• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1963-1978
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• 2015

With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users' behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.

• International Journal of Concrete Structures and Materials
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• v.5 no.1
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• pp.57-64
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• 2011

An extensive database has been constructed of reinforced concrete (RC) beam-column connection tests subjected to cyclic lateral loading. All cases within the database experienced joint shear failure, either in conjunction with or without yielding of longitudinal beam reinforcement. Using the experimental database, envelope curves of joint shear stress vs. joint shear strain behavior have been created by connecting key points such as cracking, yielding, and peak loading. Various prediction approaches for RC joint shear behavior are discussed using the constructed experimental database. RC joint shear strength and deformation models are first presented using the database in conjunction with a Bayesian parameter estimation method, and then a complete model applicable to the full range of RC joint shear behavior is suggested. An RC joint shear prediction model following a U.S. standard is next summarized and evaluated. Finally, a particular joint shear prediction model using basic joint shear resistance mechanisms is described and for the first time critically assessed.

• Research in Community and Public Health Nursing
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• v.13 no.3
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• pp.456-470
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• 2002

Objectives: The purpose of this study was to test the Theory of Planned Behavior (TPB) in the prediction of smoking cessation intention and smoking cessation behavior among adolescent smokers, in order to provide basic data to develop a future smoking cessation program as a nursing intervention. Method: The study subjects were 80 adolescent smokers who had smoked one cigarette and attended a five-day school smoking cessation program. The data were collected from October 24 to December 21, 1999. The instruments used in this study were the tools developed by Jee (1994) to measure TPB variables such as attitude toward smoking cessation behavior, subjective norm, perceived behavioral control, smoking cessation intention, and smoking cessation behavior. The data were analyzed with the SAS/PC program using descriptive statistics, hierarchical multiple regression, and logistic multiple regression. Results: 1. Attitude toward smoking cessation behavior, subjective norm, and perceived behavioral control were partially significant in predicting smoking cessation intention. 2. Smoking cessation intention and perceived behavioral control toward smoking cessation behavior did not significantly predict smoking cessation behavior. 3. There were partial interaction effects among the attitude toward smoking cessation behavior, subjective norm, and perceived behavioral control in the prediction of smoking cessation intention. 4. There were partial interaction effects between smoking cessation intention and perceiver behavioral control toward smoking cessation behavior in the prediction of smoking cessation behavior. Conclusion: This study partially demonstrated support for the TPB model that was partially useful in predicting smoking cessation intention and smoking cessation behavior among adolescent smokers. Therefore, it is recommended that attitude toward smoking cessation behavior and perceived behavioral control should be considered in developing smoking cessation programs and implementing nursing interventions to change the smoking behavior of adolescent smokers.

• Journal of Korean Academy of Nursing
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• v.28 no.3
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• pp.550-562
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• 1998

The purpose of this study was to test the Theory of Planned Behavior in the prediction of contraceptive behavior among married women. This study used a descriptive correlational design to examine the relationships among the study variables. Eighty married women in Seoul and Kyungki-do participated in this study, Research instruments used were the tool for measuring TPB variables search as attitude toward contraception, subjective norm, perceived behavioral control, and intention ; and the tool for measuring contraceptive behavior. The former was modified by the researcher according to Ajzen & Fishbein(1980)'s guidelines for tool development and Jee (1993)'s tool. The latter was developed by the researcher Data was collected from July 20, 1996 to October 25, 1996. The results are as follows ; The three factors, attitude, subjective norm and perceived behavioral control of contraception can explain 30% of the variance in contraceptive intention. Inspection of path coefficient for each of the three predictor variables revealed that subjective norm and perceived behavioral control were the predictor variables on intention, while attitude was not. ; and intention and percevied behavioral control factors can explain 42% of the variance in contraceptive behavior. Inspection of path coefficient for each of the two predictor variables revealed that intention and perceived behavioral control were the predictor variables on behavior. In conclusion, this study identified that Theory of Planned Behavior was a useful model in the prediction of contraceptive behavior, and the contraceptive service program based on the TPB variables would be an effective nursing intervention for the change in contraceptive behavior.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.2
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• pp.31-37
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• 2011

