• Journal of Digital Convergence
• /
• v.15 no.5
• /
• pp.495-503
• /
• 2017

The purpose of this study was to investigate the effect of psychobiographical intensive interview(PII) on the parent's insight, parent-child relationship characteristics and counseling session. For this, the PII was conducted to 10 mothers with school age children. PII was semi-structured form that included the intensive interview for exploring psychobiographical information and interpretation counseling of it. The results showed that the level of insight into parent's child problem and relation to parent's own problem was significantly increased. On the other hand, parent-child relationship characteristics (parental efficacy, parenting attitude, and parenting stress) were found to be significant only in some of the parenting stresses. We investigated the possibility as a counseling tool for PII through counseling session impact assessments. As a result of that, we confirmed that there were positive results in task impacts, relationship impacts, and helpful impacts.

• Journal of Aerospace System Engineering
• /
• v.14 no.2
• /
• pp.50-56
• /
• 2020

In this study, we investigated whether long short-term memory (LSTM) can be used in the future to predict F10.7 index data; the F10.7 index is a space environment factor affecting atomic oxygen erosion. Based on this, we compared the prediction performances of LSTM, the Autoregressive integrated moving average (ARIMA) model (which is a traditional statistical prediction model), and the similar pattern searching method used for long-term prediction. The LSTM model yielded superior results compared to the other techniques in the prediction period starting from the max/min points, but presented inferior results in the prediction period including the inflection points. It was found that efficient learning was not achieved, owing to the lack of currently available learning data in the prediction period including the maximum points. To overcome this, we proposed a method to increase the size of the learning samples using the sunspot data and to upgrade the LSTM model.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.2
• /
• pp.1157-1168
• /
• 2021

Hotels drive the growth and development of tourism. Despite their important role, many hotels are small and medium-sized firms (SME) that are struggling to survive against fierce competition. Experts agree that strategic planning is vital for SME survival, but it is not wholly applicable for SME managers. Meanwhile, Mintzberg's concept of crafting strategy offers a more productive insight into SME strategic planning, but its abstract nature has historically discouraged empirical research on its practical benefits. This study will be the first to empirically explore the operationalization of Mintzberg's crafting strategy characteristics, and analyze its influence on organizational learning using structural equation model. Using a sample of 50 hotels in Bali, Indonesia, this study reveals that managing pattern and stability, detecting discontinuity, and knowing the business have a positive but weak effect, whereas reconciling change and continuity proves to have a positive and significantly strong effect on organizational learning. This study has bridged the gap between the abstract concepts of crafting strategy, which is a potentially better approach for SMEs, with daily operational practices. This study also proves that Mintzberg's approach can be used to predict organizational learning. This relationship is crucial since previous studies concluded that organizational learning improves company performance.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.3
• /
• pp.377-384
• /
• 2022

Whether voluntary or involuntary in the field of education, learner participation is a basic premise for all teaching-learning. It is true that behaviorism and cognitive educational psychology have helped the development of teaching-learning theory so far but the reality is that it has not been of great help to provide learner-centered education according to the learner's learning type. We have professional theological knowledge and insight in theological college and having the knowledge to diagnose and solve difficulties and problems in the pastoral field and it is an increasingly difficult reality to educate students to have spiritual leadership that can lead the future society. We know that each student should understand the characteristics of each student and teach according to their learning type but the reason why it is difficult to implement is that each learner has different competencies, conditions, and cultural backgrounds and has particularly diverse learning types. in this respect, in order to increase the learning effect of individuals, individual learning considering the learning type of students is effective.

