• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• International Journal of Advanced Culture Technology
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• 제5권2호
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• pp.82-89
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• 2017

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the GradientDescentOptimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

• 대한한방소아과학회지
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• 제19권1호
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• pp.67-82
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• 2005

Objectives : This study was conducted to find out the effect of Yukmijiwhangtanggamibang (YM) on growth, learning and memory of rats. Methods : It was divided SD rats into Sham group, 192 Saporin injection(SA+Saline) group and Injection of 192 Saporin with YM(SA+YM) group. Growth measure length of bone and tail. Memory performance was used aquisition test and learning retention of morris water maze. It was detected acetylcholinesterase(AChE), cholineacetyltransferase(ChAT) at medial septum and hippocampus by immunohistochemistry Results : Body Weight of the SA+YM Group increased effectively, as compared with SA Saline group. Growth of bone in the SA+YM Group increased effectively, as compared with SA+Saline group. Growth of Tail in the SA+YM Group increased effectively, as compared with SA_Saline group. The SA+YM Group in Aquisition Test improved effectively, as compared with SA+Saline group. The SA+YM Group in Learning Test improved effectively, as compared with SA+Saline group. The numbers of ChAT cells in Medial septum increased effectively, as compared with SA+Saline group. The numbers of ChAT cells in CA1 of Hippocampus increased, but was not effective. Conclusion : These results suggest that YM has an improving effect on the impaired learning through the effects on memory registration and retrieval.

• 정보처리학회논문지B
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• 제12B권3호
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• pp.343-348
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• 2005

본 연구에서는 신경망 학습의 일반화 성능과 학습속도를 개선시키기 위한 인자들의 결합 효과를 살펴본다. 신경망 학습에서 중요한 평가 척도로서 여기서 고려하는 인자들에는 초기 가중값의 범위와 학습률 그리고 계수조정 등이 있다. 특히 초기 가중값과 학습률을 고정시킨 후 새롭게 조정된 계수들을 단계적으로 변화시키는 새로운 인자 결합방법을 이용한다. 이를 통하여 신경망 학습량과 학습속도를 비교해 보고, 계수조정을 통한 개선된 학습 영향을 살펴본다. 그리고 비선형의 단순한 예제를 이용한 실증분석을 통하여 신경망 모형의 일반화 성능과 학습 속도 개선을 위한 각 인자들의 개별 효과와 결합 효과를 살펴보고 그 개선 방안을 논의한다.

• 동의생리병리학회지
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• 제19권2호
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• pp.434-440
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• 2005

This study was conducted to find out the effect of Bojungikgitanggamibang(BIT) on growth, learning and memory of rats. The effects of BIT on learning and memory performance were examined in normal or memory impaired mice by using water maze task. Memory was impaired by 192 saporin. Body weight and growth of bone and tail of sample group were not significant compared with those of control groups. Acquisition test of water maze revealed that acqusitive ability of sample group significantly improved on 4,5th day compared with control group, while retentive ability of sample group was not significant. ChAT cell numbers of medial septum of sample group was significant compared with control group, and so was those of CA1, CA2 parts of hippocampus. On ChAT cell numbers of hippocampus, in CA2 part. These results suggest that BIT has an improving effect on the impaired learning through the effects on memory registration and retrieval.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.212-222
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• 2021

Multiple waves of COVID-19 highlighted one crucial aspect of this pandemic worldwide that factors affecting the spread of COVID-19 infection are evolving based on various regional and local practices and events. The introduction of vaccines since early 2021 is expected to significantly control and reduce the cases. However, virus mutations and its new variant has challenged these expectations. Several countries, which contained the COVID-19 pandemic successfully in the first wave, failed to repeat the same in the second and third waves. This work focuses on COVID-19 pandemic control and management in Saudi Arabia. This work aims to predict new cases using deep learning using various important factors. The proposed method is called Deep Learning and Dynamic Weighing-based (DLDW) COVID-19 cases prediction method. Special consideration has been given to the evolving factors that are responsible for recent surges in the pandemic. For this purpose, two weights are assigned to data instance which are based on feature importance and dynamic weight-based time. Older data is given fewer weights and vice-versa. Feature selection identifies the factors affecting the rate of new cases evolved over the period. The DLDW method produced 80.39% prediction accuracy, 6.54%, 9.15%, and 7.19% higher than the three other classifiers, Deep learning (DL), Random Forest (RF), and Gradient Boosting Machine (GBM). Further in Saudi Arabia, our study implicitly concluded that lockdowns, vaccination, and self-aware restricted mobility of residents are effective tools in controlling and managing the COVID-19 pandemic.

