• International Journal of Contents
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• v.12 no.4
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• pp.8-16
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• 2016

This research proposes the composition logic of an Active Learning Environment (ALE), to enable discovery by learning through experience, whilst increasing knowledge about modern architectural heritage. Linking information to the historical heritage using Information and Communication Technology (ICT) helps to overcome the limits of previous learning methods, by providing rich learning resources on site. Existing field trips of cultural heritages are created to impart limited experience content from web resources, or receive content at a specific place through humanities Geographic Information System (GIS). Therefore, on the basis of the blended space theory, an augmented space experience method for overcoming these shortages was composed. An ALE space framework is proposed to enable discovery through learning in an expanded space. The operation of ALE space is needed to create full coordination, such as a Content Management System (CMS). It involves a relation network to provide knowledge to the rule engine of the CMS. The application is represented with the Deoksugung Palace Seokjojeon hall example, by describing a user experience scenario.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.1
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• pp.15-28
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• 2018

Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2020-2037
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• 2017

In order to improve the energy efficiency of n-Epidemic routing protocol in opportunistic networks, in which a stable end-to-end forwarding path usually does not exist, a novel adjustment strategy for parameter n is proposed using learning atuomata principle. First, nodes dynamically update the average energy level of current environment while moving around. Second, nodes with lower energy level relative to their neighbors take larger n avoiding energy consumption during message replications and vice versa. Third, nodes will only replicate messages to their neighbors when the number of neighbors reaches or exceeds the threshold n. Thus the number of message transmissions is reduced and energy is conserved accordingly. The simulation results show that, n-Epidemic routing protocol with the proposed adjustment method can efficiently reduce and balance energy consumption. Furthermore, the key metric of delivery ratio is improved compared with the original n-Epidemic routing protocol. Obviously the proposed scheme prolongs the network life time because of the equilibrium of energy consumption among nodes.

• Journal of the Daesoon Academy of Sciences
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• v.24_1
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• pp.255-289
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• 2014

This study's aim is to identify modern and post-modern values and transnationalism embodied in Daesoon Thought and to seek for the new value to overcome irrationality of modern values in this society we live in. Several previous studies discussed about these issues, but most of them studied them on the basis of Korean new religions or Jeungsangyo, or in sociological theory perspective. Therefore, this study focused on Daesoon Thought encompassing ideological perspective as well as historical perspective of Daesoonjinrihoe. As for nationalism, while Eastern learning(Dong-Hak) is prone to ethnocentricity, Daesoon Thought shows trans-ethnic perspective. As for historical perspectivel, Mugeuikdo, a precursor to Daesoonjinrihoe showed non-relationship with any politics as contrasted with other new korean religious movement at that time. As for aspects of modern values, 3 perspectives (political system, social system, abolition of premodern values) were discussed. As for political system perspective, while Eastern learning advocates democratic modernity but accepted monarchy, Jeungsan denied monarchy. And While western political philosophy advocated rationality-based absolute person, Daesoon Thought proposes ideal human who can have political power and do religious indoctrination all together. As for social system perspective, while western humanism is based on all of he people's equity in front of God, Eastern learning on humans are Heaven (人乃天), Daesoon Thought is based on Injon thought(人尊思想) which encompasses spiritual world, human world and all of the universe. Daesoon Thought also proposes abolition of discrimination by gender, social position. As for abolition of premodern values, Daesoon Thought critics pre-modern formalism and advocate acceptance of other nations' culture, pragmatism, and humanism. As presented above, Daesoon Thought has not only modern values but also aspects of post-modernity and transnationalism. In the future, further studies are needed which tackle these issues and search for new values of Daesoon Thought which can overcome limitation of modern values.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.145-150
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• 2023

The main purpose of the study is to identify the key aspects of modern innovative research in the field of education. In the modern informatized world, education is becoming a decisive factor in social development and an important component in the development of the human personality, increasing respect for human rights and freedoms. Today it is quite obvious that without the necessary education a person will not be able to provide himself with proper living conditions and realize himself as a person. The high level of education of the population is an important factor that positively influences the creation of favorable conditions for the full realization of the rights and freedoms of man and citizen. Today, active and interactive teaching methods are widely used. The use of interactive teaching methods ensures complete immersion of students in the learning process and is the main source of learning. The radical difference between traditional and interactive learning is that the student not only replenishes and strengthens his knowledge, but also complements and constructs new ones. The methodology includes a number of theoretical methods. As a result of the study, current trends and prerequisites of modern innovative research in the field of education were investigated.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.337-342
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• 2012

The modern culture has changing its type, characteristic and genre by the times. And the modern culture has providing good resources to reader that he/she can see the times. Recently, the modern culture has changed the providing method for reader. That is, the attempt is to provide the various and many literary works to reader as the digital devices or the types of content. In this paper, we propose e-learning system based on a modern literary work's ontology. And we provide this system to reader for supporting easy and diverse process to reader. The modern literary work's contents in this system is processed by SCORM, and we construct LMS and LCMS. In order to evaluate this system, we construct the test group by 80 people, and we show the efficiency of this system process with modern literary work by the test.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.44-51
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• 2016

Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users' sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.446-465
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• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.300-306
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• 2022

The theoretical analysis and synthesis of prospects for the development of distance learning in Ukraine, the main topical problems of distance education in Ukraine are considered, the main factors that hinder the introduction of distance learning are analyzed, to pay attention to the need to increase the level of computer literacy among Ukrainian educators and the formation of modern methodology of distance learning, in particular, a single, systematic, national approach of organization, coordination and control in this area. Research methods: analytical method, method of structural and functional analysis, phenomenological method, content analysis method, philosophical reflection method, sociological methods (questionnaire, interview).

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.211-221
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• 2024

Pakistan is a top producer and exporter of high-quality rice, but traditional methods are still being used for detecting rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The modified connection skipping ResNet 50 had the highest accuracy of 99.16%, while the other models achieved 98.16%, 98.47%, and 98.56%, respectively. In addition, CNN and an ensemble model K-nearest neighbor were explored for disease prediction, and the study demonstrated superior performance and disease prediction using recommended web-app approaches.