Modelling of the Minimum Ignition Temperature (MIT) of Corn Dust using Statistical Analysis and Artificial Neural networks based on the Synergistic Effect of Concentration and Dispersion Pressure

2021 ◽  
Author(s):  
Ushtar Arshad ◽  
Syed Ali Ammar Taqvi ◽  
Azizul Buang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Synergistic Effect ◽  
Ignition Temperature ◽  
Minimum Ignition Temperature ◽  
Artificial Neural

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.

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 Classification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of Escherichia coli isolates from broilers

2015 ◽  
Vol 35 (2) ◽  
pp. 137-140 ◽  
Author(s):  
Daniela T. Rocha ◽  
Felipe O. Salle ◽  
Gustavo Perdoncini ◽  
Silvio L.S. Rocha ◽  
Flávia B.B. Fortes ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Antimicrobial Resistance ◽  
Economic Losses ◽  
Artificial Neural ◽  
Relationship Of ◽  
The Relationship

Avian pathogenic Escherichia coli (APEC) is responsible for various pathological processes in birds and is considered as one of the principal causes of morbidity and mortality, associated with economic losses to the poultry industry. The objective of this study was to demonstrate that it is possible to predict antimicrobial resistance of 256 samples (APEC) using 38 different genes responsible for virulence factors, through a computer program of artificial neural networks (ANNs). A second target was to find the relationship between (PI) pathogenicity index and resistance to 14 antibiotics by statistical analysis. The results showed that the RNAs were able to make the correct classification of the behavior of APEC samples with a range from 74.22 to 98.44%, and make it possible to predict antimicrobial resistance. The statistical analysis to assess the relationship between the pathogenic index (PI) and resistance against 14 antibiotics showed that these variables are independent, i.e. peaks in PI can happen without changing the antimicrobial resistance, or the opposite, changing the antimicrobial resistance without a change in PI.

Multidimensional statistical analysis using artificial neural networks - Astronomical applications

1993 ◽  
Vol 106 ◽  
pp. 1685 ◽  
Author(s):  
Miquel Serra-Ricart ◽  
Xavier Calbet ◽  
Lluis Garrido ◽  
Vicens Gaitan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Artificial Neural

Statistical Analysis Based on Information Obtained from Inspection in Line to Forecast Future Land Pipeline Damage

2012 ◽  
Vol 1372 ◽  
Author(s):  
J. L. González-Domínguez ◽  
D. A. Padilla-Pérez ◽  
S. L. Hernández-Mejía ◽  
M. A. Adame-González
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Market Share ◽  
Fractal Geometry ◽  
Critical Infrastructure ◽  
Oil Industry ◽  
Artificial Neural

ABSTRACTOil industry is very strategic for any country. Not only for their market share and their budgets, but also for the critical infrastructure interdependences that creates. Considering statistics and fractal geometry as a support for analysis, interpretation of data and as an aid in taking decisions, this paper deals with data obtained in 1994 and 2004 from the inspection of the onshore pipelines in Mexico. Making use of software Benoit and ITSM-2000, the time series were found to have a fractal behavior. Further analysis and contrast with a previous research base on artificial neural networks was also accomplished. As a result it was obtained a forecast for 2014, based on the sum of the time series of data.

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