• Research in Community and Public Health Nursing
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• v.6 no.2
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• pp.197-211
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• 1995

This study was designed to identify school health nurses' attitudes toward sex education through a Q-methodological approach. Research was done from Apr. 3, 1995 to Oct. 15, 1995. A final Q-sample was selected to 37 statements out of initial 128 statements after consultation from counselors, educators and writers related to sex education. The P -sample was consisted with 32 school health nurses in Chonbuk province. The collected data were analyzed by Quanal program on PC. The results of the study were as follows: School health nurses are categorized into 6 types. The first type, receiving type with cognition deficiency about general learning objectives of sex education were consisted with 4 subjects. The second type, valuing type with cognition deficiency about general learning objectives of sex education were consisted with 6 subjects. The third type, adopting behavior type with cognition deficiency about specific learning objectives of sex education were consisted with 5 subjects. The fourth type, receiving type with cognition deficiency about specific learning objectives of sex education were consisted with 5 subjects. The fifth type, making sense of information type with cognition deficiency about specific learning objectives of sex education were consisted with 5 subjects. The sixth type, adopting behavior type with cognition deficiency about general learning objectives of sex education were consisted with 7 subjects. As a result of this study, we may realize necessity of prepared sex educators. Sex the educators must be fully cognitive and affective toward sex education before practicing sex education.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.89-96
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• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.

• Korean Journal of Health Education and Promotion
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• v.21 no.3
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• pp.151-177
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• 2004

Recently, the educational community has attempted to implement the theory of multiple intelligences. In approaching multiple intelligences, teachers have applied the same structural approach which has been so successful with cooperative learning. Cooperative learning is easy to learn and implement, fun for teachers and students, and produce profoundly positive outcomes along a remarkable number of dimensions. Different structures are designed for different outcomes, including enhanced mastery of subject matter, improved thinking skills, team building, class building, development of social character and social skills, communication skills, classroom management, classroom discipline, and development of and engagement of each of the multiple intelligences. Cooperative learning is becoming an increasingly popular teaching strategy. In this study, it is aimed to clarify the application of cooperative learning in health education. Cooperative Learning in health education enhances student learning by: 1) providing a shared cognitive set of information between students, 2) motivating students to learn the material, 3) ensuring that students construct their own health knowledge, 4) providing formative feedback, 5) developing social and health group skills necessary for success outside the classroom, and 6) promoting positive interaction between members of different cultural and socio-economic groups. Cooperative Learning structures and techniques in health education are following. Flash Card, Focused Listing, Structured Problem-solving, Paired Annotations, Structured Learning Team Group Roles, Send-A-Problem, Value Line, Uncommon Commonalities, Team Expectations, Double Entry Journal, Guided Reciprocal Peer Questioning, What if. Because the purpose of health education is the practice, therefore health specialists have to guide powerful and effective teaching method The application of cooperative learning in health education may improve its effectiveness.

• Korean Journal of Clinical Pharmacy
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• v.32 no.4
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• pp.328-335
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• 2022

Background: Case-based learning (CBL) is becoming an important approach for improving interprofessional collaboration education. Previous studies have examined learners' satisfaction with interprofessional education (IPE) in medical institutions. However, there are few studies on the implementation of university-led CBL interventions and their direct effects on learning outcomes. The aim of this study was to evaluate the effectiveness of CBL interventions on changes in the participants' perception and knowledge acquisition ability. Methods: The CBL approach consisted of team-based case-based learning, self-directed learning, and post-feedback. It was conducted as a single course for pharmacy students in their 5th year in a university setting. Changes in the participants' perceptions and self-assessments of competence levels were evaluated using survey responses. The effect of the CBL intervention on knowledge acquisition ability was directly evaluated using the exam score. Results: The majority agreed or strongly agreed that team-based case-based learning, and self-directed learning helped them to improve their knowledge and skills to a higher level and to increase the self-assessment of competency level. The average score of knowledge acquisition ability (average score of 75.0, p=0.0098) was significantly higher in the CBL intervention group than the lecture-based learning intervention group (average score of 52.0). Conclusion: The participants positively perceived that CBL intervention helped them to effectively improve their knowledge and the self-assessment of competency level. It also enhanced knowledge acquisition ability. These data, based on the survey responses, suggest that it is necessary to implement CBL interventions in a university-led single professional education.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.318-326
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• 2022

Marketing stress testing is a new method of identifying the company's strengths and weaknesses in a turbulent environment. Technically, this is a complex procedure, so it involves artificial intelligence and machine learning. The main problem is currently the development of methodological approaches to the development of the company's digital model, which will provide a framework for machine learning. The aim of the study was to identify and develop an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. This aim provided the company's activities to be considered as a set of elements (business processes, products) and factors that affect them (marketing environment). The article proposes an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. The proposed approach includes four main elements that are subject to parameterization: elements of the company's internal environment, factors of the marketing environment, the company' core competency and factors impacting the company. Matrices for evaluating the results of the work of expert groups to determine the degree of influence of the marketing environment factors were developed. It is proposed to distinguish between mega-level, macro-level, meso-level and micro-level factors depending on the degree of impact on the company. The methodological limitation of the study is that it involves the modelling method as the only one possible at this stage of the study. The implementation limitation is that the proposed approach can only be used if the company plans to use machine learning for marketing stress testing.

• The Journal of Korean Academic Society of Nursing Education
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• v.14 no.1
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• pp.55-62
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• 2008

Purpose: The purpose of this study was to compare and analyze learning satisfaction, critical thinking disposition, learning attitude and motivation between Problem Based Learning and Subjective Based Learning. Method: The research was performed between September and December, 2005 and 2006, including the development of PBL packages and their application. Statistical analysis was performed using SPSS 13.0. An independent t-test, $X^2$-test, and Pearson Correlation Coefficient were performed to compare the two groups on each of the measures. Result: There were no statistically significant differences among participants in the two groups according to general characteristics. However, The PBL group scored significantly higher on learning satisfaction, critical thinking disposition, learning attitude and motivation. Conclusion: This study contributes to our understanding of student outcomes of the PBL approach compared to the SBL approach. PBL needs to be extended over individual nursing courses for the unification of related courses and a curriculum.

• Korean System Dynamics Review
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• v.9 no.1
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• pp.57-71
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• 2008

This research paper describes a complex and vague settings in which organization makes a decision and explains a role of decision maker's learning process. The original paper, written by Cohen, March, Olsen in 1972, said that all members of organization depended on the technology taken through trials and errors, which is the 'learning' process literally. But they intended to exclude the learning process in their simulation model because their PORTRAN model couldn't replicate the learning concept. As a result, they couldn't explain how all agents of garbage can simulation model resolve the problem dynamically. To overcome this original paper's limitations, we try to rebuild a learning process simulation model using by system dynamics approach that can capture the linkage between organization leanings and agents-based decision-makings. Our learning simulation results reveal two points. First, decision maker's leanings process improves the efficiency of decision making in complex situation. Second, group learning shows a superior efficiency to an individual learning because group members share organizational memory and energy.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.309-314
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• 2007

This paper presents an adaptive neural computation algorithm for multi-layered neural networks which are applied to identify the characteristic function of dynamic systems. The main feature of the proposed algorithm is that the initial learning rate for the employed neural network is assigned systematically, and also the assigned learning rate can be adjusted empirically for effective neural leaning. By employing the approach, enhanced modeling of dynamic systems is possible. The effectiveness of this approach is veri tied by simulations.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.366-371
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• 2014

This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.