• Smart Media Journal
• /
• v.1 no.1
• /
• pp.36-41
• /
• 2012

Feature selection is the important technique of selecting a subset of relevant features for building robust pattern recognition systems. Various methods have been studied for feature selection from sequential search algorithms to stochastic algorithms. In this work, we adopted a Quantum-inspired Genetic Algorithm (QGA) which is based on the concept and principles of quantum computing such as Q-bits and superposition of state for feature selection. The performance of QGA is compared to that of the Conventional Genetic Algorithm (CGA) with respect to the classification rates and the number of selected features. The experimental result using UCI data sets shows that QGA is superior to CGA.

• The KIPS Transactions:PartB
• /
• v.15B no.4
• /
• pp.331-340
• /
• 2008

Feature Selection is one of methods to improve the classification accuracy of data in the field of machine learning. Many feature selection algorithms have been proposed and discussed for years. However, the problem of finding the optimal feature subset from full data still remains to be a difficult problem. Bio-inspired algorithms are well-known evolutionary algorithms based on the principles of behavior of organisms, and very useful methods to find the optimal solution in optimization problems. Bio-inspired algorithms are also used in the field of feature selection problems. So in this paper we proposed new improved bio-inspired algorithms for feature selection. We used well-known bio-inspired algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), to find the optimal subset of features that shows the best performance in classification accuracy. In addition, we modified the bio-inspired algorithms considering the prior importance (prior relevance) of each feature. We chose the mRMR method, which can measure the goodness of single feature, to set the prior importance of each feature. We modified the evolution operators of GA and PSO by using the prior importance of each feature. We verified the performance of the proposed methods by experiment with datasets. Feature selection methods using GA and PSO produced better performances in terms of the classification accuracy. The modified method with the prior importance demonstrated improved performances in terms of the evolution speed and the classification accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.7
• /
• pp.1916-1934
• /
• 2023

The phishing attack is a malicious emerging threat on the internet where the hackers try to access the user credentials such as login information or Internet banking details through pirated websites. Using that information, they get into the original website and try to modify or steal the information. The problem with traditional defense systems like firewalls is that they can only stop certain types of attacks because they rely on a fixed set of principles to do so. As a result, the model needs a client-side defense mechanism that can learn potential attack vectors to detect and prevent not only the known but also unknown types of assault. Feature selection plays a key role in machine learning by selecting only the required features by eliminating the irrelevant ones from the real-time dataset. The proposed model uses Hyperparameter Optimized Artificial Neural Networks (H-ANN) combined with a Hybrid Firefly and Grey Wolf Optimization algorithm (H-FFGWO) to detect and block phishing websites in Internet of Things(IoT) Applications. In this paper, the H-FFGWO is used for the feature selection from phishing datasets ISCX-URL, Open Phish, UCI machine-learning repository, Mendeley website dataset and Phish tank. The results showed that the proposed model had an accuracy of 98.07%, a recall of 98.04%, a precision of 98.43%, and an F1-Score of 98.24%.

• Journal of Communications and Networks
• /
• v.17 no.3
• /
• pp.306-320
• /
• 2015

Due to an explosive growth in services running in different datacenters, there is need for service selection and routing to deliver user requests to the best service instance. In current solutions, it is generally the client that must first select a datacenter to forward the request to before an internal load-balancer of the selected datacenter can select the optimal instance. An optimal selection requires knowledge of both network and server characteristics, making clients less suitable to make this decision. Information-Centric Networking (ICN) research solved a similar selection problem for static data retrieval by integrating content delivery as a native network feature. We address the selection problem for services by extending the ICN-principles for services. In this paper we present Queue and Latency, a network-driven service selection algorithm which maps user demand to service instances, taking into account both network and server metrics. To reduce the size of service router forwarding tables, we present a statistical method to approximate an optimal load distribution with minimized router state required. Simulation results show that our statistical routing approach approximates the average system response time of source-based routing with minimized state in forwarding tables.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.117-127
• /
• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• The Journal of the Korea Contents Association
• /
• v.21 no.1
• /
• pp.311-320
• /
• 2021

