetic nanoparticles, and some cases have applied nanoparticles to enhance the sensing performance by utilizing the intrinsic catalytic activity. Furthermore, some reports have introduced fluorescent nanoparticles, such as quantum dots, to represent the lower detection limits of conventional enzyme-based colorimetric methods and lateral flow assays. Some studies using electrochemical measurements based on aptamer-nanoparticle complexes have also been announced. In addition, as the response to new toxins generated by changes in the marine environment is still lacking, further research on diagnostic and detection is also greatly needed for these kinds of marine toxins and their derivatives.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1467-1475
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• 2023

The public-interest direct payment program involves providing direct payments to agricultural producers and rural residents through public funds, premised on performing public functions such as environmental conservation, stable food supply, and maintaining rural communities via agricultural activities. Scientific estimation of crop cultivation areas and production levels is crucial for formulating agricultural policies linked to regulating food supply, which increasingly impacts the national economy. Conducting comprehensive on-site inspections for compliance monitoring of direct payment programs has shown very low efficiency in relation to budget and time. The expansion of areas subject to compliance monitoring and various challenges in on-site inspections necessitate streamlining current monitoring methods and devising effective strategies. As a solution, the application of Remote Sensing technology and spatial information utilization, allowing swift acquisition of necessary information for policies without overall on-site visits, is being discussed as an efficient compliance monitoring method. Therefore, this study evaluated the potential use of remote sensing for improving operational efficiency in monitoring compliance with public-interest direct payment programs. Using satellite images during farming seasons in Gimje and Hapcheon, vegetation indices and spatial variations were utilized to identify cultivated areas, presence of mixed crops, validated against on-site inspection data.