• Journal of the Institute of Electronics Engineers of Korea SC
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• 제46권6호
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• pp.1-8
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• 2009

Detecting entry and exit zones in a view covered by multiple cameras is an essential step to determine the topology of the camera setup, which is critical for achieving and sustaining the accuracy and efficiency of multi-camera surveillance system. In this paper, a graph theoretic clustering method is proposed to detect zones using data points which correspond to entry and exit events of objects in the camera view. The minimum spanning tree (MST) is constructed by associating the data points. Then a set of well-formed clusters is sought by removing inconsistent edges of the MST, based on the concepts of the cluster balance and the cluster density defined in the paper. Experimental results suggest that the proposed method is effective, even for sparsely elongated clusters which could be problematic for expectation-maximization (EM). In addition, comparing to the EM-based approaches, the number of data required to obtain stable outcome is relatively small, hence shorter learning period.

• Genomics & Informatics
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• 제14권2호
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• pp.53-61
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• 2016

Toxoplasma gondii is an intracellular Apicomplexan parasite and a causative agent of toxoplasmosis in human. It causes encephalitis, uveitis, chorioretinitis, and congenital infection. T. gondii invades the host cell by forming a moving junction (MJ) complex. This complex formation is initiated by intermolecular interactions between the two secretory parasitic proteins-namely, apical membrane antigen 1 (AMA1) and rhoptry neck protein 2 (RON2) and is critically essential for the host invasion process. By this study, we propose two potential leads, NSC95522 and NSC179676 that can efficiently target the AMA1 hydrophobic cleft, which is a hotspot for targeting MJ complex formation. The proposed leads are the result of an exhaustive conformational search-based virtual screen with multilevel precision scoring of the docking affinities. These two compounds surpassed all the precision levels of docking and also the stringent post docking and cumulative molecular dynamics evaluations. Moreover, the backbone flexibility of hotspot residues in the hydrophobic cleft, which has been previously reported to be essential for accommodative binding of RON2 to AMA1, was also highly perturbed by these compounds. Furthermore, binding free energy calculations of these two compounds also revealed a significant affinity to AMA1. Machine learning approaches also predicted these two compounds to possess more relevant activities. Hence, these two leads, NSC95522 and NSC179676, may prove to be potential inhibitors targeting AMA1-RON2 complex formation towards combating toxoplasmosis.

• KIPS Transactions on Software and Data Engineering
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• 제8권11호
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• pp.427-432
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• 2019

In this paper, we propose three approaches to modeling Android malware. The first method involves human security experts for meticulously selecting feature sets. With the second approach, we choose 300 features with the highest importance among the top 99% features in terms of occurrence rate. The third approach is to combine multiple models and identify malware through weighted voting. In addition, we applied a novel method of eliminating permission information which used to be regarded as a critical factor for distinguishing malware. With our carefully generated feature sets and the weighted voting by the ensemble algorithm, we were able to reach the highest malware detection accuracy of 97.8%. We also verified that discarding the permission information lead to the improvement in terms of false positive and false negative rates.

• Clinical and Experimental Pediatrics
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• 제64권11호
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• pp.575-581
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• 2021

Background: Toxocariasis is a zoonotic parasitic disease caused by Toxocara canis and Toxocara cati in humans. Various types of T. canis are important. Purpose: The current study aimed to investigate the prevalence of Toxocara spp. in pediatrics in the context of a systematic review and meta-analysis. Methods: The MEDLINE (PubMed), Web of Sciences, Embase, Google Scholar, Scopus, and Cumulative Index of Nursing and Allied Health databases were searched to identify peer-reviewed studies published between January 2000 and December 2019 that report the prevalence of Toxocara spp. in pediatrics. The evaluation of articles based on the inclusion and exclusion criteria was performed by 2 researchers individually. Results: The results of 31 relevant studies indicated that the prevalence of Toxocara spp. was 3%-79% in 10,676 cases. The pooled estimate of global prevalence of Toxocara spp. in pediatrics was 30 (95% confidence interval, 22%-37%; I²=99.11%; P=0.00). The prevalence was higher in Asian populations than in European, American, and African populations. Conclusion: Health policymakers should be more attentive to future research and approaches to Toxocara spp. and other zoonotic diseases to improve culture and identify socioeconomically important factors.

