• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.51-58
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• 2022

Various studies have been conducted from the past to the present because stock price forecasts provide stability in the national economy and huge profits to investors. Recently, there have been many studies that suggest stock price prediction models using various input data such as macroeconomic indicators and emotional analysis. However, since each study was conducted individually, it is difficult to objectively compare each method, and studies on their impact on stock price prediction are still insufficient. In this paper, the effect of input data currently mainly used on the stock price is evaluated through the predicted value of the deep learning model and the error rate of the actual stock price. In addition, unlike most papers in emotional analysis, emotional analysis using the news body was conducted, and a method of supplementing the results of each emotional analysis is proposed through three emotional analysis models. Through experiments predicting Microsoft's revised closing price, the results of emotional analysis were found to be the most important factor in stock price prediction. Especially, when all of input data is used, error rate of ensembled sentiment analysis model is reduced by 58% compared to the baseline.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.265-272
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• 2022

As the risk of lung cancer has increased, early-stage detection and treatment of cancers have received a lot of attention. Among various medical imaging approaches, computer tomography (CT) has been widely utilized to examine the size and growth rate of lung nodules. However, the process of manual examination is a time-consuming task, and it causes physical and mental fatigue for medical professionals. Recently, many computer-aided diagnostic methods have been proposed to reduce the workload of medical professionals. In recent studies, encoder-decoder architectures have shown reliable performances in medical image segmentation, and it is adopted to predict lesion candidates. However, localizing nodules in lung CT images is a challenging problem due to the extremely small sizes and unstructured shapes of nodules. To solve these problems, we utilize atrous spatial pyramid pooling (ASPP) to minimize the loss of information for a general U-Net baseline model to extract rich representations from various receptive fields. Moreover, we propose mixed-up attention mechanism of reverse, boundary and convolutional block attention module (CBAM) to improve the accuracy of segmentation small scale of various shapes. The performance of the proposed model is compared with several previous attention mechanisms on the LIDC-IDRI dataset, and experimental results demonstrate that reverse, boundary, and CBAM (RB-CBAM) are effective in the segmentation of small nodules.

• Child Health Nursing Research
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• v.29 no.2
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• pp.149-160
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• 2023

Purpose: Based on the health belief model and theory of planned behavior, this study investigated how age group (adolescence and emerging adulthood) moderated the relative effects of perceived susceptibility, perceived severity, and subjective norms on preventive behavior against coronavirus disease 2019 (COVID-19). Methods: This secondary data analysis utilized data from adolescents (n=272) and emerging adults (n=239). Hierarchical multiple regression analysis was performed to test the moderating effect of age group on the relationships among variables. Results: Higher perceived susceptibility (β=.21, p<.001), perceived severity (β=.14, p=.002), subjective norms (friends) (β=.26, p<.001), subjective norms (parents) (β=.44, p<.001), and subjective norms (schools) (β=.28, p<.001) enhanced COVID-19 preventive behaviors. Moderated regression analysis showed that subjective norms (friends and school) impacted preventive behavior in adolescents more than in emerging adults. Conclusion: Given the need to increase perceived susceptibility and severity among adolescents and emerging adults, these findings provide baseline data for designing effective COVID-19 prevention interventions that consider the developmental characteristics of different age groups. Interventions by health centers at universities can strengthen COVID-19 preventive behavior among emerging adults. As adolescents are influenced by friends, their peer roles must be strengthened to enhance adherence to COVID-19 preventive guidelines.

• Steel and Composite Structures
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• v.48 no.6
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• pp.611-623
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• 2023

This work considered an optimal control formulation in the sense of Caputo derivatives. The optimality of the fractional optimal control problem. The tumor immune interaction in fractional form provides an excellent tool for the description of memory and hereditary properties of inter and intra cells. So the interaction between effector-cells, tumor cells and are modeled by using the definition of Caputo fractional order derivative that provides the system with long-time memory and gives extra degree of freedom. In addiltion, existence and local stability of fixed points are investigated for discrete model. Moreover, in order to achieve more efficient computational results of fractional-order system, a discretization process is performed to obtain its discrete counterpart. Our technique likewise allows the advancement of results, such as return time to baseline that are unrealistic with current model solvers.

• Journal of Korean Neurosurgical Society
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• v.66 no.2
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• pp.121-132
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• 2023

Objective : The parafascicular nucleus (PF) plays important roles in controlling the basal ganglia. It is not well known whether the PF affects the development of abnormal involuntary movements (AIMs). This study was aimed to find a role of the PF in development of AIMs using optogenetic methods in an animal model. Methods : Fourteen rats were underwent stereotactic operation, in which they were injected with an adeno-associated virus with channelrhodopsin (AAV2-hSyn-ChR2-mCherry) to the lateral one third of the PF. Behavior test was performed with and without optical stimulation 14 days after the injection of the virus. AIM of rat was examined using AIM score. After the behavior test, rat's brain was carefully extracted and the section was examined using a fluorescence microscope to confirm transfection of the PF. Results : Of the 14 rats, seven rats displayed evident involuntary abnormal movements. AIM scores were increased significantly after the stimulation compared to those at baseline. In rats with AIMs, mCherry expression was prominent in the PF, while the rats without AIM lacked with the mCherry expression. Conclusion : AIMs could be reversibly induced by stimulating the PF through an optogenetic method.

