• 한국컴퓨터정보학회논문지
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• 제27권8호
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• pp.49-59
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• 2022

본 논문에서는 2020년 기준 단백질 서열을 이용한 기능과 구조 예측 분야에서 가장 많이 사용되고 있는 딥러닝 모델인 CNN과 LSTM/GRU 모델을 동일한 조건 하에 비교 평가한 연구를 토대로 새로운 효소 기능 예측 모델인 PSCREM을 설계하였다. CNN 합성곱 시 누락되는 세부 패턴을 보존하기 위하여 서열 진화정보를 이용하였으며 중첩 RNN을 통해 기능적으로 중요한 의미를 가지는 아미노산 간의 관계 정보를 추출하고 특징 맵 제작에 참조하였다. 사용된 RNN 계열의 알고리즘은 LSTM과 GRU로 보통 stacked RNN 기법으로 100 units 이상 2~3회 쌓는 것이 일반적이나 본 논문에서는 10, 20 unit으로 구성한 뒤 중첩시켜서 특징 맵 제작에 사용하였다. 모델에 들어가는 데이터는 단백질 서열 데이터로 PSSM profile로 가공한 뒤 사용되었다. 실험 결과 효소 번호 첫 번째 자리를 예측하는 문제에 대해 86.4%의 정확도를 나타냄을 입증하였고, 효소 번호 3번째 자리까지 예측 정확도 84.4%의 성능을 내는 것을 확인하였다. PSCREM은 Overlapped RNN을 통해 단백질 기능에 관련된 고유 패턴을 더 잘 파악하며 Overlapped RNN은 단백질 기능 및 구조 예측 추출 분야에 새로운 방법론으로서 제안된다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.466-467
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• 2018

최근 이미지 인식, 영상 인식, 음성 인식, 자연어 처리 등 다양한 분야에 인공지능이 적용되면서 딥러닝(Deep learning) 기술에 관한 관심이 높아지고 있다. 딥러닝 중에서도 가장 대표적인 알고리즘으로 이미지 인식 및 분류에 강점이 있고 각 분야에 많이 쓰이고 있는 CNN(Convolutional Neural Network)에 대한 많은 연구가 진행되고 있다. 본 논문에서는 일반적인 CNN 구조를 변형한 새로운 네트워크 구조를 제안하고자 한다. 일반적인 CNN 구조는 convolution layer, pooling layer, fully-connected layer로 구성된다. 그러므로 본 연구에서는 일반적인 CNN 구조 내부에 FC를 첨가한 새로운 네트워크를 구성하고자 한다. 이러한 변형은 컨볼루션된 이미지에 신경회로망이 갖는 장점인 일반화 기능을 포함시켜 정확도를 올리고자 한다.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.49-62
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• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.

• Nuclear Engineering and Technology
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• 제55권11호
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• pp.3925-3934
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• 2023

The importance of fast neutron detection for nuclear safeguards purposes has increased due to its potential advantages such as reasonable cost and higher precision for larger sample masses of nuclear materials. Pulse-shape discrimination (PSD) is inevitably used to discriminate neutron- and gamma-ray- induced signals from organic scintillators of very high gamma sensitivity. The light output (LO) threshold corresponding to several MeV of recoiled proton energy could be necessary to achieve fine PSD performance. However, this leads to neutron count losses and possible distortion of results obtained by neutron multiplicity counting (NMC)-based nuclear material accountancy (NMA). Moreover, conventional PSD techniques are not effective for counting of neutrons in a high-gamma-ray environment, even under a sufficiently high LO threshold. In the present work, PSD performance (figure-of-merit, FOM) according to LO bands was confirmed using a conventional charge comparison method (CCM) and compared with results obtained by convolution neural network (CNN)-based PSD algorithms. Also, it was attempted, for the first time ever, to reject fake neutron signals from distorted PSD regions where neutron-induced signals are normally detected. The overall results indicated that higher neutron detection efficiency with better accuracy could be achieved via CNN-based PSD algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• Current Optics and Photonics
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• 제6권1호
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• pp.92-103
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• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.

• 문화기술의 융합
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• 제9권5호
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• pp.737-745
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• 2023

본 논문은 열화상 이미지를 활용하여 배전반 화재 발생을 감지하기 위한 인공지능 모델을 개발하는 연구에 대해 다룬다. 연구의 목표는 수집한 열화상 이미지를 전처리하여 객체 탐지 모델에 적합한 데이터로 가공하고, 이를 이용하여 배전반 내 화재 발생 여부를 판단하는 모델을 설계하는 것이다. 연구에서는 AI-HUB의 산업단지 내 학습용 열화상 이미지 데이터를 활용하였으며, CNN 기반 딥러닝 객체 검출 알고리즘 중 대표적인 모델인 Faster R-CNN과 RetinaNet을 사용하여 모델을 구축하고 두 개의 모델을 비교 분석하여 최적의 모델을 제안하고 있다.

• 대한조선학회논문집
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• 제57권3호
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• pp.140-151
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• 2020

It is important to identify the location of ship hull blocks with exact block identification number when scheduling the shipbuilding process. The wrong information on the location and identification number of some hull block can cause low productivity by spending time to find where the exact hull block is. In order to solve this problem, it is necessary to equip the system to track the location of the blocks and to identify the identification numbers of the blocks automatically. There were a lot of researches of location tracking system for the hull blocks on the stockyard. However there has been no research to identify the hull blocks on the stockyard. This study compares the performance of 5 Convolutional Neural Network (CNN) models with multi-view image set on the classification of the hull blocks to identify the blocks on the stockyard. The CNN models are open algorithms of ImageNet Large-Scale Visual Recognition Competition (ILSVRC). Four scaled hull block models are used to acquire the images of ship hull blocks. Learning and transfer learning of the CNN models with original training data and augmented data of the original training data were done. 20 tests and predictions in consideration of five CNN models and four cases of training conditions are performed. In order to compare the classification performance of the CNN models, accuracy and average F1-Score from confusion matrix are adopted as the performance measures. As a result of the comparison, Resnet-152v2 model shows the highest accuracy and average F1-Score with full block prediction image set and with cropped block prediction image set.

• 한국컴퓨터정보학회논문지
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• 제27권1호
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• pp.43-50
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• 2022

CPA(Correlation Power Analysis)는 암호 알고리즘이 탑재된 공격 대상 장비의 미세한 소비전력을 측정하여 90% 이상의 확률로 암호 알고리즘에 사용된 비밀키를 추측하는 부채널 공격 방법이다. CPA는 통계를 기반으로 분석을 수행하기 때문에 반드시 많은 양의 데이터가 요구된다. 따라서 CPA는 매회 공격을 위해 약 15분 이상 소비전력을 측정해야만 한다. 본 논문에서는 CPA의 데이터 수집 문제를 해결하기 위해 입력데이터를 축적하고 결과를 예측할 수 있는 CNN(Convolutional Neural Network)을 사용하는 방법을 제안한다. 사전에 공격 대상 장비의 소비전력을 수집 및 학습을 통해 임의의 소비전력을 입력시키면 즉각적으로 비밀키를 추정할 수 있어 연산속도를 향상하고 96.7%의 비밀키 추측 정확도를 나타냈다.