• Advances in Traditional Medicine
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• v.7 no.2
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• pp.191-196
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• 2007

The ethanol extract of leaves of the mangrove Aegiceras corniculatum Blanco (Myrsinaceae) was screened for its antinociceptive, antidiarrhoeal and cytotoxic activities. The extract produced significant writhing inhibition in acetic acid-induced writhing in mice at the oral dose of 250 and 500 mg/kg body weight (P < 0.001), which was comparable to the standard drug diclofenac sodium at the dose of 25 mg/kg of body weight. When tested for its antidiarrhoeal effects on castor oil induced diarrhoea in mice, it increased mean latent period and decreased the frequency of defecation significantly at the oral dose of 500 mg/kg body weight (P<0.05; P<0.01) comparable to the standard drug loperamide at the dose of 50 mg/kg of body weight. Moreover, when tested for toxicity using brine shrimp, the extract showed potent activity against the brine shrimp Artemia salina ($LC_{50}$ 10 mg/ml). The overall results tend to suggest the antinociceptive, antidiarrhoeal and cytotoxic activities of the extract.

• Journal of Microbiology and Biotechnology
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• v.15 no.3
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• pp.466-471
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• 2005

An Escherichia coli strain with high phytase activity was screened from pig excreta. The phytase gene, appA, was amplified by PCR technique. To obtain large amounts of appA phytase, the appA gene was subcloned into the baculovirus transfer vector pVL1393 under the control of the Polyhedrin promoter. The recombinant baculovirus harboring the appA gene was obtained after co-transfection and screening. The early $5^{th}$ instar larvae of silkworm were infected with the recombinant virus. Using this system, the appA phytase was overproduced up to 7,710 U per ml hemolymph. SDS-PAGE analysis revealed the baculovirus-derived appA phytase to be approximately 47 kDa in size. The optimal temperature and pH of the expressed phytase were $60^{\circ}C$ and pH 4.5, respectively. The enzymatic activity was increased by the presence of 1 mM $Ca^{2+}$, 1 mM $Mn^{2+}$, or $0.02\%$ Triton X-100.

• Journal of the Korean Society of Tobacco Science
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• v.15 no.2
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• pp.167-173
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• 1993

Pirimiphos - methyl was fogged in a tobacco warehouse to determine the proper dose rate, Small wire cages, containing adults and larvae of cigarette beetle (Lasiodema senicorne F.) were put at different altitude in the warehouse of which the holding capacity was about 250m3. At recommended dose rate, 0.4ml/m3(25% ai), it brought about 100% mortality of adults and 98.8% -100% mortality of larvae. When the dose rate was reduced to about half the recommended one, 97.6 and 96.4% mortality could still be obtained against adults and larvae, respectively. The number of the beetles monitored by sex - pheromone trap was greatly reduced after the fogging in the warehouses at 4 geographic places in Korea. When the warehouses were ventilated for one hour immediately after the fogging done for 48 hours, workers could not detected any opposing smell in the warehouses. And panel members responded positively that there was no negative effect on the taste of the cigarettes made of the fogged tobacco. Although the residue of the chemical was detected from the cigarettes in 4 of 5 cases, the residue was 1.1-1.3$\mu\textrm{g}$/20 cigarettes except for one case from which 3.3$\mu\textrm{g}$/20 cigarettes residue was detected. The relatively low residue is about l/450-1/150 of the WHO/FAO tolerant level.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.125-131
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• 2021

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.27-32
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• 2022

As the need for eco-friendly transportation increases due to the deepening climate crisis, many local governments in Korea are introducing shared bicycles. Due to anxiety about public transportation after COVID-19, bicycles have firmly established themselves as the axis of daily transportation. The use of shared bicycles is spread, and the demand for bicycles is increasing by rental offices, but there are operational and management difficulties because the demand is managed under a limited budget. And unfortunately, user behavior results in a spatial imbalance of the bike inventory over time. So, in order to easily operate the maintenance of shared bicycles in Seoul, bicycles should be prepared in large quantities at a time of high demand and withdrawn at a low time. Therefore, in this study, by using machine learning, the linear regression algorithm and MS Azure ML are used to predict and analyze when demand is high. As a result of the analysis, the demand for bicycles in 2018 is on the rise compared to 2017, and the demand is lower in winter than in spring, summer, and fall. It can be judged that this linear regression-based prediction can reduce maintenance and management costs in a shared society and increase user convenience. In a further study, we will focus on shared bike routes by using GPS tracking systems. Through the data found, the route used by most people will be analyzed to derive the optimal route when installing a bicycle-only road.

