• The Journal of Korean Medicine
• /
• v.28 no.2 s.70
• /
• pp.22-33
• /
• 2007

Uterine myoma is a benign tumor of smooth muscle in the uterine wall. Recently, in Oriental medicine, concerns about uterine myoma patients have increased. We analyzed the medical records for 944 patients, including 257 uterine myoma patients, who visited Dongeui University Oriental Medical Center from May 2001 to June 2006. We investigated the DSOM (Diagnosis System of Oriental Medicine) symptom scores which effect uterine myoma patients using stepwise logistic regression model. Logistic regression analysis indicated as follows: In the control group composed of 558 outpatients, 18 items of DSOM were associated with myoma, 9 positively and 9 negatively, and the results showed that the correct rate was equal to 81.1%, sensitivity 72.8%, and specificity 84.9%. In 129 clinical trials data, 33 items of DSOM were associated with myoma, 18 positively and 15 negatively, and the results showed that the correct rate was equal to 85.8%, sensitivity 84.8%, and specificity 87.6%. In 687 outpatient and clinical trials data, 18 items of DSOM were associated with myoma, 10 positively and 8 negatively, and the results showed that the correct rate was equal to 82.8%, sensitivity 70.8%, and specificity 87.3%.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.8
• /
• pp.596-600
• /
• 2009

To support an efficient management of software verification and validation activities, many defect prediction models have been proposed based on object oriented metrics. They usually adopt logistic regression analysis, And, they state that the correctness of prediction is about 60${\sim}$70%, We performed a similar experiment with Eclipse 3.3 to check their prediction effectiveness, However, the result shows that correctness is about 40% which is much lower than the original results. We also found that univariate logistic regression analysis produces better results than multivariate logistic regression analysis.

• Journal of Multimedia Information System
• /
• v.3 no.2
• /
• pp.9-12
• /
• 2016

This paper is concerned with the effective and efficient identification of the gender and motion of humans. Tracking this nonverbal behavior is useful for providing clues about the interaction of different types of people and their exact motion. This system can also be useful for security in different places or for monitoring patients in hospital and many more applications. Here we describe a novel method of determining identity using machine learning with Microsoft Kinect. This method minimizes the fitting or overlapping error between an ellipsoid based skeleton.

• Journal of the Korean Society of Industry Convergence
• /
• v.20 no.5
• /
• pp.395-404
• /
• 2017

Cultivating soybeans in rice paddy field reduces labor costs and increases the yield. Soybeans, however, are highly susceptible to excessive soil water in paddy field. Controlled drainage system can adjust groundwater level (GWL) and control soil moisture content, resulting in improvement soil environments for optimum crop growth. The objective of this study was to fit the soybean growth data (canopy height and stem diameter) using Gompertz model and Logistic model at different GWL and validate those models. The soybean, Daewon cultivar, was grown on the lysimeters controlled GWL (20cm and 40cm). The soil textures were silt loam and sandy loam. The canopy height and stem diameter were measured from the 20th days after seeding until harvest. The Gompertz and Logistic models were fitted with the growth data and each growth rate and maximum growth value was estimated. At the canopy height, the $R_2$ and RMSE were 0.99 and 1.58 in Gompertz model and 0.99 and 1.33 in Logistic model, respectively. The large discrepancy was shown in full maturity stage (R8), where plants have shed substantial amount of leaves. Regardless of soil texture, the maximum growth values at 40cm GWL were greater than the value at 20cm GWL. The growth rates were larger at silt loam. At the stem diameter, the $R_2$ and RMSE were 0.96 and 0.27 in Gompertz model and 0.96 and 0.26 in Logistic model, respectively. Unlike the canopy height, the stem diameter in R8 stage didn't decrease significantly. At both GWLs, the maximum growth values and the growth rates at silt loam were all larger than the values at sandy loam. In conclusion, Gompertz model and Logistic model both well fit the canopy heights and stem diameters of soybeans. These growth models can provide invaluable information for the development of precision water management system.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.6_2
• /
• pp.1103-1110
• /
• 2020

