• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.55-64
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• 2023

The Internet of Things (IoT) is the combination of the internet and various sensing devices. IoT security has increasingly attracted extensive attention. However, significant losses appears due to malicious attacks. Therefore, intrusion detection, which detects malicious attacks and their behaviors in IoT devices plays a crucial role in IoT security. The intrusion detection system, namely IDS should be executed efficiently by conducting classification and efficient feature extraction techniques. To effectively perform Intrusion detection in IoT applications, a novel method based on a Conventional Neural Network (CNN) for classification and an improved Genetic Algorithm (GA) for extraction is proposed and implemented. Existing issues like failing to detect the few attacks from smaller samples are focused, and hence the proposed novel CNN is applied to detect almost all attacks from small to large samples. For that purpose, the feature selection is essential. Thus, the genetic algorithm is improved to identify the best fitness values to perform accurate feature selection. To evaluate the performance, the NSL-KDDCUP dataset is used, and two datasets such as KDDTEST²¹ and KDDTEST⁺ are chosen. The performance and results are compared and analyzed with other existing models. The experimental results show that the proposed algorithm has superior intrusion detection rates to existing models, where the accuracy and true positive rate improve and the false positive rate decrease. In addition, the proposed algorithm indicates better performance on KDDTEST⁺ than KDDTEST²¹ because there are few attacks from minor samples in KDDTEST⁺. Therefore, the results demonstrate that the novel proposed CNN with the improved GA can identify almost every intrusion.

• Nuclear Engineering and Technology
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• v.19 no.3
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• pp.192-197
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• 1987

Two-point kinetic equations for a compact-core-with-bulky-D$_2$O-reflector system were developed. A unique feature of the system is that certain fission gammas create retarded photoneutrons in the D$_2$O reflector by (ｒ, n) reaction. Coupling effect between the core and the reflector was investigated by simulating power transients with various ramp reactivity insertions. Special attention was paid to the phenomenon associated with spatial separation of photoneutrons and their precursors. Simulations show that accuracy of the two-point model is comparable with that of space-dependent approach. Also it is found that the explicily expressed photoneutron terms in the reflector equation slow down the power transient compared to non-photoneutron expressions. Detectors for reactor power control purpose prefer to be deployed in the core zone to be able to accurately perdict transient power.

• Smart Media Journal
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• v.12 no.10
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• pp.47-54
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• 2023

As understanding sudden climate change and agricultural productivity becomes increasingly important due to global warming, soil moisture prediction is emerging as a key topic in agriculture. Soil moisture has a significant impact on crop growth and health, and proper management and accurate prediction are key factors in improving agricultural productivity and resource management. For this reason, soil moisture prediction is receiving great attention in agricultural and environmental fields. In this paper, we collected and analyzed open field environmental data using a pilot field through random forest, a machine learning algorithm, obtained the correlation between data characteristics and soil moisture, and compared the actual and predicted values of soil moisture. As a result of the comparison, the prediction rate was about 92%. It was confirmed that the accuracy was . If soil moisture prediction is carried out by adding crop growth data variables through future research, key information such as crop growth speed and appropriate irrigation timing according to soil moisture can be accurately controlled to increase crop quality and improve productivity and water management efficiency. It is expected that this will have a positive impact on resource efficiency.

• Communications of Mathematical Education
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• v.37 no.2
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• pp.233-256
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• 2023

This study conducted foundational research to derive ways to use ChatGPT in mathematics education by analyzing ChatGPT's responses to questions from the National Assessment of Educational Achievement (NAEA) and the College Scholastic Ability Test (CSAT). ChatGPT, a generative artificial intelligence model, has gained attention in various fields, and there is a growing demand for its use in education as the number of users rapidly increases. To the best of our knowledge, there are very few reported cases of educational studies utilizing ChatGPT. In this study, we analyzed ChatGPT 3.5 responses to questions from the three-year National Assessment of Educational Achievement and the College Scholastic Ability Test, categorizing them based on the percentage of correct answers, the accuracy of the solution process, and types of errors. The correct answer rates for ChatGPT in the National Assessment of Educational Achievement and the College Scholastic Ability Test questions were 37.1% and 15.97%, respectively. The accuracy of ChatGPT's solution process was calculated as 3.44 for the National Assessment of Educational Achievement and 2.49 for the College Scholastic Ability Test. Errors in solving math problems with ChatGPT were classified into procedural and functional errors. Procedural errors referred to mistakes in connecting expressions to the next step or in calculations, while functional errors were related to how ChatGPT recognized, judged, and outputted text. This analysis suggests that relying solely on the percentage of correct answers should not be the criterion for assessing ChatGPT's mathematical performance, but rather a combination of the accuracy of the solution process and types of errors should be considered.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.

