Mathematical modeling, data mining

In the last decades not only thousands of chemical descriptors but also many advanced, powerful modeling algorithms have been made available, The older QSAR models were linear equations with one or a few parameters. Then, other tools have been introduced, such as artificial neural network, fuzzy logic, and data mining algorithms, making possible non linear models and automatic generation of mathematical solutions. 

MacKay s textbook [114] offers not only a comprehensive coverage of Shannon s theory of information but also probabilistic data modeling and the mathematical theory of neural networks. Artificial NN can be applied when problems appear with processing and analyzing the data, with their prediction and classification (data mining). The wide range of applications of NN also comprises optimization issues. The information-theoretic capabilities of some neural network algorithms are examined and neural networks are motivated as statistical models [114]. 

Hugh Chipman is Associate Professor and Canada Research Chair in Mathematical Modelling in the Department of Mathematics and Statistics at Acadia University. His interests include the design and analysis of experiments, model selection, Bayesian methods, and data mining. 

From the contents discussed above, the following conclusions can be draw The prediction of water inflow in mines must follow three principles finding out different conditions, using representative calculated parameters, and selecting the proper mathematical model. No matter which method is used to predict water inflow in mines, the amount of increment of mine groundwater must be calculated., Moreover, the rationality of calculating water inflow from the water balance must be considered in mines In order to obtain accurate water inflow data successfully, it is necessary to analyze hydrogeological conditions carefully to select the... 

That mine water chemistry data are treated and the water sources are identified by mathematical model, is the inevitable result of the combination of modern geochemistry, mathematics and computer science. The process steps of recognizing water sources are as follows (Zheng, 2004) ... 

Other Applied Sciences. There has been a dramatic rise in the power of computation and information technology. With it have come vast amounts of data in various fields of applied science and engineering. The challenge of understanding the data has led to new tools and approaches, such as data mining. Applied mathematics includes the use of mathematical models and control theory that fe-cihtate the study of epidemics, pharmacokinetics, and physiologic systems in the medical industry. In telematics, models are developed and used in the enhancement of wireless mobile communications. 

To determine the extent of bloating or expansion in an industrial rotary kiln, one must carry out laboratory tests using bench scale furnaces for the evolution kinetics and further correlation tests in a pilot rotary kiln for appropriate temperature profiles. The temporal events determined are, in turn, used to plan quarry operations for product quality control. The same data may also be useful in developing a mechanistic mathematical model that can predict temperature distribution and density changes in the raw material as they journey through the kiln (Boateng et al., 1997). Such tools have proven to be useful for the control of product quality as new mines are explored or even as different strata of the existing mine are explored for feedstock. Some of these time-temperature histories are discussed herein. 

The center of this technology is an expert system for crude assay generation. This expert system, which includes a set of mathematical algorithms based in powerful data-mining techniques and first-principle models, may generate a complete crude oil evaluation from variable levels of information available. 

There are various tools for risk prediction, ranging from complex mathematical models to a less complicated Event Tree Analysis. The following section provides a description of two types of risk prediction tools Data Mining and Failure Mode Effect Analysis (FMEA) ... 

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