• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.685-697
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• 2003

The Web based multimedia programmes/courses are becoming widely available in recent years. Most of these courses focus on Behaviorist way of learning, which does not promote deep learning in any way. For Adults this approach further incapacitated, as it does not satisfy Andragogical needs. The search for Constructivist way of learning through the web applied to Indian conditions led to need for developing a curriculum development approach that would promote construction of knowledge through web based collaboration. This paper attempts to reengineer existing curriculum development processes and lays out a framework of‘Problem Based Online Learning (PBOL)’curriculum design. In this context, entire curriculum development life cycle is evolved and explained. This is a part of doctoral work (Ph.D), which is in progress and being undertaken by K.James Mathai, and guided of Dr.D.S.Karaulia.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.352-360
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• 2023

Recently, AI pilots using reinforcement learning are developing to a level that is more flexible than rule-based methods and can replace human pilots. In this paper, a curriculum was used to help head-on combat with reinforcement learning. It is not easy to learn head-on with a reinforcement learning method without a curriculum, but in this paper, through the two circle-based head-on air combat learning technique, ownship gradually increase the difficulty and become good at head-on combat. On the two-circle, the ATA angle between the ownship and target gradually increased and the AA angle gradually decreased while learning was conducted. By performing reinforcement learning with and w/o curriculum, it was engaged with the rule-based model. And as the win ratio of the curriculum based model increased to close to 100 %, it was confirmed that the performance was superior.

• Biomolecules & Therapeutics
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• v.21 no.5
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• pp.381-390
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• 2013

The purpose of this study was to examine whether ginsenoside Rg3 (GRg3) could improve learning and memory impairments and inflammatory reactions induced by injecting lipopolysaccharide (LPS) into the brains of rats. The effects of GRg3 on proinflammatory mediators in the hippocampus and the underlying mechanisms of these effects were also investigated. Injection of LPS into the lateral ventricle caused chronic inflammation and produced deficits in learning in a memory-impairment animal model. Daily administration of GRg3 (10, 20, and 50 mg/kg, i.p.) for 21 consecutive days markedly improved the LPS-induced learning and memory disabilities demonstrated on the step-through passive avoidance test and Morris water maze test. GRg3 administration significantly decreased expression of pro-inflammatory mediators such as tumor necrosis factor-${\alpha}$, interleukin-1${\beta}$, and cyclooxygenase-2 in the hippocampus, as assessed by reverse transcription-polymerase chain reaction analysis and immunohistochemistry. Together, these findings suggest that GRg3 significantly attenuated LPS-induced cognitive impairment by inhibiting the expression of pro-inflammatory mediators in the rat brain. These results suggest that GRg3 may be effective for preventing or slowing the development of neurological disorders, including Alzheimer's disease, by improving cognitive and memory functions due to its anti-inflammatory activity in the brain.

• Journal of Gastric Cancer
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• v.18 no.1
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• pp.90-98
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• 2018

Purpose: We evaluated the learning curve and short-term surgical outcomes of robot-assisted distal gastrectomy (RADG) performed by a single surgeon experienced in open, but not laparoscopic, gastrectomy. We aimed to verify the feasibility of performing RADG without extensive laparoscopic experience. Materials and Methods: Between July 2012 and December 2016, 60 RADG procedures were performed by a single surgeon using the da $Vinci^{(R)}$ Surgical System (Intuitive Surgical). Patient characteristics, the length of the learning curve, surgical parameters, and short-term postoperative outcomes were analyzed and compared before and after the learning curve had been overcome. Results: The duration of surgery rapidly decreased from the first to the fourth case; after 25 procedures, the duration of surgery was stabilized, suggesting that the learning curve had been overcome. Cases were divided into 2 groups: 25 cases before the learning curve had been overcome (early cases) and 35 later cases. The mean duration of surgery was 420.8 minutes for the initial cases and 281.7 minutes for the later cases (P<0.001). The console time was significantly shorter during the later cases (168.6 minutes) than during the early cases (247.1 minutes) (P<0.001). Although the volume of blood loss during surgery declined over time, there was no significant difference between the early and later cases. No other postoperative outcomes differed between the 2 groups. Pathology reports revealed the presence of mucosal invasion in 58 patients and submucosal invasion in 2 patients. Conclusions: RADG can be performed safely with acceptable surgical outcomes by experts in open gastrectomy.

• Management & Information Systems Review
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• v.22
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• pp.25-59
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• 2007

These days, a lot of information and knowledge is being produced and accumulated constantly, which makes it difficult for a person to get the exact information or knowledge in simple way that he or she wants to get. It is also true in college and university. A lot of data is increasing so fast that a student cannot achieve his or her goal in learning with the text. This means that it is necessary to bring a change in the way of teaching and learning from only simple lectures. So in this treatise, we try to develop the method of the introduction and operation of center for teaching and learning. In order to accomplish the purposes, this research has examined the questionare and domestic colleges and universities.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.6
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• pp.307-316
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• 2020

Machine learning model data is highly affected by performance. preprocessing is needed to enable analysis of various types of data, such as letters, numbers, and special characters. This paper proposes a development environment that aims to process categorical and continuous data according to the type of missing values in stage 1, implementing the function of selecting the best performing algorithm in stage 2 and automating the process of checking model performance in stage 3. Using this model, machine learning models can be created without prior knowledge of data preprocessing.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.285-294
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• 2023

Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environments. In this work, the detection task is decomposed into two subtasks: exploration and localization. A hierarchical control policy (HC) is proposed to perform the subtasks at different stages. The low-level controller learns how to execute the individual subtasks by deep reinforcement learning, and the high-level controller determines which subtasks should be executed at the current stage. In experimental tests under different geometrical conditions, HC achieves the best performance among the autonomous decision policies. The robustness and generalized ability of the hierarchy have been demonstrated.

• The Journal of Korean Association of Computer Education
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• v.11 no.6
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• pp.77-84
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• 2008

The Teaching & Learning Center plays an important role in increasing the expertise of instructors and for directing the diffusion of innovation not only in primary & secondary education but also in education at the university level. In this study, the Performance Evaluation Model is devised and developed for improving the competency of the Teaching & Learning Center. It consists of three domains - (a) the planning domain, (b) the process domain, and (c) the performance domain - and 11 external indices and 8 internal indices. The Performance Evaluation Index and Guidelines are proposed based on this model.

• Psychiatry investigation
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• v.15 no.11
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• pp.1030-1036
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• 2018

Objective In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm. Methods Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R. Results The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807. Conclusion This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.