• 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 Korean Society on Water Environment
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• v.39 no.1
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• Journal of Global Scholars of Marketing Science
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• v.11
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• pp.147-163
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• 2003

Recently it is necessary that Hotel business introduce Information Technology to enhance competitive advantage and cope with changeable business promptly in management. Thus in an effort of using Information Technology strategically, Many Hotel business tries to install CRM system (Customer Relationship Management). This study tries to analyze the effects on customer performance by installing CRM system if it is in charge of major strategic system, it can get successful customer performance. I hypothesize to resolve the problem, and search preceding study results concerning the elements of CRM Installment and Customer performance. The survey was taken to emplyees in the field of CRM installment in Luxury hotel to test the hypothesis. To summarize the results, first, CRM intallment affects CRM customer performance. in short, systematic feature, management environment, information intention, and technologic element affect it. Through this study, facing the limitation and future study are below. first, additional parameter should be considered though I reviewed the elements affecting CRM customer performance by searching and abstracting preceding studies. Second, There are lack of preceding studies because it has passed only a couple of years since Korean businesses deal with CRM system and there is the limitation to compare this result with others due to few empirical analysisses. Especilly, I can hardly find the preceding study concerning hotel industry but tries to search preceding parameter as to the customer performance of CRM system. Until now, It is needed to continual study its measurement later. I believe that more specific study and precise theoretical test be performed and they deal with current CRM system installment and facing problems in all of the korean hotels.

• Journal of Pharmaceutical Investigation
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• v.39 no.3
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• pp.185-199
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• 2009

Using a strain-controlled rheometer [Advanced Rheometric Expansion System (ARES)], the steady shear flow properties and the dynamic viscoelastic properties of $Antiphlamine-S^{(R)}$ lotion have been measured at $20^{\circ}C$ (storage temperature) and $37^{\circ}C$ (body temperature). In this article, the temperature dependence of the linear viscoelastic behavior was firstly reported from the experimental data obtained from a temperature-sweep test. The steady shear flow behavior was secondly reported and then the effect of shear rate on this behavior was discussed in detail. In addition, several inelastic-viscoplastic flow models including a yield stress parameter were employed to make a quantitative evaluation of the steady shear flow behavior, and then the applicability of these models was examined by calculating the various material parameters. The angular frequency dependence of the linear viscoelastic behavior was nextly explained and quantitatively predicted using a fractional derivative model. Finally, the strain amplitude dependence of the dynamic viscoelastic behavior was discussed in full to elucidate a nonlinear rheological behavior in large amplitude oscillatory shear flow fields. Main findings obtained from this study can be summarized as follows : (1) The linear viscoelastic behavior is almostly independent of temperature over a temperature range of $15{\sim}40^{circ}C$. (2) The steady shear viscosity is sharply decreased as an increase in shear rate, demonstrating a pronounced Non-Newtonian shear-thinning flow behavior. (3) The shear stress tends to approach a limiting constant value as a decrease in shear rate, exhibiting an existence of a yield stress. (4) The Herschel-Bulkley, Mizrahi-Berk and Heinz-Casson models are all applicable and have an equivalent validity to quantitatively describe the steady shear flow behavior of $Antiphlamine-S^{(R)}$ lotion whereas both the Bingham and Casson models do not give a good applicability. (5) In small amplitude oscillatory shear flow fields, the storage modulus is always greater than the loss modulus over an entire range of angular frequencies tested and both moduli show a slight dependence on angular frequency. This means that the linear viscoelastic behavior of $Antiphlamine-S^{(R)}$ lotion is dominated by an elastic nature rather than a viscous feature and that a gel-like structure is present in this system. (6) In large amplitude oscillatory shear flow fields, the storage modulus shows a nonlinear strain-thinning behavior at strain amplitude range larger than 10 % while the loss modulus exhibits a weak strain-overshoot behavior up to a strain amplitude of 50 % beyond which followed by a decrease in loss modulus with an increase in strain amplitude. (7) At sufficiently large strain amplitude range (${\gamma}_0$>100 %), the loss modulus is found to be greater than the storage modulus, indicating that a viscous property becomes superior to an elastic character in large shear deformations.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.11-22
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• 2010

This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

• Journal of radiological science and technology
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• v.26 no.1
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• pp.71-82
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• 2003