• ETRI Journal
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• v.45 no.1
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• pp.18-27
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• 2023

We present an English-Korean speech translation corpus, named EnKoST-C. End-to-end model training for speech translation tasks often suffers from a lack of parallel data, such as speech data in the source language and equivalent text data in the target language. Most available public speech translation corpora were developed for European languages, and there is currently no public corpus for English-Korean end-to-end speech translation. Thus, we created an EnKoST-C centered on TED Talks. In this process, we enhance the sentence alignment approach using the subtitle time information and bilingual sentence embedding information. As a result, we built a 559-h English-Korean speech translation corpus. The proposed sentence alignment approach showed excellent performance of 0.96 f-measure score. We also show the baseline performance of an English-Korean speech translation model trained with EnKoST-C. The EnKoST-C is freely available on a Korean government open data hub site.

• Smart Media Journal
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• v.12 no.1
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• pp.32-46
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• 2023

Natural Language Processing (NLP) has grown tremendously in recent years. Typically, bilingual, and multilingual translation models have been deployed widely in machine translation and gained vast attention from the research community. On the contrary, few studies have focused on translating between spoken and sign languages, especially non-English languages. Prior works on Sign Language Translation (SLT) have shown that a mid-level sign gloss representation enhances translation performance. Therefore, this study presents a new large-scale Korean sign language dataset, the Museum-Commentary Korean Sign Gloss (MCKSG) dataset, including 3828 pairs of Korean sentences and their corresponding sign glosses used in Museum-Commentary contexts. In addition, we propose a translation framework based on self-supervised learning, where the pretext task is a text-to-text from a Korean sentence to its back-translation versions, then the pre-trained network will be fine-tuned on the MCKSG dataset. Using self-supervised learning help to overcome the drawback of a shortage of sign language data. Through experimental results, our proposed model outperforms a baseline BERT model by 6.22%.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.256-261
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• 2011

Labor productivity is a significant factor related to control time, cost, and quality. Many researchers have developed models to define method of measuring the relationship between productivity and various constraints such as the size of working area, maximum working hours, and the crew composition. Most of the previous research has focused on estimating productivity; however, this research concentrates on estimating labor productivity and developing time and cost data for repetitive concrete pouring activity. In Korea, "Standard Estimating" only contains the average productivity data of the construction industry, and it is difficult to predict the time and cost of any particular project; hence, there are some errors in estimating duration and cost for individual activity and project. To address these issues, this research collects data, measures productivity, and develops time and cost data using labor productivity based on field conditions from the collected data. A probabilistic approach is also proposed to develop data. A case study is performed to validate this process using actual data collected from construction sites and it is possible that the result will be used as the EVMS baseline of cost management and schedule management.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4350-4356
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• 2023

Radiation detection systems working at high count rates suffer from the overlapping of their output electric pulses, known as pulse pile-up phenomenon, resulting in spectrum distortion and degradation of the energy resolution. Pulse tail extrapolation is a pile-up correction method which tries to restore the shifted baseline of a piled-up pulse by extrapolating the overlapped part of its preceding pulse. This needs a mathematical model which is almost always nonlinear, fitted usually by a nonlinear least squares (NLS) technique. NLS is an iterative, potentially time-consuming method. The main idea of the present study is to replace the NLS technique by an integration-based non-iterative method (NIM) for pulse tail extrapolation by an exponential model. The idea of linear extrapolation, as another non-iterative method, is also investigated. Analysis of experimental data of a NaI(Tl) radiation detector shows that the proposed non-iterative method is able to provide a corrected spectrum quite similar with the NLS method, with a dramatically reduced computation time and complexity of the algorithm. The linear extrapolation approach suffers from a poor energy resolution and throughput rate in comparison with NIM and NLS techniques, but provides the shortest computation time.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.195-201
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• 2024

With the Covid-19(Corona Virus) spread all around the world, people are using this propaganda and the desperate need of the citizens to know the news about this mysterious virus by spreading fake news. Some Countries arrested people who spread fake news about this, and others made them pay a fine. And since Social Media has become a significant source of news, .there is a profound need to detect these fake news. The main aim of this research is to develop a web-based model using a combination of machine learning algorithms to detect fake news. The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis; We assumed that Natural Language Processing(NLP) wouldn't be enough alone to make context analysis as Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes and tweet-length we also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. And Finally, to get the best accuracy, we combined two of the best algorithms used SVM ( which is widely accepted as baseline classifier, especially with binary classification problems ) and Naive Base.