• The Journal of the Korean dental association
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• v.49 no.3
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• pp.153-158
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• 2011

Oral precancerous lesion is a morphologically altered tissue in which oral cancer is more likely to occur than is apparently normal counterpart. As dentists always do oral examination and dental treatment, with fundamental knowledge and attention of this lesion, it is relatively easy to find one. If followed by proper treatment and management, it is possible to minimize its oral cancer progression, or at least delay it. Even if it were to progress to oral cancer, very early detection is possible. However, no specific biomarkers are present at the moment that could reveal oral precnacerous lesion that is high risk of oral cancer progression. Since early detection of oral cancer followed by treatment could show good prognosis with just a simple ablative surgery. Dentists should also instruct people to avoid risk factor related oral cancer progression and take natural compound having anticancer effect. Hereby, As a primary care givers, dentists play an important role in prevention of oral cancer.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.577-583
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• 2007

The character strings on chips and components are so tiny and numerous that it is a very difficult work for people to perform. In this paper, we propose a mark automatic checking system, which will determine whether chip is wrong-mark or not by recognizing characters on chips. Lots of faulty detection conditions and template matching methods are used to inspect the faulty mark items. The faulty detection classifies conditions as five kinds-darkness, matching, area, broken and branch. A series of experimentation show that the method proposed here can offer an effective way to determine wrong-mark on chips.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.95-103
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• 2019

According to the Statistics Korea in 2017, the 10 leading causes of death contain a cardiac disorder disease, self-injury. In terms of these diseases, urgent assistance is highly required when people do not move for certain period of time. We propose an unusual event detection algorithm to identify abnormal user behaviors using dust, vision and activity sensors in their houses. Vision sensors can detect personalized activity behaviors within the CCTV range in the house in their lives. The pattern algorithm using the dust sensors classifies user movements or dust-generated daily behaviors in indoor areas. The accelerometer sensor in the smartphone is suitable to identify activity behaviors of the mobile users. We evaluated the proposed pattern algorithms and the fusion method in the scenarios.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1825-1844
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• 2019

Political campaigns circulate manipulative opinions in online communities to implant false beliefs and eventually win elections. Not only is this type of manipulation unfair, it also has long-lasting negative impacts on people's lives. Existing tools detect political manipulation based on a supervised classifier, which is accurate when trained with large labeled data. However, preparing this data becomes an excessive burden and must be repeated often to reflect changing manipulation tactics. We propose a practical detection system that requires moderate groundwork to achieve a sufficient level of accuracy. The proposed system groups opinions with similar properties into clusters, and then labels a few opinions from each cluster to build a classifier. It also models each opinion with features deduced from raw data with no additional processing. To validate the system, we collected over a million opinions during three nation-wide campaigns in South Korea. The system reduced groundwork from 200K to nearly 200 labeling tasks, and correctly identified over 90% of manipulative opinions. The system also effectively identified transitions in manipulative tactics over time. We suggest that online communities perform periodic audits using the proposed system to highlight manipulative opinions and emerging tactics.

• Proceedings of the KIPE Conference
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• 2018.11a
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• pp.7-8
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• 2018

In this study, we develop a leakage current detection device of lighting installation. The reed switch using in proposed device is activated when the leakage current is generated. We also design a GUI system of a management computer using LabVIEW and administrator's mobile phone app based on IoT. As results, this study is to build an IoT convergence system and it aims to protect people and property by coping with leakage current fault in real time.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.83-87
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• 2021

By the increase of internet usage, communicating online became an everyday thing. Thereby various people have experienced profanity by anonymous users. Nowadays lots of studies tried to solve this problem using artificial intelligence, but most of the solutions were for non-real time situations. In this paper, we propose a Telegram plugin that detects swear words using word2vec, and an algorithm to find the target of the sentence. We vectorized the input sentence to find connections with other similar words, then inputted the value to the pre-trained CNN (Convolutional Neural Network) model to detect any swears. For target recognition we proposed a sequential algorithm based on KoNLPY.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.413-423
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• 2022

The NHIS provides free or highly subsidized healthcare to all people by providing financial fortification. However, the financial sustainability of the scheme is threatened by numerous factors. Therefore, this work sought to provide a solution to process claims intelligently. The provided Petri net model demonstrated successful data flow among the various participant. For efficiency, scalability, and performance two main subsystems were modelled and integrated - data input and claims processing subsystems. We provided smart claims processing algorithm that has a simple and efficient error detection method. The complexity of the main algorithm is good but that of the error detection is excellent when compared to literature. Performance indicates that the model output is reachable from input and the token delivery rate is promising.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.425-426
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• 2022

Many CCTVs are installed around the area for the purpose of preventing crime. In this case, it is necessary to sound an alarm when a specific situation occurs because many people cannot be watching CCTV. It is necessary to analyze the video data to determine what kind of behavior it is. In this study, we propose a scene change detection technique through histogram operation, paying attention to the change of the histogram due to a sudden movement.

• The KIPS Transactions:PartB
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• v.19B no.4
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• pp.225-230
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• 2012

In this paper, a blind person using a white cane as an adjunct of the things available sensing technology has been implemented. Sensing technology to implement things ultrasonic sensors and a webcam was used to process the data from the server computer. Ultrasonic sensors detect objects within 4meter people distinguish between those things that if the results based on the results will sound off. In this study, ultrasonic sensors, object recognition and human perception with the introduction of techniques and technologies developed for detecting objects in the lives of the visually impaired is expected to be greater usability.