• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.16-37
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• 2022

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.

• Journal of Acupuncture Research
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• v.38 no.3
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• pp.183-191
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• 2021

Sa-am acupuncture originated in the Chosun Dynasty and is a distinct feature of Korean medicine. It has been used to treat various diseases and conditions in clinical practice however, there is insufficient scientific evidence to support the use of Sa-am acupuncture. We aimed to comprehensively review the clinical studies of Sa-am acupuncture retrieved from national and international databases (MEDLINE, EMBASE, the China National Knowledge Infrastructure, and 3 Korean databases). There were 52 articles reviewed including 29 case studies, 19 randomized controlled trials (RCTs), and 4 uncontrolled trials. Neurological disorders were the most frequently studied, and kidney tonification, and directional supplementation and draining were the most frequently used methods. Overall, the outcomes were generally positive however, there were many additional treatments together with Sa-am acupuncture reported in the case reports, and the quality of evidence was low in the RCTs. Future studies should report the detailed method of practicing Sa-am acupuncture treatment and focus on the specific effect of Sa-am acupuncture with rigorous design to scientifically support the clinical use of Sa-am acupuncture.

• Fashion & Textile Research Journal
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• v.24 no.1
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• pp.42-52
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• 2022

The goal of this study is to analyze the value of Kanga as an ethos of the Swahili culture. The theoretical background of the research method was the analysis of the domestic and foreign literature, journals, and research data from various internet sites related to the subject, and the conclusion was drawn based on these studies. With the spread Pan-Africanism, the interest in African ethos has become a source of inspiration for contemporary fashion. Moreover, as a symbol of Swahili culture in East Africa, Kanga has been developed by embracing its own diverse cultures, The unique feature of Kanga is that it can easily be transformed created ceaselessly and creatively. Consequently, the following results were obtained based on the theoretical content. Firstly, as a representative of Women's Voice, Kanga serves as an outlet for the voices of women coming from a poor social status under the political background in East Africa. Secondly, as a Reliable Advocate, Kanga performs the positive functions as a medium of communication through its traditional usage and distinctive arrangement of clothes. Thirdly, as a Versatile Messenger, the uniqueness of Kanga with the external elements in most interestingly and active mannerly, and it has become the value of communication channel which clearly inspired the fashion designers. I believe that it will be interesting and meaningful to study the strategies on the social role of Kanga in the future which has started receiving more attention in the 21st century. And it can be said that Kanga's unique identity lies in the attraction and value which influences contemporary fashion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• Journal of Microbiology and Biotechnology
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• v.33 no.1
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• pp.106-113
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• 2023

The supply of microbiological risk-free water is essential to keep food safety and public hygiene. And removal, inactivation, and destruction of microorganisms in drinking water are key for ensuring safety in the food industry. Ultraviolet-C (UV-C) irradiation is an attractive method for efficient disinfection of water without generating toxicity and adversely affecting human health. In this study, the disinfection efficiencies of UV-C irradiation on Shigella flexneri (Gram negative) and Listeria monocytogenes (Gram positive) at various concentrations in drinking water were evaluated using a water purifier. Their morphological and physiological characteristics after UV-C irradiation were observed using fluorescence microscopy and flow cytometry combined with live/dead staining. UV-C irradiation (254 nm wavelength, irradiation dose: 40 mJ/cm²) at a water flow velocity of 3.4 L/min showed disinfection ability on both bacteria up to 10⁸ CFU/4 L. And flow cytometric analysis showed different physiological shift between S. flexneri and L. monocytogenes after UV-C irradiation, but no significant shift of morphology in both bacteria. In addition, each bacterium revealed different characteristics with time-course observation after UV-C irradiation: L. monocytogenes dramatically changed its physiological feature and seemed to reach maximum damage at 4 h and then recovered, whereas S. flexneri seemed to gradually die over time. This study revealed that UV-C irradiation of water purifiers is effective in disinfecting microbial contaminants in drinking water and provides basic information on bacterial features/responses after UV-C irradiation.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.10
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• pp.935-943
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• 2017

CNN (Convolution neural network), which is used for image classification and speech recognition among neural networks learning based on positive data, has been continuously developed to have a high performance structure to date. There are many difficulties to utilize in an embedded system with limited resources. Therefore, we use GPU (General-Purpose Computing on Graphics Processing Units), which is used for general-purpose operation of GPU to solve the problem because we use pre-learned weights but there are still limitations. Since CNN performs simple and iterative operations, the computation speed varies greatly depending on the thread allocation and utilization method in the Single Instruction Multiple Thread (SIMT) based GPGPU. To solve this problem, there is a thread that needs to be relaxed when performing Convolution and Pooling operations with threads. The remaining threads have increased the operation speed by using the method used in the following feature maps and kernel calculations.

