• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.442-448
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• 2023

Artificial Intelligence (AI) technology has recently grown explosively and is being used in a variety of application fields. Accordingly, the number of AI models is rapidly increasing. AI models are adapted and developed to fit a variety of data types, tasks, and environments, and the variety and volume of models continues to grow. The need to share models and collaborate within the AI community is becoming increasingly important. Collaboration is essential for AI models to be shared and improved publicly and used in a variety of applications. Therefore, with the advancement of AI, the introduction of Model Hub has become more important, improving the sharing, reuse, and collaboration of AI models and increasing the utilization of AI technology. In this paper, we collect data on the model hub and analyze the characteristics of the model hub and the AI models provided. The results of this research can be of great help in developing various multimodal AI models in the future, utilizing AI models in various fields, and building services by fusing various AI models.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.293-301
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• 2023

With the advancement of artificial intelligence and its various use cases, accessing it through edge computing environments is gaining traction. However, due to the nature of edge computing environments, efficient management and optimization of clusters distributed in different geographical locations is considered a major challenge. To address these issues, this paper proposes a centralization and automation tool called KubEVC-Agent based on Kubernetes. KubEVC-Agent centralizes the deployment, operation, and management of edge clusters and presents a use case of the data transformation for optimizing intra-cluster communication. This paper describes the components of KubEVC-Agent, its working principle, and experimental results to verify its effectiveness.

• Journal of Information Technology Applications and Management
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• v.31 no.3
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• pp.165-179
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• 2024

Although firms with advanced analytics and machine learning (which is often called AI) capabilities are considered to be highly successful in the market by making decisions and actions based on quantitative analysis using data, the scarcity of historical data and the lack of right data infrastructure are the problems for the organizations to perform such projects. The objective of this study, is to identify a road map for the organization to reach data capability maturity to become AI matured organizations. First, this study defines the terms, AI capability, data capability and AI matured organization. Then using content analyses, organizations' data practices performed for AI system development and operation are analyzed to infer a data capability roadmap to become an AI matured organization.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.119-126
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• 2024

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.139-161
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• 2024

The Fourth Industrial Revolution has spurred widespread adoption of AI-based services, driving global interest in AI semiconductors for efficient large-scale computation. Text mining research, historically using LDA, has evolved with machine learning integration, exemplified by the 2021 BERTopic technology. This study employs BERTopic to analyze AI semiconductor-related patents and research data, generating 48 topics from 2,256 patents and 40 topics from 1,112 publications. While providing valuable insights into technology trends, the study acknowledges limitations in taking a macro approach to the entire AI semiconductor industry. Future research may explore specific technologies for more nuanced insights as the industry matures.

• Journal of Information Technology Applications and Management
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• v.28 no.5
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• pp.41-50
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• 2021

As COVID-19 spread widely, and rapidly, the number of misinformation is also increasing, which WHO has referred to this phenomenon as "Infodemic". The purpose of this research is to develop detection and rectification of COVID-19 misinformation based on Open-domain QA system and text similarity. 9 testing conditions were used in this model. For open-domain QA system, 6 conditions were applied using three different types of dataset types, scientific, social media, and news, both datasets, and two different methods of choosing the answer, choosing the top answer generated from the QA system and voting from the top three answers generated from QA system. The other 3 conditions were the Closed-Domain QA system with different dataset types. The best results from the testing model were 76% using all datasets with voting from the top 3 answers outperforming by 16% from the closed-domain model.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.7-13
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• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.203-212
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• 2024

The area under the ROC curve (AUC) is one of the most common criteria used to measure the overall performance of binary classifiers for a wide range of machine learning problems. In this article, we propose a L₁-penalized AUC-optimization classifier that directly maximizes the AUC for high-dimensional data. Toward this, we employ the AUC-consistent surrogate loss function and combine the L₁-norm penalty which enables us to estimate coefficients and select informative variables simultaneously. In addition, we develop an efficient optimization algorithm by adopting k-means clustering and proximal gradient descent which enjoys computational advantages to obtain solutions for the proposed method. Numerical simulation studies demonstrate that the proposed method shows promising performance in terms of prediction accuracy, variable selectivity, and computational costs.

• Electronics and Telecommunications Trends
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• v.39 no.5
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• pp.12-20
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• 2024

As more devices are used across various industries and their performance improves, artificial intelligence applications are being increasingly adopted. Hence, the rapid development of neural networks suitable for diverse devices can determine the competitiveness of companies. Machine learning operations (MLOps), which constitute a framework that supports neural network generation and its immediate application to devices, have become necessary for the development of artificial intelligence. Currently, most MLOps are provided by major companies such as Google, Amazon, and Microsoft, which provide cloud services supported by large-scale computing power. In addition, various services are provided by the open-source project Kubeflow. We examine basic concepts and technology trends in MLOps and unveil additional functions required in industry.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.601-612
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• 2024

A functional clustering analysis is a crucial machine learning technique in functional data analysis. Many functional clustering methods have been developed to enhance clustering performance. Moreover, due to the phase variability between functions, elastic functional clustering methods, such as applying the Fisher-Rao metric, which can manage phase variation during clustering, have been developed to improve model performance. However, aligning functions without considering the phase variation can distort functional information because phase variation can be a natural characteristic of functions. Hence, we propose a state-of-the-art functional hierarchical clustering that can manage phase and amplitude variations of functional data. This approach is based on the phase and amplitude separation method using the norm-preserving time warping of functions. Due to its invariance property, this representation provides robust variability for phase and amplitude components of functions and improves clustering performance compared to conventional functional hierarchical clustering models. We demonstrate this framework using simulated and real data.