Sheet aluminum alloys have been used in manufacturing of machine structures. In fatigue crack propagation behavior of thin sheet aluminum alloys, it is important that fatigue crack growth rate is affected by crack closure phenomenon. In this work, we analyzed the characteristics of fatigue crack propagation behavior in experiment of constant stress condition for thin sheet Al 2024-T3 alloys, and identified the retardation behavior of crack growth by comparing experimental results of thin and thick plate specimen. We attempt to operate the fatigue life estimating process using the fatigue related material constants from referred fatigue crack propagation analysis. And we analyzed the experimental and prediction results of fatigue life of thin sheet aluminum alloy in order to identify the relation between retardation behavior of fatigue crack growth and crack closure phenomenon.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.201-208
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• 2000

In this study, the behavior of saturated sandy soils under dynamic loads - pore water pressure and effective stress - was investigated using Disturbed State Concept(DSC) model. The model parameters are evaluated from laboratory test data. During the process of loading and reverse loading, DSC model is utilized to trace strain-hardening and cyclic softening behavior. The procedure of back prediction proposed in this study are verified by comparing with laboratory test results. From the back prediction of pore water pressure and effective mean pressure under cyclic loading, excess pore water pressure increases up to initial effective confining pressure and effective mean pressure decrease close to zero in good greement with laboratory test results. Those results represent the liquefaction of saturated sandy soils under dynamic loads. The number of cycles at initial liquefaction using the model prediction is in good agreement with laboratory test results. Therefore, the results of this study state that the liquefaction of saturated sandy soils can be explained by the effective tress analysis.

• Journal of the Korea institute for structural maintenance and inspection
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• v.5 no.1
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• pp.237-246
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• 2001

This study is to measure the creep coefficient by 3 days, 7 days and 28 days in the age when loading for the quality assessment of $350kgf/cm^2$ in the high-strength concrete. And it is to analyze the behavior of creep coefficient by applying the experimental data though the compressive strength test, the elastic modulus test and the dry shrinkage test to the ACI-209, AASHTO-94 and CEB/FIP-90, the prediction mode, and the basis of concrete structural design. Also it is to analyze the behavior of short-term creep coefficient during 91 days in the age when loading through the experiment by using the regression analysis, the statistical theory. As applying it to the long-term behavior during 365 days and comparing with the creep prediction mode and examining it, the result from the analysis of the quality of the concrete is as follows. As the result of comparison and analysis about the ACI-209, AASHTO-94 and CEB/FIP-90, the prediction mode, and the basis of concrete structural design, the normal Portland cement class 1 shows the approximate value with the prediction of GEE/PIP-90 and the basis of concrete structural design, but in case of the prediction of ACI-209 and AASHTO-94, there would be worry of underestimation in the application.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.10a
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• pp.285-288
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• 1999

Creep is a major parameter to represent long-term behavior of concrete structures concerning serviceability and durability. The effect of creep is recently taking account into crack resistance analysis of early-age concrete concerning durability evaluation. Since existing creep prediction models were proposed to predict creep for hardened concrete, most of them cannot consider effectively the information on microstructure formation and hydration developed in the early-age concrete. In this study, creep tests for early-age concrete made of the type I cement and the type V cement are carried out respectively and creep prediction models are evaluated for the prediction of creep behavior in early-age concrete. A creep prediction model is modified for the prediction of creep in early-age concrete and also verified by comparing prediction results with results of creep tests on early-age concrete.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.573-575
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• 2004

This study presents a new customer segmentation method based on features that can predict the customer's buying behavior. In this method, we consider all variables that can affect the customer's buying behavior including demographics, psychographics, technographics, transaction pattern-related variables, etc. We define several features which are the combination of variables with the interaction effect by using C5.0, use SOM (Self-Organizing Map) neural networks in odor to extract the feature's patterns and classify, and then make features' rules using C5.0 far the prediction of customer buying behavior

• Structural Engineering and Mechanics
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• v.16 no.6
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• pp.643-654
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• 2003

Two real-time modeling prediction (RMP) schemes are presented in this paper for analyzing the behavior of deep excavations during construction. The first RMP scheme is developed from the traditional AR(p) model. The second is based on the simplified Elman-style recurrent neural networks. An on-line learning algorithm is introduced to describe the dynamic behavior of deep excavations. As a case study, in-situ measurements of an excavation were recorded and the measured data were used to verify the reliability of the two schemes. They proved to be both effective and convenient for predicting the behavior of deep excavations during construction. It is shown through the case study that the RMP scheme based on the neural network is more accurate than that based on the traditional AR(p) model.