• Korean Journal of Computational Design and Engineering
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• v.5 no.2
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• pp.127-135
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• 2000

Emergent computing paradigms such as genetic algorithms have found increased use in problems in engineering design. These computational tools have been shown to be applicable in the solution of generically difficult design optimization problems characterized by nonconvexities in the design space and the presence of discrete and integer design variables. Another aspect of these computational paradigms that have been lumped under the bread subject category of soft computing, is the domain of artificial intelligence, knowledge-based expert system, and machine learning. The paper explores a machine learning paradigm referred to as teaming classifier systems to construct the high-quality global function approximations between the design variables and a response function for subsequent use in design optimization. A classifier system is a machine teaming system which learns syntactically simple string rules, called classifiers for guiding the system's performance in an arbitrary environment. The capability of a learning classifier system facilitates the adaptive selection of the optimal number of training data according to the noise and multimodality in the design space of interest. The present study used the polynomial based response surface as global function approximation tools and showed its effectiveness in the improvement on the approximation performance.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.127-134
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• 2022

Generally developed for medical testing, electrocardiogram (ECG) recordings seizure the cardiac electrical signals from the surface of the body. ECG study can consequently be a vital first step to support analyze, comprehend, and expect cardiac ailments accountable for 31% of deaths globally. Different tools are used to analyze ECG signals based on computational methods, and explicitly machine learning method. In all abovementioned computational simulations are prevailing tools for cataloging and clustering. This review demonstrates the different effective methods for heart disease based on computational methods for ECG analysis. The accuracy in machine learning and three-dimensional computer simulations, among medical inferences and contributions to medical developments. In the first part the classification and the methods developed to get data and cataloging between standard and abnormal cardiac activity. The second part emphases on patient analysis from entire ECG recordings due to different kind of diseases present. The last part represents the application of wearable devices and interpretation of computer simulated results. Conclusively, the discussion part plans the challenges of ECG investigation and offers a serious valuation of the approaches offered. Different approaches described in this review are a sturdy asset for medicinal encounters and their transformation to the medical world can lead to auspicious developments.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.3
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• pp.38-53
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• 2022

Recently, the acquisition and production of information using digital tools and the creation of new knowledge are emphasized as important educational elements. Therefore, in this study, the effect of learning achievement according to the digital literacy level of college students was analyzed. For the analysis, a questionnaire is conducted with college students majoring in aviation services attending universities in Seoul Capital Area and Chungcheong area. To verify the hypothesis of the study, demographic characteristics are identified based on the questionnaire, reliability and validity of measurement items are verified, and structural equation model analysis is performed to verify the hypothesis. The analysis results are as follows. First, among the sub-factors of digital literacy competency of college students majoring in aviation service, 'technology use' is found to have a positive effect on 'cognitive flow' and 'emotional flow' of learning flow except 'behavioral flow'. Second, among the sub-factors of digital literacy competency, 'self-learning' is found to have a positive effect on 'cognitive flow', 'emotional flow', and 'behavioral flow' in learning flow. Third, the sub-factors of learning flow, 'cognitive flow', 'emotional flow', and 'behavioral flow' have a positive effect on 'learning satisfaction'. Fourth, 'learning satisfaction' is found to have a positive effect on 'learning outcomes'. Based on the research results, practical support measures and strategies for educational success are presented.

• Educational Technology International
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• v.10 no.1
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• pp.1-23
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• 2009

Learners are often posited in a paradoxical situation where they are not fully involved in decision making processes on how to learn, in designing their tools. Cognitive artifacts in e-learning are supposed to effectively support learner-centered e-learning. The purpose of the study is to analyze cases of cognitive artifacts and to inquire those design principles for facilitating the learner-centered e-learning. Four research questions are suggested: First, it will be analyzed the characteristics of learners with respect to design of cognitive artifacts for supporting the learner-centered e-learning. Second, characteristics of four cases to design cognitive artifacts in learner-centered e-learning environment are analyzed. Third, it will be suggested the appropriate design principles of cognitive artifacts to facilitating learner-centered learning in e-learning environment. Four cases of cognitive artifacts design in learner-centered e-learning was identified as follows: Wiki software as cognitive artifacts in computer-supported collaborative learning; 'Play Around Network (PAN)' as cognitive artifact to monitor learning activities in knowledge community; Knowledge Forum System (KFS) as a cognitive artifact in knowledge building; cognitive artifacts in Courses-as-seeds applied meta-design. Five design principles are concluded as follows: Promoting externalization of cognitive artifacts to private media; Helping learners to initiate their learning processes; Encouraging learners to make connections with other learners' knowledge building and their cognitive artifacts; Promoting monitoring of participants' contributions in collaborative knowledge building; Supporting learners to design their cognitive artifacts.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.416-434
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• 2022

