• 문화기술의 융합
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• 제8권5호
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• pp.653-662
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• 2022

최근 이미지와 같은 다차원의 복잡한 패턴 인식에 많이 사용하는 CNN(Convolutional Neural Networks)을 적용한 지문분류 방법이 다양하게 연구되고 있다. CNN 기반 지문분류 방법은 일반적으로 특징추출과 분류 단계로 나누어진 두 단계의 과정을 하나로 통합하여 실행할 수 있다. 따라서 CNN 기반 방법은 지문 이미지의 특징을 자동으로 추출할 수 있으므로, 처리 과정을 단축시킬 수 있는 장점이 있다. 또한 불완전하거나 품질이 낮은 지문의 특징을 다양하게 학습할 수 있으므로, 예외 상황의 특징 추출에 대해 유연성이 있다. 본 논문에서는 CNN 기반 지문분류연구동향을 파악하고, 실험 방법 및 결과 분석을 통해 향후 연구방향에 대해 논의하고자 한다.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.42-49
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• 2021

As cyber attacks become more intelligent, there is difficulty in detecting advanced attacks in various fields such as industry, defense, and medical care. IPS (Intrusion Prevention System), etc., but the need for centralized integrated management of each security system is increasing. In this paper, we collect big data for intrusion detection and build an intrusion detection platform using deep learning and CNN (Convolutional Neural Networks). In this paper, we design an intelligent big data platform that collects data by observing and analyzing user visit logs and linking with big data. We want to collect big data for intrusion detection and build an intrusion detection platform based on CNN model. In this study, we evaluated the performance of the Intrusion Detection System (IDS) using the KDD99 dataset developed by DARPA in 1998, and the actual attack categories were tested with KDD99's DoS, U2R, and R2L using four probing methods.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.161-176
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• 2022

There has been active research in image classification using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of efficient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a unified framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with different characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.

• 한국전자통신학회논문지
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• 제15권1호
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• pp.133-138
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• 2020

본 논문은 대부분의 변형된 CNN(: Convolution Neural Networks)에서 사용하지 않는 첫 번째 컨볼루션 층(convolution layer)을 사용해 정확도 향상을 노리는 방법을 소개한다. GoogLeNet, DenseNet과 같은 CNN에서 첫 번째 컨볼루션 층에서는 기존방식(3×3 컨볼루션연산 및 배규정규화, 활성화함수)만을 사용하는데 이 부분을 RGB-csb(: RGB channel separation block)로 대체한다. 이를 통해 RGB값을 특징 맵에 적용시켜 정확성을 향상시킬 수 있는 선행연구 결과에 추가적으로, 기존 CNN과 제한된 영상 개수를 사용하여 정확도를 비교한다. 본 논문에서 제안한 방법은 영상의 개수가 적을수록 학습 정확도 편차가 커 불안정하지만 기존 CNN에 비해 정확도가 평균적으로 높음을 알 수 있다. 영상의 개수가 적을수록 평균적으로 약 2.3% 높은 정확도를 보였으나 정확도 편차는 5% 정도로 크게 나타났다. 반대로 영상의 개수가 많아질수록 기존 CNN과의 평균 정확도의 차이는 약 1%로 줄어들고, 각 학습 결과의 정확도 편차 또한 줄어든다.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.96-99
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• 2020

According to brilliant development of smart devices, many related services are being devised. And, almost every service is designed to provide user-centric services based on personal information. In this situation, to prevent unintentional leakage of personal information is essential. Conventionally, ID and Password system is used for the user authentication. This is a convenient method, but it has a vulnerability that can cause problems due to information leakage. To overcome these problem, many methods related to face recognition is being researched. Through this paper, we investigated the trend of user authentication through biometrics and a representative model for face recognition techniques. One is DeepFace of FaceBook and another is FaceNet of Google. Each model is based on the concept of Deep Learning and Distance Metric Learning, respectively. And also, they are based on Convolutional Neural Network (CNN) model. In the future, further research is needed on the equipment configuration requirements for practical applications and ways to provide actual personalized services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• 인터넷정보학회논문지
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• 제23권4호
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• pp.21-33
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• 2022

최근 추천 시스템은 영화, 음악, 온라인 쇼핑 및 SNS 등 다양한 분야들에서 광범위하게 활용되고 있으며, 추천 시스템 분야에서 1세대 모델이라고 할수 있는 Apriori 모델을 통한 연관분석부터 최근 많은 주목을 받는 딥러닝 기반 모델들까지 많은 모델들이 제안되어왔다. 추천 시스템에서 기본 모델들은 협업 필터링(Collaborative filtering) 방법, 콘텐츠 기반 필터링(Content-based filtering) 방법, 그리고 이 두 방법을 통합적으로 사용하는 하이브리드 필터링(Hybrid filtering) 방법으로 분류될 수 있다. 하지만 이러한 모델들은 최근 점점 빠르게 변화하는 사용자-아이템 간의 상호관계와 빅데이터의 발전과 같은 내외 변화 요인들에 적응하지 못하면서 점점 분야 내 방법론으로써의 지위를 잃어가고 있다. 반면, 추천 시스템 내에서 딥러닝 기반 모델들은 비선형 변환, 표현학습, 순차적 모델링, 그리고 유연성과 같은 장점들 때문에 그 비중이 높아지고 있는 추세이다. 본 논문에서는 딥러닝 기반 추천 모델들 중에서도 사용자-아이템 간의 상호작용에 대해 보다 정확하고, 유연성 있게 분석이 가능한 순차적 모델링에 적합한 순환 신경망, 합성곱 신경망, 그리고 생성적 적대 신경망 중심 기반 모델로 분류하여 비교 및 분석한다.

• Journal of information and communication convergence engineering
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• 제19권2호
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• pp.79-83
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• 2021

This paper proposes a novel image classification method based on few-shot learning, which is mainly used to solve model overfitting and non-convergence in image classification tasks of small datasets and improve the accuracy of classification. This method uses model structure optimization to extend the basic convolutional neural network (CNN) model and extracts more image features by adding convolutional layers, thereby improving the classification accuracy. We incorporated certain measures to improve the performance of the model. First, we used general methods such as setting a lower learning rate and shuffling to promote the rapid convergence of the model. Second, we used the data expansion technology to preprocess small datasets to increase the number of training data sets and suppress over-fitting. We applied the model to 10 monkey species and achieved outstanding performances. Experiments indicated that our proposed method achieved an accuracy of 87.92%, which is 26.1% higher than that of the traditional CNN method and 1.1% higher than that of the deep convolutional neural network ResNet50.

• Imaging Science in Dentistry
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• 제49권1호
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• pp.1-7
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• 2019

Purpose: Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about deep learning that were applied to the field of oral and maxillofacial radiology. Materials and Methods: A systematic review was performed using Pubmed, Scopus, and IEEE explore databases to identify articles using deep learning in English literature. The variables from 25 articles included network architecture, number of training data, evaluation result, pros and cons, study object and imaging modality. Results: Convolutional Neural network (CNN) was used as a main network component. The number of published paper and training datasets tended to increase, dealing with various field of dentistry. Conclusion: Dental public datasets need to be constructed and data standardization is necessary for clinical application of deep learning in dental field.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.