• Korean Journal of Head & Neck Oncology
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• v.39 no.1
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• pp.1-9
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• 2023

Technological advancement in human genome analysis and ICT (information & communication technologies) brought 'precision medicine' into our clinical practice. Precision medicine is a novel medical approach that provides personalized treatments tailored to each individual by precisely segmenting patient populations, based on robust data including a person's genetic information, disease information, lifestyle information, etc. Precision medicine has a potential to be applied to treating a range of tumors, in addition to non-small cell lung cancer, in which precision oncology has been actively practiced. In this article, we are reviewing precision medicine in head and neck cancer (HNC) with focus on tumor agnostic biomarkers and treatments such as NTRK, MSI-H/dMMR, TMB-H and BRAF V600E, all of which were recently approved by U.S. Food and Drug Administration (FDA).

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2125-2138
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• 2023

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objective functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.211-211
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• 2022

Amaranth is a nutritious and broadly adapted seed crop in high demand around the world. A preliminary approach for understanding the genetics of amaranth resources entails a morphologic characterization, which can provide the basis for breeding the first variety in Korea, leading to satisfying the needs of farmers and consumers. Therefore, this study aimed to evaluate the phenotypic characteristics of ten genetic amaranth accessions for the selection of outstanding accessions in terms of yield and grain quality. A randomized complete block design was used, with fifteen replications for each accession under field conditions. Five quantitative and three qualitative descriptors were evaluated with descriptive analysis. The results showed that the accessions with plant heights smaller than the average (>112.7 cm) presented lower yields and smaller seed sizes, thus decreasing the grain quality. The cluster analyses established groups of accessions with good yields (>30.1 g of seeds per plant) and stable morphological characteristics. Based on yield and morphological descriptors, the proposed selection index indicated four accessions as potential parents for amaranth breeding programs in Kora.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.156-156
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• 2023

Salinity is a major abiotic stress that limits rice productivity in many regions of the world. In order to develop salt stress tolerant rice plants, genetic engineering is a promising approach. We characterized the molecular function of rice C3H2C3 as a really interesting new gene (RING). Oryza sativa RING finger protein H2-3 (OsRFPH2-3) was highly expressed in 100 mM NaCl. To identify the localization of OsRFPH2-3, we fused vectors that include C-terminal GFP protein (35S;;OsRFPH2-3-GFP). OsRFPH2-3 was expressed in the nucleus in rice protoplasts. An in vitro ubiquitin assay demonstrated that OsRFPH2-3 possessed E3-ubiquitin ligase activity. However, the mutated OsRFPH2-3 were not possessed any E3-ubiquitin ligase activity. Under salinity conditions, OsRFPH2-3-overexpressing plants exhibited higher chlorophyll, proline, SOD, POD, CAT, and soluble sugar contents and lower H₂O₂ accumulation than wild-type plants, supporting transgenic plants with enhanced salinity tolerance phenotypes. OsRFPH2-3-overexpressing plants exhibited low Na+ accumulation and Na+/K+ ratios in their roots. Theses results suggest that overexpression of OsRFPH2-3 can make plant insensitivity about salinity conditions.

• Smart Media Journal
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• v.13 no.2
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• pp.129-135
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• 2024

In this paper, we propose a new approach that enhances the continuous optimization performance of the MAP-Elites algorithm. The existing self-referencing MAP-Elites algorithm employed the "DE/rand/1/bin" operator from the differential evolution algorithm, which, due to its lack of rotational invariance, led to a degradation in optimization performance when there were high correlations among variables. The proposed algorithm replaces the "DE/rand/1/bin" operator with the "DE/current-to-rand/1" operator. This operator, possessing rotational invariance, ensures robust performance even in cases where there are high correlations among variables. Experimental results confirm that the proposed algorithm performs better than the comparison algorithms.

• Journal of Drive and Control
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• v.21 no.1
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• pp.46-52
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• 2024

The advancement of autonomous driving technology has heightened the importance of Autonomous Mobile Robotics (AMR) within smart factories. Notably, in tasks involving the transportation of heavy objects, the consideration of weight in route optimization and path planning has become crucial. There is ongoing research on local path planning, such as Dijkstra, A^*, and RRT^*, focusing on minimizing travel time and distance within smart factory warehouses. Additionally, there are ongoing simultaneous studies on route optimization, including TSP algorithms for various path explorations and on minimizing energy consumption in mobile robotics operations. However, previous studies have often overlooked the weight of the objects being transported, emphasizing only minimal travel time or distance. Therefore, this research proposes route planning that accounts for the maximum payload capacity of mobile robotics and offers load-optimized path planning for multi-destination transportation. Considering the load, a genetic algorithm with the objectives of minimizing both travel time and distance, as well as energy consumption is employed. This approach is expected to enhance the efficiency of mobility within smart factories.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.227-249
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• 2003

Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as model construction process. Irrespective of the efficiency of a teaming procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network model. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables fur neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4513-4518
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• 2014

The prevalence of BRCA1 gene mutations in breast cancer differs between diverse ethnic groups. Relatively little information is known about patterns of BRCA1 mutations in early-onset breast cancer in women of Uighur or Han descent, the major ethnic populations of the Xinjiang region in China. The aim of this study was to identify BRCA1 mutations in Uighur and Han patients with early-onset (age <35 years), and sporadic breast cancer for genetic predisposition to breast cancer. For detection of BRCA1 mutations, we used a polymerase chain reaction single-stranded conformation polymorphism approach, followed by direct DNA sequencing in 22 Uighur and 13 Han women with early-onset sporadic breast cancer, and 32 women with benign breast diseases. The prevalence of BRCA1 mutations in this population was 22.9% (8/35) among early-onset sporadic breast cancer cases. Of these, 31.8% (7/22) of Uighur patients and 7.69% (1/13) of Han patients were found to have BRCA1 mutations. In 7 Uighur patients with BRCA1 mutations, there were 11 unique sequence alterations in the BRCA1 gene, including 4 clearly disease-associated mutations on exon 11 and 3 variants of uncertain clinical significance on exon 11, meanwhile 4 neutral variants on intron 20 or 2. None of the 11 BRCA1 mutations identified have been previously reported in the Breast Cancer Information Core database. These findings reflect the prevalence of BRCA1 mutations in Uighur women with early-onset and sporadic breast cancer, which will allow for provision of appropriate genetic counseling and treatment for Uighur patients in the Xinjiang region.