Results 251  300 of 802  
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Hadjmohammadi M R   2007
A quantitative structureproperty relationship (QSPR) study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques is carried out to investigate the retention time behavior of some pesticides on the DB5ms fusedsilica column in gas chromatography. Five descriptors selected in the MLR model are: first component WHIM index ...


Zhu Dazhou   2007
The nusupport vector regression (nuSVR) was used to construct the calibration model between soluble solids content (SSC) of apples and acoustooptic tunable filter nearinfrared (AOTFNIR) spectra. The performance of nuSVR was compared with the partial least square regression (PLSR) and the backpropagation artificial neural networks (BPANN). The influence of SVR ...


Quiming Noel S   2007
Retention prediction models using multiple linear regression (MLR) and artificial neural networks (ANN) were developed for adrenoreceptor agonists and antagonists chromatographed on a diol column under hydrophilic interaction chromatographic (HILIC) mode at three pH conditions (3.0, 4.0 and 5.0). Using stepwise MLR, the retention behavior of the analytes was satisfactorily ...


Wang DongXue   2007
A comprehensive numerical model for distributed Bragg reflectors (DBRs) based on thinfilm optics is developed. Detailed refractiveindex calculations for GaN, AlN, AlGaN, and InGaN can also be included in this numerical model. This model can predict DBR performances for refractiveindex variations, layerthickness fluctuations, and the number of quarterwave stack pairs ...


Rothney Megan P   2007
Accelerometers are a promising tool for characterizing physical activity patterns in free living. The major limitation in their widespread use to date has been a lack of precision in estimating energy expenditure (EE), which may be attributed to the oversimplified timeintegrated acceleration signals and subsequent use of linear regression models ...


Dou Y   2007
A method for simultaneous, nondestructive analysis of aspirin and phenacetin in compound aspirin tablets with different concentrations has been developed by principal component artificial neural networks (PCANNs) on nearinfrared (NIR) spectroscopy. In PCANNs models, the spectra data were first analyzed by principal component analysis (PCA). Then the scores of the ...


Nagendra S M Shiva   2008
This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8h average CO concentration using 1h maximum predicted CO data for the critical (winter) period (NovemberMarch). The models have been developed for three 8h groupings of 10 P.M. to 6 A.M., ...


Sofu A   2007
Changes in the physical, chemical, and microbiological structure of yogurt determine the storage and shelf life of the product. In this study, microbial counts and pH values of yogurt during storage were determined at d 1, 7, and 14. Simultaneously, image processing of yogurt was digitized by using a machine ...


Kim MinYoung   2007
This study was aimed at developing a modeling technique to accurately describe the hydrological interaction with nonpoint pollutants using Artificial Neural Networks (ANNs). Rainfall, surface discharge water, and nutrient concentrations (total nitrogen and total phosphorus) were monitored and used for ANN computation. A comparison study was conducted for two wellknown ...


Sahinkaya Erkan   2007
The performance of a fluidizedbed reactor (FBR) based sulfate reducing bioprocess was predicted using artificial neural network (ANN). The FBR was operated at high (65 degrees C) temperature and it was fed with iron (4090 mg/L) and sulfate (1,0001,500 mg/L) containing acidic (pH = 3.56) synthetic wastewater. Ethanol was supplemented ...


Fatemi M H   2007
The micellewater partition coefficients of 81 organic compounds in SDS solution were predicted by quantitative structureproperty relationship method. The multiple linear regression (MLR) and artificial neural network (ANN) techniques were used to build linear and nonlinear model, respectively. In this work the proposed QSPR models, both by MLR and ANN, ...


Janik L J   2007
This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in redgrape homogenates from their visiblenearinfrared (VisNIR) spectra. The PLS prediction of anthocyanin concentrations for newseason samples from VisNIR spectra was characterised by regression nonlinearity and ...


JalaliHeravi M   2008
A linear and nonlinear quantitative structureactivity relationship (QSAR) study is presented for modeling and predicting heparanase inhibitors' activity. A data set that consisted of 92 derivatives of 2,3dihydro1,3dioxo1Hisoindole5carboxylic acid, furanyl1,3thiazol2yl and benzoxazol5yl acetic acids is used in this study. Among a large number of descriptors, four parameters classified as physicochemical, ...


Corzo Gerald   2007
Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of ...


IbriÄ‡ Svetlana   2007
This study had two aims. Firstly, we wanted to model the effects of the percentage of Eudragit RS PO and compression pressure as the most important process and formulation variables on the time course of drug release from extendedrelease matrix aspirin tablets. Secondly, we investigated the possibility of predicting drug ...


Cannon Alex J   2007
Synoptic downscaling models are used in climatology to predict values of weather elements at one or more stations based on values of synopticscale atmospheric circulation variables. This paper presents a hybrid method for climate prediction and downscaling that couples an analog, i.e., knearest neighbor, model to an artificial neural network ...


Clinical prediction of antidepressant response in mood disorders: linear multivariate vs. neural ...
Serretti Alessandro   2007
Predicting the outcome of antidepressant treatment by pretreatment features would be of great usefulness for clinicians as up to 50% of major depressives may not have a satisfactory response in spite of adequate trials of antidepressant drugs. In the present article we compared a linear multivariate model of predictors with ...


