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Esteva Hugo - - 2007
The human brain has billions of neurons and connections that cannot be emulated by computers. This structure could explain the anatomical basis of typically human psychological activities like intuition or artistic creation. On the other hand, the computer-organized way of "reasoning" binary problems through systematic comparison of a large number ...
Hadjmohammadi M R - - 2007
A quantitative structure-property 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 DB-5ms fused-silica column in gas chromatography. Five descriptors selected in the MLR model are: first component WHIM index ...
Zhu Dazhou - - 2007
The nu-support vector regression (nu-SVR) was used to construct the calibration model between soluble solids content (SSC) of apples and acousto-optic tunable filter near-infrared (AOTF-NIR) spectra. The performance of nu-SVR was compared with the partial least square regression (PLSR) and the back-propagation artificial neural networks (BP-ANN). 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 Dong-Xue - - 2007
A comprehensive numerical model for distributed Bragg reflectors (DBRs) based on thin-film optics is developed. Detailed refractive-index calculations for GaN, AlN, AlGaN, and InGaN can also be included in this numerical model. This model can predict DBR performances for refractive-index variations, layer-thickness fluctuations, and the number of quarter-wave 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 time-integrated acceleration signals and subsequent use of linear regression models ...
Dou Y - - 2007
A method for simultaneous, non-destructive analysis of aspirin and phenacetin in compound aspirin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANNs 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 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November-March). The models have been developed for three 8-h 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 Min-Young - - 2007
This study was aimed at developing a modeling technique to accurately describe the hydrological interaction with non-point 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 well-known ...
Sahinkaya Erkan - - 2007
The performance of a fluidized-bed 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 (40-90 mg/L) and sulfate (1,000-1,500 mg/L) containing acidic (pH = 3.5-6) synthetic wastewater. Ethanol was supplemented ...
Fatemi M H - - 2007
The micelle-water partition coefficients of 81 organic compounds in SDS solution were predicted by quantitative structure-property 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 red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and ...
Jalali-Heravi M - - 2008
A linear and non-linear quantitative structure-activity relationship (QSAR) study is presented for modeling and predicting heparanase inhibitors' activity. A data set that consisted of 92 derivatives of 2,3-dihydro-1,3-dioxo-1H-isoindole-5-carboxylic acid, furanyl-1,3-thiazol-2-yl and benzoxazol-5-yl acetic acids is used in this study. Among a large number of descriptors, four parameters classified as physico-chemical, ...
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 ...
Ibrić 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 extended-release 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 synoptic-scale atmospheric circulation variables. This paper presents a hybrid method for climate prediction and downscaling that couples an analog, i.e., k-nearest neighbor, model to an artificial neural network ...
Serretti Alessandro - - 2007
Predicting the outcome of antidepressant treatment by pre-treatment 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 TiO2-photocatalytic 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 Long-jian - - 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 K-H - - 2007
OBJECTIVE: To evaluate several methods of predicting prostate cancer-related outcomes, i.e. nomograms, look-up tables, artificial neural networks (ANN), classification and regression tree (CART) analyses and risk-group 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 feed-forward type network models with back-propagation training algorithm were developed. Among various network configurations, 4-5-3-2 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 (FT-IR) 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 near-infrared (NIR) multivariate calibration of textile. First, a model-based regression method is proposed. It consists in genetic algorithm optimisation combined with partial least squares regression (GA-PLS). 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 single-wall carbon nanotube (SWNT) properties near the Fermi level were successfully predicted using a nearest-neighbor tight-binding model characterized by a single parameter, V1. We show however that this model fails for armchair-edge graphene nanostrips due to interactions directly across hexagons. These same interactions are found largely hidden in the ...
Zheng Fang - - 2007
Back-propagation 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 structure-activity relationships. A partial least squares (PLS) regression model was also developed ...
Chen Ho-Wen - - 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 GA Chemical Engineering Department, American University of Sharjah, Sharjah, United Arab Emirates. - - 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
Near-infrared (NIR) spectroscopy was used in simultaneous, non-destructive analysis of antipyriine and caffeine citrate tablets. Principal component artificial neural networks (PC-ANNs) were used to construct models for the analytes, using the testing set for external validation. Four pretreated spectra, namely, first-derivative, second-derivative, 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 intra-particle 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 GL Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, - - 2007
Experimental approaches for identifying T-cell epitopes are time-consuming, 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 Mechanistic-Empirical Pavement Design Guide, M-E 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 high-throughput data as provided by DNA or protein microarrays. Along with the increasing ...
Liu Chun-Chi - - 2007
BACKGROUND: Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive 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 30-2 visual fields, from 100 glaucoma patients and 116 ...
Poonnoy Poonpat - - 2007
Inputs for ANN (multihidden-layer feed-forward 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 case-control association studies. I and colleagues have previously described a method of association analysis using artificial neural networks (ANNs), whose performance compared favourably to single-marker methods. Here, the performance of ANN analysis is ...
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 square-root (SR) models are proposed together with different artificial ...
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