This study, for learners using online and offline tools, understood the structural relationship of user experience of smart learning app on continuous use intention through the technology acceptance model, and classified the learning type characteristics. In addition, based on the experience of using the smart learning app, we explored ways to improve the design of the user experience design for learning tools and contents. For this purpose, the usage perception of 84 middle and high school students of the developed smart learning learning app was investigated after using it for 2 months, and the data were analyzed using the PLS structural equation technique. The main results of this study are as follows. First, system and content user experience had a significant effect on perceived usability and perceived ease of use, and the effect on continued use intention through attitude was significant. Second, there was a significant difference in the effect of system user experience on perceived usefulness in multi-group comparative analysis and gender group. In the preferred learning group, it was the path from perceived ease of use and perceived usefulness to attitude and intention to continue using that showed a significant path difference. Third, as a result of classifying the most commonly used learning types by the multidimensional scale method, the types separated into low dimensions were found to be four types: offline sync type, online sync type, ubiquitous learning type, and self-direct learning type.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.873-877
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• 2010

CAPTCHA is a security tool that prevents the automatic sign-up by a spam or a robot. This CAPTCHA usually depends on the smart readability of humans. However, the common and plain CAPTCHA with text-based system is not difficult to be solved by intelligent web-bot and machine learning tools. In this paper, we propose a new sub-image based CAPTCHA system totally different from the text based system. Our system offers a set of cropped sub-image from a whole digital picture and asks user to identify the correct orientation. Though there are some nice machine learning tools for this job, but they are useless for a cropped sub-images, which was clearly revealed by our experiment. Experiment showed that our sub-image based CAPTCHA is easy to human solver, but very hard to all kinds of machine learning or AI tools. Also our CAPTCHA is easy to be generated automatical without any human intervention.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.133-139
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• 2024

The educational situation, which resulted from the announced self-isolation regime, intensified the forced decisions on the organization of the distance educational process. The study is topical because of the provision of distance learning based on the experience of Kyiv National University of Culture and Arts. The study was conducted in three stages. Systemic, socio-communicative, competence approaches, sociological methods (questionnaires and interviews) were chosen as methodological tools of the research. The results of a survey of teachers and entrants to higher education institutions on the topic "Using social networks and digital platforms for online classes under the conditions of quarantine restrictions" allowed to scientifically substantiate the need for deeper knowledge of such tools as Google Meet (79%), Zoom (13.78%) and Google Classroom (11.62%), which are preferred by entrants. Almost a third of entrants (34.26%) noted the lack of scientific and methodological support for learning the subjects. The study showed high efficiency of messengers in distance education. The study found that in the process of organizing communication in the student-teacher system, it is necessary to take into account the priority of Telegram on the basis of which it is necessary to implement a chatbot for convenient and effective exchange of information about the educational process. Further research should focus on the effectiveness of the use of Telegram. The effectiveness of using chatbots should also be considered. Chatbots can be used to automate routine components of the learning process.

• International Journal of Knowledge Content Development & Technology
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• v.14 no.1
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• pp.19-31
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• 2024

In order to support the mission of the institution in which they are attached, academic libraries provide services in both manual and digital but COVID -19 pandemic that spanned between March and September, 2020 has changed the scenario. With particular reference to Nigeria, about 249,606 cases were confirmed and in order to curb the scourge of this deadly disease, physical academic activities were prevented by Nigeria Centre for Disease Control (NCDC). With this development, innovative tools became indispensable tools for successful delivery of library services in Nigerian academic libraries. Whether or not these tools are still in use for reformation of library service during post- Covid era remains unclear, hence, need for this study. This study examined librarians' use of innovative tools for information dissemination in Nigerian academic libraries during the post-Covid era using a descriptive survey design. Data were obtained both in quantitative and qualitative formats from one hundred and forty-four librarians as respondents. A total enumeration sampling technique was adopted because the population was minimal. Findings of the study revealed that innovative tools such as videoconferencing, WhatsApp, teleconferencing, Facebook, LinkedIn, and web-based learning applications are still in use by librarians for the dissemination of information during the post-Covid era. These tools are useful and beneficial to librarians during the post-COVID era, as they facilitate easy participation and engagement of library users in various discussions. Inadequate funding and lack of advanced technology skills were also identified as major impediments to the successful use of innovative tools for information dissemination. As a result, it was suggested that academic libraries throughout Nigeria prioritize staff training on the necessary digital skills needed to cope in this advanced technology era.