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

• Asia pacific journal of information systems
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• v.16 no.2
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• pp.69-84
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• 2006

Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.155-162
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• 2021

The expansion of the world wide web has led to a huge amount of user generated content over different forums and social media platforms, these rich data resources offer the opportunity to reflect, and track changing public sentiments and help to develop proactive reactions strategies for decision and policy makers. Analysis of public emotions and opinions towards events and sentimental trends can help to address unforeseen areas of public concerns. The need of developing systems to analyze these sentiments and the topics behind them has emerged tremendously. While most existing works reported in the literature have been carried out in English, this paper, in contrast, aims to review recent research works in Arabic language in the field of thematic sentiment analysis and which techniques they have utilized to accomplish this task. The findings show that the prevailing techniques in Arabic topic-based sentiment analysis are based on traditional approaches and machine learning methods. In addition, it has been found that considerably limited recent studies have utilized deep learning approaches to build high performance models.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.417-426
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• 2024

In general, the implementation of machine learning requires prior knowledge and experience with deep learning models, and substantial computational resources and time are necessary for data processing. As a result, machine learning encounters several limitations when deployed on embedded processors. To address these challenges, this paper introduces a novel approach where a genetic algorithm is applied to the convolution operation within the machine learning process, specifically for performing a selective convolution operation.In the selective convolution operation, the convolution is executed exclusively on pixels identified by a genetic algorithm. This method selects and computes pixels based on a ratio determined by the genetic algorithm, effectively reducing the computational workload by the specified ratio. The paper thoroughly explores the integration of genetic algorithms into machine learning computations, monitoring the fitness of each generation to ascertain if it reaches the target value. This approach is then compared with the computational requirements of existing methods.The learning process involves iteratively training generations to ensure that the fitness adequately converges.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.204-205
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• 2023

This study studied a prediction model for sending collision alarms for small fishing boats based on semi-supervised learning(SSL). The supervised learning (SL) method requires a large number of labeled data, but the labeling process takes a lot of resources and time. This study used service data collected through a data pipeline linked to 'intelligent maritime traffic information service' and data collected from real-sea experiment. The model accuracy was improved as a result of learning not only real-sea experiment data with labeling determined based on actual user satisfaction but also service data without label determined together.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.170-170
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• 2020

Due to the development of technologies, complex computation of huge data set is possible with a prevalent personal computer. Therefore, machine learning methods have been widely applied in the hydrologic field such as regression-based regional frequency analysis (RFA). The main purpose of this study is to compare two frameworks of RFA based on the artificial neural network (ANN) models: quantile regression technique (QRT-ANN) and parameter regression technique (PRT-ANN). As an output layer of the ANN model, the QRT-ANN predicts quantiles for various return periods whereas the PRT-ANN provides prediction of three parameters for the generalized extreme value distribution. Rainfall gauging sites where record length is more than 20 years were selected and their annual maximum rainfalls and various hydro-meteorological variables were used as an input layer of the ANN model. While employing the ANN model, 70% and 30% of gauging sites were used as training set and testing set, respectively. For each technique, ANN model structure such as number of hidden layers and nodes was determined by a leave-one-out validation with calculating root mean square error (RMSE). To assess the performances of two frameworks, RMSEs of quantile predicted by the QRT-ANN are compared to those of the PRT-ANN.

• Educational Technology International
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• v.11 no.1
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• pp.69-92
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• 2010

The purpose of this study is to examine middle school teachers' use of technology and problem-based learning (PBL) in their teaching practice. Factors related to teachers' use of technology and PBL are also investigated including: teachers' computer and Internet skills, feelings of preparedness to use the Internet, attitudes toward the use of web resources, pedagogical beliefs, science teaching efficacy, and the use of general teaching strategies. Twenty-seven middle school science, math, and technology teachers participated in the study. Research results describe the participants as slightly proficient in computer and Internet skills, positive toward use of web resources, and neutral on feelings of preparedness toward use of computer and the Internet. Participants also tended toward constructivist pedagogical beliefs and used various teaching strategies. They, however, reported low science teaching efficacy. Teachers' use of computers and the Internet correlated with pedagogical beliefs and feelings of preparedness toward the use of computers and the Internet. The study also found the relationships between the use of PBL and teachers' computer and internet skills, pedagogical beliefs, and the use of general teaching strategies. Also discussed are meaningful implications for teachers' professional development, especially for the programs designed to facilitate the use of web-enhanced PBL.

• Journal of Korean Library and Information Science Society
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• v.40 no.1
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• pp.363-384
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• 2009

School library programs are a specific strategy connecting user to resources. Accordingly in order to strengthen school libraries, it is necessary for teacher librarians to develop partnerships that can form relationships between internal and external resources. As a result of analyses of partnerships appeared in current middle school library programs, its most programs are based on the internal reading events. In addition to, classroom teachers and external human resources rarely participate in the program. To improve these problems, it is essential to develop teacher librarians' leadership and collaborative networks fostering educational presence of school libraries in a learning community.

• The Journal of Information Systems
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• v.14 no.2
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• pp.75-99
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• 2005

Today, many universities are confronted with the changing education paradigm such as e-learning, Distance Education, Virtual University, This IT-based teaming paradigm shift is certainly a new opportunity or a threat to our universities. To overcome this problem the universities should think e-Learning as strategic weapon, such as many firms created competitive weapons from the information systems at the 1980s. So, e-Learning system can be a SIS(Strategic Information System) which supports university's future education strategies. To build a e-Learning system, not only many H/W and S/W resources but also expert personnels are required. An organization such as local university who is week at financial status can't himself plan the system. The Local University Community e-Learning Centers that support the demand of e-learning for their community are recommended. In order to operate these centers efficiently, the strategic roles of the e-Learning center should first be defined. To define the strategic roles, We classified the strategic roles of the e-Learning center into four dimensions, (1) to improve management efficiency, (2) to enhance educational service, (3) to acquire competitive advantages, (4) to build new education infrastructure, and each dimension has 5 or 6 measurement items. As result, to enhance the educational service was considered as the most significant factor among the four dimensions of strategic roles, and the infrastructure building was the next. We also tried to find the difference for each factor by the characteristics of responsor. The data showed that there was litter difference between the groups in evaluating the significance of strategic roles of e-learning centers. Through the strategic roles definition and analysis of expected role ratings, we could have recommended the direction and operation policies of the e-Loaming centers.

• Journal of Agricultural Extension & Community Development
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• v.8 no.2
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• pp.179-190
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• 2001

The purpose of this study was to analysis the education effects of web-based on-line cyber program mesaured by Kirkpatrick’s evaluation process. The average score on satisfaction of the program was 4.28(.59), which was designed to evaluate the level 1, reaction. To test level 2, learning, the average score that students achieved was calculated and it was 86.87(std.=7.05) in the term examinations. The level 3, job months. It was reported that most employees who took the course are utilizing the knowledge that they acquired from the course(mean=3.80, std.=.77). To identify the level 4, business results, the mean score of the number of accidents and near misses that happened in their factories for 3 months before and after the course were compared. There was statistically significant difference between the number of accidents that happened 3 months before the course and 3 months after the course, at the significance level of .01, which was tested by Paired t-test.