• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.8-16
• /
• 2021

The tourism industry is influenced by a large number of factors that affect the development scenarios of the tourism in different ways. At the same time, tourism is an important component of the national economy of any state, forms its image, investment attractiveness, is a source of income and a stimulus for business development. The aim of the article is to conduct an empirical study to identify the importance of cognitive determinants in the development of tourism. The study used general and special methods: systems analysis, synthesis, grouping, systematization, cognitive modeling, cognitive map, pulse method, predictive extrapolation. Target factors, indicators, and control factors influencing the development of tourism in Ukraine are determined and a cognitive model is built, which graphically reflects the nature of the influence of these factors. Four main scenarios of the Ukrainian tourism industry are established on the basis of creating a matrix of adjacency of an oriented graph and forecast modeling based on a scenario approach. The practical significance of the obtained results lies in the possibility of their use to forecast the prospects of tourism development in Ukraine, the definition of state policy to support the industry that will promote international and domestic tourism.

• Safety and Health at Work
• /
• v.8 no.3
• /
• pp.267-275
• /
• 2017

Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

• Health Policy and Management
• /
• v.22 no.2
• /
• pp.263-274
• /
• 2012

This study describes the hypertensive patients characteristics associated with the health incentive point program, and develops and analyzes a simple predictive model of participation in the program. Based on the Incheon Chronic Disease Management System(iCDMS), a model program of community partnership for hypertensive or diabetic patients detection and follow-up since 2005 in Incheon metropolitan city, a cross-sectional design was used in this study. An effective 10.844 adults sample was divided into groups according to participation in the health incentive point program and continuing treatment, and individual and health characteristics among groups were compared. Furthermore, the predictors associated with participation in the program were identified by the logistic regression analysis. After the health incentive point program in iCDMS was introduced, the number of hypertensive patients participation in the program increased 23.9 times which is vastly high given the various programs were provided. There were statistically significant differences among the groups: age(p=0.000), treatment compliance(p=0.000), and blood pressure control at the last measurement(p=0.000), in particular, between participation group(GroupI, n=246) and non-participation group(GroupIII, n=10,408). Furthermore, age over 60 years(OR: 0.33), treatment compliance(OR: 3.49~3.78) and blood pressure controls(OR: 2.13~2.30) were statistically significant predictors associated with participation in the program, based on the logistic regression analysis with GroupI and GroupIII. To increase participation in the health incentive point program, variables such as age, treatment compliance and blood pressure controls are more concerned. And, high-risk patients and family members need targeted health incentive programs.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.3
• /
• pp.107-114
• /
• 2022

In this paper, we have enhanced the risk prediction of hypertension using the feature selection method in the Korean National Health and Nutrition Examination Survey (KNHANES) database of the Korea Centers for Disease Control and Prevention. The study identified various risk factors correlated with chronic hypertension. The paper is divided into three parts. Initially, the data preprocessing step of removes missing values, and performed z-transformation. The following is the feature selection (FS) step that used a factor analysis (FA) based on the feature selection method in the dataset, and feature importance (FI) and multicollinearity analysis (MC) were compared based on FS. Finally, in the predictive analysis stage, it was applied to detect and predict the risk of hypertension. In this study, we compare the accuracy, f-score, area under the ROC curve (AUC), and mean standard error (MSE) for each model of classification. As a result of the test, the proposed MC-FA-RF model achieved the highest accuracy of 80.12%, MSE of 0.106, f-score of 83.49%, and AUC of 85.96%, respectively. These results demonstrate that the proposed MC-FA-RF method for hypertension risk predictions is outperformed other methods.

