• Journal of Information Technology Services
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• v.22 no.6
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• pp.37-53
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• 2023

With the development of the 4th industrial revolution, companies are trying to introduce new AI technologies and improve their performance. In particular, chatbot technology has developed and can not only communicate smoothly with humans, but also perform many complex tasks, so it has high market potential. However, there is still little research on chatbots in the field of e-commerce. Accordingly, this study aims to suggest ways to improve corporate performance through chatbot user satisfaction analysis. With the rapid development of China's e-commerce platform, In this study, through previous studies, the characteristics of chatbots were classified into accessibility, accuracy, empathy, reliability, and intimacy as factors influencing perceived usefulness, perceived ease, and perceived enjoyment of the Technology Acceptance Model (TAM). Five were selected and used as independent variables, and a model that affects customer satisfaction was set up. This paper sets user satisfaction as an important indicator of chatbot service and analyzes the path that affects user satisfaction, thereby improving chatbot service technology. It is important in that it provides a way to improve the smart chatbot service by understanding the degree of user acceptance in depth.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.53-58
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• 2023

The storage capacity of 3D-NAND flash memory has been enhanced by the multi-layer dielectrics. The deposition process has become more challenging due to the tight process margin and the demand for accurate process control. To reduce product costs and ensure successful processes, process diagnosis techniques incorporating artificial intelligence (AI) have been adopted in semiconductor manufacturing. Recently there is a growing interest in process diagnosis, and numerous studies have been conducted in this field. For higher model accuracy, various process and sensor data are required, such as optical emission spectroscopy (OES), quadrupole mass spectrometer (QMS), and equipment control state. Among them, OES is usually used for plasma diagnostic. However, OES data can be distorted by viewport contamination, leading to misunderstandings in plasma diagnosis. This issue is particularly emphasized in multi-dielectric deposition processes, such as oxide and nitride (ON) stack. Thus, it is crucial to understand the potential misunderstandings related to OES data distortion due to viewport contamination. This paper explores the potential for misunderstanding OES data due to data distortion in the ON stack process. It suggests the possibility of excessively evaluating process drift through comparisons with a QMS. This understanding can be utilized to develop diagnostic models and identify the effects of viewport contamination in ON stack processes.

• Asia pacific journal of information systems
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• v.32 no.1
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• pp.125-150
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• 2022

Technology companies launched their intelligent personal assistants (IPAs). IPAs not only provide individuals with a convenient way to interact with technology but also offer them the opportunity to interact with AI in a useful and meaningful form. Therefore, the global IPAs have experienced tremendous growth over the past decade. But maintaining continuous usage intention is still a massive challenge for developers and marketers and previous technology adoption models are not enough to explain continuous usage intention of IPAs. Thus, we adopted the bi-dimensional perspectives of utilitarian and hedonic value in this research model, and investigated how three characteristics of IPAs - anthropomorphism, autonomy, and intelligence - affect utilitarian value and hedonic value, which in turn continuous usage intentions. 227 data were collected from IPA users. The results showed that IPAs' continuous usage intention is significantly determined by both utilitarian and hedonic value, with the hedonic value being more prominent. In addition, the results showed that anthropomorphism and intelligence are the most important antecedents of utilitarian and hedonistic value. The results also illustrated that autonomy is a crucial predictor of utilitarian value rather than hedonistic value. Our work contributes to current research by widening the theoretical understanding of the effect of IPA characteristics on continuous usage intention through bi-dimensional values. Our paper also provides IPAs' developer and marketer guidelines for enhancing continuous usage intention.

