• Computers and Concrete
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• 제30권2호
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• pp.99-111
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• 2022

In this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze-thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.

• 한국인터넷방송통신학회논문지
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• 제22권6호
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• pp.113-119
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• 2022

소프트웨어 결함 예측 작업 시 단순히 결함 유무만을 예측하는 이진 분류 모델에 비해 결함의 심각도 범주를 예측하는 다중 분류 모델은 훨씬 유용하게 사용될 수 있다. 소수의 심각도 기반 결함 예측 모델들이 제안되었지만 딥러닝 기법을 사용한 분류기는 없었다. 본 논문은 3개, 5개의 은닉층을 갖고 은닉층 노드수가 고정된 구조와 변화하는 구조의 MLP 모델들을 제작하였다. 모델 평가 실험 결과 기존 기계학습 모델들 중 가장 좋은 성능을 보인 MLPs보다 MLP 기반 딥러닝 모델들은 Accuracy와 AUC 모두 유의미하게 더 우수한 성능을 보였다. 특히 노드수 고정 구조에서는 은닉 층수 3, 배치사이즈 32, 노드수 64인 모델 구조가 가장 좋은 성능을 보였다.

• ETRI Journal
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• 제42권1호
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• pp.90-100
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• 2020

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

• 대한임베디드공학회논문지
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• 제17권2호
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• pp.77-83
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• 2022

In this paper, the two-stage object detection approach is proposed to implement a deep learning-based label inspection system on edge computing environments. Since the label printed on the products during the production process contains important information related to the product, it is significantly to check the label information is correct. The proposed system uses the lightweight deep learning model that able to employ in the low-performance edge computing devices, and the two-stage object detection approach is applied to compensate for the low accuracy relatively. The proposed Two-Stage object detection approach consists of two object detection networks, Label Area Detection Network and Character Detection Network. Label Area Detection Network finds the label area in the product image, and Character Detection Network detects the words in the label area. Using this approach, we can detect characters precise even with a lightweight deep learning models. The SF-YOLO model applied in the proposed system is the YOLO-based lightweight object detection network designed for edge computing devices. This model showed up to 2 times faster processing time and a considerable improvement in accuracy, compared to other YOLO-based lightweight models such as YOLOv3-tiny and YOLOv4-tiny. Also since the amount of computation is low, it can be easily applied in edge computing environments.

• 한국컴퓨터정보학회논문지
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• 제26권7호
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• pp.37-44
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• 2021

뇌 MRI 영상의 자동 분류는 뇌종양의 조기 진단을 하는 데 있어 중요한 역할을 한다. 본 연구에서 우리는 심층 특징 앙상블을 사용한 MRI 영상에서의 딥 러닝 기반 뇌종양 분류 모델을 제안한다. 우선 사전 학습된 3개의 합성 곱 신경망을 사용하여 입력 MRI 영상에 대한 심층 특징들을 추출한다. 그 이후 추출된 심층 특징들은 완전 연결 계층들로 구성된 분류 모듈의 입력 값으로 들어간다. 분류 모듈에서는 우선 3개의 서로 다른 심층 특징들 각각에 대해 먼저 완전 연결 계층을 거쳐 특징 차원을 줄인다. 그 이후 3개의 차원이 준 특징들을 결합하여 하나의 특징 벡터를 생성한 뒤 다시 완전 연결 계층의 입력값으로 들어가서 최종적인 분류 결과를 예측한다. 우리가 제안한 모델을 평가하기 위해 웹상에 공개된 뇌 MRI 데이터 셋을 사용하였다. 실험 결과 우리가 제안한 모델이 다른 기계학습 기반 모델보다 더 좋은 성능을 나타냄을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4625-4647
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• 2020

The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of "generative" and "adversarial", researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• 정보보호학회논문지
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• 제31권1호
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• pp.51-59
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• 2021

딥러닝 모델은 변이를 통해 훈련 데이터에서 벗어난 입력으로부터 잘못된 예측 결과를 산출할 수 있으며 이는 자율주행, 보안 분야 등에서 치명적인 사고로 이어질 수 있다. 딥러닝 모델의 신뢰성 보장을 위해서는 다양한 변이를 통해 예외적인 상황에 대한 모델의 처리 능력이 검증되어야 한다. 하지만, 기존 연구가 제한된 모델을 대상으로만 수행되었으며, 여러 입력 변이 유형에 구분을 짓지 않고 사용했다. 본 연구에서는 딥러닝 검증 데이터 세트로 널리 사용되고 있는 CIFAR10 데이터 세트를 기반으로 다양한 상용화된 모델과 추가 버전을 포함하여 총 6개의 모델에 대한 신뢰성 검증을 수행한다. 이를 위해 실생활에서 발생할 수 있는 6가지 유형의 입력 변이 알고리즘을 다양한 파라미터와 함께 데이터 세트에 개별적으로 적용하여 각각에 대한 모델의 정확도를 비교함으로써 특정 변이 유형과 관련된 모델의 취약점을 구체적으로 파악한다.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.57-66
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• 2023

Rice is an important food crop for most of the population in the world and it is largely cultivated in Pakistan. It not only fulfills food demand in the country but also contributes to the wealth of Pakistan. But its production can be affected by climate change. The irregularities in the climate can cause several diseases such as brown spots, bacterial blight, tungro and leaf blasts, etc. Detection of these diseases is necessary for suitable treatment. These diseases can be effectively detected using deep learning such as Convolution Neural networks. Due to the small dataset, transfer learning models such as vgg16 model can effectively detect the diseases. In this paper, vgg16, inception and xception models are used. Vgg16, inception and xception models have achieved 99.22%, 88.48% and 93.92% validation accuracies when the epoch value is set to 10. Evaluation of models has also been done using accuracy, recall, precision, and confusion matrix.