Applications ANNs approach

In recent decades, much attention has been paid to the application of artificial neural networks as a tool for spectral interpretation (see, e.g.. Refs. [104, 105]). The ANN approach app]ied to vibrational spectra allows the determination of adequate functional groups that can exist in the sample, as well as the complete interpretation of spectra. Elyashberg [106] reported an overall prediction accuracy using ANN of about 80 % that was achieved for general-purpose approaches. Klawun and Wilkins managed to increase this value to about 95% [107]. 

Aqueous solubility is selected to demonstrate the E-state application in QSPR studies. Huuskonen et al. modeled the aqueous solubihty of 734 diverse organic compounds with multiple linear regression (MLR) and artificial neural network (ANN) approaches [27]. The set of structural descriptors comprised 31 E-state atomic indices, and three indicator variables for pyridine, ahphatic hydrocarbons and aromatic hydrocarbons, respectively. The dataset of734 chemicals was divided into a training set ( =675), a vahdation set (n=38) and a test set (n=21). A comparison of the MLR results (training, r =0.94, s=0.58 vahdation r =0.84, s=0.67 test, r =0.80, s=0.87) and the ANN results (training, r =0.96, s=0.51 vahdation r =0.85, s=0.62 tesL r =0.84, s=0.75) indicates a smah improvement for the neural network model with five hidden neurons. These QSPR models may be used for a fast and rehable computahon of the aqueous solubihty for diverse orgarhc compounds. 

Di Pietro, L. 1998. Strategies for describing preferential flow The continuum approach and cellular automaton fluids, p. 437-453. In H.M. Selim and L. Ma (ed.) Physical non-equilibrium in soils, modelling and applications. Ann Arbor Press, MI. 

The use of the ANN approach was demonstrated for the first time on polaro-graphic data [85,86], It was first applied to the evaluation of equilibria (using ANN and experimental design methods) as a tool in electrochemical data evaluation for fully inert metal complexes [85] and later on for fully dynamic (labile) metal complexes [86], The general application in chemical equilibria, for the evaluation of potentiometric or nuclear magnetic resonance (NMR) data, for example, was shown recently [87], where it was stressed that the method is general and can also be applied in extraction. 

These capabilities of ANNs make them a unique tool for a large number of industrial applications. In this chapter, the authors demonstrate, with case studies, the advantages of using this approach to physical property predictions in polymer science. 

For PyMS to be used for (1) routine identification of microorganisms and (2) in combination with ANNs for quantitative microbiological applications, new spectra must be comparable with those previously collected and held in a data base.127 Recent work within our laboratory has demonstrated that this problem may be overcome by the use of ANNs to correct for instrumental drift. By calibrating with standards common to both data sets, ANN models created using previously collected data gave accurate estimates of determi-nand concentrations, or bacterial identities, from newly acquired spectra.127 In this approach calibration samples were included in each of the two runs, and ANNs were set up in which the inputs were the 150 new calibration masses while the outputs were the 150 old calibration masses. These associative nets could then by used to transform data acquired on that one day to data acquired at an earlier data. For the first time PyMS was used to acquire spectra that were comparable with those previously collected and held in a database. In a further study this neural network transformation procedure was extended to allow comparison between spectra, previously collected on one machine, with spectra later collected on a different machine 129 thus calibration transfer by ANNs was affected. Wilkes and colleagues130 have also used this strategy to compensate for differences in culture conditions to construct robust microbial mass spectral databases. 

In an early application of in silico approaches to predict human VD, Ritschel and coworkers described an approach using artificial neural networks (ANN), in this case for VDp [34]. However, this was not a truly in silico-only approach as the ANN that yielded accurate predictions of human VD required animal pharmacokinetic data as input parameters, along with in vitro data (protein binding and logP). 

Mckeigue, P.M., Carpenter, J., Parra, E.J., and Shriver, M.D. (2000) Estimation of admixture and detection of linkage in admixed populations by a Bayesian approach application to African-American populations. Ann. Hum. Genet. 64, 171-186. 

Applications to batch processes have been less common, but there has been some work done on the use of an ANN to control autoclave curing. Joseph et al. used an ANN successfully to cure a part, reducing cure times and improving qualities, such as thickness control and void content [37], When more variables were included, however, the computational problem became intractable. This particular approach to using an ANN broke variables down into time, temperature, and pressure recipes, which, as noted in the Section 15.3.2, can lead to exponential growth of necessary training cases. 

The approach to power being put forward here is therefore essenti ly transformational . Transformational leadership was explained in Chapter 3 as gaining compliance by means of getting others to change their viewpoint, attitudes or beliefs. This concept, which has been developed extensively by Noel Tichy and Mary Ann Devanna (1986), is equally applicable to the use of personal power. 

Engineered variants of enzymes could be another approach in biosensor design for the discrimination and detection of various enzyme-inhibiting compounds when used in combination with chemometric data analysis using ANN. The crucial issues that should be addressed in the development of new analytical methods are the possibility of simultaneous and discriminative monitoring of several contaminants in a multi-component sample and the conversion of the biosensing systems to marketable devices suitable for large-scale environmental and food applications. 

Refs. [i] Clark LC, Lyons C (1962) Ann NY Acad Sci 102 29 [ii] Turner APT, Karuhe I, Wilson GS (1987) Biosensors fundamentals and applications. Oxford University Press, Oxford [in] Scheller FS, Schubert F (1992) Biosensors. Elsevier, Amsterdam [iv] Heller A (1990) Acc Chem Res 23 128 [v] Scheller F, Wollenberger U (2002) Enzyme electrodes. Ire Bard AJ, Stratmann M, Wilson GS (eds) Bioelectrochemistry. Encyclopedia of electrochemistry, vol. 9. Wiley-VCH, Weinheim [vi] Scheller F, LisdatF, Wollenberger U (2005) Application of electrically contacted enzymes for biosensors. In Willnerl, KatzE (eds) Bioelectronics from theory to applications. Wiley-VCH, Weinheim [vii] Cass AEG (ed) (1990) Biosensors a practical approach. Oxford University Press, Oxford... 

The application of ANN for a representation of reaction kinetics can be a very promising method to solve modelling problems. Besides intrinsic kinetics also internal diffusion resistances can be included into the neural network based model. This approach significantly reduces the time required for experimental studies. Despite that neural networks do not help to understand and develop a real reaction mechanism, they make the prediction of the reactor behaviour possible. This approach can be essential in the case of complex or uncertain kinetics - e.g. for polymerization reactions. In this study the neural network approach has been tested for a batch reactor. A trained network can be successfully implemented into any type reactor model. 

BIA/CAR] Biader Ceipidor, U., Carunchio, V., Pascucci, G., An approach to the study of complex formation by solvent modification. An application to the nickel(ll)-chloride system, Ann. Chim. (Rome), 66, (1976), 727-740. Cited on pages 152, 376. 

Hundt W, Leuenberger D, Rehsteiner F, Gygax P (1994) An approach to monitoring of the grinding process using acoustic emission (AE) technique. CIRP Ann Manuf Technol 43(l) 295-298 hiasaki I (1998) Application of acoustic emission sensor for monitoring machining processes. Ultrason Int 36(l-5) 273-281... 

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