• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.285-291
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• 2001

This paper describes a natural language question answering system that can be used by students in getting as solution to their queries. Unlike AI question answering system that focus on the generation of new answers, the present system retrieves existing ones from question-answer files. Unlike information retrieval approaches that rely on a purely lexical metric of similarity between query and document, it uses a semantic knowledge base (WordNet) to improve its ability to match question. Paper describes the design and the current implementation of the system as an intelligent tutoring system. Main drawback of the existing tutoring systems is that the computer poses a question to the students and guides them in reaching the solution to the problem. In the present approach, a student asks any question related to the topic and gets a suitable reply. Based on his query, he can either get a direct answer to his question or a set of questions (to a maximum of 3 or 4) which bear the greatest resemblance to the user input. We further analyze-application fields for such kind of a system and discuss the scope for future research in this area.

• Journal of Gastric Cancer
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• v.12 no.1
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• pp.7-12
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• 2012

More than ten years have passed since the sentinel node (SN) concept for gastric cancer surgery was first discussed. Less invasive modified surgical approaches based on the SN concept have already been put into practice for malignant melanoma and breast cancer, however the SN concept is not yet placed in a standard position in gastric cancer surgery even after two multi-institutional prospective clinical trials, the Japan Clinical Oncology Group trial (JCOG0302) and the Japanese Society for Sentinel Node Navigation Surgery (SNNS) trial. What is the problem in the clinical application of the SN concept to gastric cancer surgery? There is no doubt that we need reliable indicator(s) to determine with certainty the absence of metastasis in the lymph nodes in order to avoid unnecessary lymphadenectomy. There are several matters of debate in performing the actual procedure, such as the type of tracer, the site of injection, how to detect and harvest, how to detect metastases of SNs, and learning period. These issues have to be addressed further to establish the most suitable procedure. Novel technologies such as indocyanine green (ICG) fluorescence imaging and one-step nucleic acid amplification (OSNA) may overcome the current difficulties. Once we know what the problems are and how to tackle them, we can pursue the goal.

• Korean System Dynamics Review
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• v.3 no.2
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• pp.49-68
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• 2002

The intent of this study is to develop system dynamics model for assessment of organizational and human factors in nuclear power plant which can contribute to secure the nuclear safety. Previous studies are classified into two major approaches. One is engineering approach such as ergonomics and probability safety assessment(PSA). The other is social science approach such like sociology, organization theory and psychology. Both have contributed to find organization and human factors and to present guideline to lessen human error in NPP. But, since these methodologies assume that relationship among factors is independent they don't explain the interactions among factors or variables in NPP. To overcome these limits, we have developed system dynamics model which can show cause and effect among factors and quantify organizational and human factors. The model we developed is composed of 16 functions of job process in nuclear power, and shows interactions among various factors which affects employees' productivity and job quality. Handling variables such like degree of leadership, adjustment of number of employee, and workload in each department, users can simulate various situations in nuclear power plant in the organization side. Through simulation, user can get insight to improve safety in plants and to find managerial tools in the organization and human side. Analyzing pattern of variables, users can get knowledge of their organization structure, and understand stands of other departments or employees. Ultimately they can build learning organization to secure optimal safety in nuclear power plant.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.665-673
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• 2021

Default has become an extreme concern in the current world due to the financial crisis. The previous prediction of companies' bankruptcy exhibits evidence of decision assistance for financial and regulatory bodies. Notwithstanding numerous advanced approaches, this area of study is not outmoded and requires additional research. The purpose of this research is to find the best classifier to detect a company's default risk and bankruptcy. This study used secondary data from the Pakistan Stock Exchange (PSX) and it is time-series data to examine the impact on the determinants. This research examined several different classifiers as per their competence to properly categorize default and non-default Pakistani companies listed on the PSX. Additionally, PSX has remained consistent for some years in terms of growth and has provided benefits to its stockholders. This paper utilizes machine learning techniques to predict financial distress in companies listed on the PSX. Our results indicate that most multi-stage mixture of classifiers provided noteworthy developments over the individual classifiers. This means that firms will have to work on the financial variables such as liquidity and profitability to not fall into the category of liquidation. Moreover, Adaptive Boosting (Adaboost) provides a significant boost in the performance of each classifier.