In this paper, I proposed a classifier of liver cirrhotic step using T1-weighted MRI(magnetic resonance imaging) and hierarchical neural network. The data sets for classification of each stage, which were normal, 1type, 2type and 3type, were obtained in Pusan National University Hospital from June 2001 to december 2001. And the number of data was 46. We extracted liver region and nodule region from T1-weighted MR liver image. Then objective interpretation classifier of liver cirrhotic steps in T1-weighted MR liver images. Liver cirrhosis classifier implemented using hierarchical neural network which gray-level analysis and texture feature descriptors to distinguish normal liver and 3 types of liver cirrhosis. Then proposed Neural network classifier teamed through error back-propagation algorithm. A classifying result shows that recognition rate of normal is 100%, 1type is 82.3%, 2type is 86.7%, 3type is 83.7%. The recognition ratio very high, when compared between the result of obtained quantified data to that of doctors decision data and neural network classifier value. If enough data is offered and other parameter is considered, this paper according to we expected that neural network as well as human experts and could be useful as clinical decision support tool for liver cirrhosis patients.

• Journal of Preventive Medicine and Public Health
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• v.43 no.2
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• pp.109-116
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• 2010

Objectives: The pandemic of novel influenza A (H1N1) virus has required decision-makers to act in the face of the substantial uncertainties. In this study, we evaluated the potential impact of the pandemic response strategies in the Republic of Korea using a mathematical model. Methods: We developed a deterministic model of a pandemic (H1N1) 2009 in a structured population using the demographic data from the Korean population and the epidemiological feature of the pandemic (H1N1) 2009. To estimate the parameter values for the deterministic model, we used the available data from the previous studies on pandemic influenza. The pandemic response strategies of the Republic of Korea for novel influenza A (H1N1) virus such as school closure, mass vaccination (70% of population in 30 days), and a policy for anti-viral drug (treatment or prophylaxis) were applied to the deterministic model. Results: The effect of two-week school closure on the attack rate was low regardless of the timing of the intervention. The earlier vaccination showed the effect of greater delays in reaching the peak of outbreaks. When it was no vaccination, vaccination at initiation of outbreak, vaccination 90 days after the initiation of outbreak and vaccination at the epidemic peak point, the total number of clinical cases for 400 days were 20.8 million, 4.4 million, 4.7 million and 12.6 million, respectively. The pandemic response strategies of the Republic of Korea delayed the peak of outbreaks (about 40 days) and decreased the number of cumulative clinical cases (8 million). Conclusions: Rapid vaccination was the most important factor to control the spread of pandemic influenza, and the response strategies of the Republic of Korea were shown to delay the spread of pandemic influenza in this deterministic model.

• Journal of Pharmaceutical Investigation
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• v.37 no.3
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• pp.137-148
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• 2007

Using a strain-controlled rheometer [Rheometrics Dynamic Analyzer (RDA II)], the steady shear flow properties of a semi-solid ointment base (vaseline) have been measured over a wide range of shear rates at temperature range of $25{\sim}60^{\circ}C$. In this article, the steady shear flow properties (shear stress, steady shear viscosity and yield stress) were reported from the experimentally obtained data and the effects of shear rate as well as temperature on these properties were discussed in detail. In addition, several inelastic-viscoplastic flow models including a yield stress parameter were employed to make a quantitative evaluation of the steady shear flow behavior, and then the applicability of these models was examined by calculating the various material parameters (yield stress, consistency index and flow behavior index). Main findings obtained from this study can be summarized as follows : (1) At temperature range lower than $40^{\circ}C$, vaseline is regarded as a viscoplastic material having a finite magnitude of yield stress and its flow behavior beyond a yield stress shows a shear-thinning (or pseudo-plastic) feature, indicating a decrease in steady shear viscosity as an increase in shear rate. At this temperature range, the flow curve of vaseline has two inflection points and the first inflection point occurring at relatively lower shear rate corresponds to a static yield stress. The static yield stress of vaseline is decreased with increasing temperature and takes place at a lower shear rate, due to a progressive breakdown of three dimensional network structure. (2) At temperature range higher than $45^{\circ}C$, vaseline becomes a viscous liquid with no yield stress and its flow character exhibits a Newtonian behavior, demonstrating a constant steady shear viscosity regardless of an increase in shear rate. With increasing temperature, vaseline begins to show a Newtonian behavior at a lower shear rate range, indicating that the microcrystalline structure is completely destroyed due to a synergic effect of high temperature and shear deformation. (3) Over a whole range of temperatures tested, the Herschel-Bulkley, Mizrahi-Berk, and Heinz-Casson models are all applicable and have an almostly equivalent ability to quantitatively describe the steady shear flow behavior of vaseline, whereas the Bingham, Casson,and Vocadlo models do not give a good ability.