• Journal of Service Research and Studies
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• v.13 no.4
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• pp.191-205
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• 2023

Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.315-320
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• 2021

Currently, cloud computing is being used as a technology that greatly changes the IT field. For many businesses, many cloud services are available in the form of custom, reliable, and cost-effective web applications. Most cloud service providers provide functions such as IoT, machine learning, AI services, blockchain, AR & VR, mobile services, and containers in addition to basic cloud services that support the scalability of processors, memory, and storage. In this paper, we will look at the most used cloud service providers and compare the services provided by the cloud service providers.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.1-19
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• 2022

Purpose: The purpose of this study is to design a system for quality management and improvement of overall public services. Methods: Literature Review, Framework Design Method, Case Studies Analysis Results: Public Service Quality Management Principles, Definition of Public Services Quality Management Areas, Quality Management Guidelines, Service Quality Management Tools Conclusion: In this study, a study case of the public service quality management framework, which is a system that supports overall quality management and continuous quality improvement of public services, is presented. The management system was designed based on the existing research results and domestic and foreign cases of public service standardization, targeting the entire public service.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1160-1163
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• 2021

DOOH(Degital Out of Home) advertisement market is developing steadily, and the case of use is also increasing, In advertisement market, personalized services is actively being provided with technological development. On the other hand, personalized services are difficult to be provided in DOOH and are p rovided by only personal information, not feelings. This study aims to construct personalized DOOH se rvices by using AI facial expression recognition and suggesting a solution optimized for interaction bet ween user and services by providing healing and advertisement.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.77-84
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• 2017

With the entry into the aging society, we are increasingly interested in wellness, and personalized medical services through artificial intelligence are expanding. In order to provide personalized medical services, it is difficult to provide accurate medical analysis services only with the existing hospital system components PM / PA, OCS, EMR, PACS, and LIS. Therefore, it is necessary to present the hospital system model and the construction plan suitable for personalized medical service. Currently, some medical cloud services and artificial intelligence diagnosis services using Watson are being introduced in domestic. However, there are not many examples of systematic hospital system construction. Therefore, this paper proposes a hospital system model suitable for personalized medical service. To do this, we design a model that integrates medical big data construction and AI medical analysis system into the existing hospital system components, and suggest development plan of each module. The proposed model is meaningful as a basic research that provides guidelines for the construction of new hospital system in the future.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.138-154
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• 2019

This study was conducted to identify the use motivations of AI speaker and examine the characteristics of AI speaker users. Based on the uses and gratifications theory, The study results show that the user motivations of AI speaker are four dimensional, namely escaping from daily problems and maintaining social relationships, information acquisition and learning, entertainment and relaxation and pursuit of practicability. The main AI speaker users are in their 30s, and they are innovative to actively use AI speakers for entertainment purposes such as listening to music. The four sub-dimensions differed as we compared them with user characteristics. Specifically, the motivation for escaping from daily problems and maintaining social relationships varied with gender and age. Moreover, age and informativeness were identified to have an influence on the motivations of information acquisition and learning and entertainment and relaxation. In sum, this research provides practical implications into how to strategically create contents and services for AI speakers.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.129-164
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• 2023

In a situation where the use and introduction of Large Language Models (LLMs) is expanding due to recent developments in generative AI technology, it is difficult to find actual application cases or implementation methods for the use of internal company data in existing studies. Accordingly, this study presents a method of implementing generative AI services using the LLM application architecture using the most widely used LangChain framework. To this end, we reviewed various ways to overcome the problem of lack of information, focusing on the use of LLM, and presented specific solutions. To this end, we analyze methods of fine-tuning or direct use of document information and look in detail at the main steps of information storage and retrieval methods using the retrieval augmented generation (RAG) model to solve these problems. In particular, similar context recommendation and Question-Answering (QA) systems were utilized as a method to store and search information in a vector store using the RAG model. In addition, the specific operation method, major implementation steps and cases, including implementation source and user interface were presented to enhance understanding of generative AI technology. This has meaning and value in enabling LLM to be actively utilized in implementing services within companies.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.101-110
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• 2020

Devices that add emotion-specific services or various functions are appearing in AI speakers and related devices. To this end, this study performed topic modeling analysis on the topics of post-purchase texts written by AI speaker users, and compared them with the data collected via survey questionnaires. Furthermore, data on the emotional intelligence of AI speakers and relationship quality were collected from 600 users and analyzed using structural equation modeling. The findings of the study are as follows: First, the analysis results of topic modeling showed that most of the articles mainly mention the functional aspects of AI speakers. Second, emotional intelligence of AI speaker perceived by consumer affected relationship quality, and relationship quality had a positive effect on customer satisfaction. Therefore, this study expands the area of AI research by integrating the concept of emotional intelligence and relationship quality to provide new theoretical and practical implications.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.2
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• pp.109-133
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• 2024

This study investigated the impact of the technical characteristics of Generative AI (GAI) and librarians' personal traits on their intention to use GAI. Personalization, interaction, and context awareness were considered as technical characteristics of GAI that influence the intention to use GAI, while innovativeness and frequency of GAI use were considered as librarians' personal traits. The study targeted 187 librarians working in libraries, and 165 questionnaires were collected and analyzed. The results showed that the technical characteristics of GAI had a statistically significant impact on the intention to use GAI. Additionally, librarians' personal traits, namely innovativeness and frequency of GAI use, were also found to have a significant impact on the intention to use GAI. The findings of this study can be used as valuable information to help librarians increase their intention to use GAI and improve the quality and satisfaction of library services.