• 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.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.98-107
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• 2018

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2180-2197
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• 2019

With the proliferation of the Android malicious applications, malware becomes more capable of hiding or confusing its malicious intent through the use of code obfuscation, which has significantly weaken the effectiveness of the conventional defense mechanisms. Therefore, in order to effectively detect unknown malicious applications on the Android platform, we propose DroidVecDeep, an Android malware detection method using deep learning technique. First, we extract various features and rank them using Mean Decrease Impurity. Second, we transform the features into compact vectors based on word2vec. Finally, we train the classifier based on deep learning model. A comprehensive experimental study on a real sample collection was performed to compare various malware detection approaches. Experimental results demonstrate that the proposed method outperforms other Android malware detection techniques.

• International journal of advanced smart convergence
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• 제10권3호
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• pp.163-171
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• 2021

Recently, Machine Learning-based visualization approaches have been proposed to combat the problem of malware detection. Unfortunately, these techniques are exposed to Adversarial examples. Adversarial examples are noises which can deceive the deep learning based malware detection network such that the malware becomes unrecognizable. To address the shortcomings of these approaches, we present Block-matching and 3D filtering (BM3D) algorithm and deep image prior based denoising technique to defend against adversarial examples on visualization-based malware detection systems. The BM3D based denoising method eliminates most of the adversarial noise. After that the deep image prior based denoising removes the remaining subtle noise. Experimental results on the MS BIG malware dataset and benign samples show that the proposed denoising based defense recovers the performance of the adversarial attacked CNN model for malware detection to some extent.

• 디지털융복합연구
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• 제20권4호
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• pp.389-396
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• 2022

딥러닝(Deep Learning)은 퍼셉트론을 기반으로 하고 있으며 현재에는 이미지 인식, 음성 인식, 객체 검출 및 약물 개발 등과 같은 다양한 영역에서 사용되고 있다. 이에 따라 학습 알고리즘이 다양하게 제안되었고 신경망을 구성하는 뉴런수도 연구자마다 많은 차이를 보이고 있다. 본 연구는 현재 대표적으로 사용되고 있는 확률적 경사하강법(SGD), 모멘텀법(Momentum), AdaGrad, RMSProp 및 Adam법의 뉴런수에 따른 학습 특성을 분석하였다. 이를 위하여 1개의 입력층, 3개의 은닉층, 1개의 출력층으로 신경망을 구성하였고 활성화함수는 ReLU, 손실 함수는 교차 엔트로피 오차(CEE)를 적용하였고 실험 데이터셋은 MNIST를 사용하였다. 그 결과 뉴런수는 100~300개, 알고리즘은 Adam, 학습횟수(iteraction)는 200회가 딥러닝 학습에서 가장 효율적일 것으로 결론을 내렸다. 이러한 연구는 향후 새로운 학습 데이터가 주어졌을 경우 개발될 알고리즘과 뉴런수의 기준치에 함의를 제공할 것이다.

• 한국정보전자통신기술학회논문지
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• 제12권6호
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• pp.591-596
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• 2019

본 논문에서는 로컬 PC의 성능이 주는 영향이 적은 딥러닝 동작 구조를 구현하였다. 일반적으로, 딥러닝 모델은 많은 연산량을 가지고 있어 처리하는 PC의 성능에 영향을 많이 받는다. 본 논문에서는 이와 같은 제약 사항을 줄이기 위하여 AWS와 스트리밍 서버를 이용하여 딥러닝 동작을 구현하였다. 첫 번째, AWS에서 딥러닝 연산을 하여 로컬 PC의 성능이 떨어지더라도 딥러닝 동작이 정상적으로 작동할 수 있도록 하였다. 하지만 AWS를 통해 연산 시 입력에 대해 출력의 실시간성이 떨어진다. 두 번째, 스트리밍 서버를 이용하여 딥러닝 모델의 실시간성을 증가시킨다. 스트리밍 서버를 사용하지 않았을 경우 한 이미지씩 처리하거나 이미지를 쌓아서 동영상으로 만들어 처리하여야 하기 때문에 실시간성이 떨어진다. 성능 비교 실험을 위한 딥러닝 모델로는 YOLO v3모델을 사용하였고, AWS의 인스턴스들 및 고성능 GPU인 GTX1080을 탑재한 로컬 PC의 성능을 비교하였다. 시뮬레이션 결과 AWS의 인스턴스인 p3 인스턴스를 사용하였을 때 한 이미지 당 테스트 시간이 0.023444초로써 고성능 GPU인 GTX1080을 탑재한 로컬 PC의 한 이미지 당 테스트 시간인 0.027099초와 유사하다는 결과를 얻었다.

