• Journal of the Korea Society of Computer and Information
• /
• v.22 no.1
• /
• pp.153-159
• /
• 2017

In recent years there has been a rise in the use and interest of the flipped learning as a teaching and learning paradigm. The flipped learning model includes any use of Internet technology to enrich the learning in a classroom, so that a professor can spend more time interacting with students instead of lecturing. In the flipped model, students viewed video lectures online outside of class time. Students then performed two kinds of assignments, a teamwork assignment and an individual work assignment, through the class time. In this paper, we propose a flipped educational model for a college class. This experimental research compares class of college algorithm using the flipped classroom methods and the traditional lecture-homework structure and its effect on student achievement. The result data of mid-term exam and final exam were analyzed and compared with previous year data. The findings of this research show that there was not a significant difference in the scores of student between two lecturing methods. The survey result and lecture evaluation by students show that students are in favor of the flipped learning.

• Electronics and Telecommunications Trends
• /
• v.36 no.5
• /
• pp.82-91
• /
• 2021

This study reviews application of data-driven anomaly detection techniques to the aviation domain. Recent advances in deep learning have inspired significant anomaly detection research, and numerous methods have been proposed. However, some of these advances have not yet been explored in aviation systems. After briefly introducing aviation safety issues, data-driven anomaly detection models are introduced. Along with traditional statistical and well-established machine learning models, the state-of-the-art deep learning models for anomaly detection are reviewed. In particular, the pros and cons of hybrid techniques that incorporate an existing model and a deep model are reviewed. The characteristics and applications of deep learning models are described, and the possibility of applying deep learning methods in the aviation field is discussed.

• The Journal of Information Systems
• /
• v.30 no.4
• /
• pp.247-275
• /
• 2021

Purpose This study attempted to predict corporate innovation and business performance using ensemble learning. Design/methodology/approach The ensemble techniques uses weak learning to create robust learning, which combines several weak models to derive improved performance. In this study, XGboost, LightGBM, and Catboost were used among ensemble techniques. It was compared and evaluated with traditional machine learning methods. Findings The summary of the research results is as follows. First, the type of innovation is expanding from technical innovation to non-technical areas. Second, it was confirmed that LightGBM performed best for radical innovation prediction, and XGboost performed best for incremental innovation prediction. Third, Catboost performed best for firm performance prediction. Although there was no significant difference in predictive power between ensemble techniques, we found that comparative analysis was necessary to confirm better prediction performance.

• Research in Mathematical Education
• /
• v.25 no.2
• /
• pp.135-158
• /
• 2022

This study aimed to building models to understand the relationships between reasoning resources and content knowledge. We applied Support Vector Machine and linear models to the data including fifth graders' scores in the Cornel Critical Thinking Test and the Iowa Assessments, demographic information, and learning science approach (a student-centered approach to learning called the Science Writing Heuristic [SWH] or traditional). The SWH model showing the relationships between critical thinking domains and academic achievement at grade 5 was developed, and its validity was tested across different learning environments. We also evaluated the stability of the model by applying the SWH models to the data of the grade levels. The findings can help mathematics educators understand how critical thinking and achievement relate to each other. Furthermore, the findings suggested that reasoning in mathematics classrooms can promote performance on standardized tests.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.55 no.5
• /
• pp.638-644
• /
• 2022

The measurement of morphological features is essential in aquaculture, fish industry and the management of fishery resources. The measurement of fish requires a large investment of manpower and time. To save time and labor for fish measurement, automated and reliable measurement methods have been developed. Automation was achieved by applying computer vision and machine learning techniques. Recently, machine learning methods based on deep learning have been used for most automatic fish measurement studies. Here, we review the current status of automatic fish measurement with traditional computer vision methods and deep learning-based methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.47-49
• /
• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.