In this study, a segmented model with Upside-Down bathtub shaped failure intensity for a repairable system are proposed under the assumption that the occurrences of the failures of a repairable system follow the Non-Homogeneous Poisson Process. The proposed segmented model is the compound model of S-PLP and LIP (Segmented Power Law Process and Logistic Intensity Process), that fits the separate failure intensity functions on each segment of time interval. The maximum likelihood estimation is used for estimating the parameters of the S-PLP and LIP model. The case study of system A shows that the S-PLP and LIP model fits better than the other models when compared by AICc (Akaike Information Criterion corrected) and MSE (Mean Squared Error). And it also implies that the S-PLP and LIP model can be useful for explaining the failure intensities of similar systems.

• Journal of Korea Multimedia Society
• /
• v.25 no.8
• /
• pp.1109-1121
• /
• 2022

As the demand for quality sleep increases in modern society, the importance of sleep technology has increased. Recently, development of sleep environment improvement products and research on the user's sleep improvement have been activated. Representatively, user sleep pattern analysis research is being conducted through the existing polysomnography, but it is difficult to use it in the sleep environment of daily life. Therefore, in this paper, we propose a system design that can provide a customized deep sleep service to users by detecting sleep disturbance factors in a motion bed environment. In order to improve the user's sleep satisfaction, a logistic regression-based sleep pattern analysis model is proposed and accuracy and significance are verified through experiments. And to improve user's sleep satisfaction, we propose a logistic regression-based sleep pattern analysis model and verify accuracy and significance through experiments. The proposed system is expected to improve the user's sleep quality and effectively prevent and manage sleep disorders.

• Journal of Korean Society for Geospatial Information Science
• /
• v.19 no.3
• /
• pp.33-40
• /
• 2011

This paper presents the prediction and evaluation of landslide using LRA(logistic regression analysis) and ANN (Artificial Neural Network) methods. In order to assess the landslide, we selected Sarabong, Byeoldobong area and Mt. Song-ak in Jeju Island. Five factors which affect the landslide were selected as: slope angle, elevation, porosity, dry density, permeability. So as to predict and evaluate the landslide, firstly the weight value of each factor was analyzed by LRA(logistic regression analysis) and ANN(Artificial Neural Network) methods. Then we got two prediction maps using AcrView software through GIS(Geographic Information System) method. The comparative analysis reveals that the slope angle and porosity play important roles in landslide. Prediction map generated by LRA method is more accurate than ANN method in Jeju. From the prediction map, we found that the most dangerous area is distributed around the road and path.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.294-302
• /
• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.30 no.4
• /
• pp.111-117
• /
• 2007

Comparing with bar-code systems, RFID systems can supply more efficient work. Using RFID systems, logistic management systems could be helped effectively to gather real-time information. It's available to reduce the working time and object's delay time, and to deal with real-time information by using RFID system. Until now, based on how many pallets used, there is few study about best workload of RFID system. Therefore, in this study, both bar-code and RFID system simulations were executed for inspection process in distribution center. As a result, following the ware pallet quantity, the total cost of both working time and other delay times were calculated and the sensitivity analysis of total cost trend was executed.

• The Journal of Information Systems
• /
• v.17 no.1
• /
• pp.131-153
• /
• 2008

So many location based service systems, including automobile navigation system logistic management, taxi fleet management, and so on, are being used everywhere. However, these are all outdoors. This paper provides a stepping stone for commercial indoor location based services by developing an integrated system of our indoor positioning and map viewer modules. For the indoor positioning, we propose WLAN (Wireless Local Area Network) based EKF (Extended Kalman Filter) which estimates user's current location and tracts user's trace in the sequence of time. Our map viewer renders a map recorded in an Autocad DXF file and provides functions of map manipulation such as zoom-in, zoom-out, and move. We integrate our indoor positioning and map viewer modules and discuss the experimental results of the integrated system.