Assessing of the Importance of Medical Parameters on the Risk of the Myocardial Infraction Using Statistical Analysis and Neural Networks

2017 ◽  
pp. 237-243
Author(s):  
Andrea Peterkova ◽  
German Michalconok
Keyword(s):  
Neural Networks ◽  
Statistical Analysis ◽  
Myocardial Infraction

Underwriting Automobile Insurance Using Artificial Neural Networks

2011 ◽  
pp. 3865-3870
Author(s):  
Fred Kitchens
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Automobile Insurance ◽  
Long Time ◽  
Artificial Neural ◽  
Pencil And Paper

For hundreds of years, actuaries used pencil and paper to perform their statistical analysis It was a long time before they had the help of a mechanical adding machine. Only recently have they had the benefit of computers. As recently as 1981, computers were not considered important to the process of insurance underwriting. Leading experts in insurance underwriting believed that the judgment factor involved in the underwriting process was too complex for any computer to handle as effectively as a human underwriter (Holtom, 1981). Recent research in the application of technology to the underwriting process has shown that Holtom’s statement may no longer hold true (Gaunt, 1972; Kitchens, 2000; Rose, 1986). The time for computers to take on an important role in the insurance underwriting process may be upon us. The author intends to illustrate the applicability of artificial neural networks to the insurance underwriting process.

Using hierarchical statistical analysis and deep neural networks to detect covert timing channels

2019 ◽  
Vol 82 ◽  
pp. 105546 ◽  
Author(s):  
Omar Darwish ◽  
Ala Al-Fuqaha ◽  
Ghassen Ben Brahim ◽  
Ilyes Jenhani ◽  
Athanasios Vasilakos
Keyword(s):  
Neural Networks ◽  
Statistical Analysis ◽  
Deep Neural Networks ◽  
Timing Channels ◽  
Covert Timing Channels

Identification of critical brand community variables and constructs using importance-performance analysis and neural networks

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Matti Haverila ◽  
Kai Christian Haverila ◽  
Caitlin McLaughlin
Keyword(s):  
Neural Networks ◽  
Statistical Analysis ◽  
Online Survey ◽  
Customer Engagement ◽  
Analysis Tool ◽  
Brand Community ◽  
Content Type ◽  
Target Construct ◽  
Statistical Analysis Tool ◽  
And Performance

Purpose This paper aims to use a unique statistical analysis tool to examine the importance and performance of critical brand community constructs and indicators to make concrete recommendations for brand community managers going forward. Design/methodology/approach An online survey was used to gather 501 responses from North American members of the Qualtrics panel. The data was analyzed with partial least squares (PLS) modeling software SmartPLS and neural networks available in statistical software JMP by SAS. Findings Using the brand community motives by Madupy and Cooley (2010), the results of this paper indicated that there was significant room for improvement in customer engagement. Based on further analysis, entertainment and identification with the brand community were the most important constructs in driving community engagement so that the identification construct received a “do better” ruling meaning that the improvement of the indentification construct score would enhance significantly the score of the target construct engagement score. Originality/value For brand community managers, it is important to know the true importance of the critical brand community constructs and indicators, along with an assessment of current performance. This helps to increase satisfaction and relationship quality among brand community members. The current study uses unique statistical analysis tools to make such concrete recommendations.

scholarly journals Prediction of Compressive Strength of Concrete in Wet-Dry Environment by BP Artificial Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chengyao Liang ◽  
Chunxiang Qian ◽  
Huaicheng Chen ◽  
Wence Kang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Influencing Factors ◽  
Mechanical Performance ◽  
Tidal Zone ◽  
Structure Degradation ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

Engineering structure degradation in the marine environment, especially the tidal zone and splash zone, is serious. The compressive strength of concrete exposed to the wet-dry cycle is investigated in this study. Several significant influencing factors of compressive strength of concrete in the wet-dry environment are selected. Then, the database of compressive strength influencing factors is established from vast literature after a statistical analysis of those data. Backpropagation artificial neural networks (BP-ANNs) are applied to establish a multifactorial model to predict the compressive strength of concrete in the wet-dry exposure environment. Furthermore, experiments are done to verify the generalization of the BP-ANN model. This model turns out to give a high accuracy and statistical analysis to confirm some rules in marine concrete mix and exposure. In general, this model is practical to predict the concrete mechanical performance.

scholarly journals Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks

2007 ◽  
Vol 10 (1) ◽  
pp. 69-74 ◽  
Author(s):  
Luciano Pivoto Specht ◽  
Oleg Khatchatourian ◽  
Lélio Antônio Teixeira Brito ◽  
Jorge Augusto Pereira Ceratti
Keyword(s):  
Neural Networks ◽  
Statistical Analysis ◽  
Asphalt Rubber ◽  
Rotational Viscosity

scholarly journals A Review of Computer Vision Techniques in the Detection of Metal Failures

2021 ◽  
Vol 349 ◽  
pp. 02021
Author(s):  
Deborah Fitzgerald ◽  
Roselita Fragoudakis
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Statistical Analysis ◽  
Error Detection ◽  
Spatial Data ◽  
Fourier Transforms ◽  
Vision System ◽  
Gabor Filtering ◽  
Destructive Testing ◽  
Occurrence Matrix

This paper considers and contrasts several computer vision techniques used to detect defects in metallic components during manufacturing or in service. Methodologies include statistical analysis, weighted entropy modification, Fourier transformations, neural networks, and deep learning. Such systems are used by manufacturers to perform non-destructive testing and inspection of components at high speeds [1]; providing better error detection than traditional human visual inspection, and lower costs [2]. This is a review of the computer vision system comparing different mathematical analysis in order to illustrate the strengths and weaknesses relative to the nature of the defect. It includes exemplar that histograms and statistical analysis operate best with significant contrast between the defect and background, that co-occurrence matrix and Gabor filtering are computationally expensive, that structural analysis is useful when there are repeated patterns, that Fourier transforms, applied to spatial data, need windowing to capture localized issues, and that neural networks can be utilized after training.

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