• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.507-516
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• 2011

Comparative studies on generalized binomial models (Moon, 2003; Ng, 1989; Paul, 1985; Kupper and Haseman, 1978; Griffiths, 1973) are restrictive in that the models compared are rather limited and MSE of the estimates is the only measure considered for the model adequacy. This paper is aimed to report simulation results which provide possible guidelines for selecting a proper model. We examine Pearson type of goodness-of-fit statistic to its degrees of freedom and AIC for the overall model quality. MSE and Bias of the individual estimates are also considered as the component fit measures. Performance of some models varies widely for a certain range of the parameter space while most of the models are quite competent. Our evaluation shows that the Extended Beta-Binomial model (Prentice, 1986) turns out to be particularly favorable in the point that it provides consistently excellent fit almost all over the values of the intra-class correlation coefficient and the probability of success.

• 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 Digital Convergence
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• v.20 no.4
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• pp.791-797
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• 2022

Through recent metaverse marketing case studies, success factors and activation methods were analyzed from the perspective of content, platform, network, and device of the metaverse ecosystem in each industry. The importance of contents and platform of metaverse could be found in entertainment, fashion, office space and real estate, education, advertisement and commerce industries. In order to vitalize the metaverse, firstly, it is necessary to strengthen active participation and retention by providing a stable revenue model for market participants. Secondly, the importance of attractive content to expand subscribers is a key trigger for metaverse activation. Thirdly, it is necessary to increase the convenience of using metaverse service by using a light and simple device for the user. Fourthly, a win-win cooperation strategy should be supported in the value chain of the industry through ecosystem scalability. In addition, business opportunities for market participants and additional revenue models should be continuously provided.

• Journal of Information Science Theory and Practice
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• v.11 no.2
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• pp.82-103
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• 2023

The Zimbabwean healthcare sector faces huge challenges due to increased demands for improved services for a growing number of patients with fewer resources. The use of information and communications technologies, prevalent in many industries, but lacking in Zimbabwean healthcare, could increase productivity and innovation. The adoption of health management information systems (HMISs) can lead to improved patient safety and high-level patient care. These technologies can change delivery methods to be more patient focused by utilising integrated models and allowing for a continuum of care across healthcare providers. However, implementation of these technologies in the health care sector remains low. The purpose of this study is to demonstrate the advantages to be attained by using HMISs in healthcare delivery and to ascertain the factors that influence the uptake of such systems in the public healthcare sector. A conceptual model, extending the technology, organization, and environment framework by means of other adoption models, underpins the study of adoption behavior. A mixed method methodology was used to conduct the study. For the quantitative approach, questionnaires were used to allow for regression analysis. For the qualitative approach, thematic analysis was used to analyse interview data. The results showed that the critical success factors (namely, relative advantage, availability, complexity, compatibility, trialability, observability, management support, information and communication technology expertise, communication processes, government regulation, infrastructure support, organizational readiness, industry and competitive support, external support, perceived ease of use, perceived usefulness, attitude, and intention to use) influenced adoption of HMISs in public hospitals in Zimbabwe.

• Journal of Dental Rehabilitation and Applied Science
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• v.22 no.4
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• pp.341-348
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• 2006

Accurate fit of the implant prosthesis is important in ensuring long term success of osseointegrated implant. Inaccurate fit of the implant prosthesis may give rise to complications and mechanical failure. To evaluate fite of the implant prosthesis, the development of the methods of analyzing the degree of misfit is important in clinical practice. To analyze the degree of the misfit of implant prosthesis, modal testing was used. A 2-dimensional finite element modal testing was accomplished. Four 2-dimensional finite element models with various levels of misfit of implant prostheses were constructed. Thickness gauges were simulated to make misfit in the implant prostheses. With eigenvalue analysis, the natural frequencies of the models were found in the frequency domain representation of vibration. According to the difference of degree of misfit, natural frequencies of the models were changed.

• IE interfaces
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• v.24 no.4
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• pp.408-419
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• 2011

R&D is a foundation for new business chance and productivity improvement leading to enormous expense and a long-term multi-step process. During the R&D process, decision-makers are confused due to the various future uncertainties that influence economic and technical success of the R&D projects. For these reasons, several decision-making models for R&D project investment have been suggested; they are based on traditional methods such as Discounted Cash Flow (DCF), Decision Tree Analysis (DTA) and Real Option Analysis (ROA) or some fusion forms of the traditional methods. However, almost of the models have constraints in practical use owing to limits on application, procedural complexity and incomplete reflection of the uncertainties. In this study, to make the constraints minimized, we propose a new model named Real Option Decision Tree Model which is a conceptual combination form of ROA and DTA. With this model, it is possible for the decision-makers to simulate the project value applying the uncertainties onto the decision making nodes.

