• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권1호
• /
• pp.90-113
• /
• 2021

Electronic Records Management System (ERMS) is a computer program or set of applications that is utilized for keeping up to date records along with their storage. ERMS has been extensively utilized for enhancing the performance of academic institutions. The system assists in the planning and decision-making processes, which in turn enhances the competencies. However, although ERMS is significant in supporting the process of decision-making, the majority of organizations have failed to take an initiative to implement it, taking into account that are some implementing it without an appropriate framework, and thus resulted in the practice which does not meet the accepted standard. Therefore, this study identifies the factors influencing the adoption of ERMS among employees of HLI in Yemen and the role of such adoption in the decision-making process, using the Unified Theory of Acceptance and Use of Technology (UTAUT) along with Technology, Organization and Environment (TOE) as the underpinning theories. The study conducts a cross-sectional survey with a questionnaire as the technique for data collection, distributed to 364 participants in various Yemeni public Higher Learning Institutions (HLI). Using AMOS as a statistical method, the findings revealed there are significant and positive relationships between technology factors (effort expectancy, performance expectancy, IT infrastructure and security), organizational factors (top management support, financial support, training, and policy),environmental factors (competitiveness pressure, facilitating conditions and trust) and behavioral intention to adopt ERMS, which in return has a significant relationship with the process of decision-making in HLI. The study also presents a variety of theoretical and empirical contributions that enrich the body of knowledge in the field of technology adoption and the electronic record's domain.

• International Journal of Computer Science & Network Security
• /
• 제24권2호
• /
• pp.31-42
• /
• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.

• 한국기록관리학회지
• /
• 제20권1호
• /
• pp.177-199
• /
• 2020

본 연구는 기록이 생산되어 보존기록관까지 도달하는 과정에서 평가라는 중요한 기록관리 업무가 어떻게 유기적으로 수행되는 지를 탐구한다. 이러한 통시적인 관점에서 미국뉴욕주기록관의 기록관리 및 평가시스템을 하나의 사례로 살펴보고자 한다. 미국의 기록환경은 아키비스트와 레코드매니저의 역할과 전문성을 구분하는 점에서 한국의 사례와는 다르다. 이런 환경에서도 뉴욕주기록관은 생산기관의 기록관리 과정의 평가업무를 적극지원한다. 본 연구는 미국 뉴욕주의 사례를 통하여 아키비스트의 기록물 평가가 보존기록물의 이관과 수집 시점에 시작한다는 소극적인 관점에서 벗어나, 기록물 생애주기의 시작점에서부터 아키비스트가 가지는 책임과 역할이 있다는 점을 강조한다.

• 한국기록관리학회지
• /
• 제6권1호
• /
• pp.65-92
• /
• 2006

기록정보의 관리와 보존은 활용을 전제로 행해질 때 그 의미와 가치가 살아난다. 따라서 기록관들은 기록정보의 활용을 보다 극대화하기 위하여 기록정보의 검색 및 열람이라는 단순 서비스를 탈피하여 이용자 중심의 보다 적극적 기록정보서비스를 제공하기 위한 많은 노력을 하고 있다. 본 연구는 이러한 노력을 활발히 하고 있는 각국의 국립기록관의 기록정보서비스를 유형별로 즉 1) 교육자와 학생을 위한 학습지원서비스, 2) 연구자를 위한 연구지원서비스, 3) 일반 이용자를 위한 특정주제서비스를 중심으로 하여 각각의 서비스 내용을 심층 비교 분석하였다. 마지막으로 각국의 서비스 현황 분석을 기반으로 우리나라 국가기록원을 비롯한 기록관에서 기록정보 활용을 활성화하기 위하여 제공되어야 할 기록정보서비스 방안을 제시하였다. 이러한 연구는 궁극적으로 기록관별 기록정보 이용 활성화 계획을 수립하는데 기초 자료로 활용될 수 있을 것이다.

• 대한수학교육학회지:수학교육학연구
• /
• 제27권3호
• /
• pp.391-410
• /
• 2017

본 연구는 학습유형이 상이한 초등학교 6학년 수학영재학생들의 일반화 및 정당화의 특징을 분석함으로써 학습유형에 따른 개별화 지도방안에 대한 교수학적 시사점을 도출하는 것을 목적으로 한다. 이를 위해, 초등학교 6학년 수학영재학생 3명의 학습유형을 판별하고 주어진 수학적 과제를 해결하는 수행과정을 추적 관찰하였다. 학생들에게는 지필환경과 함께 지오지브라를 활용한 동적기하환경이 제공되었으며, 학생들이 작성한 활동지, 지오지브라의 활동이 기록된 학생의 산물, 두 연구자가 관찰하며 작성한 현장관찰일지, 과제 탐구 후 개별면담 등을 통해 자료를 수집하여 질적 분석을 실시하였다. 그 결과, 초등학교 6학년 수학영재학생들의 일반화 특성은 다양하게 나타났으나 그에 비해 정당화 수준은 동일한 것으로 드러났다. 또한, 학습유형에 따라 학습 환경에 대한 선호도의 차이를 보였다. 이러한 연구 결과를 바탕으로 수학영재학생들의 학습유형에 따른 개별화 지도방안에 대해 제안하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권1호
• /
• pp.30-45
• /
• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Journal of Information Processing Systems
• /
• 제13권4호
• /
• pp.951-969
• /
• 2017

