• 기본간호학회지
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• 제11권3호
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• pp.248-255
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• 2004

Purpose: This study was done to compare the effectiveness of web based learning versus traditional education for learning foley-catheterization in Basic Nursing. Method: This study was a quasi-experimental research. The participants were 60 students who were taking Basic Nursing at A nursing college (3 years) in C city. Thirty students each were assigned to the experimental or control group. Data were collected between October 20 and November 4, 2003. The data were analyzed by descriptive statistics, t-test and ANCOVA. Results: The mean score for competence in foley-catheterization practice in the experimental group was 48.63 and in the control group, 44.67. This result was statistically significant (t=7.655, p=.001). The mean score for knowledge in the experimental group was 63.0, while fur the control group, 64.0. This result was not statistically significant (t=-.330, p=.743). The mean score for confidence in learning in the experimental group was 26.70 for the pre-test and 30.73 for the post-test, and in the control group 27.93 and 28.37 respectively, but this result was not statistically significant (F=.858, p=.358). Conclusion: The Web based learning was found to be effective in nursing practice but not nursing knowledge. It is necessary to continue to develop approaches to teaching nursing and to evaluate these approaches with further research.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.583-587
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• 2015

Scenario-based learning (SBL) has been used in a variety of training situations across different disciplines. Despite its seemly widespread use in construction management discipline, very few attempts have been made to explore its effectiveness and the respective students' learning experience. Using a survey research design, this study aims to investigate students' perceptions on SBL approach in construction management courses. The specific objectives are: (i) to identify the characteristics of a favourable SBL environment, and (ii) to explore the students' learning experience and effectiveness of the SBL approach. The results show that the four characteristics of a favourable SBL environment are: effective team formulation, constant engagement with lecturer, working in a group, and incorporation of motivational incentive for participation. The students really appreciated the opportunities to apply concepts learnt in the lectures in their SBL group work. Also, they perceived that the SBL approach is effective in developing their reflective and critical thinking skills, analytic and problem-solving skills and their ability to work as a team. These findings should facilitate more critical approaches to similar form of teaching methods.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.91-94
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• 2023

According to most experts and health workers, a living creature's body heat is little understood and crucial in the identification of disorders. Doctors in ancient medicine used wet mud or slurry clay to heal patients. When either of these progressed throughout the body, the area that dried up first was called the infected part. Today, thermal cameras that generate images with electromagnetic frequencies can be used to accomplish this. Thermography can detect swelling and clot areas that predict cancer without the need for harmful radiation and irritational touch. It has a significant benefit in medical testing because it can be utilized before any observable symptoms appear. In this work, machine learning (ML) is defined as statistical approaches that enable software systems to learn from data without having to be explicitly coded. By taking note of these heat scans of breasts and pinpointing suspected places where a doctor needs to conduct additional investigation, ML can assist in this endeavor. Thermal imaging is a more cost-effective alternative to other approaches that require specialized equipment, allowing machines to deliver a more convenient and effective approach to doctors.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1085-1092
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• 2022

Travel distance is a parameter mainly used in the objective function of Construction Site Layout Planning (CSLP) automation models. To obtain travel distance, common approaches, such as linear distance, shortest-distance algorithm, visibility graph, and access road path, concentrate only on identifying the shortest path. However, humans do not necessarily follow one shortest path but can choose a safer and more comfortable path according to their situation within a reasonable range. Thus, paths generated by these approaches may be different from the actual paths of the workers, which may cause a decrease in the reliability of the optimized construction site layout. To solve this problem, this paper adopts reinforcement learning (RL) inspired by various concepts of cognitive science and behavioral psychology to generate a realistic path that mimics the decision-making and behavioral processes of wayfinding of workers on the construction site. To do so, in this paper, the collection of human wayfinding tendencies and the characteristics of the walking environment of construction sites are investigated and the importance of taking these into account in simulating the actual path of workers is emphasized. Furthermore, a simulation developed by mapping the identified tendencies to the reward design shows that the RL agent behaves like a real construction worker. Based on the research findings, some opportunities and challenges were proposed. This study contributes to simulating the potential path of workers based on deep RL, which can be utilized to calculate the travel distance of CSLP automation models, contributing to providing more reliable solutions.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.127-134
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• 2022

