• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.457-464
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• 2009

The weak classifier of AdaBoost algorithm is a central classification element that uses a single criterion separating positive and negative learning candidates. Finding the best criterion to separate two feature distributions influences learning capacity of the algorithm. A common way to classify the distributions is to use the mean value of the features. However, positive and negative distributions of Haar-like feature as an image descriptor are hard to classify by a single threshold. The poor classification ability of the single threshold also increases the number of boosting operations, and finally results in a poor classifier. This paper proposes a weak classifier that uses multiple criterions by adding a probabilistic criterion of the positive candidate distribution with the conventional mean classifier: the positive distribution has low variation and the values are closer to the mean while the negative distribution has large variation and values are widely spread. The difference in the variance for the positive and negative distributions is used as an additional criterion. In the learning procedure, we use a new classifier that provides a better classifier between them by selective switching between the mean and standard deviation. We call this new type of combined classifier the "Mixed Weak Classifier". The proposed weak classifier is more robust than the mean classifier alone and decreases the number of boosting operations to be converged.

• Korean Medical Education Review
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• v.18 no.2
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• pp.106-113
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• 2016

The purpose of this study is to identify factors associated with effective peer-assisted learning (PAL) for nursing students. This review examined studies on PAL in nursing education. The literature was searched using terms including 'nursing & peer assisted learning,' 'nursing & peer learning,' and 'nursing & peer teaching.' Potentially relevant research on PAL was identified, and 12 studies were determined to meet the inclusion criteria. This review includes five qualitative, three mixed-methods, and three experimental studies published on the topic. In the studies reviewed, practicum classes were found to use PAL the most. Students of the same age were most commonly the subjects of PAL, as indicated in six papers. PAL has been suggested to affect participants' knowledge, self-efficacy, confidence, and anxiety. The findings indicate that interactions between peers promote learning and lead to mutually positive responses, which provide opportunities for self-development. Finally, students' learning outcomes improve and their confidence in their knowledge and skills increases as they experience the role of student nurse. These findings indicate that PAL can be utilized as an efficient learning method in nursing education programs. However, effective strategies for using PAL to achieve learning objectives and maximize learning outcomes are needed.

• Convergence Security Journal
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• v.21 no.3
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• pp.57-66
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• 2021

As the detection performance using deep learning and machine learning of the intrusion detection field has been verified, the cases of using it are increasing day by day. However, it is difficult to collect the data required for learning, and it is difficult to apply the machine learning performance to reality due to the imbalance of the collected data. Therefore, in this paper, A mixed sampling technique using t-SNE visualization for imbalanced data processing is proposed as a solution to this problem. To do this, separate fields according to characteristics for intrusion detection events, including payload. Extracts TF-IDF-based features for separated fields. After applying the mixed sampling technique based on the extracted features, a data set optimized for intrusion detection with imbalanced data is obtained through data visualization using t-SNE. Nine sampling techniques were applied through the open intrusion detection dataset CSIC2012, and it was verified that the proposed sampling technique improves detection performance through F-score and G-mean evaluation indicators.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• The Journal of Korean Academic Society of Nursing Education
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• v.30 no.3
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• pp.290-300
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• 2024

Purpose: Nursing clinical practice education is transforming with the advent of mobile education and the unique experiences it offers in caring for virtual patients. For this innovative approach, this study aims to evaluate the efficacy of mobile-based virtual women's breast cancer nursing simulation training content on nursing students' confidence, satisfaction, and learning flow. It also examines the nursing students' virtual patient care experiences. Methods: A mixed methods approach using a convergent design was employed to examine students' cancer care confidence and satisfaction, learning flow, and learning experiences. Quantitative data through online questionnaires and qualitative data through focus group interviews were collected, merged, and analyzed. Results: This study developed a virtual nursing training module aimed at caring for women with breast cancer, a novel approach to facilitate mobile-based simulation training for nursing students. Data were analyzed using descriptive analysis, a chi-squared test, Fisher's exact test, t-test for participant homogeneity (experimental: 20, control: 20), independent t-test, and paired t-test. Satisfaction (t=3.53, p=.001) and confidence (t=4.07, p=.001), as well as flow (t=3.78, p=.001), significantly improved in the experimental group compared to the control group. Two core themes and five sub-themes were derived from the experimental group's experiences acquired by caring for women with breast cancer virtually, including that the students "Virtually cared for breast cancer patients, learning as if real." Conclusion: The mobile-based virtual nursing simulation training content allowed nursing students to upgrade their comprehensive nursing care skills by experiencing a fun and practical environment made possible by a new learning method.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.672-680
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• 2003