• Journal of Bio-Environment Control
• /
• v.31 no.1
• /
• pp.1-7
• /
• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• Journal of the Korea Convergence Society
• /
• v.8 no.5
• /
• pp.117-125
• /
• 2017

This is a convergence study to present strategies for performance enhancement by verifying the causal relationship between the influencing factor on the performance of the healthcare-associated infection control guidelines in hospital nurses. Participants were 388 nurses recruited from 16 different tertiary and general hospitals in Korea. Data collection was conducted using self-report questionnaires and analyzed using SPSS 21.0 and AMOS 21.0 programs. The overall fitness was ${\chi}^2=99.64$ (df=14, p<.01), GFI=.94, RMSEA=.10, NFI=.84, CFI=.90. The explanatory power of predictive variables on intention were 23.8%, and those on behavior were 17.7%. As a result of this study, it was found that TPB is an appropriate theory to explain the performance of healthcare-associated infection control guidelines, and repeated studies including multi-level modeling of career experience and organizational influences on behavior with strong social characteristics are needed.

• Journal of Information Science Theory and Practice
• /
• v.12 no.1
• /
• pp.60-76
• /
• 2024

Broadband Internet has proven to be vital for economic growth in developed countries. Developing countries have implemented several initiatives to increase their broadband access. However, its full potential can only be realized through adoption and use. With lower-middle-income countries accounting for the majority of the world's unconnected population, this study employs the theory of planned behavior (TPB) to investigate users' intentions to adopt broadband. Rural Tanzania was chosen as a case study. A cross-sectional study was conducted over three weeks, using 155 people from seven villages with the lowest broadband adoption rates. Non-probability voluntary response sampling was used to recruit the participants. Using the TPB constructs: attitude toward behavior (ATB), subjective norms (SN), and perceived behavioral control (PBC), ordinal regression analysis was employed to predict intention. Descriptive statistical analysis yielded mean scores (standard deviation) as 3.59 (0.46) for ATB, 3.34 (0.40) for SN, 3.75 (0.29) for PBC, and 4.12 (0.66) for intention. The model adequately described the data based on a comparison of the model with predictors and the null model, which revealed a substantial improvement in fit (p<0.05). Moreover, the predictors accounted for 50.3% of the variation in the intention to use broadband Internet, demonstrating the predictive power of the TPB constructs. Furthermore, the TPB constructs were all significant positive predictors of intention: ATB (β=1.938, p<0.05), SN (β=2.144, p<0.05), and PBC (β=1.437, p=0.013). The findings of this study provide insight into how behavioral factors influence the likelihood of individuals adopting broadband Internet and could guide interventions through policies meant to promote broadband adoption.

• Journal of Korea Water Resources Association
• /
• v.57 no.3
• /
• pp.151-164
• /
• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.11 no.3
• /
• pp.11-18
• /
• 1991

Volatile organic compounds(VOC) present in or generated by many sources, can be toxic, mutagenic or even carcinogenic, so that control of such emissions is significant. The 6 chlorinated organic chemicals as VOC were examined in this study. Prediction of the behavior of VOC on activated carbon beds is an important part of control system design. The objective of this study was to predict gas phase sorption isotherms from physical properties and liquid phase isotherms obtained with the same adsorbent and adsorbate. One of the techniques that was investigated was quantitative structure-activity relationships(QSAR) for the predicition procedures. It was possible to estimate sorption isotherms in the gas phase($a_g$) using either connectivity index, $^2{\chi}$, and the Henry's law coefficient ($H_a$) or the solubility and the equilibrium concentration in the gas phase. As a result of study, the predictive equation based on Freundlich model for $a_g$ was ${\log}\;a_g=0.238\;^2{\chi}+0.573\;{\log}\;H_a+4.330(r^2=0.94)$. Finally, this would provide a potentially useful tool to describe and predict sorption capacity without time-consuming tests.

• Applied Chemistry for Engineering
• /
• v.7 no.3
• /
• pp.443-452
• /
• 1996

Fouling is caused by sedimentation and corrosion of polymer, heavy paraffine, chemicals, heavy organics, asphaltene, etc. in the entire chemical process of heat exchanger, boiler, desalter, etc. Fouling phenomena remains a serious operating problem which results in increased energy consumption, increased pressure drops, reduction or complete loss of products yield, and increased maintenance costs. In order to calculate the separated amounts of foulants and to control the fouling process, the predictive model is developed which is based on Scott & Magat polymer solution theory, Peng-Robinson EOS, BWR EOS, and continuous and multicomponent thermodynamics.