• KIPS Transactions on Software and Data Engineering
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• 제8권8호
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• pp.343-348
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• 2019

In recent years, extractive summarization systems based on end-to-end deep learning models have become popular. These systems do not require human-crafted features and adopt data-driven approaches. However, previous related studies have shown that linguistic analysis features such as part-of-speeches, named entities and word's frequencies are useful for extracting important sentences from a document to generate a summary. In this paper, we propose an extractive summarization system based on deep neural networks using conventional linguistic analysis features. In order to prove the usefulness of the linguistic analysis features, we compare the models with and without those features. The experimental results show that the model with the linguistic analysis features improves the Rouge-2 F1 score by 0.5 points compared to the model without those features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2390-2406
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• 2022

Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP.

• Journal of The Korean Association For Science Education
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• 제42권3호
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• pp.341-356
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• 2022

This study aims to derive the implications of actor-network theory in science education research. To this end, previous studies applying the actor-network theory were analyzed. The study results show that educational research using actor-network theory can be divided into three main approaches. First, ANT was used as an epistemological perspective to construct an educational method or perspective, Second, ANT was used as an ontological perspective to recognize non-human agency, Third, ANT was used as a methodology for educational research. Based on the results, the possibility of contributing to science education research is discussed. As a new theoretical point of view, we hope that actor-network theory will be meaningful in science education practice and empirical research.

• Journal of Korean Home Economics Education Association
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• 제35권1호
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• pp.73-88
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• 2023

Since the enactment of the School Meal Support Act in 2009, South Korean dietary education has been evolving, placing health, environment, and consideration as its core values. The 3rd Basic Plan for Dietary Education (2020-2024) aims to achieve a sustainable dietary lifestyle that civic agriculture for collective consumption, healthy citizens, and an inclusive society. However, the digital civilization of the Fourth Industrial Revolution is significantly impacting dietary education in schools. Therefore, this study examines sustainable dietary education content in South Korean school meals, diagnoses the phenomena of dietary education facing digital transformation in education due to the Fourth Industrial Revolution, and explores directions for inclusive dietary education through concrete structures and content systems for inclusive dietary education that foster sustainable dietary capabilities. To achieve inclusive dietary education, a structure and system that allow cognitive, normative, and practical learning to be combined in an inclusive way is required. Furthermore, to practice sustainable dietary education, alternative approaches that emphasize the development of learners' core competencies are necessary in the direction of inclusive dietary education that fosters inclusivity.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.135-146
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• 2023

An effective educational program warrants the inclusion of an innovative construction which enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational Decision Support System (EDSS) has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. Insufficient information systems encounter trouble and hurdles in making the sufficient advantage from EDSS owing to the deficit of accuracy, incorrect analysis study of the characteristic, and inadequate database. DMTs (Data Mining Techniques) provide helpful tools in finding the models or forms of data and are extremely useful in the decision-making process. Several researchers have participated in the research involving distributed data mining with multi-agent technology. The rapid growth of network technology and IT use has led to the widespread use of distributed databases. This article explains the available data mining technology and the distributed data mining system framework. Distributed Data Mining approach is utilized for this work so that a classifier capable of predicting the success of students in the economic domain can be constructed. This research also discusses the Intelligent Knowledge Base Distributed Data Mining framework to assess the performance of the students through a mid-term exam and final-term exam employing Multi-agent system-based educational mining techniques. Using single and ensemble-based classifiers, this study intends to investigate the factors that influence student performance in higher education and construct a classification model that can predict academic achievement. We also discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development.

• Journal of Korean Society of Water and Wastewater
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• 제37권6호
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• pp.347-361
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• 2023

Water utilities are making various efforts to reduce water losses from water networks, and an essential part of them is to recognize the moment when a pipe burst occurs during operation quickly. Several physics-based methods and data-driven analysis are applied using real-time flow and pressure data measured through a SCADA system or smart meters, and methodologies based on machining learning are currently widely studied. Water utilities should apply various approaches together to increase pipe burst detection. The most intuitive and explainable water balance method and its procedure were presented in this study, and the applicability and detection performance were evaluated by applying this approach to water supply pipelines. Based on these results, water utilities can establish a mass balance-based pipe burst detection system, give a guideline for installing new flow meters, and set the detection parameters with expected performance. The performance of the water balance analysis method is affected by the water network operation conditions, the characteristics of the installed flow meter, and event data, so there is a limit to the general use of the results in all sites. Therefore, water utilities should accumulate experience by applying the water balance method in more fields.