Generally developed for medical testing, electrocardiogram (ECG) recordings seizure the cardiac electrical signals from the surface of the body. ECG study can consequently be a vital first step to support analyze, comprehend, and expect cardiac ailments accountable for 31% of deaths globally. Different tools are used to analyze ECG signals based on computational methods, and explicitly machine learning method. In all abovementioned computational simulations are prevailing tools for cataloging and clustering. This review demonstrates the different effective methods for heart disease based on computational methods for ECG analysis. The accuracy in machine learning and three-dimensional computer simulations, among medical inferences and contributions to medical developments. In the first part the classification and the methods developed to get data and cataloging between standard and abnormal cardiac activity. The second part emphases on patient analysis from entire ECG recordings due to different kind of diseases present. The last part represents the application of wearable devices and interpretation of computer simulated results. Conclusively, the discussion part plans the challenges of ECG investigation and offers a serious valuation of the approaches offered. Different approaches described in this review are a sturdy asset for medicinal encounters and their transformation to the medical world can lead to auspicious developments.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.301-308
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• 1997

The learning speed of the neural networks, the most important factor in applying to real problems, greatly depends on the learning rate of the networks, Three approaches-empirical, deterministic, and stochastic ones-have been proposed to date. We proposed a new learning-rate selection algorithm using an evolutionary programming search scheme. Even though the performance of our method showed better than those of the other methods, it was found that taking much time for selecting evolutionary learning rates made the performance of our method degrade. This was caused by using static intervals (called static windows) in order to update learning rates. Out algorithm with static windows updated the learning rates showed good performance or didn't update the learning rates even though previously updated learning rates shoved bad performance. This paper introduce a window control scheme to avoid such problems. With the window control scheme, our algorithm try to update the learning ra es only when the learning performance is continuously bad during a specified interval. If previously selected learning rates show good performance, new algorithm will not update the learning rates. This diminish the updating time of learning rates greatly. As a result, our algorithm with the window control scheme show better performance than that with static windows. In this paper, we will describe the previous and new algorithm and experimental results.

• 정보저장시스템학회논문집
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• 제3권4호
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• pp.167-172
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• 2007

The recently-developed off-line learning control approaches for the rejection of periodic disturbances utilize the specific property that the learning system tends to oscillate in steady state. Unfortunately, the prior works have not clarified how closely the learning system should approach the steady state to achieve the rejection of periodic disturbances to satisfactory level. In this paper, we address this issue extensively for the class of linear systems. We also attempt to remove the effect of other aperiodic disturbances on the rejection of the periodic disturbances effectively. In fact, the proposed learning control algorithm can provide very fast convergence performance in the presence of aperiodic disturbance. The effectiveness and practicality of our work is demonstrated through mathematical performance analysis as well as various simulation results.

• 응용통계연구
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• 제35권5호
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• pp.667-684
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• 2022

유전자 발현 데이터는 각 유전자에 대해 mRNA 양의 정도를 나타내고, 그러한 유전자 발현량에 대한 분석은 질병 발생에 대한 메커니즘을 이해하고 새로운 치료제와 치료 방법을 개발하는데 중요한 아이디어를 제공해오고 있다. 오늘날 DNA 마이크로어레이와 RNA-시퀀싱과 같은 고출력 기술은 수천 개의 유전자 발현량을 동시에 측정하는 것을 가능하게 하여 고차원성이라는 유전자 발현 데이터의 특징을 발생시켰다. 이러한 고차원성으로 인해 유전자 발현 데이터를 분석하기 위한 학습 모형들은 과적합 문제에 부딪히기 쉽고, 이를 해결하기 위해 차원 축소 또는 변수 선택 기술들이 사전 분석 단계로써 보통 사용된다. 특히, 사전 분석 단계에서 우리는 유전자 선택법을 이용하여 부적절하거나 중복된 유전자를 제거할 수 있고 중요한 유전자를 찾아낼 수도 있다. 현재까지 다양한 유전자 선택 방법들이 기계학습의 맥락에서 개발되어왔다. 본 논문에서는 기계학습 접근법을 사용하는 최근의 유전자 선택 방법들을 집중적으로 살펴보고자 한다. 또한, 현재까지 개발된 유전자 선택 방법들의 근본적인 문제점과 앞으로의 연구 방향에 대해 논의하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2381-2399
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• 2019

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

• International Journal of Computer Science & Network Security
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• 제22권8호
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• pp.208-216
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• 2022

In Saudi Arabia, female students tend to struggle with the basics of computer programming, especially coding. To better understand why female students sometimes perform poorly in this discipline, this qualitative study aims to obtain the views of female computer programming teachers at a Saudi university on using mobile learning (m-learning) methods in computer programming lectures. Ten teachers from the all-female Aljouf University were interviewed to assess their perceptions of m-learning, in particular, the usefulness of ViLLE visualisation software. Data were analysed using thematic analysis. Most interview responses about m-learning and ViLLE were positive, although there were some notable negative responses. The Saudi culture-related responses were evenly divided between positive and negative, reflecting the culture's limitations.