• 자동차안전학회지
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• 제15권1호
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• pp.55-62
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• 2023

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.213-219
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• 2023

In recent years, popularity of deep learning (DL) is increased due to its ability to extract features from Hyperspectral images. A lack of discrimination power in the features produced by traditional machine learning algorithms has resulted in poor classification results. It's also a study topic to find out how to get excellent classification results with limited samples without getting overfitting issues in hyperspectral images (HSIs). These issues can be addressed by utilising a new learning network structure developed in this study.EfficientNet-B4-Based Convolutional network (EN-B4), which is why it is critical to maintain a constant ratio between the dimensions of network resolution, width, and depth in order to achieve a balance. The weight of the proposed model is optimized by Search and Rescue Operations (SRO), which is inspired by the explorations carried out by humans during search and rescue processes. Tests were conducted on two datasets to verify the efficacy of EN-B4, with Indian Pines (IP) and the University of Pavia (UP) dataset. Experiments show that EN-B4 outperforms other state-of-the-art approaches in terms of classification accuracy.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2019년도 추계학술대회
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• pp.41-43
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• 2019

Image-to-image 변환에서 인상적인 성능을 보이는 StarGAN 은 모델의 성능에 중요한 영향을 끼치는 adversarial weight, classification weight, reconstruction weight 라는 세가지 하이퍼파라미터의 결정을 전제로 하고 있다. 본 연구에서는 이 중 conditional GAN loss 인 adversarial loss 와 classification loss 를 대치할 수 있는 attribute loss를 제안함으로써, adversarial weight와 classification weight 를 최적화하는 데 걸리는 시간을 attribute weight 의 최적화에 걸리는 시간으로 대체하여 하이퍼파라미터 탐색에 걸리는 시간을 획기적으로 줄일 수 있게 하였다. 제안하는 attribute loss 는 각 특징당 GAN 을 만들 때 각 GAN 의 loss 의 합으로, 이 GAN 들은 hidden layer 를 공유하기 때문에 연산량의 증가를 거의 가져오지 않는다. 또한 reconstruction loss 를 단순화시켜 연산량을 줄인 simplified content loss 를 제안한다. StarGAN 의 reconstruction loss 는 generator 를 2 번 통과하지만 simplified content loss 는 1 번만 통과하기 때문에 연산량이 줄어든다. 또한 이미지 Framing 을 통해 배경의 왜곡을 방지하고, 양방향 성장을 통해 학습 속도를 향상시킨 아키텍쳐를 제안한다.

• 한국가정과교육학회지
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• 제32권2호
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• pp.99-115
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• 2020

이 연구의 목적은 가정 교과와 미디어 리터러시 교육의 관련성을 알아보는 것이다. 이를 위해 중학교 '기술·가정 2' 교과서 12종의 학습 자료를 분석하였다. 분석 기준으로 '미디어 리터러시 수행 목표'를 선택한 후, 빈도 및 내용 분석 및 세 번의 협의를 통해 미디어 자료의 분포와 '미디어 리터러시 수행 목표'의 반영을 분석하였다. 본 연구의 결과는 다음과 같다. 첫째, 12종 교과서의 전체 학습자료 중 미디어자료를 이용한 학습자료는 39.6%를 차지하였고 출판사별로 미디어자료를 이용한 학습자료의 빈도와 비중에서 차이가 있었다. 미디어 유형에 따라서는 '인쇄' 68.3%, '영상' 16.7%, '디지털' 13.5%로 분포하였고, 단방향 미디어의 사용은 86.5%로 대부분을 차지하였다. 둘째, 출판사별로 미디어 리터러시 수행목표가 반영된 학습자료의 빈도와 비중에서 차이가 있었고 총체적인 미디어 리터러시 함양을 위해 학습내용의 보완이 필요하였다. 학습자료에 반영된 미디어 리터러시 수행목표 중 '의미 이해와 전달'은 58.8%로 가장 많이 반영된 수행목표였지만 미디어를 통한 쌍방향 소통은 부재하였다. 이러한 교과서 분석 결과를 바탕으로 가정과 미디어 리터러시 수행목표를 수정하여 제안하면 다음과 같다. '의미 이해와 자기표현', '소통과 사회참여', '책임있는 미디어 이용', '감상과 향유', '미디어 기술 활용', '정보 검색과 선택', '창작과 제작', '비판적 이해와 평가'이다. 이상의 연구 결과를 바탕으로 현장의 가정 수업에서 가정과 미디어 리터러시 수행목표를 학습요소로 반영하여 가정과 교육에서 융합적인 미디어 리터러시 교육을 수행하는데 도움이 되는 기초 자료로 활용되길 기대한다.