• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.27-33
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• 2021

The whole world now is dealing with Coronavirus, and it has turned to be one of the most widespread and long-lived pandemics of our times. Reports reveal that the infectious disease has taken toll of the almost 80% of the world's population. Amidst a lot of research going on with regards to the prediction on growth and transmission through Symptomatic carriers of the virus, it can't be ignored that pre-symptomatic and asymptomatic carriers also play a crucial role in spreading the reach of the virus. Classification Algorithm has been widely used to classify different types of COVID-19 carriers ranging from simple feature-based classification to Convolutional Neural Networks (CNNs). This research paper aims to present a novel technique using a Random Forest Machine learning algorithm with hyper-parameter tuning to classify different types COVID-19-carriers such that these carriers can be accurately characterized and hence dealt timely to contain the spread of the virus. The main idea for selecting Random Forest is that it works on the powerful concept of "the wisdom of crowd" which produces ensemble prediction. The results are quite convincing and the model records an accuracy score of 99.72 %. The results have been compared with the same dataset being subjected to K-Nearest Neighbour, logistic regression, support vector machine (SVM), and Decision Tree algorithms where the accuracy score has been recorded as 78.58%, 70.11%, 70.385,99% respectively, thus establishing the concreteness and suitability of our approach.

• Journal of the Korean Chemical Society
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• v.48 no.5
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• pp.519-526
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• 2004

The purpose of this study was to search pre-service teachers?and chemistry teachers?conceptions related to electrolysis process by predicting electrolysis products in NaI solution. A questionnaire developed by the researchers and following interviews were adopted for the research. By the methods, the conceptions of the groups were compared. Also, the relationship between their conceptions and explanations of chemistry II textbooks and general chemistry books was examined. From the analysis, it was found that most of the pre-service teachers had difficulties in using standard electrode potential when they predicted products of electrolysis. Most of the chemistry teachers could use standard electrode potential, but it was difficult to understand water electrolysis in redox reaction. The explanations of chemistry II textbooks also contained misconceptions.

• Journal of the Korea Concrete Institute
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• v.18 no.4 s.94
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• pp.479-487
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• 2006

This paper deals with a research about the time-dependent behavior analysis for pre-tensioned high-performance concrete(HPC) members. By improving AASHTO-LRFD(2004) method for predicting the creep and shrinkage of normal concrete, and the relaxation of prestressing tendon, a time-dependent behavior analysis of high-performance concrete structures has been introduced. Two methods, the step-function method and the time-step method have been incorporated in the time-dependent analysis. The developed program can predict the initial and time-dependent losses of prestressing forces and the deflections of high-performance concrete structures. The present model has been verified by comparing with the experimental results from the test of time-dependent behaviors of pre-tensioned members using high-performance concrete. From this, the current model gives good relations with the experimental results, but the AASHTO method is not good for the prediction of time-dependent behaviors of high-performance concrete members.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.357-376
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• 2022

This study examines circulation status and trends of a public library during three year periods from January 2019 to December 2021. There was a statistically significant difference in the mean number of circulation between the pre-Covid-19 period and the Covid-19 period, and the Covid-19 period and the Covid-19 recovery period. However, no significant difference was found between the pre-Covid-19 period and the Covid-19 recovery period. Across three years, there was a significant difference in the distribution of circulation per month. Circulation distribution was also significantly different among different days of the week and different hours of the day. Monthly circulation distribution and hourly circulation distribution during the pre-Covid-19 period was similar to those of the Covid-19 recovery period, whereas those of the Covid-19 period differed from the pre-Covid-19 period and the Covid-19 recovery period. It is expected that the results of this study could contribute to the collection development, and the management and improvement of services of public libraries. It is also expected that the results of this study could contribute to the prediction of circulation patterns and information needs of public library users.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Journal of the Korean Society of Tobacco Science
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• v.35 no.1
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• pp.1-6
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• 2013

