• Journal of Digital Convergence
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• v.20 no.3
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• pp.117-129
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• 2022

In the perspective of value-based adoption mode, this study empirically examined the factors that affect the intention of users of Fintech payment services to stop using them. A survey of college students who are familiar with digital devices, have no objection to payment and settlement services, and have high service access. A total of 148 questionnaires were analyzed using SPSS and SmartPLS. The study results show that perceived benefits, complexity, and security concerns are significant factors influencing the discontinue intention of Fintech payment services. Among them, the perceived benefit showed the most significant influence. Based on the results of this study, Fintech providers will be able to build a service environment to provide continuous benefits for maintaining long-term relationships with users, improve systems to secure various uses, and reduce users' negative perceptions of security. Recently, the use of services by the elderly has increased, so it is necessary to expand the scope of this study to target various age groups in future research.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.291-298
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• 2022

COVID-19 has spread across the world in the last two years, confining people to their homes and shutting down businesses and markets. The world is currently experiencing a catastrophic economic and social crisis. To benefit people and to protect them, industries invented new products. These products were made by small and medium-sized businesses across the globe. In South Asia, there was also a rigorous lockdown, people were laid off, and SMEs adopted E-commerce to assist clients and customers. Therefore, the study aims to analyze the impact of the COVID-19 pandemic on E-commerce adoption through open innovation strategies in South Asian countries. 500 respondents were selected through an online questionnaire to collect data from different countries of South Asia. The prominent countries are; India, Pakistan, and Bangladesh. The results of the study show that perceived compatibility and complexity have a positive influence on E-commerce adoption. In normal circumstances, however, the open innovation model is feasible. Knowledge and experience sharing and management attitude have a moderate impact on E-commerce adoption. These results are beneficial for researchers and SME managers in South Asia to overcome the challenges of the COVID-19 pandemic and increase the number of skilled people employed. This study suggests that SMEs should hire skilled workers to upgrade their systems.

• Korea Trade Review
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• v.48 no.1
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• pp.83-126
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• 2023

The revenue histogram of venture businesses is shifting from bell-shaped normal distribution to power-law distribution, which implies that the fitness landscape of the venture businesses ecosystem is changing to be more rugged terrain. We argue that the firm should adopt both exploitation (fast follower) and exploration (or first mover) strategies not to get stuck in local maxima in the rugged fitness landscape from the complex system perspective. By designing and performing agent-based modeling simulation experiments which consist of three types of agents (new technologies, entrepreneurs, and consumers), we demonstrated that the ambidexterity strategy showed the highest performance score in three of four different environment except 'Fast Widening' case where the exploitation strategy showed the highest performance score under low technology appropriation and fast disruptive technology development speed. By investigating the financial and other statistics of 617 top venture businesses who have earned 100B won or higher annual revenue, we concluded that 82% and 9% of firms are bent on the exploitation and exploration strategy.

• Knowledge Management Research
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• v.24 no.3
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• pp.127-148
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• 2023

Traditional Business Intelligence environment is limited to support the rapidly changing businesses and the exponential growth of data in both volume and complexity of data. Companies should shift their business intelligence environment into Self-Service Business Intelligence (SSBI) environment in order to make smarter and faster decisions. However, firms seem to face various challenges in implementing and leveraging the effective business intelligence system, and academics do not provide sufficient studies related including the success factors of SSBI. This study analyzes the three cases of Korean companies in depth, their development process and the assessment of business intelligence, based on the theoretical model on the key success factors of business intelligence systems. The comparative analysis of the three cases including project managers' interviews and performance evaluations provide rich implications for the successful adoption and the use of business intelligence systems of firms. The study is expected to provide useful references for firms to fully leverage the effects of business intelligence systems and upgrade towards self-service business intelligence systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.1-6
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• 2023

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.417-430
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• 2024

As modern technology advances and the proliferation of the internet continues, cyber threats are also on the rise. To effectively counter these threats, the importance of utilizing Cyber Threat Intelligence (CTI) is becoming increasingly prominent. CTI provides information on new threats based on data from past cyber incidents, but the complexity of data and changing attack patterns present significant analytical challenges. To address these issues, this study aims to utilize graph data that can comprehensively represent multidimensional relationships. Specifically, the study constructs a heterogeneous graph based on malware data, and uses the metapath2vec node embedding technique to more effectively identify cyber attack groups. By analyzing the impact of incorporating topology information into traditional malware data, this research suggests new practical applications in the field of cyber security and contributes to overcoming the limitations of CTI analysis.