• Journal of Information Science Theory and Practice
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• v.12 no.2
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• pp.79-95
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• 2024

The new AI based conversational chatbot, ChatGPT, launched in November 2022, is causing a stir. There are many opinions about this being a 'threat or a promise,' and thus it is important to understand what has been said about this tool and, based on the growing literature that has emerged on the subject, demystify its effective impact on society. To analyse this impact, a systematic literature review with the support of the preferred reporting items for systematic reviews and meta-analysis protocol was used. The data, scientific documents, were collected using the main scientific databases - SCOPUS and Web of Science - and the results were presented based on a bibliometric and thematic exploration of content. The main findings indicate that people are increasingly using this chatbot in more diverse areas. Therefore, this study contributes at the practical level, aiming to enlighten people in general - both in professional and personal life - about this tool and its impacts. Also, it contributes at the theoretical level, which involves expanding understanding and elucidation of the impacts of ChatGPT in different areas of study.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.143-150
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• 2024

With the development of AI, multimedia tools in education offer personalized learning environments, which foster individual competencies. This study aims to examine the effectiveness of smart education as perceived by learners through a case study of university writing classes utilizing multimedia learning tools, and to explore potential applications. To achieve this, a writing course incorporating various multimedia tools to promote interaction was designed and implemented over the course of one semester, targeting 42 university students. Through the semester, student reactions and survey results were analyzed to investigate the effects and satisfaction levels regarding the use of multimedia learning tools in writing instruction as perceived by students. The analysis revealed that multimedia-assisted writing classes effectively fostered learners' autonomy by focusing on individual needs, while also promoting interaction and encouraging spontaneous participation. Students reported recognizing the presence of diverse perspectives by comparing and communicating about each other's writing, leading to an expansion of their own thinking. In using ChatGPT, it was found that students attempted to refine their questions until they obtained the desired answers. They reported that this process deepened their understanding of the essence of the questions. These benefits led to results of high levels of students' active class engagement and satisfaction. This study contributes foundational and empirical data regarding the effectiveness and potential applications of learner-centered smart education as part of fourth industrial revolution integration research.

• Asia pacific journal of information systems
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• v.30 no.1
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• pp.31-52
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• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• Korean Journal of Radiology
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• v.25 no.6
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• pp.518-539
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• 2024

Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.

• Korean Journal of Poultry Science
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• v.37 no.4
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• pp.361-372
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• 2010

Serological evaluation for the poultry is important for various reasons, such as designing and assessing the vaccination program and diagnosing diseases and for this reason, serologic tests for the layer flocks have been conducted on a regular basis. Moreover, the nationwide serological survey and analysis are essential to understand the epidemiological status of national poultry industry. In this sense, the study was conducted to evaluate the immune status of the layer flocks with the sera submitted to Avian Disease Laboratory, Chungbuk National University in 2009, and several important viral diseases were selected for evaluation including low pathogenic avian influenza (LPAI), Newcastle disease (ND), infectious bronchitis (IB) and avian metapneumovirus (aMPV). For LPAI and ND, the age-related patterns of geometric mean titer (GMT) changes were similar but there were differences in the flock positive rate and the level of GMT due to the different vaccination policy. In the case of IB, the values of GMT showed that the field infection was more prevalent than expected. For aMPV, positive birds in a flock increased as the layers got older, which reflected the course of field infection because vaccination against aMPV was not allowed in 2009. From this study, the immune status for the main viral diseases in layers became more clarified but this information was limited because of only one year study. Therefore, serological survey needs to be conducted on a yearly basis and furthermore include broilers and breeders for a better understanding of the health status in the national poultry industry.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.123-131
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• 2020

Along with the rapid development of artificial intelligence technology, natural language processing, which deals with human language, is also actively studied. In particular, BERT, a language model recently proposed by Google, has been performing well in many areas of natural language processing by providing pre-trained model using a large number of corpus. Although BERT supports multilingual model, we should use the pre-trained model using large amounts of Korean corpus because there are limitations when we apply the original pre-trained BERT model directly to Korean. Also, text contains not only vocabulary, grammar, but contextual meanings such as the relation between the front and the rear, and situation. In the existing natural language processing field, research has been conducted mainly on vocabulary or grammatical meaning. Accurate identification of contextual information embedded in text plays an important role in understanding context. Knowledge graphs, which are linked using the relationship of words, have the advantage of being able to learn context easily from computer. In this paper, we propose a system to extract Korean contextual information using pre-trained BERT model with Korean language corpus and knowledge graph. We build models that can extract person, relationship, emotion, space, and time information that is important in the text and validate the proposed system through experiments.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.