• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.49-56
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• 2023

Machine learning methods diversely applied to the Internet of Things (IoT) field have been successful due to the enhancement of computer processing power. They offer an effective way of detecting malicious intrusions in IoT because of their high-level feature extraction capabilities. In this paper, we proposed a novel feature selection method for malicious intrusion detection in IoT by using an evolutionary technique - Genetic Algorithm (GA) and Machine Learning (ML) algorithms. The proposed model is performing the classification of BoT-IoT dataset to evaluate its quality through the training and testing with classifiers. The data is reduced and several preprocessing steps are applied such as: unnecessary information removal, null value checking, label encoding, standard scaling and data balancing. GA has applied over the preprocessed data, to select the most relevant features and maintain model optimization. The selected features from GA are given to ML classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) and the results are evaluated using performance evaluation measures including recall, precision and f1-score. Two sets of experiments are conducted, and it is concluded that hyperparameter tuning has a significant consequence on the performance of both ML classifiers. Overall, SVM still remained the best model in both cases and overall results increased.

• Journal of Korean Clinical Nursing Research
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• v.16 no.2
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• pp.57-67
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• 2010

Purpose: The purposes of this study were to compare the level of perception and to identify factors associated with perception on patient safety culture among health care providers working in an operating room(OR). Methods: A cross-sectional survey design was used. Data were collected conveniently from 154 RNs and 116 physicians working in a tertiary hospital in Seoul. Patient safety culture was measured using "The Hospital Survey on Patient Safety Culture" developed by the Agency for Healthcare Research and Quality (AHRQ). Descriptive statistics, t-test, ANOVA, and Spearman rank correlation coefficients were used for statistical analysis with the SPSS version 17.0. Results: The perception level of nurses and physicians on patient safety culture was "moderate" (3.14). Compared to physicians, nurses showed a significantly lower perception on the items of "teamwork within units" (t=-6.904, p<.001) and "overall perception of patient safety" (t=-4.327, p<.001), but had a higher perception about "frequency of events reported" (t=2.769, p=.006). The physicians' professional positions, years of working experience, age, and working hour per week were identified as factors associated with patient safety culture. Conclusion: Level of perception on patient safety culture may vary among health care providers working in the OR. The study finding suggests that a tailored education and training strategies should be considered to develop an effective safety culture for healthcare professionals working in OR.

• Animal Bioscience
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• v.36 no.6
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• pp.980-989
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• 2023

Objective: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Methods: Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. Results: The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. Conclusion: This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.

• Korean Educational Research Journal
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• v.43 no.1
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• pp.89-123
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• 2022

This research aimed to explore the nature of teachers' experiences of multicultural education in primary schools with ethnic diversity in the Republic of Korea and draw policy implications. For this study, the researchers interviewed 15 primary school teachers using semi-structured questionnaires in mine different schools. The participating teachers were in charge of the multicultural education in schools with ethnic diversity in two rural counties in the Republic of Korea. The analysis of the empirical data suggests that teachers stationed in ethnic diversity have not been trained for the diverse population nor multicultural education in general. In addition, they were struggling with the lack of teaching resources including textbooks for multicultural education, support for students and their parents in need of learning Korean as a foreign language, accurate data of those students etc. These teacher policy implications are suggested while discussing the findings; such as the importance of practical in-service training opportunities, quality teaching resources, Korean as Second Language(KSL) experts, and accurate data of students with ethnic diversity.

• Physical Therapy Korea
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• v.30 no.2
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• pp.110-119
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• 2023

Background: Osteoarthritis is a common condition with an increasing prevalence and is a common cause of disability. Osteoarthritic pain decreases the quality of life, and simple gait training is used to alleviate it. Knee osteoarthritis limits joint motion in the sagittal and lateral directions. Although many recent studies have activated orthotic research to increase knee joint stabilization, no study has used patellar tendon straps to treat knee osteoarthritis. Objects: This study aimed to determine the effects of patellar tendon straps on kinematic, mechanical, and electromyographic activation in patients with knee osteoarthritis. Methods: Patients with knee osteoarthritis were selected. After creating the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), leg length difference, Q-angle, and thumb side flexion angle of the foot were measured. Kinematic, kinetic, and muscle activation data during walking before and after wearing the orthosis were viewed. Results: After wearing the patellar tendon straps, hip adduction from the terminal stance phase, knee flexion from the terminal swing phase, and ankle plantar flexion angle increased during the pre-swing and initial swing phases. The cadence of spatiotemporal parameters and velocity increased, and step time, stride time, and foot force duration decreased. Conclusion: Based on the results of this study, the increase in plantar flexion after strap wearing is inferred by an increase due to neurological mechanisms, and adduction at the hip joint is inferred by an increase in adduction due to increased velocity. The increase in cadence and velocity and the decrease in gait speed and foot pressure duration may be due to joint stabilization. It can be inferred that joint stabilization is increased by wearing knee straps. Thus, wearing a patellar tendon strap during gait in patients with knee osteoarthritis influences kinematic changes in the sagittal plane of the joint.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1720-1728
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• 2022

