• Journal of Korean Academy of Nursing Administration
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• v.7 no.3
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• pp.473-485
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• 2001

This study is intended to develop a reliable and appropriate instrument of the clinical nursing education. This research consisted of 4 steps. First step is construction of the content validity by 1 Korean literature professor and 1 teaching professor in Ga Chon Gil College, the pilot study for the content validity by 14 professors and survey with four points Likert Scale, which includes from the point 'strongly valid' to the point of 'strongly non-valid', by 113 head nurses who guide and evaluate the students in clinical practice. The third step is the test of validity and reliability of the preliminary evaluation tool. The fourth step is the test of validity and reliability of the developmental evaluation tool. The data were collected from Sep. 10th, 2001 to Sep. 28th, 2001. This study was analyzed by SPSS PC+ for descriptive statistics, factor analysis and Cronbach's Co-efficient Alpha of the collected data. The results of these analysis are like as follows. 1. Evaluation tool of Clinical practice consists of 16 items including four categories : factor 1 was labeled 'desirable attitude'(5 items), factor 2 was labeled 'correctly judgement and nursing problem solving'(4 items), factor 3 was labeled 'adaptive ability of nursing knowledge and skill'(4 items), factor 4 was labeled 'desirable human relationship'(3 items) and these contributed 71.992% of the variance in the total score. 2. Cronbach's Co-efficient Alpha for internal consistency was .9128 for the total 16 items. For further research, it need to develop a variable and reliable instrument of the student self-evaluation and instrument that based on community.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.531-536
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• 2002

In this paper, we have applied Genetic Algorithms(GAs) to Intrusion Detection System(TDS), and then proposed and simulated the misuse detection model firstly. We have implemented with the KBD contest data, and tried to simulated in the same environment. In the experiment, the set of record is regarded as a chromosome, and GAs are used to produce the intrusion patterns. That is, the intrusion rules are generated. We have concentrated on the simulation and analysis of classification among the Data Mining techniques and then the intrusion patterns are produced. The generated rules are represented by intrusion data and classified between abnormal and normal users. The different rules are generated separately from three models "Time Based Traffic Model", "Host Based Traffic Model", and "Content Model". The proposed system has generated the update and adaptive rules automatically and continuously on the misuse detection method which is difficult to update the rule generation. The generated rules are experimented on 430M test data and almost 94.3% of detection rate is shown.3% of detection rate is shown.

• Journal of Microbiology and Biotechnology
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• v.27 no.5
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• pp.943-946
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• 2017

Although the genus Leuconostoc, generally found in various fermented foods, has often been suggested to be a novel probiotic for food fermentation and health promotion, the strains in this genus showed low acid tolerance and low osmotic stress resistance activities, which are required for survival during food fermentation events. Recently, a novel species of Weissella, W. jogaejeotgali $FOL01^T$ (= KCCM 43128 = JCM 30580), was isolated from Korean fermented clams. To determine the genomic features of this new species, its genome was completely sequenced and analyzed. The genome consists of a circular chromosome of 2,114,163 bp of DNA with a G+C content of 38.8%, and the plasmid pFOL01 consists of 35,382 bp of DNA with a G+C content of 39.1%. The genome analysis showed its potential for use in food fermentation and osmotic stress resistance abilities for processing in food industries. In addition, this strain was predicted to have acid tolerance and adhesion to the mucosal layer for survival and colonization in the gut. Subsequent experiments substantiated these abilities, suggesting that W. jogaejeotgali may have probiotic potential and a high survival rate during food fermentation. Therefore, it may be suitable as a novel probiotic strain for various applications in food industries.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.21-30
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• 2015

