• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1021-1031
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• 2023

State-sponsored cyber attacks are highly impactful because they are carried out to achieve pre-planned goals. As a defender, it is difficult to respond to them because of the large scale of the attack and the possibility that unknown vulnerabilities may be exploited. In addition, overreacting can reduce the availability of users and cause business disruption. Therefore, there is a need for a response policy that can effectively defend against attacks while ensuring user availability. To solve this problem, this paper proposes a method to collect the number of processes and sessions of defense assets in real time and use them for learning. Using this method to learn reinforcement learning-based policies on a cyber attack simulator, the attack duration based on 100 time-steps was reduced by 27.9 time-steps and 3.1 time-steps for two attacker models, respectively, and the number of "restore" actions that impede user availability during the defense process was also reduced, resulting in an overall better policy.

• School Mathematics
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• v.7 no.1
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• pp.17-32
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• 2005

The purpose of this study is to analyze the effects of calculator use, which is drawing more attention in elementary mathematics, on students' learning of mathematics and to suggest effective ways of using calculators. The present study examined appropriate items commonly used in other papers in the areas of number sense and concepts, problem solving, pattern exploration and reasoning ability. The process of item selection about calculator use were investigated through preservice elementary school teachers' responses to the Questionnaire. The use of calculators In elementary school should be based on teachers' under-standing about why calculators are useful tools for learning mathematics. For more effective use of calculators, more sophisticated experimental studies need to be conducted about selected questions.

• Journal of radiological science and technology
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• v.41 no.5
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• pp.487-491
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• 2018

The purpose of this study was to analyze questionnaires of 161 college students attending radiology departments in order to investigate the actual condition of internet use of radiology students. As a result, 95% of college students using the Internet showed 5.8% of general knowledge, 56.9% of radiation major, and 45.8% of general education. In the field of Internet use, basic medicine was 71.2%, anatomy 59.5% and physiology 51.6%. Radiation theory was 39.9% in radiation physics, 31.4% in radiation biology, and 18.3% in radiation management. The radiological applications were followed by radiography and radiography in order of 31.4% and 20.3%, respectively. The radiological imaging was 45.8%, MRI was 37.9%, CT was 37.3%, ultrasound was 24.2%, And radiation nuclear medicine 25.5%. The results of the descriptive statistics of the satisfaction of the contents using the Internet media showed that the overall satisfaction was below 2.5 Based on the results of this study, it is necessary to develop a program with high accessibility to provide various opportunities for internet-based opportunities to increase the academic achievement value of major subjects through the internet and to solve the difficulties in the major subject.

• International Journal of Knowledge Content Development & Technology
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• v.9 no.2
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• pp.45-64
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• 2019

This study examined the perception and use of web 2.0 applications for academic purposes by medical students of Ambrose Alli University, Ekpoma. The objective was to investigate the medical students' perceptions of web 2.0 applications, web 2.0 tools use, extent of use, perception and purpose for using web 2.0 applications. Descriptive survey method was used for this study. The total population of this study was 3670 and the sample size was 367 representing 10% of the study. The purposive sampling technique was adopted, and the instrument used for this study was questionnaire, a total of 367 copies were administered and 321 were found useful for the study. Percentage means and standard deviation on table and chart were used to analyze the data collected using Statistical Package for the Social Sciences (SPSS) software. Findings showed that the perception of web 2.0 applications of medical students AAU was positive and few of web 2.0 applications were used for academic purposes. It was recommended in the study that medical students should be provided with the facilities in a format more familiar to them and used by most of them and institutions need to equip the learning process with the needed facilities which will be of utmost benefit even for future purposes.

• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.139-146
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• 2011

The present study proposes an innovative approach to develop educational contents for the field trip with creative activities. In the study, the field trip with creative activities extends students' learning spaces from not only the classroom but also social circumstances. With the use of the Creative Resource Map (CRM) for students' field trip, students can approach to rich contents whenever and wherever they want. For this, it is necessary to have a variety of curriculum-related contents. For the content development, it is important to enhance mutual cooperation between school and local community as well as to create local capacity for the development of the u-learning contents with smart devices.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.149-160
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• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.182-188
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• 2023

Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1223-1237
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• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.

• Journal of Animal Science and Technology
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• v.66 no.4
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• pp.846-858
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• 2024

In a duck cage, ducks are placed in various states. In particular, if a duck is overturned and falls or dies, it will adversely affect the growing environment. In order to prevent the foregoing, it was necessary to continuously manage the cage for duck growth. This study proposes a method using an object detection algorithm to improve the foregoing. Object detection refers to the work to perform classification and localization of all objects present in the image when an input image is given. To use an object detection algorithm in a duck cage, data to be used for learning should be made and the data should be augmented to secure enough data to learn from. In addition, the time required for object detection and the accuracy of object detection are important. The study collected, processed, and augmented image data for a total of two years in 2021 and 2022 from the duck cage. Based on the objects that must be detected, the data collected as such were divided at a ratio of 9 : 1, and learning and verification were performed. The final results were visually confirmed using images different from the images used for learning. The proposed method is expected to be used for minimizing human resources in the growing process in duck cages and making the duck cages into smart farms.