• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4317-4335
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• 2018

Semi-supervised multi-view latent space learning is gaining considerable popularity recently in many machine learning applications due to the high cost and difficulty to obtain the large amount of label information of data. Although some semi-supervised multi-view latent space learning methods have been presented, there is still much space for improvement: 1) How to learn latent discriminant intact feature representations by employing data of multiple views; 2) How to exploit the manifold structure of both labeled and unlabeled point in the learned latent intact space effectively. To address the above issues, we propose an approach called semi-supervised multi-view manifold discriminant intact space learning ($SM^2DIS$) for image classification in this paper. $SM^2DIS$ aims to seek a manifold discriminant intact space for data of different views by making use of both the discriminant information of labeled data and the manifold structure of both labeled and unlabeled data. Experimental results on MNIST, COIL-20, Multi-PIE, and Caltech-101 databases demonstrate the effectiveness and robustness of our proposed approach.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1251-1258
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• 2019

Intelligent tutoring system enables users to effectively learn by utilizing various artificial intelligence techniques. For instance, it can recommend a proper curriculum or learning method to individual users based on their learning history. To do this effectively, user's characteristics need to be analyzed and classified based on various aspects such as interest, learning ability, and personality. Even though data labeled by the characteristics are required for more accurate classification, it is not easy to acquire enough amount of labeled data due to the labeling cost. On the other hand, unlabeled data should not need labeling process to make a large number of unlabeled data be collected and utilized. In this paper, we propose a semi-supervised learning method based on feedback variational auto-encoder(FVAE), which uses both labeled data and unlabeled data. FVAE is a variation of variational auto-encoder(VAE), where a multi-layer perceptron is added for giving feedback. Using unlabeled data, we train FVAE and fetch the encoder of FVAE. And then, we extract features from labeled data by using the encoder and train classifiers with the extracted features. In the experiments, we proved that FVAE-based semi-supervised learning was superior to VAE-based method in terms with accuracy and F1 score.

• Asia pacific journal of information systems
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• 제32권2호
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• International Journal of Contents
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• 제16권2호
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• pp.8-18
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• 2020

Data is critical in deep learning but the scarcity of data often occurs in research, especially in the preparation of the paired training data. In this paper, document image binarization with unpaired data is studied by introducing adversarial learning, excluding the need for supervised or labeled datasets. However, the simple extension of the previous unpaired training to binarization inevitably leads to poor performance compared to paired data training. Thus, a new deep learning approach is proposed by introducing a multi-diversity of higher quality generated images. In this paper, a two-stage model is proposed that comprises the generative adversarial network (GAN) followed by the U-net network. In the first stage, the GAN uses the unpaired image data to create paired image data. With the second stage, the generated paired image data are passed through the U-net network for binarization. Thus, the trained U-net becomes the binarization model during the testing. The proposed model has been evaluated over the publicly available DIBCO dataset and it outperforms other techniques on unpaired training data. The paper shows the potential of using unpaired data for binarization, for the first time in the literature, which can be further improved to replace paired data training for binarization in the future.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.1006-1018
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• 2020

Dataset is getting more import to develop a learning based algorithm. Quality of the algorithm definitely depends on dataset. So we introduce new dataset over 200 thousands images which are fully labeled multi modal sensor data. Proposed dataset was designed and constructed for researchers who want to develop detection, tracking, and action classification in outdoor environment for surveillance scenarios. The dataset includes various images and multi modal sensor data under different weather and lighting condition. Therefor, we hope it will be very helpful to develop more robust algorithm for systems equipped with difference kinds of sensors in outdoor application. Case studies with the proposed dataset are also discussed in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1104-1120
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• 2020

The chest X-rays are a common way to diagnose lung cancer or pneumonia. In particular, the finding of a lung nodule is the most important problem in the early detection of lung cancer. Recently, a lot of automatic diagnosis algorithms have been studied to find the lung nodules missed by doctors. The algorithms are typically based on segmentation network like U-Net. However, the occurrence of false positives that similar to lung nodules present outside the lungs can severely degrade performance. In this study, we propose a multi-task learning method that simultaneously learns the lung region and nodule-labeled data based on the prior knowledge that lung nodules exist only in the lung. The proposed method significantly reduces false positives outside the lung and improves the recognition rate of lung nodules to 83.8 F1 score compared to 66.6 F1 score of single task learning with U-net model. The experimental results on the JSRT public dataset demonstrate the effectiveness of the proposed method compared with other baseline methods.

