• International Journal of Internet, Broadcasting and Communication
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• 제10권4호
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• pp.50-64
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• 2018

This paper describes a visual object detection approach utilizing ensemble based machine learning. Object detection methods employing 1D features have the benefit of fast calculation speed. However, for real image with complex background, detection accuracy and performance are degraded. In this paper, we propose an ensemble learning algorithm that combines a 1D feature classifier and 2D DNF (Disjunctive Normal Form) classifier to improve the object detection performance in a single input image. Also, to improve the computing efficiency and accuracy, we propose a feature selecting method to reduce the computing time and ensemble algorithm by combining the 1D features and 2D DNF features. In the verification experiments, we selected the Haar-like feature as the 1D image descriptor, and demonstrated the performance of the algorithm on a few datasets such as face and vehicle.

• Journal of Communications and Networks
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• 제13권2호
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• pp.125-131
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• 2011

Rapid strides are being made to bring to reality the technology of wearable sensors for monitoring patients' physiological data.We study the problem of automatically detecting anomalies in themeasured blood glucose levels. The normal daily measurements of the patient are used to train a hidden Markov model (HMM). The structure of the HMM-its states and output symbols-are selected to accurately model the typical transitions in blood glucose levels throughout a 24-hour period. The learning of the HMM is done using historic data of normal measurements. The HMM can then be used to detect anomalies in blood glucose levels being measured, if the inferred likelihood of the observed data is low in the world described by the HMM. Our simulation results show that our technique is accurate in detecting anomalies in glucose levels and is robust (i.e., no false positives) in the presence of reasonable changes in the patient's daily routine.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.305-318
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• 2022

Abusive behaviors have become a common issue in many online social media platforms. Profanity is common form of abusive behavior in online. Social media platforms operate the filtering system using popular profanity words lists, but this method has drawbacks that it can be bypassed using an altered form and it can detect normal sentences as profanity. Especially in Korean language, the syllable is composed of graphemes and words are composed of multiple syllables, it can be decomposed into graphemes without impairing the transmission of meaning, and the form of a profane word can be seen as a different meaning in a sentence. This work focuses on the problem of filtering system mis-detecting normal phrases with profane phrases. For that, we proposed the deep learning-based framework including grapheme and syllable separation-based word embedding and appropriate CNN structure. The proposed model was evaluated on the chatting contents from the one of the famous online games in South Korea and generated 90.4% accuracy.

• 상하수도학회지
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• 제38권2호
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• pp.83-93
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• 2024

In this study, we present a sewer pipe inspection technique through a combination of active sonar technology and deep learning algorithms. It is difficult to inspect pipes containing water using conventional CCTV inspection methods, and there are various limitations, so a new approach is needed. In this paper, we introduce a inspection method using active sonar, and apply an auto encoder deep learning model to process sonar data to distinguish between normal and abnormal pipelines. This model underwent training on sonar data from a controlled environment under the assumption of normal pipeline conditions and utilized anomaly detection techniques to identify deviations from established standards. This approach presents a new perspective in pipeline inspection, promising to reduce the time and resources required for sewer system management and to enhance the reliability of pipeline inspections.

• Journal of Information Processing Systems
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• 제14권3호
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• pp.727-739
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• 2018

This study evaluates the viewpoints of user focus incidents using microblog sentiment analysis, which has been actively researched in academia. Most existing works have adopted traditional supervised machine learning methods to analyze emotions in microblogs; however, these approaches may not be suitable in Chinese due to linguistic differences. This paper proposes a new microblog sentiment analysis method that mines associated microblog emotions based on a popular microblog through user-building combined with spectral clustering to analyze microblog content. Experimental results for a public microblog benchmark corpus show that the proposed method can improve identification accuracy and save manually labeled time compared to existing methods.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.453-461
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• 2021