• World Technopolis Review
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• v.1 no.2
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• pp.92-106
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• 2012

This study focuses on exploring the behaviours of high-tech start-up firms in response to the policy interventions undertaken to promote regional innovation in South Korea since 1997. High-tech start-ups and their technological entrepreneurship are increasingly considered by policy makers and academics to play a crucial role in the generation of innovation and economic development. However, this study started from a basic concern of why government intervention does not necessarily result in an increase of regional innovation capacity. To explain this concern, we constructed a new conceptual framework of 'government dependency' and apply this to 'Daedeok,' a regional innovation system in South Korea, to explore the reproduction of path dependency as an impact induced by innovation policy. This conceptual framework was developed by remodeling path dependency approaches through a systemic and interactive lens. An empirical study used qualitative interviews of start-up founders to delineate the emergence of a new development path and the extent to which dependency was reproduced in the Daedeok regional innovation system. Empirical analysis suggested that 'reliance' and 'persistence' were the crucial factors in the production and reproduction of the government dependency. Some firms accepted dependency as reliance, but others regarded it as policy utilization. Thus, a critical juncture could not be clearly identified in actors' behaviour. It was also unclear if dependency had hindered innovation, but it was shown that the regional and institutional contexts strongly influenced the reproduction process. The study concludes that the construct of government dependency can also provide useful insights into policy learning as well as the success of government interventions.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Journal of Information Science Theory and Practice
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• v.6 no.4
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• pp.28-38
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• 2018

Wikipedia is composed of millions of articles, each of which explains a particular entity with various languages in the real world. Since the articles are contributed and edited by a large population of diverse experts with no specific authority, Wikipedia can be seen as a naturally occurring body of human knowledge. In this paper, we propose a method to automatically identify key entities and relations in Wikipedia articles, which can be used for automatic ontology construction. Compared to previous approaches to entity and relation extraction and/or identification from text, our goal is to capture naturally occurring entities and relations from Wikipedia while minimizing artificiality often introduced at the stages of constructing training and testing data. The titles of the articles and anchored phrases in their text are regarded as entities, and their types are automatically classified with minimal training. We attempt to automatically detect and identify possible relations among the entities based on clustering without training data, as opposed to the relation extraction approach that focuses on improvement of accuracy in selecting one of the several target relations for a given pair of entities. While the relation extraction approach with supervised learning requires a significant amount of annotation efforts for a predefined set of relations, our approach attempts to discover relations as they occur naturally. Unlike other unsupervised relation identification work where evaluation of automatically identified relations is done with the correct relations determined a priori by human judges, we attempted to evaluate appropriateness of the naturally occurring clusters of relations involving person-artifact and person-organization entities and their relation names.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.204-214
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• 2022

Speedy development has been witnessed in communication technologies and the adoption of the Internet across the world. Information dissemination is the primary goal of these technologies. One of the rapidly developing nations in the Middle East is Saudi Arabia, where the use of communication technologies, including mobile and Internet, has drastically risen in recent times. These advancements are relatively new to the region when contrasted to developed nations. Thus, offenses arising from the adoption of these technologies may be new to Saudi Arabians. This study examines cyber security awareness among Saudi Arabian citizens in distinct settings. A comparison is made between the cybersecurity policy guidelines adopted in Saudi Arabia and three other nations. This review will explore distinct essential elements and approaches to mitigating cybercrimes in the United States, Singapore, and India. Following an analysis of the current cybersecurity framework in Saudi Arabia, suggestions for improvement are determined from the overall findings. A key objective is enhancing the nationwide focus on efficient safety and security systems. While the participants display a clear knowledge of IT, the surveyed literature shows limited awareness of the risks related to cyber security practices and the role of government in promoting data safety across the Internet. As the findings indicate, proper frameworks regarding cyber security need to be considered to ensure that associated threats are mitigated as Saudi Arabia aspires to become an efficient smart nation.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.64-72
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• 2024

In recent times cyber attackers can use Artificial Intelligence (AI) to boost the sophistication and scope of attacks. On the defense side, AI is used to enhance defense plans, to boost the robustness, flexibility, and efficiency of defense systems, which means adapting to environmental changes to reduce impacts. With increased developments in the field of information and communication technologies, various exploits occur as a danger sign to cyber security and these exploitations are changing rapidly. Cyber criminals use new, sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable and strong cyber defense systems that can identify a wide range of threats in real-time. In recent years, the adoption of AI approaches has increased and maintained a vital role in the detection and prevention of cyber threats. In this paper, an Ensemble Deep Restricted Boltzmann Machine (EDRBM) is developed for the classification of cybersecurity threats in case of a large-scale network environment. The EDRBM acts as a classification model that enables the classification of malicious flowsets from the largescale network. The simulation is conducted to test the efficacy of the proposed EDRBM under various malware attacks. The simulation results show that the proposed method achieves higher classification rate in classifying the malware in the flowsets i.e., malicious flowsets than other methods.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.