• Knowledge Management Research
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• v.19 no.4
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• pp.171-185
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• 2018

Studies on online review have carried out analysis of the rating and topic as a whole. However, it is necessary to analyze opinions on various dimensions of service quality. This study classifies reviews of healthcare services into service quality dimensions, and proposes a method to identify words that are mainly referred to in each dimension. Service quality was based on the dimensions provided by SERVQUAL, and patient reviews have collected from NHSChoice. The 2,000 sentences sampled were classified into service quality dimension of SERVQUAL and a method of extracting important keywords from sentences by service quality dimension was suggested. The RAKE algorithm is used to extract key words from a single document and an index is considered to consider frequently used words in various documents. Since we need to identify key words in various reviews, we have considered frequency and discrimination (IDF) at the same time, rather than identifying key words based only on the RAKE score. In SERVQUAL dimension, we identified the words that patients mentioned mainly, and also identified the words that patients mainly refer to by review rating.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.47-49
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• 2019

본 논문은 노화로 인하여 감소하는 전기자동차용 배터리의 전류 용량을 실시간으로 추정하는 SOH (State-of-health) 알고리즘을 제안한다. 제안하는 알고리즘은 노화에 따른 OCV (Open circuit voltage) 곡선의 변화 경향을 분석하고, 저주파 통과 필터를 이용하여 추정된 OCV를 기반으로 전류 용량 및 SOH를 산출한다. 알고리즘을 검증하기 위하여 전기자동차용 배터리를 이용한 실험 및 시뮬레이션을 진행한다.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.10-14
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• 2021

Today people adopt various contents from their mobile devices which lead to numerous platforms. As technology of 5G, IOT, and smart phone develops, the number of people who create, edit, collect, and share their own videos, photos, and articles continues to increase. As more contents are shared online, the numbers of data being stolen continue to increase too. To prevent these cases, an authentication method is needed to encrypt the content and prove it as its own content. In the report, we propose a few methods to secure various misused content with secondary security. A unique private key is designed when people create new contents through sending photos or videos to platforms. The primary security is to encrypt the "Private Key" with a public key algorithm, making its data-specific "Timeset" that doesn't allow third-party users to enter. For the secondary security, we propose to use Message Authentication Codes(MACs) to certify that we have produced the content.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.31-34
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• 2021

The classical HMM is defined by a parameter triple �� = (��, A, B), where each parameter represents a collection of probability distributions: initial state, state transition and output distributions in order. This paper proposes a new stationary parameter e = (e1, e2, …, eN) where N is the number of states and et = P(|xt = i, y) for describing how an input pattern y ends in state xt = i at time t followed by nothing. It is often said that all is well that ends well. We argue here that all should end well. The paper sets the framework for the theory and presents an efficient inference and training algorithms based on dynamic programming and expectation-maximization. The proposed model is applicable to analyzing any sequential data with two or more finite segmental patterns are concatenated, each forming a context to its neighbors. Experiments on online Hangul handwriting characters have proven the effect of the proposed augmentation in terms of highly intuitive segmentation as well as recognition performance and 13.2% error rate reduction.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.627-630
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• 2022

Over the past few years, fake news has become one of the most significant problems. Since it is impossible to prevent people from spreading misinformation, people should analyze the news themselves. However, this process takes some time and effort, so the routine part of this analysis should be automated. There are many different approaches to this problem, but they only analyze the text and messages, ignoring the images. The fake news problem should be solved using a complex analysis tool to reach better performance. In this paper, we propose the approach of training an Artificial Intelligence using an unsupervised learning algorithm, combined with online data parsing tools, providing independence from subjective data set. Therefore it will be more difficult to spread fake news since people could quickly check if the news or article is trustworthy.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.406-409
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• 2023

With the explosion of information, recommendation algorithms are becoming increasingly important in providing people with appropriate content, enhancing their online experience. In this paper, we propose a recommender system using advanced deep reinforcement learning(DRL) techniques. This method is more adaptive and integrative than traditional methods. We selected the MovieLens dataset and employed the precision metric to assess the effectiveness of our algorithm. The result of our implementation outperforms other baseline techniques, delivering better results for Top-N item recommendations.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.1-10
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• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.4
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• pp.242-250
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• 2014

Conventional researches of standard tool validating cryptographic algorithm have been studied for the internet environment, for the mobile internet. It is important to develop the validation tool for establishment of interoperability and convenience of users in the information systems. Therefore, this paper presents the validation tool of Elliptic Curve Cryptography algorithm that can test if following X9.62 technology standard specification. The validation tool can be applied all information securities using DES, SEED, AES, SHA-1/256/384/512, RSA-OAEP V2.0, V2.1, ECDSA, ECKCDSA, ECDH, etc. Moreover, we can enhance the precision of validation through several experiments and perform the validation tool in the online environment.

• Nuclear Engineering and Technology
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• v.38 no.1
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• pp.81-92
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• 2006

In this study, the core dynamics of a PWR reactor is identified online by a recursive least-squares method. Based on the identified reactor model consisting of the control rod position and the core average coolant temperature, the future average coolant temperature is predicted. A model predictive control method is applied to designing an automatic controller for the thermal power control of PWR reactors. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, this procedure for solving the optimization problem is repeated. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted core coolant temperature and the desired temperature, as well as minimizing the variation of the control rod positions. In addition, the objectives are subject to the maximum and minimum control rod positions as well as the maximum control rod speed. Therefore, a genetic algorithm that is appropriate for the accomplishment of multiple objectives is utilized in order to optimize the model predictive controller. A three-dimensional nuclear reactor analysis code, MASTER that was developed by the Korea Atomic Energy Research Institute (KAERI) , is used to verify the proposed controller for a nuclear reactor. From the results of a numerical simulation that was carried out in order to verify the performance of the proposed controller with a $5\%/min$ ramp increase or decrease of a desired load and a $10\%$ step increase or decrease (which were design requirements), it was found that the nuclear power level controlled by the proposed controller could track the desired power level very well.