In this paper, we newly propose a traffic information service model that collects traffic information sensed by an individual vehicle in real time by using a smart device, and which enables drivers to share traffic information on all roads in real time using an application installed on a smart device. In particular, when the driver requests traffic information for a specific area, the proposed driver-personalized service model provides him/her with traffic information on the driving directions in advance by predicting the driving directions of the vehicle based on the learning of the driving records of each driver. To do this, we propose a traffic information management model to process and manage in real time a large amount of online-generated traffic information and traffic information requests generated by each vehicle. We also propose a road node-based indexing technique to efficiently store and manage location-based traffic information provided by each vehicle. Finally, we propose a driving learning and prediction model based on the hidden Markov model to predict the driving directions of each driver based on the driver's driving records. We analyze the traffic information processing performance of the proposed model and the accuracy of the driving prediction model using traffic information collected from actual driving vehicles for the entire area of Seoul, as well as driving records and experimental data.

• 한국수학교육학회지시리즈E:수학교육논문집
• /
• 제29권3호
• /
• pp.423-439
• /
• 2015

본 연구는 대학 신입생의 고등학교 재학 시 선행학습경험, 내신등급, 수능수리영역등급과 대학에서의 대학수학교과성적, 대학학업성취도(GPA) 간의 관계를 보았다. 이를 통해, 고등학교 재학 시의 수학교과 학업능력이 대학 신입생의 대학수학교과성적에 어느 정도 영향을 미치는지 분석하고자 하였다. 연구의 목적을 위해, A대학교 2014학년 1학기에 개설된 '기초미적분학'을 수강한 이과대학 및 공과대학 신입생 193명을 대상으로 설문을 실시하고 성적 등 관련 자료를 활용하였다. 이들 자료는 기술통계, 상관분석, 차이검정, 일원변량분석(ANOVA), 사후검정 및 회귀분석을 실시하였다. 연구결과, 첫째, 연구 대상 대학생의 90% 이상이 고등학교 재학 시 수학교과 선행학습을 한 것으로 나타났으며, 둘째, 선행학습의 효과성에 대한 인식은 필요성 인식보다 유의미하게 낮게 나타났다. 셋째, 대학수학교과성적과 대학학업성취도 간에는 높은 정적 상관관계가 있었으나, 내신등급과 대학학업성취도 간 및 수능수리영역등급과 대학수학교과성적 간에는 미미한 수준의 상관관계만 있었다. 넷째, 고교성적(내신등급, 수능수리영역등급), 선행학습노력, 선행학습만족도, 선행학습필요성이 대학수학학업성취도에 미치는 영향력은 미미한 수준이었다. 연구결과를 바탕으로, 대학수학교과의 학업성취도를 높이기 위한 방안을 제언하였다.

• Asian review of World Histories
• /
• 제4권1호
• /
• pp.3-34
• /
• 2016

This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.

• 한국문헌정보학회지
• /
• 제45권2호
• /
• pp.61-74
• /
• 2011

The author analyses the challenging activities of Japanese libraries in this decade by launching two keywords; "product-sharing" and "outcome generation." "Product-sharing" means that libraries share knowledge, skills, and records which are produced as the result of the services or in the process of activities. And "outcome generation" means that libraries generate any efficiency or effectiveness through their services to users. Using these concepts, reported are the current situation and aspects of Japanese libraries which try to make various contributions to the society; research and learning of the people, and education and training for professional librarians, and so on. In the analysis, the author shows some examples of "product-sharing" at first, including the records of reference transaction and the multi-functioned online public access catalogue. Especially, focused is on the various possibility and adoptability of the Collaborative Reference Database System of the National Diet Library of Japan. This system is one of digital reference service in Japan, and the database of reference transaction records is expected to be useful for research and academic studyies as knowledge-base of professional librarians. And the system is also expected to be a platform for LIS education and professional development in the e-learning environment. Secondly, as the examples of "outcome generation", explained are the problem-solving-type activities, and provision of the collection about books on struggling against disease and illness. A few examples of outcome in the problem-solving-type activities are these; increase of sales in the services for shop managers, business persons, and entrepreneurs, contribution to affluent daily life by providing the local information services to residents and neighbourhoods, and etc. And for both the patients with serious cases and their family or those who nurse them, books about other persons' notes or memorandum are the greatest support, and sometime healing. The author discuss the 'raison d'etre' of these activities focusing on public libraries in Japan.