Emilio Carina A   2007
A chemometric study on the TiO2photocatalytic degradation of nitrilotriacetic acid (NTA) in aqueous media under UV radiation has been carried out taking into account the multiple variables that take part in the system. To save redundant number of experiments, the system has been managed under chemometric techniques for several variables ...


Chen Longjian   2007
AIM: To develop an artificial neural network (ANN) model for predicting skin permeability (log K(p)) of new chemical entities. METHODS: A large dataset of 215 experimental data points was compiled from the literature. The dataset was subdivided into 5 subsets and 4 of them were used to train and validate ...


Chun Felix KH   2007
OBJECTIVE: To evaluate several methods of predicting prostate cancerrelated outcomes, i.e. nomograms, lookup tables, artificial neural networks (ANN), classification and regression tree (CART) analyses and riskgroup stratification (RGS) models, all of which represent valid alternatives. METHODS: We present four direct comparisons, where a nomogram was compared to either an ANN, ...


Serpen Arda   2007
The artificial neural network (ANN) modeling approach was used to predict acrylamide formation and browning ratio (%) in potato chips as influenced by time x temperature covariants. A series of feedforward type network models with backpropagation training algorithm were developed. Among various network configurations, 4532 configuration was found as the ...


Lasch Peter   2007
In this report the applicability of an improved method of image segmentation of infrared microspectroscopic data from histological specimens is demonstrated. Fourier transform infrared (FTIR) microspectroscopy was used to record hyperspectral data sets from human colorectal adenocarcinomas and to build up a database of spatially resolved tissue spectra. This database ...


Durand A   2007
In this work, different approaches for variable selection are studied in the context of nearinfrared (NIR) multivariate calibration of textile. First, a modelbased regression method is proposed. It consists in genetic algorithm optimisation combined with partial least squares regression (GAPLS). The second approach is a relevance measure of spectral variables ...


Annadurai Gurusamy   2007
Biodegradation of phenol using Pseudomonas pictorum (NICM 2074) a potential biodegradant of phenol was investigated for its degrading potential under different operating conditions. The neural network input parameter set consisted of the same set of four levels of maltose (0.025, 0.05, 0.075 g/l), phosphate (3, 12.5, 22 g/l), pH (7, ...


Zhang Ya Xiong   2007
Two clinical data sets were applied for pattern recognition in order to discover the correlation between urinary nucleoside profiles and tumours. One data set contains 168 clinical urinary samples, of which 84 specimens are from female thyroid cancer patients (malignant tumour group), and the other samples were collected from healthy ...


Kang S H   2007
An ordinary sigmoid E(max) model could not predict overshoot of electroencephalographic approximate entropy (ApEn) during recovery from remifentanil effect in our previous study. The aim of this study was to evaluate the ability of an artificial neural network (ANN) to predict ApEn overshoot and to evaluate the predictive performance of ...


White Carter T   2007
Many singlewall carbon nanotube (SWNT) properties near the Fermi level were successfully predicted using a nearestneighbor tightbinding model characterized by a single parameter, V1. We show however that this model fails for armchairedge graphene nanostrips due to interactions directly across hexagons. These same interactions are found largely hidden in the ...


Zheng Fang   2007
Backpropagation artificial neural networks (ANNs) were trained on a dataset of 104 VMAT2 ligands with experimentally measured log(1/K(i)) values. A set of related descriptors, including topological, geometrical, GETAWAY, aromaticity, and WHIM descriptors, was selected to build nonlinear quantitative structureactivity relationships. A partial least squares (PLS) regression model was also developed ...


Chen HoWen   2007
This paper applies artificial neural network (ANN) to model the observed effluent quality data. The ANN's structure, involving the number of hidden layer and node and their connection, is determined endogenously by resorting to the compromise of data cost minimization and prediction accuracy maximization. To obtain the best compromise possible, ...


Tompos András   2007
In this study, artificial neural networks (ANNs) were used to reveal a quantitative relationship between catalytic composition and catalytic activity. This relationship was predefined using a hypothetical experimental space described by a multidimensional polynomial. The predictive ability of ANNs was investigated, i.e. an attempt was done to evaluate how ANNs ...


Husseini Ghaleb A   2007
This paper models steady state acoustic release of Doxorubicin (Dox) from Pluronic P105 micelles using Artificial Neural Networks (ANN). Previously collected release data were compiled and used to train, validate, and test an ANN model. Sensitivity analysis was then performed on the following operating conditions: ultrasonic frequency, power density, Pluronic ...


Dou Ying   2007
Nearinfrared (NIR) spectroscopy was used in simultaneous, nondestructive analysis of antipyriine and caffeine citrate tablets. Principal component artificial neural networks (PCANNs) were used to construct models for the analytes, using the testing set for external validation. Four pretreated spectra, namely, firstderivative, secondderivative, standard normal variate (SNV) and multiplicative scatter correction ...


