• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.109-117
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• 2012

The purpose of this study is to give the preliminary data and suggestion for introducing and spreading e-learning engineering education through analyzing learning behaviors, learning motivations, and satisfaction of e-learning engineering students. Especially, this comparative study analyzes each research domain according to majors and grades, thereby suggesting more specific and practical results. 2,745 students registered in 38 subjects of e-learning in 2 Universities were analyzed for this study. The study result shows that engineering students are attending around 2 e-learning subjects with a duration of about 30 minutes once a week. The main of learning motivation for e-learning was not easy test level and feasibility of acquiring credit but advantages of e-learning such as freedom of time and space, learning by repetition. The satisfaction scores of e-learning were lower compared to the aspects of system and contents Based on these results, first, an active spread of e-learning to engineering education is necessary because the demand from the engineering students is high enough and they have desirable learning behavior and learning motivation for it. Second, the characteristics of grades need to be taken into consideration on operation of e-learning. Third, a successful e-learning process needs more meticulous and active operation.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.

• English Language & Literature Teaching
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• v.13 no.3
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• pp.133-156
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• 2007

Although the understanding of English tense system is a crucial factor for communicative English learning and teaching for EFL students, it has been neglected over the years. As with other areas of the grammar, difficulties may arise from the nature of the system itself or from differences between time, tense and aspect. Consequently, many learners face a considerable difficulty with the English tense system as they are more often unable to grasp the basic conceptual differences of present/present continuous, past/present perfect, will/be going to along with many others. More concerning fact is that lots of instructors or so-called native English teachers seem not to be aware of the importance of teaching English tense system. The purpose of this study is to review and examine various theories and practical usages of tense in order to establish and/or present better methods for teaching tenses. This paper is focused on comparatively exact distinction of time, physical notion from tense, grammatical category as well as sequences of tenses in view of school grammar and communicative function. At the end or middle of each chapter, efficient teaching and learning techniques or strategies on tenses are suggested to help instructors or learners who relentlessly face confusions in understanding tense and its usage for communicative English learning and teaching. This study attempts to influence learners' ability to recognize and write tense in authentic contexts not to mention spoken English.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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• pp.72-78
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• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.6
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• pp.502-505
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• 2005

According to use of a multimedia content being increased in an e-learning, a size deviation of a content to provide in an e-teaming system is increased. It is becoming a factor to drop efficiency of education because this makes a deviation of response time on an e-learning user. Therefore, studies about a deviation improvement of user response speed is necessary in the e-learning system that used a multimedia content. In this study, we designe the hybrid e-teaming system that used multi-layed structire. A proposed system is based on a characteristics of media and size of a content that a multimedia data had. Also, it proved superiority of performance through experimentation.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.37-43
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• 2001

New product development has been a key element fur organizational evolution. The bulk of research about new product strategy has focused solely on new product development function itself. This paper investigates cross-functional elements in new product development. More specifically, we suggest that there must exist a fit between new product strategy and value chain strategy. It means that, in order to support new product development activity, there must exist a relevant value chain strategy. We consider three types of integration - internal integration, customer integration, and supplier integration - as strategic elements of value chain strategy. For the case of new product strategy, we consider market newness and product technology unfamiliarity as strategic elements. We also consider two types of learning characteristic, i.e., \\\"fast-adaptive learning\\\" and \\\"slow-adaptive leaning\\\" as control factor. Learning characteristic represents firms organizational capability related with organizational learning. For example, fur fast-adaptive learning case, the effect of integration appears early in time. System dynamics simulation is employed to verify our research framework. The results exhibit that there must exist cross-functional relationships between value chain strategy and new product strategy in order to shorten total development time.al development time.

• Journal of Creative Information Culture
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• v.6 no.3
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• pp.149-158
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• 2020

Corona19 has experienced radical changes in teaching methods in primary and secondary schools and higher education institutions, and the Ministry of Education has continued various attempts and support to ensure the quality of teaching and to promote learning participation in distance education. In this study, the support policy of the Ministry of Education for the post-Corona era was reviewed, and the professors' experiences in remote education were investigated and analyzed through intensive interviews. As a way to utilize programs to support participation in learning in real-time distance education, first, consideration of the proper period of concentration of learning by learners, second, coping with unexpected problems during learning activities, and using small meeting rooms and chatting as a collaboration tool were presented.

• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.2
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• pp.27-31
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• 2023

Purpose: Establishment of a real-time monitoring system for odor control in traditional markets in Gangwon-do and a system for linking prevention facilities. Research design, data and methodology: Build server and system logic based on data through real-time monitoring device (sensor-based). A temporary data generation program for deep learning is developed to develop a model for odor data. Results: A REST API was developed for using the model prediction service, and a test was performed to find an algorithm with high prediction probability and parameter values optimized for learning. In the deep learning algorithm for AI modeling development, Pandas was used for data analysis and processing, and TensorFlow V2 (keras) was used as the deep learning library. The activation function was swish, the performance of the model was optimized for Adam, the performance was measured with MSE, the model method was Functional API, and the model storage format was Sequential API (LSTM)/HDF5. Conclusions: The developed system has the potential to effectively monitor and manage odors in traditional markets. By utilizing real-time data, the system can provide timely alerts and facilitate preventive measures to control and mitigate odors. The AI modeling component enhances the system's predictive capabilities, allowing for proactive odor management.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.190-194
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• 2023

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.