• 융합정보논문지
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• 제12권1호
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• pp.31-38
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• 2022

본 논문은 전문가 독립적 비지도 신경망 학습 기반 다변량 시계열 데이터 분석 모델인 MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder)의 실제 현장에서의 적용과 Auto-encoder 기반인 MSCRED 모델의 한계인, 학습 데이터가 오염되지 않아야 된다는 점을 극복하기 위한 학습 데이터 샘플링 기법인 Subset Sampling Validation을 제시한다. 라벨 분류가 되어있는 발전소 장비의 진동 데이터를 이용하여 1) 학습 데이터에 비정상 데이터가 섞여 있는 상황을 재현하고, 이를 학습한 경우 2) 1과 같은 상황에서 Subset Sampling Validation 기법을 통해 학습 데이터에서 비정상 데이터를 제거한 경우의 Anomaly Score를 비교하여 MSCRED와 Subset Sampling Validation 기법을 유효성을 평가한다. 이를 통해 본 논문은 전문가 독립적이며 오류 데이터에 강한 이상 진단 프레임워크를 제시해, 다양한 다변량 시계열 데이터 분야에서의 간결하고 정확한 해결 방법을 제시한다.

• 지능정보연구
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• 제24권1호
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• pp.1-23
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• 2018

E-commerce 환경의 발전으로 소비자들은 다양한 상품들을 한 자리에서 폭 넓게 비교할 수 있게 되었다. 하지만 온라인 쇼핑몰에 올라와있는 상당량의 주요 상품 정보들이 이미지 형태이기 때문에 컴퓨터가 인지할 수 있는 텍스트 기반 검색 시스템에 반영될 수 없다는 한계가 존재한다. 이러한 한계점은 일반적으로 기존 기계학습 기술 및 OCR(Optical Character Recognition) 기술을 활용해, 이미지 형태로 된 키워드를 인식함으로써 개선할 수 있다. 그러나 기존 OCR 기술은 이미지 안에 글자가 아닌 그림이 많고 글자 크기가 작으면 낮은 인식률을 보인다는 문제가 있다. 이에 본 연구에서는 기존 기술들의 한계점을 해결하기 위하여, 딥러닝 기반 사물인식 모형 중 하나인 SSD(Single Shot MultiBox Detector)를 개조하여 이미지 형태의 상품 카탈로그 내의 텍스트 인식모형을 설계하였다. 하지만 이를 학습시키기 위한 데이터를 구축하는 데 상당한 시간과 비용이 필요했는데, 이는 지도학습의 방법론을 따르는 SSD 모형은 훈련 데이터마다 직접 정답 라벨링을 해줘야 하기 때문이다. 본 연구는 이러한 문제점을 해결하기 위해 '훈련 데이터 자동 생성 프로그램'을 함께 개발하였다. 훈련 데이터 자동 생성 프로그램을 통해 수작업으로 데이터를 만드는 것에 비하여 시간과 비용을 대폭 절감할 수 있었으며, 생성된 훈련용 데이터를 통해 모형의 인식 성능을 높일 수 있었다. 더 나아가 실험연구를 통해 자동으로 생성된 훈련 데이터의 특징별로 인식기 모형의 성능에 얼마나 큰 영향을 끼치는지 알아보고, 성능 향상에 효과적인 데이터의 특징을 분석하였다. 본 연구를 통해서 개발된 상품 카탈로그 내 텍스트 인식모형과 훈련 데이터 자동 생성 프로그램은 온라인 쇼핑몰 판매자들의 상품 정보 등록 수고를 줄여줄 수 있으며, 구매자들의 상품 검색 시 결과의 정확성을 향상시키는 데 기여할 수 있을 것으로 기대한다.

• 한국컴퓨터정보학회논문지
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• 제28권10호
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• pp.27-35
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• 2023

본 논문에서는 복합소재 생산 분야에서 수요가 높은 프리프레그 섬유 제조 공정에 딥러닝 기반의 결함 검출 및 분류 시스템을 적용하여 생산성을 높이는 과정을 제안한다. 다양한 조건별 다량의 불량 발생으로 해결방안이 필요한 토우 프리프레그 제조 장비에 적용하기 위해 우선 결함 감지와 분류 모델 제작에 필요한 카메라 및 조명을 선정하여 최적의 환경을 구축하였다. 그리고 다중 분류 모델 제작에 필요한 데이터를 수집하고 정상 및 불량 조건에 따라 라벨링을 진행하였다. 다중 분류 모델은 CNN 기반으로 제작하였으며 VGGNet과 MobileNet, ResNet 등의 사전 학습모델을 적용하여 성능을 비교하고 정확도 및 손실 그래프로 개선 방향을 파악한다. 주요 문제로 과적합 문제를 확인하여 개선하기 위해 데이터 증강 및 Dropout 기법을 적용하여 보완하였다. 모델에 대한 성능 평가를 위해 혼돈행렬을 성능지표로 한 성능 평가를 진행하였으며 99% 이상의 성능을 확인하였다. 또한, 실제 공정에 적용하여 실시간 획득된 이미지에 대한 분류 결과를 확인해보며 판별 값이 정확히 도출되는지 확인한다.