• Korean Journal of Acupuncture
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• v.26 no.4
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• pp.119-133
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• 2009

Objectives : The object of this study is to report on the theory of Ohaeng-Hwa Acupuncture Therapy. Methods : The theory of Ohaeng-Hwa Acupuncture Therapy(OHAT; 五行和針法) is a part of the Five Elements Theory unique to Korea. This research Classic of Difficulty Issues-Nan Jing review Ohaeng-Hwa Acupuncture Therapy. Results : OHAT, created and developed by Jae-hoon Song, integrates the victor-vanquished as well as the son-mother relationship of the Five Elements of breakdown and restoration of balance between yin and yang. And also, it provides resources and data on The seventy fifth Nan(75難), The sixty ninth Nan(69難) of Classic of Difficulty Issues - Nan Jin 75, 69. OHAT establishes objectiveness and accuracy of diagnosis based upon the traditional method and procedure of pulse taking. In OHAT, a person's state of illness is diagnosed by applying the comparative examination of the palpitation of the pulse. It is the fact that the pulse varies according to the state, and that OHAT treatment has proven the positive results by using the victor-vanquished relationship on The Nan Jin 75. On the basis of this, it is necessary to add the sixty ninth Nan(69難), to research the theory of the generation of the Five Element. Conclusions : Ohaeng-Hwa Acupuncture is very effective in treating the wide range of illness, and thus it has gained an increasing attention of many scholars and practitioners in the field of traditional Korean oriental medicine. However, it is the first theoretical attempt to the clinical research and scientific methodology of Ohaeng(Five) Ohaeng-Hwa Acupuncture, and more active Ohaeng-Hwa Acupuncture R&D is being conducted nationwide.

• Journal of Physiology & Pathology in Korean Medicine
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• v.22 no.5
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• pp.1119-1124
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• 2008

The theory of Hwa Acupuncture Therapy (HAT), called Hwa Chim Therapy, is a part of the Five Elements Theory unique to Korea. H99AT, created and developed by Jaehoon Song, integrates the victor-vanquished as well as the son-mother relationship of the Five Elements of breakdown and restoration of balance between yin and yang. And also, it provides resources and data on The seventy fifth Difficulty Nan(75難), The sixty ninth Difficulty Nan(69難) of Classic on Difficulty - Nan Jin 75, 69. HAT establishes objectiveness and accuracy of diagnosis based upon the traditional method and procedure of pulse taking. In HAT, a person's state of illness is diagnosed by applying the comparative examination of the palpitation of the pulse. It is the fact that the pulse varies according to the state, and that HAT treatment has proven the positive results by using the victor-vanquished relationship of Classic on The Nan Jin 75. On the basis of this, it is necessary to add the sixty ninth Difficulty(69難), to research the theory of the generation of the Five Element. Despite a the concise and simple theory, Hwa Chim is very effective in treating the wide range of illness, and thus it has gained an increasing attention of many scholars and practitioners in the field of traditional Korean oriental medicine. However, it is the first theoretical attempt to the clinical research and scientific methodology of Ohaeng(Five) Hwa Chim, and more active Hwa Acupuncture R&D is being conducted nationwide.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.37
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• pp.187-216
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• 2008

Risk regarding the possibility of loss can be especially problematic. If a loss is certain to occur, it may be planned for in advance and treated as a definite, known expense. It is when there is uncertainty about the occurrence of a loss that risk becomes an important problem. The word risk is often used in connection with insurance. No one generally accepted definition of risk exists, however. Of the many definitions, two distinctive ones are commonly used. One defines risk as the variation in possible outcomes of an event based on chance. That is, the greater the number of different outcomes that may occur, the greater the risk. Another way of expressing this concept is to state: The greater the variation around an average expected loss, the greater the risk. The second definition of risk is the uncertainty concerning a possible loss. The definition of risk as a useful one because it focuses attention on the degree of risk in given situations. The degree of risk is a measure of the accuracy with which the outcome of an event based on chance can be predicted. For now, it will serve our purpose to note the more accurate the prediction of the outcome of an event based on chance, the lower the degree of risk. After sources of risks are identified and measured, a decision can be made as to how the risk should be handled. A pure risk that is not identified does not disappear, the business merely loses the opportunity to consciously decide on the best technique for dealing with that risk. The process used to systematically manage risk exposures is known as risk management. Some persons use the term risk management only in connection with businesses, and often the term refers only to the management of pure risks. In this sense, the traditional risk management goal has been to minimize the cost of pure risk to the company. But as firms broaden the ways that they view and manage many different types of risk, the need for new terminology has become apparent. The terms integrated risk management and enterprise risk management reflect the intent to manage all forms of risk, regardless of type. International trade transaction is called between countries has features of globalism, cultural gap, long distance and long terms for the transaction. It is riskier than domestic transaction has its specific risks, such as foreign exchange risk and political risk, and requires various active risk management skills. Risks in relation to the international trade transaction are the contract risk, transit risk and payment risk, etc. The risk management in relation to the international trade transaction is to identify and measure these risks. The purpose of this study is to analyse the practical problems and its solution plan by analyzing various cases related to the risk management of bill of lading in the international trade transaction.