• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.316-318
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• 2019

Recently, healthy life has become an issue in an aging society, and the number of people who have been interested in continuous health care for better life is increasing. In this paper, we implemented a personalized recommendation systm to provide convenient healthcare management for user. The PHR (Personal Health Record) of user could be stored in the server along with health related information such as lifestyle, disease, and physical condition. The users could be classified into similar clusters according to the PHR profile in order to provide healthcare contents to the users who had similar PHR profile. K-Means clustering was applied to generate clusters based on PHR profile and ACDT(Ant Colony Decision Tree) algorithm was used to provide personalised recommendation of health information stored in knowledge base. The app system developed in this paper is useful for users to perform healthcare themselves by providing information on serious diseases and lifestyle habits to be improved according to the clusters classified by PHR profile.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.11
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• pp.155-162
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• 2019

The construction industry is the highest safety accident causing industry as 28.55% portion of all industries' accidents in Korea. In particular, falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and types of accidents according to the site conditions from the real time data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.3
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• pp.95-105
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• 2009

Spam call is one of the main security threat in VoIP services. In this paper, we have designed simulation model for performance evaluation of VoIP spam defense mechanism. The simulation model has functions for performance evaluation such as calls generation and input/output comparison. Four representative caller models have been developed for performance evaluation and each model has its own characteristics as statistical parameters. The target mechanism of performance evaluation is SPIT(Spam over Internet Telephony) level decision algorithm, and we have derived SPIT levels of caller models. The performance evaluation model is designed using the DEVS formalism and DEVSJAVA$^{TM}$ is exploited for development and execution of simulation models.

• Journal of Biomedical Engineering Research
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• v.41 no.6
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• pp.247-255
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• 2020

Accurate diagnosis of movement disorders is important for providing right patient care at right time. In general, assessment of motor impairment relies on clinical ratings conducted by experienced clinicians. However, this may introduce subjective opinions into scoring the severity of motor impairment. Digital devices such as table PC and smart band with accelerometer can be used for more accurate and objective assessment and possibly helpful for clinicians to make right decision of patient's states. In this study, we introduce quantification algorithms of motor impairment which uses the digital data acquired during four clinical motor tests (Line drawing, Spiral drawing, Nose to finger and Hand flip tests). The step by step procedure of quantifying metrics (Tremor Frequency, Tremor Magnitude, Error Distance, Time, Velocity, Count and Period) are provided with flowchart. The effectiveness of the proposed algorithm is presented with the result from simulated data (normal, normal with tremor and slowness, poor with tremor, poor with tremor and slowness).

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.31-38
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• 2021

In this paper, in order to obtain the optimization of the RNN model used for sentiment analysis, the correlation of each model was studied by observing the trend of loss and accuracy according to hyperparameter tuning. As a research method, after configuring the hidden layer with LSTM and the embedding layer that are most optimized to process sequential data, the loss and accuracy of each model were measured by tuning the unit, batch-size, and embedding size of the LSTM. As a result of the measurement, the loss was 41.9% and the accuracy was 11.4%, and the trend of the optimization model showed a consistently stable graph, confirming that the tuning of the hyperparameter had a profound effect on the model. In addition, it was confirmed that the decision of the embedding size among the three hyperparameters had the greatest influence on the model. In the future, this research will be continued, and research on an algorithm that allows the model to directly find the optimal hyperparameter will continue.

• The Journal of Korean Institute of Information Technology
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• v.17 no.8
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• pp.1-9
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• 2019

With the development of imaging information communication technology in modern society, imaging acquisition and mass production technology have developed rapidly. However, crime rates using these technology are increased and forensic studies are conducted to prevent it. Identification techniques for image acquisition devices are studied a lot, but the field is limited to images. In this paper, camera model identification technique for video, not image is proposed. We analyzed video frames using the trained model with images. Through training and analysis by considering the frame characteristics of video, we showed the superiority of the model using the P frame. Then, we presented a video camera model identification system by applying a majority-based decision algorithm. In the experiment using 5 video camera models, we obtained maximum 96.18% accuracy for each frame identification and the proposed video camera model identification system achieved 100% identification rate for each camera model.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Korean Journal of Head & Neck Oncology
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• v.38 no.1
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• pp.17-24
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• 2022

Background/Objectives: This study analyzed the prognostic significance of clinico-pathologic factors including comprehensive nodal factors in parotid gland cancers (PGCs) patients and constructed a survival prediction model for PGCs patients using machine learning techniques. Materials & Methods: A total of 131 PGCs patients were enrolled in the study. Results: There were 19 cases (14.5%) of lymph nodes (LNs) at the lower neck level and 43 cases (32.8%) involved multiple level LNs metastases. There were 2 cases (1.5%) of metastases to the contralateral LNs. Intraparotid LNs metastasis was observed in 6 cases (4.6%) and extranodal extension (ENE) findings were observed in 35 cases (26.7%). Lymphovascular invasion (LVI) and perineural invasion findings were observed in 42 cases (32.1%) and 49 cases (37.4%), respectively. Machine learning prediction models were constructed using clinico-pathologic factors including comprehensive nodal factors and Decision Tree and Stacking model showed the highest accuracy at 74% and 70% for predicting patient's survival. Conclusion: Lower level LNs metastasis and LNR have important prognostic significance for predicting disease recurrence and survival in PGCs patients. These two factors were used as important features for constructing machine learning prediction model. Our machine learning model could predict PGCs patient's survival with a considerable level of accuracy.

• Archives of Plastic Surgery
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• v.49 no.5
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• pp.614-616
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• 2022

As of November 2021, TikTok has one billion monthly active users and is recognized as the most engaging social media platform. TikTok has seen a surge in users and content creators, ranging from athletes to medical professionals. In the past year, content creators have utilized the app to advocate for social reforms, education, and other uses that were not previously considered. Breast cancer is the most commonly diagnosed cancer in women, with an expected 281,550 new cases of invasive breast cancer in 2021. As more individuals with breast cancer choose to undergo resection, the demand for autologous fat grafting in breast reconstruction has increased due to the natural look and feel of breast tissue. The purpose of this article is to analyze content related to breast reconstruction with fat grafting found on TikTok and recommend methods to improve patient education, care, and outcomes. We searched TikTok on November 1, 2021, for videos using the phrase "breast reconstruction with fat grafting." The top 200 videos retrieved from the TikTok search algorithm were analyzed, and all commentaries, duplicates, and nonrelevant videos were removed. Video characteristics were collected, and two independent reviewers generated a DISCERN score A total of 131 videos were included in the study. They were found to have a combined 1,871,980 likes, 41,113 comments, and 58,662 shares. The videos had an average DISCERN score of 2.16. Content creators had an overall low DISCERN score in items involving the use of references, disclosure of risks for not obtaining treatment, and support for shared decision-making. When stratified, the DISCERN score was higher for videos created by physicians (DISCERN average 2.48) than for videos created by nonphysicians (DISCERN average 1.99; p < 0.001).

• Journal of Korea Port Economic Association
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• v.37 no.3
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• pp.1-17
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• 2021

Forecasting the daily volume of container is important in many aspects of port operation. In this article, we utilized a machine-learning algorithm based on decision tree to predict future container throughput of Busan port. Accurate volume forecasting improves operational efficiency and service levels by reducing costs and shipowner latency. We showed that our method is capable of accurately and reliably predicting container throughput in short-term(days). Forecasting accuracy was improved by more than 22% over time series methods(ARIMA). We also demonstrated that the current method is assumption-free and not prone to human bias. We expect that such method could be useful in a broad range of fields.