• Journal of Korean Academy of Fundamentals of Nursing
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• v.17 no.1
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• pp.82-89
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• 2010

Purpose: The purpose of this study was to compare nursing students before using problem-based learning and after the experience in: class satisfaction, tendency to critical thinking, learning attitude and motivation. Method: The data were collected on March 20 and June 5, 2008. The PBL study was given for 15 weeks from March through June involving 216 junior nursing students. To test effects of PBL, a one group pretest-posttest design was used. Statistical analysis was performed with SPSS 13.0. Paired t-test, $x^2$-test, and Pearson correlation coefficient were performed. Results: The results are summarized as follows: Following PBL, the students scored significantly higher on the class satisfaction (t=-3.321, p= .001), tendency to critical thinking (t=-2.218, p= .034), learning attitude (t=-2.910, p= .004) and motivation (t=-4.407, p<.001). The Pearson correlation coefficients among the three variables were significantly positive. Conclusion: This study contributes to our understanding of outcomes from the PBL approach. The students undertaking PBL showed that they developed a more positive attitude with their educational experience. Also, students' tendency to think critically improved through the use of the PBL approach.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.74-81
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA(Dropout Genetic Algorithm) which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• Journal of Ocean Engineering and Technology
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• v.34 no.4
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• pp.277-284
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• 2020

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.849-859
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• 2021

Prostate cancer (PCa) is a fatal disease that occurs in men. In general, PCa cells are found in the prostate gland. Early diagnosis is the key to prevent the spreading of cancers to other parts of the body. In this case, deep learning-based systems can detect and distinguish histological patterns in microscopy images. The histological grades used for the analysis were benign, grade 3, grade 4, and grade 5. In this study, we attempt to use transfer learning and fine-tuning methods as well as different model architectures to develop and compare the models. We implemented MobileNet, ResNet50, and DenseNet121 models and used three different strategies of freezing layers techniques of fine-tuning, to get various pre-trained weights to improve accuracy. Finally, transfer learning using MobileNet with the half-layer frozen showed the best results among the nine models, and 90% accuracy was obtained on the test data set.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.89-96
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• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.77-82
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• 2023

The article considers online discussion as an interactive learning method in the conditions of distance learning. The essence of discussion and the stages of its organization are described. The main objective of discussion in distance learning is defined as the stimulation of interest in learning and the involvement of various viewpoints in an active discussion of the stated problems. The key role in ensuring the efficiency of a discussion is identified. The article develops a model for organizing asynchronous online discussions on the Moodle platform, highlighting the sequence of stages and their content. An experimental study of the use of the discussion method in the training of students in distance learning conditions is carried out. Based on the results of the methodological experiment, conclusions are drawn about student interest in online discussions. The authors conclude that the interest of students of different specialties in asynchronous online discussions varies, and the greatest interest is demonstrated by linguistics students. Nevertheless, the differences in student interest in online discussions by groups (specialties) are more likely attributable to subjective factors, which do not affect the overall picture in a major way.

• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.1-13
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• 2018

Earthwork planning is one of the critical issues in a construction process management. For the construction process management, there are some different approaches such as optimizing construction with either mathematical methodologies or heuristics with simulations. This paper propose a simulated earthwork scenario and an optimal path for the simulation using a reinforcement learning. For reinforcement learning, we use two different Markov decision process, or MDP, formulations with interacting excavator agent and truck agent, sequenced learning, and independent learning. The simulation result shows that two different formulations can reach the optimal planning for a simulated earthwork scenario. This planning could be a basis for an automatic construction management.

• Journal of Drive and Control
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• v.20 no.2
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• pp.15-23
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• 2023

In this study, two-dimensional location of crops for auto weeding was detected using deep learning. To construct a dataset for soybean detection, an image-capturing system was developed using a mono camera and single-board computer and the system was mounted on a weeding robot to collect soybean images. A dataset was constructed by extracting RoI (region of interest) from the raw image and each sample was labeled with soybean and the background for classification learning. The deep learning model consisted of four convolutional layers and was trained with a weakly supervised learning method that can provide object localization only using image-level labeling. Localization of the soybean area can be visualized via CAM and the two-dimensional position of the soybean was estimated by clustering the pixels associated with the soybean area and transforming the pixel coordinates to world coordinates. The actual position, which is determined manually as pixel coordinates in the image was evaluated and performances were 6.6(X-axis), 5.1(Y-axis) and 1.2(X-axis), 2.2(Y-axis) for MSE and RMSE about world coordinates, respectively. From the results, we confirmed that the center position of the soybean area derived through deep learning was sufficient for use in automatic weeding systems.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.197-204
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• 2011

In this paper, There is a study of a smartphone-based App for e-learning when the process control of manufacturing. First, That is obtained the control limit lines after inputted by the measured data and able to look up the assignable causes and then can display those causes. A User of this App can access the record about assignable causes using the record menu and can use with an e-Learning tool. Because that were provided in the form of a control process theory and bulletin announcements. Helped to exchange information. In addition, the user's guide how to use this App. The result of this process control is provided by charts. The alarm message to the alertsymbol, depending on the level of color clearly was designed to UI which displays the results. After the questionnaire responses with respect to satisfaction of Utilization and satisfaction of the learning experience. The Utilization' satisfaction results Appeared that 82% of the participants were satisfied. And The learning's satisfaction results Appeared that 90% of the participants were satisfied.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.59-68
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• 2022

As real-time live streaming broadcasting and non-face-to-face classes are spreading in the Corona era, it is time to take academic interest in online streaming classes. In particular, it is important to clarify why users use online streaming classes. Therefore, this study proposes social presence, interest, convenience of use, and interactivity as characteristics of online streaming classes, and aims to verify how these characteristics affect learning satisfaction and furthermore, recommendation intention. As a result of conducting a survey on 338 Chinese collegestudents, it was found that interactivity, social presence, and interest had a positive effect on learning satisfaction, but the effect of ease did not appear. On the other hand, it was confirmed that learning satisfaction had a positive effect on the online streaming class recommendation intention.