• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.11-17
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• 2007

One of the popular methods used for pattern classification is the MBR (Memory-Based Reasoning) algorithm. Since it simply computes distances between a test pattern and training patterns or hyperplanes stored in memory, and then assigns the class of the nearest training pattern, it cannot explain how the classification result is obtained. In order to overcome this problem, we propose an incremental teaming algorithm based on RPA (Recursive Partition Averaging) to extract IF-THEN rules that describe regularities inherent in training patterns. But rules generated by RPA eventually show an overfitting phenomenon, because they depend too strongly on the details of given training patterns. Also RPA produces more number of rules than necessary, due to over-partitioning of the pattern space. Consequently, we present the IREA (Incremental Rule Extraction Algorithm) that overcomes overfitting problem by removing useless conditions from rules and reduces the number of rules at the same time. We verify the performance of proposed algorithm using benchmark data sets from UCI Machine Learning Repository.

• Journal of IKEEE
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• v.23 no.2
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• pp.529-533
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• 2019

Combined cycle power plants are often used to produce power. These days prediction of power plant output based on operating parameters is a major concern. This paper presents an approach to using computational intelligence technique to predict the output power of combined cycle power plant. Computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are considered to predict the output power. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, combined cycle power plant dataset obtained from the UCI Machine Learning Repository Database is considered.

• Journal of Service Research and Studies
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• v.6 no.4
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• pp.47-57
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• 2016

Open source has an amazing spread with the advent of smartphones. Open-source Moodle in e-learning areas are free of LMS (Learning Management System) and the most widely used worldwide, except for the black board commercial programs. One reason is well designed to support collaborative learning and interaction based on constructivist principles, which is the core principle of e-learning in particular that the theoretical basis of educational technology has a high educational effectiveness and benefits. This study examines the operational practices of collaborative learning using open source learning management system Moodle program. It introduces specific information to support the user of the collaborative learning. It looks at the advantages and singularity of collaborative learning in e-learning through examples shown. The purpose of this study is the importance of the relationship between learners and the importance of self-learning of collaborative learning through collaborative learning in a knowledge repository of Moodle. In addition, collaborative learning outcomes are is based on the motivation of learners and playfulness.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• Annual Conference of KIPS
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• 2005.11a
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• pp.607-610
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• 2005

SCORM의 Content Repository는 Asset이나 컨텐츠의 Metadata를 가지고 컨텐츠나 Asset을 검색할 수 있도록 한다. 이런 Metadata 기반 검색은 아주 많은 컨텐츠를 대상으로 검색을 할 경우, 검색을 통한 컨텐츠 결과가 너무 많을 경우 결과 내에서 재검색을 하는데 많은 시간을 들일 수 있다는 단점이 있다. 본 논문에서는 검색 효율을 높이기 위한 방법으로 SCORM 기반 LMS에 협력 필터링 방법을 적용한 시스템을 제안하였다.

• Safety and Health at Work
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• v.14 no.1
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• pp.50-58
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• 2023

Background: Occupational health services are not available for more than 80% of the global workforce. This pertains especially to informal workers, workers in agriculture and in small enterprises, and self-employed. Many are working in hazardous conditions. The World Health Organization, the International Labor Organization, the International Commission on Occupational Health, and the World Organization of Family Doctors promote as part of a solution, basic occupational health services (BOHS) integrated in primary or community health care. Quality information on this topic is difficult to find. The objective of this study is to develop an open access bibliography, a repository, referring to publications on BOHS and similar innovations, to support progress and research. Methods: The database design and sustaining literature searches (PubMed, Google Scholar, SciELO) are described. For each publication selected, basic bibliographic data, a brief content description considering copyright restrictions, and a hyperlink are included. Results: Searches resulted in a database containing 189 references to publications on BOHS such as articles in scientific journals, reports, policy documents, and abstracts of lectures. A global perspective is applied in 43 publications, a national or regional perspective is applied in 146 publications. Operational and evaluative research material is still scarce. Examples of references to publications are shown. Conclusion: The repository can inspire pioneers by showing practices in different countries and can be used for reviews and in-depth analyses. Missing publications such as from China, Russia, Japan, Republic of Korea, and Spanish/Portuguese speaking countries, can be added in the future, and translated. Search functions can be developed. International collaboration for the promotion of occupational health coverage for all workers must be intensified.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.754-764
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• 2022

Code refactoring refers to a maintenance task to change the code of a software system in order to consider new requirements, fix bugs, and restructure code. There have been various studies of refactoring subjects such as refactoring types, refactoring benefits, and CASE tools. However, Java applications rather than python ones have been benefited by refactoring-based coding practices. There are few cases of refactoring stuides on Python applications. This paper finds and analyzes single refactoring operations and composite refactoring operations for Python-based deep learning systems. In addition, we find that there is a statistically significant difference in the frequency of occurrence of single and complex refactoring operations in the two groups of deep learning applications and typical Python applications. Furthermore, we analyze keywords of commit messages to catch refactoring intentions of software developers.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.522-531
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• 2021

We describe a new deep learning model - Convolutional eXtreme Gradient Boosting (ConvXGB) for classification problems based on convolutional neural nets and Chen et al.'s XGBoost. As well as image data, ConvXGB also supports the general classification problems, with a data preprocessing module. ConvXGB consists of several stacked convolutional layers to learn the features of the input and is able to learn features automatically, followed by XGBoost in the last layer for predicting the class labels. The ConvXGB model is simplified by reducing the number of parameters under appropriate conditions, since it is not necessary re-adjust the weight values in a back propagation cycle. Experiments on several data sets from UCL Repository, including images and general data sets, showed that our model handled the classification problems, for all the tested data sets, slightly better than CNN and XGBoost alone and was sometimes significantly better.

• Journal of Korean Library and Information Science Society
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• v.45 no.1
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• pp.5-25
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• 2014

The goal of this study is to propose the plan for activating collection services of public library in Seoul. For this goal, author analyzed and compared the core infrastructure, collection services, and their correlation of public libraries in 16 local governments and evaluated environment and the current situation of collection service of public libraries in Seoul. Based on the these results, author suggested five plans or strategies for vitalizing the collection services of public libraries. All public libraries in Seoul must maximize the visibility of the new collections, supply the culture(life-long learning) programs based on library collections, expand the breadth of the collection service through library cooperation system, strengthen interlibrary loan by establishment of the collaborative repository, and raise the awareness of the citizens about the library and collections.