• Journal of Convergence for Information Technology
• /
• v.8 no.1
• /
• pp.201-206
• /
• 2018

Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12spc
• /
• pp.526-538
• /
• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.241-248
• /
• 2011

Past theories on construction price formation have been shown to be inadequate in terms of their ability to represent real-life industry practice and price formation predictability. In this paper, we develop a theoretical framework on construction price formation that integrates four theories within the domains of marketing, learning, resource management and economics. These are: (i) marketing pricing theory; (ii) experiential and organisational learning theory; (iii) resourced based theory and (iv) microeconomic theory. Utilising pricing theory from marketing, a foundation is able to be created for the procedure of construction price formation, namely: (i) identifying the objectives; (ii) assessing the tendering environment; and (iii) formation of the price. However, understanding contractors' decision making process in tender pricing as such can be attributed to theories of experiential learning and consequently organisational learning. It is argued that contractors do learn from past experience and history and are able to adapt to different market conditions. In formation of the price, neoclassical microeconomics is able to provide additional insight in terms of the supply and demand model and consideration of the market conditions. Interrelated with the microeconomic concept of scarcity, we appreciate that contractors do have limited resources that affect their tender pricing decisions and resource based theory is used to substantiate this. Integrating the various theories as a unity allows the broader reality to be visualised and add to our theoretical understanding of construction price formation.

• Journal of Digital Convergence
• /
• v.9 no.3
• /
• pp.249-260
• /
• 2011

This study aimed to develop a problem-based learning model for mind study based on the insight into a general problem-based learning and contemplation of mind study. Focusing on the main stages commonly regarded important in precedent studies on problem-based learning process, the procedures were designed as follows: 1) choosing a text 2) setting a goal for learning 3) developing a problem 4) preparing a set of learning materials 5) developing an assessment tool 6) designing a plan for teaching and learning. The content and range of the stages were presented and how the main activities should be conducted was also discussed in a main body. These procedures were specified through examples of problem-based learning on the subject of 'mind'. It also suggested how to play a role of teacher as a guide or coach with presenting various examples of teacher talk and specific activities for learners to keep the intention of primary problem-based learning by performing a set of procedures.

• 한국디지털정책학회:학술대회논문집
• /
• 2006.06a
• /
• pp.57-64
• /
• 2006

Arguably, the term "Learning Organisation" (LO) was coined in the 1970's, in the organisational learning context, by Chris Argyris. Certainly it has been around for many years. But it achieved new heights of popularity after the publication of Peter Senge's book "The Fifth Discipline the Art and Practice of the Learning Organisation". Now every respectable Government Agency and major company feels obliged to call themselves a L0. A review of the academic literature and organisation documents show many different concepts being described. Indeed, it seems that some organisations claiming to be a L0 have no clear idea of what they mean by the concept. This paper seeks to go behind the confusion to see whether there is still value for serious practitioners to continue using this concept, or whether it is now such a hackneyed phrase that more precise concepts are desirable. The Literature relating to the L0 is vast and it is beyond the scope of a conference presentation to give a comprehensive literature review. Instead, the paper gives an overview of the broad groups using the term and summarises their similarities and differences. It then reviews the key concepts in Senge's work in the light of this cacophony. The paper concludes that the diversity of definitions render the term "Learning Organisation" virtually meaningless. unless it is accompanied by a specific definition. The paper also concludes that the central tenet of Senge's work, which played a major role in popularising the concept, has been largely overlooked by the many organisations claiming this proud title "A Learning Organisation." It is argued that Senge's contribution to the literature in this field, the centrality of systems thinking to effective organisation learning remains a little understood, but critical insight.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.55-65
• /
• 2023

In this paper, we apply KeyBERT(Keyword extraction with Bidirectional Encoder Representations of Transformers) algorithm-driven topic extraction and topic frequency analysis to deep learning and reinforcement learning research to discover the rapidly changing trends in them. First, we crawled abstracts of research papers on deep learning and reinforcement learning, and temporally divided them into two groups. After pre-processing the crawled data, we extracted topics using KeyBERT algorithm, and then analyzed the extracted topics in terms of topic occurrence frequency. This analysis reveals that there are distinct trends in research work of all analyzed algorithms and applications, and we can clearly tell which topics are gaining more interest. The analysis also proves the effectiveness of the utilized topic extraction and topic frequency analysis in research trend analysis, and this trend analysis scheme is expected to be used for research trend analysis in other research fields. In addition, the analysis can provide insight into how deep learning will evolve in the near future, and provide guidance for select research topics and methodologies by informing researchers of research topics and methodologies which are recently attracting attention.