• Journal of Biomedical Engineering Research
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• v.45 no.2
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• pp.90-94
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• 2024

Diagnosing wounds presents a significant challenge in clinical settings due to its complexity and the subjective assessments by clinicians. Wound deep learning algorithms quantitatively assess wounds, overcoming these challenges. However, a limitation in existing research is reliance on specific datasets. To address this limitation, we created a comprehensive dataset by combining open dataset with self-produced dataset to enhance clinical applicability. In the annotation process, machine learning based on Gradient Vector Flow (GVF) was utilized to improve objectivity and efficiency over time. Furthermore, the deep learning model was equipped U-net with residual blocks. Significant improvements were observed using the input dataset with images cropped to contain only the wound region of interest (ROI), as opposed to original sized dataset. As a result, the Dice score remarkably increased from 0.80 using the original dataset to 0.89 using the wound ROI crop dataset. This study highlights the need for diverse research using comprehensive datasets. In future study, we aim to further enhance and diversify our dataset to encompass different environments and ethnicities.

• Convergence Security Journal
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• v.23 no.5
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• pp.135-142
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• 2023

In addressing the prominent issues of climate change, resource scarcity, and environmental pollution associated with household waste, extensive research has been conducted on intelligent waste classification methods. These efforts range from traditional classification algorithms to machine learning and neural networks. However, challenges persist in effectively classifying waste in diverse environments and conditions due to insufficient datasets, increased complexity in neural network architectures, and performance limitations for real-world applications. Therefore, this paper proposes i-YOLOX as a solution for rapid classification and improved accuracy. The proposed model is evaluated based on network parameters, detection speed, and accuracy. To achieve this, a dataset comprising 10,000 samples of household waste, spanning 17 waste categories, is created. The i-YOLOX architecture is constructed by introducing the Involution channel convolution operator and the Convolution Branch Attention Module (CBAM) into the YOLOX structure. A comparative analysis is conducted with the performance of the existing YOLO architecture. Experimental results demonstrate that i-YOLOX enhances the detection speed and accuracy of waste objects in complex scenes compared to conventional neural networks. This confirms the effectiveness of the proposed i-YOLOX architecture in the detection and classification of multiple household waste objects.

• Smart Media Journal
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• v.13 no.2
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• pp.108-121
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• 2024

In the IT industry, the complexity and volatility of corporate networks are gradually evolving, and concurrently, the significance of corporate networks is increasing. Previous research has employed network analysis to scrutinize inter-corporate trade relationships for strategic and policy making. However, previous studies focused on the overall network structure from a macroscopic perspective, presenting limitations in applicability at the individual IT corporation level. This study develops a novel research model incorporating sector and region-level network analysis based on connection strength, along with the derivation of a composite node centrality indicator. Using this methodology, we analyzed corporate networks across nine metropolitan areas using IT corporate transaction data. The results means that cities with a manufacturing base, such as Incheon, Busan, and Daegu, have recently established cooperative networks with IT companies. We also found that in the IT industry in Gwangju and Daejeon, certain companies dominate the transaction network.

• BMB Reports
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• v.57 no.5
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• pp.256-261
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• 2024

In the context of aging, the susceptibility to infectious diseases increases, leading to heightened morbidity and mortality. This phenomenon, termed immunosenescence, is characterized by dysregulation in the aging immune system, including abnormal alterations in lymphocyte composition, elevated basal inflammation, and the accumulation of senescent T cells. Such changes contribute to increased autoimmune diseases, enhanced infection severity, and reduced responsiveness to vaccines. Utilizing aging animal models becomes imperative for a comprehensive understanding of immunosenescence, given the complexity of aging as a physiological process in living organisms. Our investigation focuses on Cisd2, a causative gene for Wolfram syndrome, to elucidate on immunosenescence. Cisd2 knockout (KO) mice, serving as a model for premature aging, exhibit a shortened lifespan with early onset of aging-related features, such as decreased bone density, hair loss, depigmentation, and optic nerve degeneration. Intriguingly, we found that the Cisd2 KO mice present a higher number of neutrophils in the blood; however, isolated neutrophils from these mice display functional defects. Through mass spectrometry analysis, we identified an interaction between Cisd2 and Calnexin, a protein known for its role in protein quality control. Beyond this function, Calnexin also regulates calcium homeostasis through interaction with sarcoendoplasmic reticulum calcium transport ATPase (SERCA). Our study proposes that Cisd2 modulates calcium homeostasis via its interaction with Calnexin and SERCA, consequently influencing neutrophil functions.