• Anatomy and Cell Biology
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• v.56 no.3
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• pp.382-393
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• 2023

Cell clusters are a histological hallmark feature of intervertebral disc degeneration. Clusters arise from cell proliferation, are associated with replicative senescence, and remain metabolically, but their precise role in various stages of disc degeneration remain obscure. The aim of this study was therefore to investigate small, medium, and large size cell-clusters. For this purpose, human disc samples were collected from 55 subjects, aged 37-72 years, 21 patients had disc herniation, 10 had degenerated non-herniated discs, and 9 had degenerative scoliosis with spinal curvature <45°. 15 non-degenerated control discs were from cadavers. Clusters and matrix changes were investigated with histology, immunohistochemistry, and Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). Data obtained were analyzed with spearman rank correlation and ANOVA. Results revealed, small and medium-sized clusters were positive for cell proliferation markers Ki-67 and proliferating cell nuclear antigen (PCNA) in control and slightly degenerated human discs, while large cell clusters were typically more abundant in severely degenerated and herniated discs. Large clusters associated with matrix fissures, proteoglycan loss, matrix metalloproteinase-1 (MMP-1), and Caspase-3. Spatial association findings were reconfirmed with SDS-PAGE that showed presence to these target markers based on its molecular weight. Controls, slightly degenerated discs showed smaller clusters, less proteoglycan loss, MMP-1, and Caspase-3. In conclusion, cell clusters in the early stages of degeneration could be indicative of repair, however sustained loading increases large cell clusters especially around microscopic fissures that accelerates inflammatory catabolism and alters cellular metabolism, thus attempted repair process initiated by cell clusters fails and is aborted at least in part via apoptosis.

• Smart Media Journal
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• v.12 no.11
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• Journal of Microbiology and Biotechnology
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• v.33 no.11
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• pp.1448-1456
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• 2023

A Gram-positive, non-motile, and non-spore-forming lactic acid bacterium, designated as BK2^T, was isolated from kimchi, a Korean traditional fermented vegetable food, and the taxonomic characteristics of strain BK2^T, along with strain LMG 11983, were analyzed. Both strains optimally grew at 30℃, pH 7.0, and 1.0% NaCl. Cells of both strains were heterofermentative and facultatively anaerobic rods, demonstrating negative reactions for catalase and oxidase. Major fatty acids (>10%) identified in both strains were C_18:1 ω9c, C_16:0, and summed feature 7 (comprising C_19:1 ω6c and/or C_19:1 ω7c). The genomic DNA G+C contents of both strains were 44.7 mol%. The 16S rRNA gene sequence similarity (99.9%), average nucleotide identity (ANI; 99.9%), and digital DNA-DNA hybridization (dDDH; 99.7%) value between strains BK2^T and LMG 11983 indicated that they are different strains of the same species. Strain BK2^T was most closely related to Weissella confusa JCM 1093^T and Weissella cibaria LMG 17699^T, with 100% and 99.4% 16S rRNA gene sequence similarities, respectively. However, based on the ANI and dDDH values (92.3% and 48.1% with W. confusa, and 78.4% and 23.5% with W. cibaria), it was evident that strain BK2^T represents a distinct species separate from W. confusa and W. cibaria. Based on phylogenetic, phenotypic, and chemotaxonomic features, strains BK2^T and LMG 11983 represent a novel species of the genus Weissella, for which the name Weissella fermenti sp. nov. is proposed. The type of strain is BK2^T (=KACC 22833^T=JCM 35750^T).

• Dementia and Neurocognitive Disorders
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• v.23 no.3
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• pp.146-160
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• 2024

Background and Purpose: The emotions of people at various stages of dementia need to be effectively utilized for prevention, early intervention, and care planning. With technology available for understanding and addressing the emotional needs of people, this study aims to develop speech emotion recognition (SER) technology to classify emotions for people at high risk of dementia. Methods: Speech samples from people at high risk of dementia were categorized into distinct emotions via human auditory assessment, the outcomes of which were annotated for guided deep-learning method. The architecture incorporated convolutional neural network, long short-term memory, attention layers, and Wav2Vec2, a novel feature extractor to develop automated speech-emotion recognition. Results: Twenty-seven kinds of Emotions were found in the speech of the participants. These emotions were grouped into 6 detailed emotions: happiness, interest, sadness, frustration, anger, and neutrality, and further into 3 basic emotions: positive, negative, and neutral. To improve algorithmic performance, multiple learning approaches were applied using different data sources-voice and text-and varying the number of emotions. Ultimately, a 2-stage algorithm-initial text-based classification followed by voice-based analysis-achieved the highest accuracy, reaching 70%. Conclusions: The diverse emotions identified in this study were attributed to the characteristics of the participants and the method of data collection. The speech of people at high risk of dementia to companion robots also explains the relatively low performance of the SER algorithm. Accordingly, this study suggests the systematic and comprehensive construction of a dataset from people with dementia.