• 로봇학회논문지
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• 제15권4호
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• 로봇학회논문지
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• 제19권1호
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• pp.92-97
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• 2024

This paper provides a study of distributional perspective on reinforcement learning for application in mobile robot navigation. Mapless navigation algorithms based on deep reinforcement learning are proven to promising performance and high applicability. The trial-and-error simulations in virtual environments are encouraged to implement autonomous navigation due to expensive real-life interactions. Nevertheless, applying the deep reinforcement learning model in real tasks is challenging due to dissimilar data collection between virtual simulation and the physical world, leading to high-risk manners and high collision rate. In this paper, we present distributional reinforcement learning architecture for mapless navigation of mobile robot that adapt the uncertainty of environmental change. The experimental results indicate the superior performance of distributional soft actor critic compared to conventional methods.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2024년도 춘계학술발표대회
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• pp.294-297
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• 2024

Malware detection has become increasingly critical with the proliferation of end devices. To improve detection rates and efficiency, the research focus in malware detection has shifted towards leveraging machine learning and deep learning approaches. This shift is particularly relevant in the context of the widespread adoption of end devices, including smartphones, Internet of Things devices, and personal computers. Machine learning techniques are employed to train models on extensive datasets and evaluate various features, while deep learning algorithms have been extensively utilized to achieve these objectives. In this research, we introduce TabNet, a novel architecture designed for deep learning with tabular data, specifically tailored for enhancing malware detection techniques. Furthermore, the Synthetic Minority Over-Sampling Technique is utilized in this work to counteract the challenges posed by imbalanced datasets in machine learning. SMOTE efficiently balances class distributions, thereby improving model performance and classification accuracy. Our study demonstrates that SMOTE can effectively neutralize class imbalance bias, resulting in more dependable and precise machine learning models.

• 융합보안논문지
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• 제23권4호
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• pp.61-70
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• 2023

클러스터링은 데이터의 정답값(실제값)이 없는 데이터를 기반으로 데이터의 특징벡터의 거리 기반 등으로 군집화를 하는 비지도학습 방법이다. 이 방법은 이미지, 텍스트, 음성 등 다양한 데이터에 대해서 라벨링이 없이 적용할 수 있다는 장점이 있다. 기존 클러스터링을 하기 위해 차원축소 기법을 적용하거나 특정 특징만을 추출하여 군집화하는 방법이 적용되었다. 하지만 딥러닝 기반 모델이 발전하면서 입력 데이터를 잠재 벡터로 표현하는 오토인코더, 생성 적대적 네트워크 등을 통해서 딥 클러스터링의 기술이 연구가 되고 있다. 본 연구에서, 딥러닝 기반의 딥 클러스터링 기법을 제안하였다. 이 방법에서 오토인코더를 이용하여 입력 데이터를 잠재 벡터로 변환하고 이 잠재 벡터를 클러스터 구조에 맞게 벡터 공간을 구성 및 k-평균 클러스터링을 하였다. 실험 환경으로 pytorch 머신러닝 라이브러리를 이용하여 데이터셋으로 MNIST와 Fashion-MNIST을 적용하였다. 모델로는 컨볼루션 신경망 기반인 오토인코더 모델을 사용하였다. 실험결과로 k가 10일 때, MNIST에 대해서 89.42% 정확도를 가졌으며 Fashion-MNIST에 대해서 56.64% 정확도를 가진다.