At present, the secondary school teacher employment examination(SSTEE) is the only gateway to become a national and public secondary teacher in Korea, and after the revision from the 2014 academic year, all the questions of the exam have been converted to supply-type test items, requiring more definitive, accurate, and solid answers. Compared to the selection-type test items that measure recognition memory, the supply-type questions, testing recall memory, require constant memorization and retrieval practices to furnish answers; however, there is not enough learning tools available to support the practices. At this juncture, this study invented a mobile app, called ONE PASS, for the SSTEE. By unpacking the functional mechanisms of the brain, the basis of cognitive processing, this ONE PASS app offers a set of tools that feature brain-based learning principles, such as a personalized study planner, motivation measurement scales, mind mapping, brainstorming, and sample questions from previous tests. This study is expected to contribute to the research on the development of learning contents for applications, and at the same time, it hopes to be of some help for candidates in their exam preparation process.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.27 no.3
• /
• pp.18-31
• /
• 1999

The research deals with external space Seowon(lecture hall) dedicated to education and memorial rises in Sangju area of Choson Dynasty. Characteristics of Seowon as follow; 1. Seowon of Sangju area were built from the middle of 17C to the beginning of 18C. Ideological background of building functioning were grafted into the belief in the three God governing Childbirth, the theory of feng-shui(wind-and water-magic) which is in close connection with the principles of yin and yang, and confucianism and the philosophy of lao-tze and chung-tze. The formation of space were horizontally arrangement and vertical arrangement as the first-learning and then-ancestor shrine of Youngnam provinces. 2. Background and factors of site selection were applied geographical feature, tried to connect owner home town. 3. The shape of path of flow were simple of vertical and curved composition, were continued, were stabilized through composition of human scale's space by reasonable internal. A case of Sangju area, D/H ratio of the front area of buildings and courts was as follows. D/H=1>Hyangkyo> houses on the river>temples>lecture halls. D/H ratio ot the backside areas is as follows. D/H=1>Hyangkyo>houses on the river>lecture halls. 4. Inner garden were planted deciduous than evergreen trees with Lagerstroemia indica. Enclosed dominant trees were planted by Pinus densiflora, Querces seuata.construct GEM strain, and examined for the expression and functional stability in microcosms.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.33 no.2
• /
• pp.75-95
• /
• 2022

This study was carried out with the aim of analyzing the development and functional changes of Webdewey, which has become a basic tool of classification learning, analyzing it in terms of usability for learners, and suggesting specific ways to improve WebDewey's usability. In order to achieve this research objective, the concepts and principles of UI and usability were first laid out, and Webdewey's structure and key functions were analyzed. Since then, Webdewey's media changes and periodical feature changes have been analyzed. In addition, an opinion survey was conducted on the usability of WebDewey among learners who used WebDewey in the learning process, and proposed ways to improve WebDewey's usability based on the implications and direction of improvement derived from it. In terms of UI, proposals have been made to introduce display methods, visualization devices, the advantages of printed versions, and the development of Korean versions. In terms of the 'Create built number' function, suggestions have been made to improve usability in terms of basic number selection, composite route guidance and error message provision, new reference and route construction, screen and button design, and built-number component guidance.

• Korean Journal of Health Education and Promotion
• /
• v.18 no.2
• /
• pp.45-63
• /
• 2001

