• Journal of Fashion Business
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• v.27 no.1
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• pp.1-15
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• 2023

This study divided articles into two time periods, from 2012 to 2022, with the aim of using big data analysis to look at patterns in the ecosystem of fashion start-ups. The research method extracted top keywords based on TF(Term Frequency) and TF-IDF(Term Frequency-Inverse Document Frequency), analyzed the network, and derived centrality values. As a result of comparing the first and second fashion startup ecosystems, elements of policy, support, market, finance, and human capital were derived in the first period. In addition, in the second period, elements of policy, support, market, finance, and culture were derived. In the first period, the fashion startup ecosystem focused on fostering new designer startups by emphasizing support, finance, and human capital factors and focusing on policies. Meanwhile, in the second period, online-based fashion platform startups and fashion tech startups appeared with the support of digital transformation and fulfillment services triggered by COVID-19(Corona Virus Disease 19), private finances were emphasized, and cultural factors were derived along with success stories of fashion startups. This study is meaningful in that it helps in developing strategies for fashion startups to grow into sustainable companies.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.32-42
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• 2023

There are serious problems worldwide, such as a pandemic due to an unprecedented infection caused by COVID-19. On previous approaches, they invented medical vaccines and preemptive testing tools for medical engineering. However, it is difficult to access poor medical systems and medical institutions due to disparities between countries and regions. In advanced nations, the damage was even greater due to high medical and examination costs because they did not go to the hospital. Therefore, from a software engineering-based perspective, we propose a learning model for determining coronavirus infection through symptom data-based software prediction models and tools. After a comparative analysis of various models (decision tree, Naive Bayes, KNN, multi-perceptron neural network), we decide to choose an appropriate decision tree model. Due to a lack of data, additional survey data and overseas symptom data are applied and built into the judgment model. To protect from thiswe also adapt human normalization approach with traditional Korean medicin approach. We expect to be possible to determine coronavirus, flu, allergy, and cold without medical examination and diagnosis tools through data collection and analysis by applying decision trees.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.275-284
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• 2023

Gene expression data can be utilized in various studies, including the prediction of disease prognosis. However, there are challenges associated with collecting enough data due to cost constraints. In this paper, we propose a gene expression data generation model based on Conditional Variational Autoencoder. Our results demonstrate that the proposed model generates synthetic data with superior quality compared to two other state-of-the-art models for gene expression data generation, namely the Wasserstein Generative Adversarial Network with Gradient Penalty based model and the structured data generation models CTGAN and TVAE.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.313-315
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• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.

• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.4
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• pp.11-17
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• 2022

Purpose: This study discusses how Indonesia's response to the Corona Virus Disease-19 pandemic based on the perspective of housing policy capacity which consists of resources, organizations, and networks, politics, systems, and finance. Research design, data and methodology: This study used a qualitative method through a literature review. Data collection techniques were carried out by searching various sources and literature related to housing capacity theory and various data on Indonesia's response to the Covid 19 pandemic. Based on a literature review, this study adapted and modified the five components of capacity, namely resource capacity, organizational and network capacity, political capacity, system capacity and financial capacity in Indonesia in responding to the Covid-19 pandemic. Data analysis used analytical themes which consist of understanding the data, generating initial codes, looking for themes, reviewing themes, defining and naming themes, producing of manuscripts. Results: The results show that the weakness of the system capacity greatly affects Indonesia's housing policy capacity in responding to the Covid-19 pandemic and on the other hand the five housing capacities are an integrated process within the housing policy framework in Indonesia, especially to overcome the Covid-19 pandemic. Conclusions: The findings of this study are the importance of building a system capacity that is directly integrated with housing policy and the strengthening of the resources capacity, organizations, and networks, politics, and finance in the context of Indonesia's housing policy, especially in dealing with the Covid-19 pandemic situation.

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1254-1269
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• 2023

This study aims to predict the change in corn share according to the grazing of 20 gestational sows in a mature corn field by taking images with a camera-equipped unmanned air vehicle (UAV). Deep learning based on convolutional neural networks (CNNs) has been verified for its performance in various areas. It has also demonstrated high recognition accuracy and detection time in agricultural applications such as pest and disease diagnosis and prediction. A large amount of data is required to train CNNs effectively. Still, since UAVs capture only a limited number of images, we propose a data augmentation method that can effectively increase data. And most occupancy prediction predicts occupancy by designing a CNN-based object detector for an image and counting the number of recognized objects or calculating the number of pixels occupied by an object. These methods require complex occupancy rate calculations; the accuracy depends on whether the object features of interest are visible in the image. However, in this study, CNN is not approached as a corn object detection and classification problem but as a function approximation and regression problem so that the occupancy rate of corn objects in an image can be represented as the CNN output. The proposed method effectively estimates occupancy for a limited number of cornfield photos, shows excellent prediction accuracy, and confirms the potential and scalability of deep learning.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.34-41
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• 2023

