• International Journal of Advanced Culture Technology
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• v.8 no.2
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• pp.297-304
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• 2020

Recently, open source automatic machine learning solutions have been applied in many fields. To apply open source automated machine learning to real world problems, you need to write code with expertise in machine learning. Writing code without machine learning knowledge is challenging. To solve this problem, the automatic machine learning solutions provided by startups are made easy to use with a clean user interface. In this paper, we review automatic machine learning solutions of startups.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2785-2791
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• 2012

In this paper, a method for automatic classification of learning objects is proposed for effective management and reuse of learning contents. Proposed method will create cohesion of learning objects using cases of learning objects and perform automatic classification of learning objects by measuring their relationship based on cohesion. Application of proposed method to learning management system has the advantage of reducing the costs for developing learning contents. Simulation has shown the average accuracy of 28.20% with probability-based method and 56.38% with cohesion-based method. Simulation has proved that the method proposed in this paper is effective in automatic classification of learning objects.

• Journal of KIISE
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• v.44 no.11
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• pp.1156-1164
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• 2017

Existing studies on automatic bug triage were mostly used the method of designing the prediction system based on the machine learning algorithm. Therefore, it can be said that applying a high-performance machine learning model is the core of the performance of the automatic bug triage system. In the related research, machine learning models that have high performance are mainly used, such as SVM and Naïve Bayes. In this paper, we apply Deep Learning, which has recently shown good performance in the field of machine learning, to automatic bug triage and evaluate its performance. Experimental results show that the Deep Learning based Bug Triage system achieves 48% accuracy in active developer experiments, un improvement of up to 69% over than conventional machine learning techniques.

• Journal of information and communication convergence engineering
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• v.1 no.3
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• pp.157-162
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• 2003

We propose a learning method for improving the performance of the backpropagation algorithm. The proposed method is using a fuzzy logic system for automatic tuning of the learning rate of each weight. Instead of choosing a fixed learning rate, the fuzzy logic system is used to dynamically adjust the learning rate. The inputs of fuzzy logic system are delta and delta bar, and the output of fuzzy logic system is the learning rate. In order to verify the effectiveness of the proposed method, we performed simulations on the XOR problem, character classification, and function approximation. The results show that the proposed method considerably improves the performance compared to the general backpropagation, the backpropagation with momentum, and the Jacobs'delta-bar-delta algorithm.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.82-88
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• 2021

In this paper, we propose an automatic detection scheme of work distraction for remote management of telecommuting. The proposed scheme periodically captures two consequent computer screens and generates the difference image of these two captured images. The scheme applies the difference image to our deep learning model and makes a decision of abnormal patterns in the difference image. Our deep learning model is designed with the transfer learning technique of VGG16 deep learning. When the scheme detects an abnormal pattern in the difference image, it hides all texts in the difference images to protect disclosure of privacy-related information. Evaluation shows that the proposed scheme provides about 96% detection accuracy.

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

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.769-781
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• 2017

Deep learning networks like Convolutional Neural Networks (CNNs) show successful performances in many computer vision applications such as image classification, object detection, and so on. For implementation of deep learning networks in embedded system with limited processing power and memory, deep learning network may need to be simplified. However, simplified deep learning network cannot learn every possible scene. One realistic strategy for embedded deep learning network is to construct a simplified deep learning network model optimized for the scene images of the installation place. Then, automatic training will be necessitated for commercialization. In this paper, as an intermediate step toward automatic training under fisheye camera environments, we study more precise human localization in fisheye images, and propose an accurate human localization method, Automatic Ground-Truth Labelling Method (AGTLM). AGTLM first localizes candidate human object bounding boxes by utilizing GoogLeNet-LSTM approach, and after reassurance process by GoogLeNet-based CNN network, finally refines them more correctly and precisely(tightly) by applying saliency object detection technique. The performance improvement of the proposed human localization method, AGTLM with respect to accuracy and tightness is shown through several experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.171-178
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• 2020

Many countries are establishing and operating blended learning that combines the advantages of online and offline education. However, online education lecture-based Mooc courses have a very low level, with a graduation rate of less than 5-10%. Therefore, in order to increase the graduation rate of students taking online Mooc distance education lectures that anyone can easily take lectures anytime, anywhere on the web-based basis, it is necessary to introduce automatic analysis of students' understanding level of lectures and an automatic academic warning system. Moreover, in order to enter an advanced education country, it is necessary to develop an automatic judgment SW for wrong answer rate, automatic summary SW for lectures, and automatic analysis SW education for lecture-based weak subjects based on mixed learning levels. In order to improve this problem, in this paper, we proposed and simulated an automatic summarization system for lecture contents, an automatic warning system for incorrect answers, and an automatic judgment algorithm for weak subjects.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.108-115
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• 2020

Blended learning is a teaching method utilizing all the advantages in 'on and off-line' learning circumstances in order to enhance the learning effect and efficiency, more than the simple use of online factors in the classroom education. In this paper, we present the realization and simulation of algorithm for the realtime evaluation of low-grade and high-grade subjects in order to implement smart e-learning system, considering a lecture intelligibility. In order to grasp the levels of student's intelligibility, we simulated a function that automatically summarizes the study contents of class given by a lecturer. Especially, in administrator mode of smart e-learning system, we suggested and simulated a system in order to help the lecturer to easily manage the student's grades, and we have provided software to tell the student's intelligibility of lecture, analyzed the rate of incorrect answers, automatic judgment of lecture intelligibility and judge the weakest subject.

• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.271-286
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• 2006

In this paper, we introduce automatic question generation technique using the language resources like User-Word Intelligent Network(U-WIN) and Korean dictionary including quite a for of information. And we present Korean word learning system with this technique. The item pool method which almost learning-system are using makes some problems. As a solution of the problems, we classified into 8 question type and implemented the Korean word learning system which is making the Korean question automatically by using the morphological and semantic information according to the automatic question generation pattern of each type.