• International Journal of Computer Science & Network Security
• /
• v.24 no.5
• /
• pp.212-216
• /
• 2024

Feature Selection have turned into the main point of investigations particularly in bioinformatics where there are numerous applications. Deep learning technique is a useful asset to choose features, anyway not all calculations are on an equivalent balance with regards to selection of relevant features. To be sure, numerous techniques have been proposed to select multiple features using deep learning techniques. Because of the deep learning, neural systems have profited a gigantic top recovery in the previous couple of years. Anyway neural systems are blackbox models and not many endeavors have been made so as to examine the fundamental procedure. In this proposed work a new calculations so as to do feature selection with deep learning systems is introduced. To evaluate our outcomes, we create relapse and grouping issues which enable us to think about every calculation on various fronts: exhibitions, calculation time and limitations. The outcomes acquired are truly encouraging since we figure out how to accomplish our objective by outperforming irregular backwoods exhibitions for each situation. The results prove that the proposed method exhibits better performance than the traditional methods.

• Journal of Science Education
• /
• v.37 no.2
• /
• pp.389-404
• /
• 2013

This study aimed to explore elementary teachers' beliefs about science teaching, science learning and the nature of science and consistency among these beliefs. Data was collected by using an open questionnaire and semi-structured individual interview. Teachers' beliefs were classified as traditional beliefs and constructivist beliefs. Traditional beliefs were further divided into content knowledge-centered beliefs and procedural knowledge-centered beliefs. The result showed that a relatively large number of teachers among the total 30 teachers had traditional beliefs about science teaching, science learning, and the nature of science(respectively 60.0%, 66.7%, 83.3%). Most of traditional beliefs were identified as content knowledge-centered beliefs. The proportion of teachers with consistent beliefs for all three aspects was 40.0%, the proportion of those with consistent beliefs for two of them (those with related beliefs) was 53.3%, the proportion of those with different beliefs for them (those with divergent belief) was 6.7%. Most of the teachers with the consistent beliefs had the content knowledge-centered beliefs of traditional beliefs. Although constructivism has been widely emphasized in science education from the 1980's, the rate of the teachers with the consistent beliefs in constructivism was as low as 6.7%.

• Journal of The Korean Association For Science Education
• /
• v.20 no.2
• /
• pp.250-261
• /
• 2000

In this study, a concept learning strategy emphasizing social consensus during discussion (SCS) was developed. The instructional effects of this strategy were compared with those of cognitive conflict strategy (CCS) and traditional instruction in the aspects of students' achievement, conceptions, communication apprehension, perceptions of science learning environment, and perceptions of small group discussion. There were no significant differences in the scores of an achievement test. For the students of low communication competency, however, the scores of the CCS group were significantly higher than those of the traditional group. The adjusted mean of the SCS group was higher than those of the other groups in a conceptions test. The social consensus strategy was also found to be more effective in learning concept for those who were more competent in communicating. No significant differences were found in the communication apprehension. The scores of three groups did not differ significantly in the subcategories of 'personal relevance' and 'students' negotiation' of the test of the perceptions of science learning environment. However, the students in the SCS group scored higher in 'participation'. The students in the SCS group perceived small group discussions more positively.

• Journal of Broadcast Engineering
• /
• v.20 no.5
• /
• pp.676-686
• /
• 2015

A 4D light field image is represented in traditional 2D spatial domain and additional 2D angular domain. The 4D light field has a resolution limitation both in spatial and angular domains since 4D signals are captured by 2D CMOS sensor with limited resolution. In this paper, we propose a dictionary learning-based superresolution algorithm in 4D light field domain to overcome the resolution limitation. The proposed algorithm performs dictionary learning using a large number of extracted 4D light field patches. Then, a high resolution light field image is reconstructed from a low resolution input using the learned dictionary. In this paper, we reconstruct a 4D light field image to have double resolution both in spatial and angular domains. Experimental result shows that the proposed method outperforms the traditional method for the test images captured by a commercial light field camera, i.e. Lytro.