The purpose of this paper is to consider the expansion of non-traditional security threats and the national-level response to the emergence of emerging security threats in ultra-uncertain VUCA situations. As a major research method for better analysis, the theoretical approach was referred to papers published in books and academic journals, and technical and current affairs data were studied through the Internet and literature research. The instability and uncertainty of the international order and security environment in the 21st century brought about a change in the security paradigm. Human security emerged as the protection target of security was expanded to individual humans, and emerging security was emerging as the security area expanded. Emerging security threatsthat have different characteristicsfrom traditionalsecurity threats are expressed in various ways, such as cyber threats, new infectious disease threats, terrorist threats, and abnormal climate threats. First, the policy and strategic response to respond to emerging security threats is integrated national crisis management based on artificial intelligence applying the concept of Foresight. Second, it is to establish network-based national crisis management smart governance. Third, it is to maintain the agile resilience of the concept of Agilience. Fourth, an integrated response system that integrates national power elements and national defense elements should be established.

• IMMUNE NETWORK
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• v.23 no.1
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• pp.2.1-2.19
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• 2023

Immune diversification helps protect the host against a myriad of pathogens. CD8⁺ T cells are essential adaptive immune cells that inhibit the spread of pathogens by inducing apoptosis in infected host cells, ultimately ensuring complete elimination of infectious pathogens and suppressing disease development. Accordingly, numerous studies have been conducted to elucidate the mechanisms underlying CD8⁺ T cell activation, proliferation, and differentiation into effector and memory cells, and to identify various intrinsic and extrinsic factors regulating these processes. The current knowledge accumulated through these studies has led to a huge breakthrough in understanding the existence of heterogeneity in CD8⁺ T cell populations during immune response and the principles underlying this heterogeneity. As the heterogeneity in effector/memory phases has been extensively reviewed elsewhere, in the current review, we focus on CD8⁺ T cells in a "naive" state, introducing recent studies dealing with the heterogeneity of naive CD8⁺ T cells and discussing the factors that contribute to such heterogeneity. We also discuss how this heterogeneity contributes to establishing the immense complexity of antigen-specific CD8⁺ T cell response.

• IMMUNE NETWORK
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• v.23 no.1
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• pp.5.1-5.28
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• 2023

Th cell lineage determination and functional specialization are tightly linked to the activation of lineage-determining transcription factors (TFs) that bind cis-regulatory elements. These lineage-determining TFs act in concert with multiple layers of transcriptional regulators to alter the epigenetic landscape, including DNA methylation, histone modification and threedimensional chromosome architecture, in order to facilitate the specific Th gene expression programs that allow for phenotypic diversification. Accumulating evidence indicates that Th cell differentiation is not as rigid as classically held; rather, extensive phenotypic plasticity is an inherent feature of T cell lineages. Recent studies have begun to uncover the epigenetic programs that mechanistically govern T cell subset specification and immunological memory. Advances in next generation sequencing technologies have allowed global transcriptomic and epigenomic interrogation of CD4+ Th cells that extends previous findings focusing on individual loci. In this review, we provide an overview of recent genome-wide insights into the transcriptional and epigenetic regulation of CD4+ T cell-mediated adaptive immunity and discuss the implications for disease as well as immunotherapies.

• IMMUNE NETWORK
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• v.23 no.1
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• pp.6.1-6.24
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• 2023

Intestinal microorganisms interact with various immune cells and are involved in gut homeostasis and immune regulation. Although many studies have discussed the roles of the microorganisms themselves, interest in the effector function of their metabolites is increasing. The metabolic processes of these molecules provide important clues to the existence and function of gut microbes. The interrelationship between metabolites and T lymphocytes in particular plays a significant role in adaptive immune functions. Our current review focuses on 3 groups of metabolites: short-chain fatty acids, bile acids metabolites, and polyamines. We collated the findings of several studies on the transformation and production of these metabolites by gut microbes and explained their immunological roles. Specifically, we summarized the reports on changes in mucosal immune homeostasis represented by the Tregs and Th17 cells balance. The relationship between specific metabolites and diseases was also analyzed through latest studies. Thus, this review highlights microbial metabolites as the hidden treasure having potential diagnostic markers and therapeutic targets through a comprehensive understanding of the gut-immune interaction.