Near InfraRed Spectroscopy(NIRS) is a quick and accurate analytical method to measure multiple components in tobacco manufacturing process. This study was carried out to develop calibration equation of near infrared spectroscopy for the prediction of the amount of chemical components and hot water solubles(HWS) of reconstituted tobacco leaf. Calibration samples of reconstituted tobacco leaf were collected from every lot produced during one year. The calibration equation was formulated as modified partial least square regression method (MPLS) by analyzing laboratory actual values and mathematically pre-treated spectra. The accuracy of the acquired equation was confirmed with the standard error of prediction(SEP) of chemical components in reconstituted tobacco leaf samples, indicated as coefficient of determination($R^2$) and prediction error of sample unacquainted, followed by the verification of model equation of laboratory actual values and these predicted results. As a result of monitoring, the standard error of prediction(SEP) were 0.25 % for total sugar, 0.03 % for nicotine, 0.03 % for chlorine, 0.16 % for nitrate, and 0.38 % for hot water solubles. The coefficient of determination($R^2$) were 0.98 for total sugar, 0.97 for nicotine, 0.96 for chlorine, 0.98 for nitrate and 0.92 for hot water solubles. Therefore, the NIRS calibration equation can be applicable and reliable for determination of chemical components of reconstituted tobacco leaf, and NIRS analytical method could be used as a rapid and accurate quality control method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.28-34
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• 2010

In this paper, we proposes a emergency medical information system for predicting emergency situation by using the body's vital signs. Main research of existing emergency system has focused on body sensor networks. The problem of these studies have a delay of the emergency first aid since occurring of an emergency situation send a message of emergency situation to user. In the serious situation, patients of these problem can lead to death. To solve this problem, it need to the prediction of emergency situation for doing quickly the First Aid with identify signs of a pre-emergency situations until an emergency occurs. In this paper, the sensor network technology, the security technology, the internet information retrieval techniques, data mining technology, and medical information are studied for the convergence of medical information systems of the prediction of emergency situations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1328-1334
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• 2009

This paper presents a prediction mechanism of performance for an efficient interleave-division multiple access (IDMA) scheme that is being considered as 4th generation mobile radio system. The scheme is based upon log-likelihood ratio (LLR) to predict the performance of the IDMA. The conventional IDMA system simply passes the LLR values to a coarse estimation process in the receiver over a pre-defined number of iterations for an acceptable performance. The proposed IDMA system uses the LLRs to predict its BER performance and thus the iterative operation at the receiver can significantly be reduced when the performance attains an acceptable level. Performance evaluation shows that the proposed scheme of the IDMA with the LLRs used for the prediction provides a comparable BER performance. The use of the LLRs can facilitate an efficient design of the IDMA system that is a strong candidate system for 4G mobile radio systems.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.703-710
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• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.12
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• pp.1863-1870
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• 2018

Objective: The Bayesian first-order antedependence models, which specified single nucleotide polymorphisms (SNP) effects as being spatially correlated in the conventional BayesA/B, had more accurate genomic prediction than their corresponding classical counterparts. Given advantages of $BayesC{\pi}$ over BayesA/B, we have developed hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$, and ante-hyper-$BayesC{\pi}$ to evaluate influences of the antedependence model and hyperparameters for $v_g$ and $s_g^2$ on $BayesC{\pi}$.Methods: Three public data (two simulated data and one mouse data) were used to validate our proposed methods. Genomic prediction performance of proposed methods was compared to traditional $BayesC{\pi}$, ante-BayesA and ante-BayesB. Results: Through both simulation and real data analyses, we found that hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$ and ante-hyper-$BayesC{\pi}$ were comparable with $BayesC{\pi}$, ante-BayesB, and ante-BayesA regarding the prediction accuracy and bias, except the situation in which ante-BayesB performed significantly worse when using a few SNPs and ${\pi}=0.95$. Conclusion: Hyper-$BayesC{\pi}$ is recommended because it avoids pre-estimated total genetic variance of a trait compared with $BayesC{\pi}$ and shortens computing time compared with ante-BayesB. Although the antedependence model in $BayesC{\pi}$ did not show the advantages in our study, larger real data with high density chip may be used to validate it again in the future.