• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Journal of the Korean Data and Information Science Society
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• 제21권1호
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• pp.155-162
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• 2010

In this paper we propose a doubly penalized kernel method which estimates both the mean function and the variance function simultaneously by kernel machines for heteroscedastic autoregressive data. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which aect the performance of proposed method. Simulated examples are provided to indicate the usefulness of proposed method for the estimation of mean and variance functions.

• Journal of the Korean Data and Information Science Society
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• 제21권4호
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• pp.765-775
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• 2010

For the accelerated failure time (AFT) model a lot of effort has been devoted to develop effective estimation methods. AFT model assumes a linear relationship between the logarithm of event time and covariates. In this paper we propose a semiparametric support vector machine to consider situations where the functional form of the effect of one or more covariates is unknown. The proposed estimating equation can be computed by a quadratic programming and a linear equation. We study the effect of several covariates on a censored response variable with an unknown probability distribution. We also provide a generalized approximate cross-validation method for choosing the hyper-parameters which affect the performance of the proposed approach. The proposed method is evaluated through simulations using the artificial example.

• Journal of the Korean Data and Information Science Society
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• 제24권2호
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• pp.391-399
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• 2013

Partially linear regression is capable of providing more complete description of the linear and nonlinear relationships among random variables. In support vector regression (SVR) the hyper-parameters are known to affect the performance of regression. In this paper we propose an iterative reweighted least squares (IRWLS) procedure to solve the quadratic problem of partially linear support vector regression with a modified loss function, which enables us to use the generalized approximate cross validation function to select the hyper-parameters. Experimental results are then presented which illustrate the performance of the partially linear SVR using IRWLS procedure.

• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.925-931
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• 2009

In this paper we propose a variance function estimation method for replicated data based on averages of squared residuals obtained from estimated mean function by the least squares support vector machine. Newton-Raphson method is used to obtain associated parameter vector for the variance function estimation. Furthermore, the cross validation functions are introduced to select the hyper-parameters which affect the performance of the proposed estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• 제16권2호
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• pp.383-388
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• 2009

In this paper we propose a variance function estimation method based on kernel trick for replicated data or data consisted of sample variances. Newton-Raphson method is used to obtain associated parameter vector. Furthermore, the generalized approximate cross validation function is introduced to select the hyper-parameters which affect the performance of the proposed variance function estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.379-382
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• 1997

We present a hybrid self-tuning method using hyper elliptic Gaussian membership function. The proposed method applies a GA to identify the structure and the parameters of a fuzzy inference system. The parameters obtained by a GA, however, are near optimal solutions. So we solve this problem through a backpropagation-type gradient method. It is called GA hybrid self-tuning method in this paper. We provide a numerical example to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.

• Nuclear Engineering and Technology
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• 제48권3호
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• pp.778-784
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• 2016

HyperGam-U was recently developed to determine uranium enrichment based on ${\gamma}$- and X-ray spectroscopy analysis. The $XK_{\alpha}$ region of the uranium spectrum contains 13 peaks for $^{235}U$ and $^{238}U$ and is used mainly for analysis. To describe the X-ray peaks, a Lorentzian broadened shape function was used, and methods were developed to reduce the number of fitting parameters for decomposing the strongly overlapping peaks using channel-energy, energy-width, and energy-efficiency calibration functions. For validation, eight certified reference material uranium samples covering uranium enrichments from 1% to 99% were measured using a high-resolution planar high-purity germanium detector and analyzed using the HyperGam-U code. When corrections for the attenuation and true coincidence summing were performed for the detection geometry in this experiment, the goodness of fit was improved by a few percent. The enrichment bias in this study did not exceed 2% compared with the certified values for all measured samples.

• 한국컴퓨터정보학회논문지
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• 제25권7호
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• pp.47-55
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• 2020

본 논문에서는 앵커 프리 방법을 이용한 FCN(Fully Convolutional Network)기반의 1단계 다중 크기 얼굴 검출기를 제안한다. 최근 대부분의 연구들은 사전 정의된 앵커를 사용하여 얼굴이 있을 만한 위치를 예측한다. 그러나 사전 정의 앵커를 이용함으로써 학습 시 하이퍼 파라미터의 설정과 추가적인 계산이 필요하다. 제안하는 방법의 핵심 아이디어는 앵커 프리 방법을 사용하여 하이퍼 파라미터를 없애고 여러 개의 특징 맵을 사용함으로써 클래스 내 불균형 문제를 완화하는 것이다. 이 방법들은 다음과 같은 효과가 있다. 첫째로 사전정의 앵커를 없앰으로써 앵커와 관련된 하이퍼 파라미터와 추가적인 계산을 피한다. 둘째로 클래스 내 불균형을 완화하기 위해 여러개의 특징 맵으로부터 얼굴을 예측한다. 정량적 평가를 통해 제안하는 방법에 따른 검출 성능을 평가 및 분석한다. FDDB(Face Detection Dataset & Benchmark) 데이터 셋의 실험 결과에서 제안하는 방법이 효과가 있음을 증명했다.

• 한국방사선학회논문지
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• 제17권5호
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• pp.709-715
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• 2023

본 연구의 목적은, U-net 딥러닝 모델을 이용하여 CT 영상에서의 노이즈 감소 효과를 다양한 하이퍼 파라미터를 적용하여 평가하였다. 노이즈가 포함된 입력 영상 생성을 위하여 Gaussian 노이즈를 적용하였고, 총 1300장의 CT 영상에서 train, validation, test 셋의 비율을 8:1:1로 유지하여 U-net 모델을 적용하여 학습하였다. 연구에서 적용된 하이퍼파라미터는 최적화 함수 Adagrad, Adam, AdamW와 학습횟수 10회, 50회, 100회와 학습률 0.01, 0.001, 0.0001을 적용하였으며, 최대 신호 대 잡음비와 영상의 변동계수 값을 계산하여 정량적으로 분석하였다. 결과적으로 U-net 딥러닝 모델을 적용한 노이즈 감소는 영상의 질을 향상시킬 수 있으며 노이즈 감소 측면에서 유용성을 입증하였다.