• KIPS Transactions on Computer and Communication Systems
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• v.5 no.5
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• pp.117-126
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• 2016

Recently, increasing utilization of Big Data or Social Network Service involves the increases in demand for NoSQL Database that overcomes the limitations of existing relational database. A forensic examination of Relational Database has steadily researched in terms of Digital Forensics. In contrast, the forensic examination of NoSQL Database is rarely studied. In this paper, We introduce Redis (which is) based on Key-Value Store NoSQL Database, and research the collection and analysis of forensic artifacts then propose recovery method of deleted data. Also we developed a recovery tool, it will be verified our recovery algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.11
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• pp.329-331
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• 2020

Edutech is a compound word of education and technology, and is an educational paradigm in the era of the 4th industrial revolution. This refers to next-generation education using information and communication technology (ICT) such as big data, artificial intelligence (AI), robots, and virtual reality (VR) of the 4th industrial revolution. e-Learning is being used as an online lecture for education in ICT, but edutech is attracting attention along with e-learning as the feeding of non-face-to-face education has rapidly increased due to COVID-19. Therefore, this paper summarizes the reviewed papers on the blockchain-based badge service platform, simulation-based collaborative e-Learning system, video English dictionary, and blockchain-based access control audit system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.109-110
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• 2019

최근 IoT 환경에서는 고밀도로 노드가 분포되어진다. 이러한 센서 노드들은 데이터 전송 시 혼잡을 초래하는 중복 데이터를 생성하여 데이터의 정확도를 저하시킨다. 이에 따라 본 연구에서는 데이터 집중으로 인해 발생하는 네트워크의 정체 문제를 해결하기 위해 제안 기법은 사 분위(Interquatile, IRQ) 분석과 코사인 유사도 함수를 통해 데이터의 이상치와 중복성을 측정하여 중복 데이터 및 특이치를 제거한다. 본 연구를 통하여 최적의 데이터 전송을 통하여 IoT의 통신 성능을 향상시킬 수 있으며 결과적으로 데이터 감소율, 네트워크 수명 및 에너지의 효율성을 높일 수 있다.

• Journal of Information Technology Services
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• v.20 no.2
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• pp.91-109
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• 2021

Recently, social and economic environment has been rapidly changed. In particular, the development of IT technology accelerated the introduction of databases, communication networks, information processing and analyzing systems, making the use of such information and communication technology an essential factor for corporate management innovation. This change also affected the accounting areas. The purpose of this study is to document changes in accounting areas due to the adoption of IT technologies in the era of technology and information, to define the required accounting professions in this era, and to present the efficient educational methodologies for training such accounting experts. An accounting expert suitable for the era of technology and information means an accounting profession not only with basic accounting knowledge, competence, independency, reliability, communication skills, and flexible interpersonal skills, but also with IT skills, data utilization and analysis skills, the understanding big data and artificial intelligence, and blockchain-based accounting information systems. In order to educate future accounting experts, the accounting curriculum should be reorganized to strengthen the IT capabilities, and it should provide a wide variety of learning opportunities. It is also important to provide a practical level of education through industry and academic cooperation. Distance learning, web-based learning, discussion-type classes, TBL, PBL, and flipped-learnings will be suitable for accounting education methodologies to foster future accounting experts. This study is meaningful because it can motivate to consider accounting educational system and curriculum to enhance IT capabilities.

• International Journal of Contents
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• v.17 no.1
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• pp.1-10
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• 2021

Visual analytics is an emerging research field that combines the strength of electronic data processing and human intuition-based social background knowledge. This study demonstrates useful visual analytics with Tableau in conjunction with semantic network analysis using examples of sentiment flow and strategic communication strategies via Twitter in a blockchain domain. We comparatively investigated the sentiment flow over time and language usage patterns between companies with a good reputation and firms with a poor reputation. In addition, this study explored the relations between reputation and marketing communication strategies. We found that cryptocurrency firms more actively produced information when there was an increased public demand and increased transactions and when the coins' prices were high. Emotional language strategies on social media did not affect cryptocurrencies' reputations. The pattern in semantic representations of keywords was similar between companies with a good reputation and firms with a poor reputation. However, the reputable firms communicated on a wide range of topics and used more culturally focused strategies, and took more advantages of social media marketing by expanding their outreach to other social media networks. The visual big data analytics provides insights into business intelligence that helps informed policies.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.61-67
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• 2024