Image coding based on mixed raster content model generates don't care regions (DCR) in foreground and background layers, and its overall coding performance is greatly affected by region filling methods for DCRs. Most conventional methods for DCR filling fail in utilizing the local signal properties in hole regions and thus the high frequency components in non-DCR regions are reflected into DCR after signal filling. In addition, further high frequency components are induced to the filled signal because of signal discontinuities in the boundary of DCR. To solve this problem, a new DCR filling algorithm using the priority-based adaptive region growing is proposed in this paper. The proposed method uses the watershed algorithm and the flooding priority of each pixel for region filling is determined from the degree of smoothness in the neighborhood area. By growing the filled region into DCR based on the computed priority, the expansion of high-textured area can be minimized which can improve the overall coding performance. Experimental results show that the proposed method outperforms conventional algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.791-793
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• 2015

Recently, UCC (User Created Contents) and VoD (Video on Demand), and multimedia content are growing, IP-TV, Smart TV, OHTV (Open Hybrid TV) various services such as multi platform (Multi-platform) environment, services and QoS issues. To solve this problem, the network efficiently, and improve the quality of content is necessary for the system. In this paper, the network of channels State and transmission of multimedia data based on dynamic resource usage, TCP and UDP, Adaptive dual-streaming system used for design and analysis. In addition, the existing TCP and UDP streaming system using a single protocol for analysis and verification of the effectiveness of the difference between and. This is a disaster, and medical/first aid system will be utilized in the field of feed, are ubiquitous.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.251-263
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• 2019

Recently the huge demand for Internet media streaming has dramatically increased the cost of the CDN (Content Delivery Network) and the need for a means to reduce it is increasing day by day. In this situation, a P2P-assisted DASH technology has recently emerged, which uses P2P (Peer-to-Peer) communications based on WebRTC (Web Real-Time Communication) standards to reduce the CDN cost. This paper proposes an algorithm that can significantly improve CDN cost savings in this technology by selecting peers based on the transport history. Also we implemented this algorithm in an experimental system and, after setting experimental conditions that emulate the actual mobile network environment, we measured the performance of the experimental system. As a result, we demonstrated that the proposed algorithm can achieve higher CDN cost savings compared to the conventional algorithm where peers are selected at random.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.4
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• pp.29-46
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• 2020

The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.159-166
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• 2022

Artificial Intelligence has stimulated every aspect of today's life. Human thinking quality is trying to be involved through digital tools in all research areas of the modern era. The education industry is also leveraging artificial intelligence magical power. Uses of digital technologies in pedagogical paradigms are being observed from the last century. The widespread involvement of artificial intelligence starts reshaping the educational landscape. Adaptive learning is an emerging pedagogical technique that uses computer-based algorithms, tools, and technologies for the learning process. These intelligent practices help at each learning curve stage, from content development to student's exam evaluation. The quality of information technology students and professionals training has also improved drastically with the involvement of artificial intelligence systems. In this paper, we will investigate adopted digital methods in the education sector so far. We will focus on intelligent techniques adopted for information technology students and professionals. Our literature review works on our proposed framework that entails four categories. These categories are communication between teacher and student, improved content design for computing course, evaluation of student's performance and intelligent agent. Our research will present the role of artificial intelligence in reshaping the educational process.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.369-386
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• 2005

Although Intelligent Tutoring System(ITS) offers individualized learning environment that overcome limited function of existent CAI, and consider many learners' variable, there is little development to be using at the sites of schools because of inefficiency of investment and absence of pedagogical content knowledge representation techniques. To solve these problem, we should study a method, which represents knowledge for ITS, and which reuses knowledge base. On the pedagogical content knowledge, the knowledge in education differs from knowledge in a general sense. In this paper, we shall primarily address the multi-complex structure of knowledge and explanation of learning vein using multi-complex structure. Multi-Complex, which is organized into nodes, clusters and uses by knowledge base. In addition, it grows a adaptive knowledge base by self-learning. Therefore, in this paper, we propose the 'Extended Neural Logic Network(X-Neuronet)', which is based on Neural Logic Network with logical inference and topological inflexibility in cognition structure, and includes pedagogical content knowledge and object-oriented conception, verify validity. X-Neuronet defines that a knowledge is directive combination with inertia and weights, and offers basic conceptions for expression, logic operator for operation and processing, node value and connection weight, propagation rule, learning algorithm.