The industrial 4.0 era has been opened with the development of artificial intelligence technology, and the realization of smart farms incorporating ICT technology is receiving great attention in the livestock industry. Among them, the quality management technology of livestock products and livestock operations incorporating computer vision-based artificial intelligence technology represent key technologies. However, the insufficient number of livestock image data for artificial intelligence model training and the severely unbalanced ratio of labels for recognizing a specific defective state are major obstacles to the related research and technology development. To overcome these problems, in this study, combining oversampling and adversarial case generation techniques is proposed as a method necessary to effectively utilizing small data labels for successful defect detection. In addition, experiments comparing performance and time cost of the applicable techniques were conducted. Through experiments, we confirm the validity of the proposed methods and draw utilization strategies from the study results.

• Advances in concrete construction
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• v.15 no.1
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• pp.23-39
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• 2023

One of the important problems of concrete placing is the concrete compaction, which can affect the strength, durability and apparent quality of the hardened concrete. Therefore, vibrating operations might be accompanied by much noise and the need for training the involved workers, while inappropriate functioning can result in many problems. One of the most important methods to solve these problems is to utilize self-compacting cementitious composites instead of the normal concrete. Due to their benefits of these new materials, such as high tensile, compressive, and flexural strength, have drawn the researchers' attention to this type of cementitious composite more than ever. In this experimental investigation, six mixing designs were selected as a base to acquire the best mechanical properties. Moreover, forty-eight rectangular composite panels with dimensions of 300 mm × 400 mm and two thickness values of 30 mm and 50 mm were cast and tested to compare the flexural and impact energy absorption. Steel fibers with volume fractions of 0%, 0.5% and 1% and with lengths of 25 mm and 50 mm were imposed in order to prepare the required cement composites. In this research, the composite panels with two thicknesses of 30 mm and 50 mm, classified into 12 different groups, were cast and tested under three-point flexural bending and repeated drop weight impact test, respectively. Also, the examination and comparison of flexural energy absorption with impact energy absorption were one of the other aims of this research. The obtained results showed that the addition of fibers of longer length improved the mechanical properties of specimens. On the other hand, the findings of the flexural and impact test on the self-compacting composite panels indicated a stronger influence of the long-length fibers.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.753-761
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• 2023

This study investigates strategies to increase learning efficiency for programming subjects to which flipped learning teaching method is applied targeting non-major students. Design a learner-centered flipped learning-based programming class and get strategies for effective application methods for field application. Also, the purpose is to explore the efficient application of the flipped learning teaching method to the computational thinking subject of liberal arts classes at this university. By applying the flipped learning teaching method, one of the innovative teaching methods, we consider ways to improve the quality of programming subject classes, the efficiency of practical education, and the improvement of learner achievement. The purpose of this study is to design an efficient learning model for software education targeting non-majors by applying various teaching methods and learning design models convergence away from the traditional teaching method.

• 한국노년학
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• v.30 no.3
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• pp.803-814
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• 2010

This study was conducted to understand current management status of welfare medical device centers and to suggest complementary point. Method: We surveyed 194 welfare medical device centers through the mail. The survey was done in three domains, i. e. management of service, assuring the health resources, offering the service. Results & Conclusions: According to the result of our study, several problems, which should be improved in the near future, were suggested. That were improving facilities(especially in sanitization of the devices), operating an education or training program for the personnel, and making up for the current management. It was very important for soft landing of long-term care insurance and improving quality of the elderly's life that 'Ministry of Health and Welfare' and 'National Health Insurance corporation' must support welfare medical device centers for discharging their roles

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.