A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.145-156
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• 2015

Incorporation of individual learning and forgetting behaviors within worker-task assignment models produces a mixed integer nonlinear program (MINLP) problem, which is difficult to solve as a NP hard due to its nonlinearity in the objective function. Previous studies commonly assume homogeneity among workers in workforce scheduling that takes account of learning and forgetting characteristics. This paper expands previous researches by considering heterogeneous individual learning/forgetting, and investigates the impact of worker heterogeneity in initial expertise, steady-state productivity, learning and forgetting on system performance to assist manager's decision-making in worker-task assignments without tackling complex MINLP models. In order to understand the performance implications of workforce heterogeneity, this paper examines analytically how heterogeneity in each of the four parameters of the exponential learning and forgetting (L/F) model affects system performance in three cases : consecutive assignments with no break, n breaks of s-length each, and total b break-periods occurred over T periods. The study presents the direction of change in worker performance under different assignment schedules as the variance in initial expertise, steady-state productivity, learning or forgetting increases. Thus, it implies whether having more heterogenous workforce in terms of each of four parameters in the L/F model is desired or not in different schedules from the perspective of system productivity measurement.

• Journal of Korean Biological Nursing Science
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• v.20 no.2
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• pp.92-102
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• 2018

Purpose: The purpose of this study was to analyze the current status of studies related to the learning methods of biological nursing subjects in Korea. Methods: Five databases (KoreaMed, KMbase, NDSL, KISS, KiSTi) and grey literature were searched prior to February 2018. A total 12 studies met the inclusion criteria including 11 articles and 1 proceeding. Results: We included five experimental studies, five non-experimental studies, and two mixed method studies. First, most of the studies that applied a learning method focused on the subject of human anatomy and physiology; team-based learning was the method that was utilized the most. Second, the necessity of well-designed research was confirmed because the quality of included studies was low. Third, the research variables identified were mainly concentrated on the affective domain, and included satisfaction, motivation, self-efficacy, self-directed learning, confidence, attitude. We confirmed the need to develop a learning program that can also improve the cognitive and psychomotor domain variables in future research. Conclusion: The results of this study suggest that further research should be conducted with consideration the domain of research variables evenly. In addition, future studies should apply various learning methods and included randomized controlled trials.

• Educational Technology International
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• v.19 no.2
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• pp.175-197
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• 2018

Flipped classroom (FC) has gained attention as an active learning approach. Designing effective out-of-class activities to help prepare students for in-class activities is fundamental for successful implementation of FC. This study investigated the effectiveness of Goal-Based Scenarios (GBS) for out-of-class learning in FC. Four out of twelve units in a medical humanities course for Year 2 medical students was redesigned into a FC format, where e-learning modules were designed using a GBS approach for out-of-class activities and classroom debates were implemented for in-class activities. The other eight units were delivered in a conventional classroom debate format, which included reading text materials as pre-class assignments. A formative evaluation study was conducted using questionnaires and interview methods and students' academic achievements were evaluated by comparing their pre- and post-test scores between FC and conventional units. Students had positive perceptions of the e-learning modules in GBS approach and preferred the structure of learning in the FC format. Students' pre-test scores were slightly higher in the FC units, yet their post-test scores were comparable with conventional units. This study illustrates students' perceptions that the learning was bettered structured in FC and that the out-of-class learning using the GBS approach helped them better prepared for in-class activities.

• Educational Technology International
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• v.16 no.2
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• pp.183-200
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• 2015

This study investigated the effect of learning achievements and cognitive load according to different types of presenting learning materials and epistemological beliefs (EB). Learning achievements in this study were composed by retention and transfer of ill-structured problem. A total of 80 college students participated in the study. Prior to the learning, students were guided to fill out a questionnaire regarding epistemological beliefs and a prior knowledge test. The students of each group studied with a different type of reading material: full text (FT), full text including key questions (KeyFT) and full text including a concept map (CmFT). After a session of study was finished, they were asked to complete the posttest: retention and transfer. The results showed that there was a significant difference in transfer achievements. CmFT outperformed higher scores than the other types. There was no significant difference in retention among the groups. It is strongly believed that the types of presenting learning materials may have affected the understanding of ill-structured problem solving skills. Students with sophisticated EB showed higher achievements on retention and transfer than naive-EB and mixed-EB. Even though the data showed decrease of the cognitive load on the type of materials and EB, there were no significant differences on the cognitive load. We should consider a positive effect of types of presenting learning materials and EB enhancing capabilities of solving ill-structured problems in real life.