• International journal of advanced smart convergence
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• 제9권4호
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• pp.156-166
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• 2020

In this paper, to improve spectral efficiency and mitigate interference in coordinated millimeter-wave systems, we proposes an optimal user group and beam selection scheme. The proposed scheme improves spectral efficiency by mitigating intra- and inter-cell interferences (ICI). By examining the effective channel capacity for all possible user combinations, user combinations and beams with minimized ICI can be selected. However, implementing this in a dense environment of cells and users requires highly complex computational abilities, which we have investigated applying multiclass classifiers based on machine learning. Compared with the conventional scheme, the numerical results show that our proposed scheme can achieve near-optimal performance, making it an attractive option for these systems.

• 한국컴퓨터정보학회논문지
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• 제26권10호
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• pp.19-26
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• 2021

딥러닝 기반 악성코드 탐지 및 분류모델의 성능은 특성집합을 어떻게 구성하느냐에 따라 크게 좌우된다. 본 논문에서는 CNN 기반의 안드로이드 악성코드 탐지 시 탐지성능을 극대화할 수 있는 최적의 특성집합(feature set)을 선정하는 방법을 제안한다. 특성집합에 포함될 특성은 기계학습 및 딥러닝에서 특성추출을 위해 널리 사용되는 Chi-Square test 알고리즘을 사용하여 선정하였다. CICANDMAL2017 데이터세트를 대상으로 선정된 36개의 특성을 이용하여 CNN 모델을 학습시킨 후 악성코드 탐지성능을 측정한 결과 이진분류에서는 99.99%, 다중분류에서는 98.55%의 Accuracy를 달성하였다.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1486-1495
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• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• 지능정보연구
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• 제18권2호
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• pp.29-45
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• 2012

회사채 신용등급은 투자자의 입장에서는 수익률 결정의 중요한 요소이며 기업의 입장에서는 자본비용 및 기업 가치와 관련된 중요한 재무의사결정사항으로 정교한 신용등급 예측 모형의 개발은 재무 및 회계 분야에서 오랫동안 전통적인 연구 주제가 되어왔다. 그러나, 회사채 신용등급 예측 모형의 성과와 관련된 가장 중요한 문제는 등급별 데이터의 불균형 문제이다. 예측 문제에 있어서 데이터 불균형(Data imbalance) 은 사용되는 표본이 특정 범주에 편중되었을 때 나타난다. 데이터 불균형이 심화됨에 따라 범주 사이의 분류경계영역이 왜곡되므로 분류자의 학습성과가 저하되게 된다. 본 연구에서는 데이터 불균형 문제가 존재하는 다분류 문제를 효과적으로 해결하기 위한 다분류 기하평균 부스팅 기법 (Multiclass Geometric Mean-based Boosting MGM-Boost)을 제안하고자 한다. MGM-Boost 알고리즘은 부스팅 알고리즘에 기하평균 개념을 도입한 것으로 오분류된 표본에 대한 학습을 강화할 수 있으며 불균형 분포를 보이는 각 범주의 예측정확도를 동시에 고려한 학습이 가능하다는 장점이 있다. 회사채 신용등급 예측문제를 활용하여 MGM-Boost의 성과를 검증한 결과 SVM 및 AdaBoost 기법과 비교하여 통계적으로 유의적인 성과개선 효과를 보여주었으며 데이터 불균형 하에서도 벤치마킹 모형과 비교하여 견고한 학습성과를 나타냈다.

• Journal of Multimedia Information System
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• 제6권2호
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• pp.49-60
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• 2019

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.

• 반도체디스플레이기술학회지
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• 제21권3호
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• pp.92-97
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• 2022

Plastics are classified into 7 types such as PET (PETE), HDPE, PVC, LDPE, PP, PS, and Other for separation and recycling. Recently, large corporations advocating ESG management are replacing them with bioplastics. Incineration and landfill of disposal of plastic waste are responsible for air pollution and destruction of the ecosystem. Because it is not easy to accurately classify plastic materials with the naked eye, automated system-based screening studies using various sensor technologies and AI-based software technologies have been conducted. In this paper, NIR scanning devices considering the NIR wavelength characteristics that appear differently for each plastic material and a system that can identify the type of plastic by learning the NIR spectrum data collected through it. The accuracy of plastic material identification was evaluated through a decision tree-based SVM model for multiclass classification on NIR spectral datasets for 8 types of plastic samples including biodegradable plastic.

• 정보보호학회논문지
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• 제32권5호
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• pp.1009-1017
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• 2022

로그 데이터는 정보 시스템의 주요 동작과 상태를 이해하고 판단하는 근거로 사용되어 왔으며, 여러 보안 분야 응용에서도 중요한 입력 데이터로 사용된다. 로그 데이터로부터 필요한 정보를 얻어 이를 근거로 의사 결정을 하고, 적절한 대응 방안을 취하는 것은 시스템을 보호하고 안정적으로 운영하는 데 있어 필수적인 요소이지만, 로그의 종류와 양이 폭발적으로 증가함에 따라 기존 도구들로는 효과적이고 효율적인 대응이 쉽지 않은 상황이다. 이에 본 연구에서는 자연어 처리 기반의 머신 러닝을 이용해 멀티 소스 이벤트 로그의 보안 심각도를 여러 단계로 분류하는 방법을 제안하였으며, 472,972건의 훈련 및 테스트 샘플을 이용하여 실험을 수행한 결과 99.59%의 정확도를 달성하였다.

• 열처리공학회지
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• 제36권2호
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• pp.69-76
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• 2023

In this study, we present a system for identifying equivalent grades of standardized wire rod steel based on alloy composition using machine learning techniques. The system comprises two models, one based on a supervised multi-class classification algorithm and the other based on unsupervised autoencoder algorithm. Our evaluation showed that the supervised model exhibited superior performance in terms of prediction stability and reliability of prediction results. This system provides a useful tool for non-experts seeking similar grades of steel based on alloy composition.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• Communications for Statistical Applications and Methods
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• 제15권1호
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• pp.43-50
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• 2008

Multinomial logistic regression is a well known multiclass classification method in the field of statistical learning. More recently, the development of sparse multinomial logistic regression model has found application in microarray classification, where explicit identification of the most informative observations is of value. In this paper, we propose a sparse multinomial kernel logistic regression model, in which the sparsity arises from the use of a Laplacian prior and a fast exact algorithm is derived by employing a bound optimization approach. Experimental results are then presented to indicate the performance of the proposed procedure.