The objective of this study is to give basic materials for selection and improvement of contents of safety education, which is substantially helpful to elementary students, by analysis of contents of safety education in some subjects and assessment of the needs of elementary students for safety education. For this purpose, this study was analyzed the contents of safety education in five subjects for elementary school and conducted the survey of 883 students in some elementary schools in Seoul from April 7 to 22, 2000. The results were as follows; 1. As a result of analysis of the proportion of contents regarding safety-related education in some subjects, Physical Education occupied the highest proportion (14.09％), and that was followed by Practical Subject (9.55％) and Moral Education (9.34％). However, the proportions in Social Study and Natural Science were very low, 1.85％ and 1.31％ each. In total lines of these five subjects, the numbers of line regarding safety education was contained by 5.78％. 2. Analyzing the proportion of domains of safety education in five textbooks, the Meaning of Safety and Basic Principles occupied the highest portion (29.5％), and that was followed by the Home Safety (24.0％), the Safety in School (17.1％), and the Play and Leisure Safety (14.0％). The Coping with Accidents and First Aid, the Safety from Fire and Explosion, and the Traffic Safety occupied relatively low portion, 6.9％, 5.7％, and 2.8％ each. 3. As a result of analysis of the proportion of the safety education domain in each subject, the Meaning of Safety and Basic Principles occupied the highest portion (23.6％) in Moral Education, the Home Safety (12.7％) in Practical Subject, and the Play and Leisure Safety (10.9％) in Physical Education. 4. Most of the participants in this survey experienced the Home Accidents (71.1％). And also, they experienced the Play and Leisure Accidents (57.9％), the Accidents in School (49.7％), the Traffic Accidents (45.3％), and the Fire and Explosion Accidents (24.7％) in order. 5. In the average proportion of the needs of participants for safety education in each domain, the Coping with Accidents and First Aid has the highest point (4.05). And, that was followed by the Home safety (3.79), the Safety from Fire and Explosion (3.73), the Meaning of Safety and Basic Principles (3.65), the Play and Leisure Safety (3.50), the Safety in School (3.37), and the Traffic Safety (3.35). The average proportion of the needs for safety education of total domains was 3.66. 6. In the needs for safety education regarding the feature of participants, it showed higher scores in female students than male ones (p〈0.001), in lower grader than higher grader (p〈0.05), and in the students born to wealth than those born poor (p〈0.05). Also, the children who recognize the necessity of safety education showed higher scores of the needs for safety education (p〈0.001). And it also showed the same results of high score to the children whose parents did the safety education (p〈0.00l) and to the children and their parents who have the higher degree of practicing safety (p〈0.001), and these differences were statistically significant. 7. In the extent of preference for methods of safety education, it showed high score to the Field Learning, followed by the Audio- Visual Education, the Discussion, and the Instruction of teacher. In the extent of preference for subjects regarding the contents of safety education by each domain, it showed high score to the subject of Safety for 4 domains - the Meaning of Safety and Basic Principles, the Traffic Safety, the Safety from Fire and Explosion, and the Coping with Accidents and First Aid. And also, they preferred Moral Education for 2 domains - the Home safety and the Safety in School, and Physical Education for a domain of the Play and Leisure Safety. 8. While 27 of 36 detail items was contained the contents of safety education, the proportion of needs of participants for safety education showed more than average 3.00 score in 34 of 36 detail items. However, none of 9 detail items was included in five textbooks. Also, 2 detail items - the Coping with Disasters and the Safety from Poisoning - were included together 2 parts; One part had the higher ranked 7 items acquired by analysis of the needs, and the other had the higher ranked 7 items acquired by analysis of the contents. But, except those 2 items, none of items were matched with each part.

• Journal of the Korean Society for information Management
• /
• v.25 no.1
• /
• pp.211-233
• /
• 2008

This study examines various weighting methods for improving the performance of automatic classification based on Rocchio algorithm on two collections(LISA, Reuters-21578). First, three factors for weighting are identified as document factor, document factor, category factor for each weighting schemes, the performance of each was investigated. Second, the performance of combined weighting methods between the single schemes were examined. As a result, for the single schemes based on each factor, category-factor-based schemes showed the best performance, document set-factor-based schemes the second, and document-factor-based schemes the worst. For the combined weighting schemes, the schemes(idf*cat) which combine document set factor with category factor show better performance than the combined schemes(tf*cat or ltf*cat) which combine document factor with category factor as well as the common schemes (tfidf or ltfidf) that combining document factor with document set factor. However, according to the results of comparing the single weighting schemes with combined weighting schemes in the view of the collections, while category-factor-based schemes(cat only) perform best on LISA, the combined schemes(idf*cat) which combine document set factor with category factor showed best performance on the Reuters-21578. Therefore for the practical application of the weighting methods, it needs careful consideration of the categories in a collection for automatic classification.