Many deep learning approaches have been studied for image classification in computer vision. However, there are not enough data to generate accurate models in medical fields, and many datasets are not annotated. This study presents a new method that can use both unlabeled and labeled data. The proposed method is applied to classify cervix images into normal versus cancerous, and we demonstrate the results. First, we use a patch self-supervised learning for training the global context of the image using an unlabeled image dataset. Second, we generate a classifier model by using the transferred knowledge from self-supervised learning. We also apply attention learning to capture the local features of the image. The combined method provides better performance than state-of-the-art approaches in accuracy and sensitivity.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• Educational Technology International
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• 제6권2호
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• pp.59-67
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• 2005

The primary purposes of this study are to develop a Situated Learning Support System based on an MMORPG (Massively Multiplayer Online Role Playing Game) and to investigate applications of Situated Learning theory both hypothetically and practically. In Situated Leaning theory, cognition is interpreted as a dynamic system related to situation, context and activity. According to this theory, learning context, social interaction and personal direct experience are also emphasized. A virtual reality learning system based on an MMORPG provides context, social interaction and a learning environment able to provide direct experiences. However, such a system has been difficult for teachers to develop. This study aims to develop a support system facilitating the construction of a Situated Learning System based on an MMORPG. This study proposes new research and practical applications of Situated Learning theory using educational games.

• 한국융합학회논문지
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• 제9권6호
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• pp.33-38
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• 2018

본 논문에서는 단일 클래스만을 학습하여 차량에 대한 새로운 공격을 탐지한다. 분류 성능 평가를 위해 Car-Hacking 데이터셋을 사용한다. Car-Hacking 데이터셋은 실제 차량의 OBD-II 포트를 통해 CAN (Controller Area Network) 트래픽을 로깅하여 생성된다. 이 데이터셋에는 네 가지 공격 유형이 포함된다. 실험에 사용한 단일 클래스 분류기법은 정상 클래스만을 학습하여 비정상인 공격 클래스를 분류해내는 비지도 학습이다. 비지도 학습 방법을 사용하는 경우에 훈련 과정에서 네거티브 인스턴스를 사용하지 않기 때문에 고효율의 분류 성능을 내는 것은 어렵다. 하지만, 비지도 학습은 라벨이 없는 새로운 공격 데이터를 분류하는데 적합한 장점이 있다. 본 연구에서는 네트워크 침입탐지 시스템에서 서명기반의 규칙으로 탐지하기 어려운 새로운 공격 유형을 탐지하기 위해 단일 클래스 분류기를 사용한다. 제안 방법은 새로운 공격을 모두 탐지하고 정상데이터에 대해서도 효율적인 분류 성능을 보이는 파라미터 조합을 제시한다.

• 한국도서관정보학회지
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• 제43권2호
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• pp.227-246
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• 2012

출판업계가 불황을 타개하기 위한 목적 등으로 이른바 학습만화를 고안해 내면서 만화를 통해 교과학습 및 관련 사물을 이해하려는 학생들의 수가 날로 증가하고 있다. 하지만 만화에 대한 유해성이 높은 독서자료라는 과거의 인식은 아직도 많은 학부모들로 하여금 자녀들의 만화독서를 금지시키는 요인이 되고 있는 것이 현실이다. 그렇다면 실제 만화를 독서하고 있는 학생들과 이를 대하는 학부모들은 만화에 대하여 어떠한 인식을 가지고 있는가를 알아볼 필요가 있다. 본 연구는 3개 초등학교 3학년부터 6학년까지 한반씩 총 12개 반의 291명의 학생들과 203명의 학부모 설문지가 유효하다고 판단하여 분석한 결과, 상당수의 학생들이 학교도서관 등의 정보원으로부터 학습만화를 입수하여 독서한 경험을 가지고 있으며 학습만화가 학업성취에 상당히 도움이 되고 있으며 두 집단에서 학습만화 독서가 일반 독서로 이어지는 독서전이에 도움이 된다고 인식한 것으로 나타났다. 따라서 초등학교 도서관운영자는 학습만화를 중요한 교수학습매체로써 인식해야 하며 아울러 독서흥미유발을 위한 견인매체로써 학습만화를 활용하여야 하고 정규교과목 시간에서도 교수학습활동에 학습만화를 보조교재로 활용할 것을 제안하였다.