Du Xueling   2007
Herein, two models, the general rate model taking into account convection, axial dispersion, external and intraparticle mass transfer resistances and particle size distribution (PSD) and the artificial neural network model (ANN) were developed to describe solanesol adsorption process in packed column using macroporous resins. First, Static equilibrium experiments and kinetic ...


Zhang Guang Lan   2007
Experimental approaches for identifying Tcell epitopes are timeconsuming, costly and not applicable to the large scale screening. Computer modeling methods can help to minimize the number of experiments required, enable a systematic scanning for candidate major histocompatibility complex (MHC) binding peptides and thus speed up vaccine development. We developed a ...


Wolf Gundula   2007
An improved method for deconvoluting complex spectral maps from bidimensional fluorescence monitoring is presented, relying on a combination of principal component analysis (PCA) and feedforward artificial neural networks (ANN). With the aim of reducing ANN complexity, spectral maps are first subjected to PCA, and the scores of the retained principal ...


Saberali S F   2007
This study shows the ability of Artificial Neural Network (ANN) technology to be used for the prediction of the correlation between common lambsquarters (Chenopodium album L.) population, corn (Zea mays L.) population and planting pattern in different days after planting (as inputs) with common lambsquarters biomass production (as output). The ...


Hanittinan, Wichai
The resilient modulus (MR) of subgrade or unbound materials is a key parameter current and proposed methods for predicting the structural response of pavements (the 2002 MechanisticEmpirical Pavement Design Guide, ME PDG). Backpropagation neural network algorithms were adopted to construct artificial neural networks (ANNs) were then used to predict the ...


Parthiban Rangasamy   2007
Anaerobic treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor (ATFBR) with a mesoporous granular activated carbon (GAC) as a support material. The experimental protocol was defined to examine the effect of the maximum organic loading rate (OLR), hydraulic retention time (HRT), the efficiency ...


Linder Roland   2007
Systems biology has enjoyed explosive growth in both the number of people participating in this area of research and the number of publications on the topic. The field of systems biology encompasses the in silico analysis of highthroughput data as provided by DNA or protein microarrays. Along with the increasing ...


Liu ChunChi   2007
BACKGROUND: Genomewide identification of specific oligonucleotides (oligos) is a computationallyintensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence ...


Bizios Dimitrios   2007
PURPOSE: To evaluate and confirm the performance of an artificial neural network (ANN) trained to recognize glaucomatous visual field defects, and compare its diagnostic accuracy with that of other algorithms proposed for the detection of visual field loss. METHODS: SITA Standard 302 visual fields, from 100 glaucoma patients and 116 ...


Poonnoy Poonpat   2007
Inputs for ANN (multihiddenlayer feedforward artificial neural network) models were drying time (t(i + 1)), initial temperature (T0), moisture content (MC0), microwave power, and vacuum pressure. The outputs were temperature (T(i + 1)) and moisture content (MC(i + 1)) at a given t(i + 1). After training the ANN models ...


Curtis David   2007
BACKGROUND: Debate remains as to the optimal method for utilising genotype data obtained from multiple markers in casecontrol association studies. I and colleagues have previously described a method of association analysis using artificial neural networks (ANNs), whose performance compared favourably to singlemarker methods. Here, the performance of ANN analysis is ...


Moreau R   2007
This paper presents a method to evaluate a gesture carried out by a resident obstetrician doctors by comparing it to a gesture carried out by an expert obstetrician doctors. The studied gesture is the forceps blade placement. Residents were recorded on a childbirth simulator while placing forceps blades. Their paths ...


Bugliosi R   2007
This paper describes the further results of the study that has been described in session 5 of the 58th International Symposium on Crop Protection (Ghent 2006). Since then our attention has been focused on verifying the previous communication results working on a two years basis data set belonging to a ...


Liew Gerald   2007
PURPOSE: The arteriole to venule ratio (AVR) is widely used in studies of the associations of retinal microvascular disease with systemic and ocular outcomes. This is a discussion of the limitations of AVR; a comparison of its predictive information with that of its components, arteriolar and venular caliber; and a ...


Grossi Enzo   2007
The author describes a refiguration of medical thought that originates from nonlinear dynamics and chaos theory. The coupling of computer science and these new theoretical bases coming from complex systems mathematics allows the creation of "intelligent" agents capable of adapting themselves dynamically to problems of high complexity: the artificial neural ...


Bhasin Manoj   2007
In the present study, a systematic attempt has been made to develop an accurate method for predicting MHC class I restricted T cell epitopes for a large number of MHC class I alleles. Initially, a quantitative matrix (QM)based method was developed for 47 MHC class I alleles having at least ...


Greer Braden   2007
Herein we have set forth a detailed method to analyze microarray data using artificial neural networks (ANN) for the purpose of classification, diagnosis, or prognosis. All aspects of this analysis can be carried out online via a website. The reader is guided through each step of the analysis including data ...


Valero A   2007
Different secondary modeling approaches for the estimation of Listeria monocytogenes growth rate as a function of temperature (4 to 30 degrees C), citric acid (0% to 0.4% w/v), and ascorbic acid (0% to 0.4% w/v) are presented. Response surface (RS) and squareroot (SR) models are proposed together with different artificial ...


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