• Structural Engineering and Mechanics
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• v.84 no.1
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• pp.101-111
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• 2022

It is widely known that axially loaded fiber-reinforced polymer (FRP) confined concrete presents significant and enhanced mechanical properties with reference to the unconfined concrete. Therefore, to predict the mechanical behavior of FRP-confined concrete two quantities-peak strength and ultimate strain are required. Despite the significant advances, the determination of the ultimate strain of FRP-confined concrete is one of the most challenging problems to be resolved. This is often attributed to our persistence in desiring the conventional methods as the sole technique to examine this phenomenon and the complex nature of the ultimate strain of FRP-confined concrete. To bridge the research gap, this study adopted two machine learning (ML) techniques-artificial neural network (ANN) and Gaussian process regression (GPR)-to analyze observations obtained from 627 datasets of FRP-confined concrete circular and non-circular sections under axial loading test. Besides, the techniques are also used to predict the ultimate strain of FRP-confined concrete. Seven parameters namely width/diameter of the specimens, corner radius ratio, the strength of concrete, FRP elastic modulus, FRP thickness, FRP tensile rupture strain, and the axial strain of unconfined concrete-are the input parameters used to predict the ultimate strain of FRP-confined concrete. The results of the current study highlight the merit of using AI techniques in structural engineering applications given their extraordinary ability to comprehend multidimensional phenomena of FRP-confined concrete structures with ease, low computational cost, and high performance over the existing empirical models.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• Asia pacific journal of information systems
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• v.30 no.1
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• pp.31-52
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• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.255-268
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• 2023

This paper focuses on the tests for generalized fluctuation in the context of assessing structural changes based on linear regression models. For efficient estimation there has been a growing focus on the structural change monitoring, particularly in relation to fields such as artificial intelligence(hereafter AI) and machine learning(hereafter ML). Specifically, the investigation elucidates the implementation of structural changes and presents a coherent approach for the practical application to the BDI(Baltic Dry-bulk Index), which serves as a representative maritime trade index in global market. The framework encompasses a range of F-statistics type methodologies for fitting, visualization, and evaluation of empirical fluctuation processes, including CUSUM, MOSUM, and estimates-based processes. Additionally, it provides functionality for the computation and evaluation of sequences of pruned exact linear time(hereafter PELT).

• Journal of Haehwa Medicine
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• v.24 no.2
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• pp.35-57
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• 2016

Objectives : The purpose of this study is to prove the effect and mechanism of Gamikyejakjimogawusul-tang(GKHA) herbal acupuncture on induced rheumatoid arthritis model of DBA/1 mice. Methods : We check effect of GKHA extract on the AST, ALT, Creatinine, BUN of serum and cell viability of GK extract in RAW 264.7 cells to test the stability of this study. In vitro, we measure total phenol contents, total flavonoid contents, DPPH free radical scavenging activity, ABTS cation radical scavenging activity of Gamikyejakjimogawusul-tang, effect of GK extract on ROS(Reactive Ooxygen Species) production to estimate a anti-oxidant capacity, and we also measure effect of GK extract on NO (Nitric Oxid), IL-$1{\beta}$, IL-6, IL-17, IL-21, TNF-${\alpha}$, MCP-1, GM-CSF production in RAW 264.7 cells to estimate a anti-inflammatory efficacy. In vivo, we compare a rheumatoid arthritis manifestation between control and experimental group and estimate a AI. Then we check effect of GKHA on the level of WBC, neutrophil, lympocyte, monocyte in the blood to see the effect of immune cells in blood. In addition we measure effect of GKHA on the level of hs-CRP, IgM, IgG, IL-$1{\beta}$, IL-6, IL-17, IL-21, TNF-${\alpha}$, MCP-1, GM-CSF in serum. We observe effects of GKHA on imaging of cartilage degeneration using micro CT-arthrography in paw hind. And we calculate effects of GKHA that reduced BV ratio, BS/BV ratio using 3D Micro-CT. Lastly we observe effects of GKHA histopathologic examination analysis. Results : 1. The toxicity on liver and kidney was disregardable and the cytotoxicity against RAW 264.7 cells was also disregardable. 1. Total phenol contents and total flavonoid contents in GK extract were in high level. 2. DPPH free radical scavenging activity and ABTS cation radical scavenging activity were increased according to concentration of GK extract 3. ROS production was significantly decreased in GK extract (at 10, $100{\mu}g/ml$). 4. NO, IL-6, TNF-${\alpha}$, MCP-1 production were significantly decreased in GK extract(at 10, $100{\mu}g/ml$). IL-17, GM-CSF production were significantly decreased in GK extract(at 1, 10, $100{\mu}g/ml$). IL-$1{\beta}$, IL-21 production were also decreased but there was no statistical significance. 5. 25x observation after H&E and M-T staining, infiltration of immune cells and subsidence of the cartilage and damage to the synovial cells were decreased. Conclusions : This study showed that GKHA extract had anti-oxidant capacity, anti-inflammatory efficacy. GKHA extract also had inhibiting effect on the process of rheumatoid arthritis and can protect joint and cartilage. So we expect that GKHA extract can be a meaningful treatment to rheumatoid arthritis patients.