• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.1-7
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• 2018

Feature selection, one of data preprocessing techniques, is one of major research areas in many applications dealing with large dataset. It has been used in pattern recognition, machine learning and data mining, and is now widely applied in a variety of fields such as text classification, image retrieval, intrusion detection and genome analysis. The proposed method is based on a genetic algorithm which is one of meta-heuristic algorithms. There are two methods of finding feature subsets: a filter method and a wrapper method. In this study, we use a wrapper method, which evaluates feature subsets using a real classifier, to find an optimal feature subset. The training dataset used in the experiment has a severe class imbalance and it is difficult to improve classification performance for rare classes. After preprocessing the training dataset with SMOTE, we select features and evaluate them with various machine learning algorithms.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.555-563
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• 2022

This study explored the educational effects of e-PBL by using metaverse tools to strengthen PBL interactions among foreign learners. The university's 3-hour, 15-week PBL subject was systematically reorganized to satisfy the needs of online groups of students. Metaverse technology was also used as a tool for interaction in the process of solving practical problems closely related to our social issues through e-PBL. e-PBL lectures are composed of foreign learners from various countries. More than half of the 43 participating students are from 11 different nations. Learners in an e-PBL class are able to partake in task-based learning activities through the use of the metaverse. This qualitative study identified the metaverse is an effective communication tool which transcends language and nationality. It is also a unique space where both verbal and non-verbal communication between team members are possible online. This study can demonstrate the positive effects of e-PBL teaching methods. By using the metaverse's various tools of interaction to improve communication among foreign learners whose Korean levels are not perfect, we can create a digital space which more closely resembles an offline, interpersonal learning experience.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Journal of Science Education
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• v.33 no.2
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• pp.207-219
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• 2009

This study aims to investigate the key common topics identified and discussed in relevant literature associated with the integrative efforts among STEM disciplines. The key methodology and pedagogy were examined and the significant benefits of using the design method for STEM education were discussed. Meta-analysis was employed and qualitative approach was mainly used to synthesize the major findings and conclusions of the 33 empirical studies. The findings of this meta-analysis revealed that the types and names describing the design methods used the various terms, but the key features have reflected the similar pedagogical benefits and key characteristics. The engineering design is an effective strategic methodology and pedagogy for STEM education. In addition, the design methods show the key benefits including (1) to improve academic achievement, (2) to promote students' affective gains, (3) to facilitate collaborative learning, and (4) to explore STEM related careers and jobs. The collaborative works among STEM professions are needed to promote the benefits of using design methods for integrating STEM subjects.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.171-181
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• 2008

Home care, known also as domiciliary care, is part of the community care service that is a responsibility of the local government authorities in the UK as well as many other countries around the world. The aim is to provide the care and support needed to assist people, particularly older people, people with physical or learning disabilities and people who need assistance due to illness to live as independently as possible in their own homes. It is performed primarily by care workers visiting clients' homes where they provide help with daily activities. This paper is concerned with the dispatching of care workers to clients in an efficient manner. The optimized routine for each care worker determines a schedule to achieve the minimum total cost (in terms of distance traveled) without violating the capacity and time window constraints. A collaborative population-based meta-heuristic called Particle Swarm Optimization (PSO) is applied to solve the problem. A particle is defined as a multi-dimensional point in space which represents the corresponding schedule for care workers and their clients. Each dimension of a particle represents a care activity and the corresponding, allocated care worker. The continuous position value of each dimension determines the care worker to be assigned and also the assignment priority. A heuristic assignment scheme is specially designed to transform the continuous position value to the discrete job schedule. This job schedule represents the potential feasible solution to the problem. The Earliest Start Time Priority with Minimum Distance Assignment (ESTPMDA) technique is developed for generating an initial solution which guides the search direction of the particle. Local improvement procedures (LIP), insertion and swap, are embedded in the PSO algorithm in order to further improve the quality of the solution. The proposed methodology is implemented, tested, and compared with existing solutions for some 'real' problem instances.

• Journal of the Korean Applied Science and Technology
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• v.36 no.4
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• pp.1041-1049
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• 2019

This study gathered previous studies on the effects of image training in school physical education conducted in korea in order to investigate the average effect size as well as the factors that influence the effect sizes. This study connoted findings of individual studies related to image training in school physical education from 1995 to 2018. The results of this study were as follows: Firstly, the overall mean effect size of the image training in school physical education was large size(Cohen, 1988). Secondly, motor skills showed the large effect size than psychological variable. Thirdly, major factors that influence the effect of image training in school physical education appeared to be the type of motor learning, age, gender, training period, training frequency, training ime. Based on these findings, implications for future research and application of image training in school physical education were suggested.

• Clinical and Experimental Pediatrics
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• v.64 no.11
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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.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.201-210
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• 2023

The purpose of this study is to analyze the general characteristics, effect size, and qualitative indicators of self-advocacy studies of people with developmental disabilities published in domestic academic journals and theses. For this purpose, among a total of 2153 papers related to self-advocacy published from 2000 to 2023, 41 studies with developmental disabilities as the keyword were selected, and the specific research results are as follows. Based on the results of this study, when developing a language intervention program related to self-advocacy for people with developmental disabilities, it is recommended to develop an intervention program based on the number of sessions of 10-19 in a learning situation with 20-30 people in adolescents and adults, or during the transition period. There are many studies limited to educational aspects such as special education and integrated education, and by applying this, it is hoped that a self-advocacy language intervention program will be developed at the level of language rehabilitation that can effectively and sophisticatedly assert self-assertion and self-rights after experiencing difficulties in communication.

• Journal of Korean Home Economics Education Association
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• v.23 no.3
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• pp.53-68
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• 2011

This study analyzed the learning objectives in the 2007 revised 10th grade Home Economics textbooks of 6 different publishing companies and the achievement standards developed by Ministry of Education, Science and Technology(2009). Two experienced coders performed initial analysis based on the 'revision of Bloom's taxonomy of educational objectives' and had subsequent conferences to reach an agreement on different results between coders. For knowledge dimension, the results show that the major types of learning objectives in the "Future Family Life" unit are mainly consisted of factual knowledge and procedural knowledge, where as those of "Family Life Culture" unit are consisted of factual knowledge and conceptual knowledge. The achievement standards in both "Future Family Life" and "Family Life Culture" units are solely in a factual knowledge major type. The sub-type of knowledge dimension of both learning objectives and achievement standards fall into 'a specific facts and knowledge component'. For cognitive process dimension, the results show that the leaning objectives are focused on 'understand' and 'analyze'. Those of achievement standards are 'analyse' in the "Future Family Life" unit and 'understand' in the "Family Life Culture" units. From the result of this study, we can conclude that both learning objectives and achievement standards do not adapt any meta-cognitive knowledge, higher order thinking, and cognitive process.