In this paper, we presented a strengthening plan to prevent personal information leakage incidents by securing legal compliance for personal information impact assessment and suggesting measures to strengthen privacy during personal information impact assessment. Recently, as various services based on big data have been created, efforts are being made to protect personal information, focusing on the EU's GDPR and Korea's Personal Information Protection Act. In this society, companies entrust processing of personal information to provide customized services based on the latest technology, but at this time, the problem of personal information leakage through consignees is seriously occurring. Therefore, the use of personal information by trustees.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Quality Improvement in Health Care
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• v.5 no.2
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• pp.202-215
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• 1998

Purpose : To shorten processing time for variety of medical affairs of the patient at the outpatient clinic of a big hospital is very important to qualify medical care of the patient. Therefore, patient's waiting time for drug delivery after doctor's prescription is often utilized as a strong tool to evaluate patient satisfaction with a medical care provided. We performed this study to investigate factors influencing patient satisfaction related with waiting time for drug delivery. Methods : The data were collected from July 21 to August 12, 1998. A total 535 patients or their families who visited outpatient clinics of Inha University Hospital were subjected to evaluate the drug delivery time and the level of their satisfaction related, which were compared with those objectively evaluated by Quality Improvement Team. The reliability of the scale was tested with Cronbach's alpha, and the data were analyzed using frequency, t-test, ANOVA, correlation analysis and multiple regression. Results : The mean drug delivery time subjectively evaluated by the patient (16.1 13.0 min) was longer than that objectively evaluated (10.9 7.6 min) by 5.2 min. Drug delivery time objectively evaluated was influenced by the prescription contents, total amount or type of drug dispensed, etc, as expected. The time discrepancy between two evaluations was influenced by several causative factors. One of those proved to be a patient's late response to the information from the pharmacy which the drug is ready to deliver. Interestingly, this discrepancy was found to be more prominent especially when waiting place for drug delivery was not less crowded. Other factors, pharmaceutical counseling at the pharmacy, emotional status or behavior of a patient while he waits for the medicine, were also found to influence the time subjectively evaluated. Regarding the degree of patient satisfaction with the drug delivery, majority of patients accepted drug delivery time with less than 10 min. It was also found to be influenced by emotional status of the patient as well as kindness or activity of pharmaceutical counselor. Conclusion : The results show that, besides prescription contents, behavior pattern or emotional status of a patient, environment of the waiting place, and quality of pharmaceutical counseling at the pharmacy, may influence the patient's subjective evaluation of waiting time for drug delivery and his satisfaction related with the service in the big hospital. In order to improve patient satisfaction related with waiting time for drug delivery, it will be cost effective to qualify pharmaceutical counseling and information system at the drug delivery site or waiting place rather than to shorten the real processing time within the pharmacy.

• Journal of Convergence Society for SMB
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• v.6 no.1
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• pp.33-38
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• 2016

Disease of patient who visited the hospital can cause different symptoms of the disease, depending on the environment and lifestyle. Recent medical services offered in patients has changed in the environment that can be selected for treatment by analyzing the patient according to the disease symptoms. In this paper, we propose an efficient method to manage disease control because the treatment method may change at any patients suffering from the disease according to the patient conditions by grouping the different treatments to patients for disease information. The proposed scheme has a feature that can be ingested by the patient big disease information, as well as to improve the treatment efficiency of the medical treatment the increase patient satisfaction. The proposed sheme can handle big data by clustering of disease information for patients suffering from diseases such as patient consent small groups. In addition, the proposed scheme has the advantage that can be conveniently accessed via a particular keyword, the treatment method according to patient disease information. The experimental results, the proposed method has been improved by 23% in terms of efficiency compared to conventional techniques, disease management time is gained 11.3% improved results. Medical service user satisfaction seen from the survey is to obtain a high 31.5% results.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.15-22
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• 2019

Recent technological advances in three-dimensional (3D) sensing devices and machine learning such as deep leaning has enabled data-driven 3D applications. Research on artificial intelligence has developed for the past few years and 3D deep learning has been introduced. This is the result of the availability of high-quality big data, increases in computing power, and development of new algorithms; before the introduction of 3D deep leaning, the main targets for deep learning were one-dimensional (1D) audio files and two-dimensional (2D) images. The research field of deep leaning has extended from discriminative models such as classification/segmentation/reconstruction models to generative models such as those including style transfer and generation of non-existing data. Unlike 2D learning, it is not easy to acquire 3D learning data. Although low-cost 3D data acquisition sensors have become increasingly popular owing to advances in 3D vision technology, the generation/acquisition of 3D data is still very difficult. Even if 3D data can be acquired, post-processing remains a significant problem. Moreover, it is not easy to directly apply existing network models such as convolution networks owing to the various ways in which 3D data is represented. In this paper, we summarize technological trends in AI-based 3D content generation.