• Journal of the Korea Safety Management & Science
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• v.17 no.1
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• pp.337-348
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• 2015

This research introduces the Financial Effect Measurement (FEM) models which measures both the improvement and the innovation performance of Quality Control Circle (QCC) and activities of Six Sigma. Concepts and principle of Comprehensive Income Statement (CIS), Balanced Scorecard (BSC), Time-Driven Activity Based-Costing (TDABC) and Total Productive Maintenance (TPM) are applied in order to develop the 4 FEM models presented in this paper. First of all, FEM using CIS depicts the improvement effects of production capacity and yield using relationships between demand and supply, and line balancing efficiency between bottleneck process and non-bottleneck processes. Secondly, cause-and-effect relation of Key Performance Indicator (KPI) is used to present Critical Success Factor (CSF) effects for QC Story 15 steps of QCC and DMAIC (Define, Measure, Analyze, Improve, and Control) of Six Sigma. The next is FEM model for service management innovation activities that uses TDABC to calculate the time-driven effect for improving the indirect activities according to the cost object. Lastly, FEM model for TPM activities presents the interpretation of improvement effect model of TPM Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) maintenance using profit, cash and Economic Added Value (EVA) as metrics of enterprise values. To better understand and further investigate FEMs, recent cases on National Quality Circle Contest are used to evaluate new financial effect measurement developed in this paper.

• Advances in Computational Design
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• v.5 no.4
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• pp.445-465
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• 2020

This paper presents a study conducted with the aim of developing a model of tendering based on a technique of artificial intelligence by managing and controlling the factors of success or failure of construction projects through the evaluation of the process of invitation to tender. Aiming to solve this problem, analysis of the current environment based on SWOT (Strengths, Weaknesses, Opportunities, and Threats) is first carried out. Analysis was evaluated through a case study of the construction projects in Algeria, to bring about the internal and external factors which affect the process of invitation to tender related to the construction projects. This paper aims to develop a mean to identify threats-opportunities and strength-weaknesses related to the environment of various national construction projects, leading to the decision on whether to continue the project or not. Following a SWOT analysis, novel artificial intelligence models in forecasting the project status are proposed. The basic principal consists in interconnecting the different factors to model this phenomenon. An artificial neural network model is first proposed, followed by a model based on fuzzy logic. A third model resulting from the combination of the two previous ones is developed as a hybrid model. A simulation study is carried out to assess performance of the three models showing that the hybrid model is better suited in forecasting the construction project status than RNN (recurrent neural network) and FL (fuzzy logic) models.

• Journal of Dental Rehabilitation and Applied Science
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• v.22 no.3
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• pp.251-260
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• 2006

Accurate fit of the implant prosthesis is important in ensuring long term success of osseointegrated implant. Inaccurate fit of the implant prosthesis may give rise to complications and mechanical failure. To evaluate fite of the implant prosthesis, the development of the methods of analyzing the degree of misfit is important in clinical practice. To analyze the degree of the misfit of implant prosthesis, modal testing was used. A 2-dimensional finite element modal testing was accomplished. Four 2-dimensional finite element models with various levels of misfit of implant prostheses were constructed. Thickness gauges were simulated to make misfit in the implant prostheses. With eigenvalue analysis, the natural frequencies of the models were found in the frequency domain representation of vibration. According to the difference of degree of misfit, natural frequencies of the models were changed.

• Asian Journal of Business Environment
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• v.6 no.4
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• pp.5-12
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• 2016

Purpose - Not all firms are able to completely eliminate the risk arising out of the crisis. Success hides in the ability to perceive the market expectations accurately and take correct decisions. This study aims to analyze the firms' decisions at gross-root level. Research Design, Data, and Methodology - Primary data is obtained with the help of specially designed questionnaires from the agriproducts export firms that are members of export union of Turkey. The study is based on four theoretical structures: general planned behavior theory model, perception-leading behavior control and subjective norm model, perceived-behavioral-control leading perception and subjective norm models, and perceptions and subjective norms leading behavior control model. Structural Equation Models (SEM) is used to conduct the empirical analysis. Results - The findings show perceptions and subjective norms leading behavior control model as the best one, concluding that the environmental pressures and positive perceptions have significant effect on the strategic decisions of the agriproducts export firms. Conclusion - Policy tools like creating positive perception in the markets, providing sufficient information and financial support to the firms and increasing market competition can be used effectively to achieve the said objective.