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Niaei A - - 2010
The catalytic activity of transition metals (Cr,Mn,Fe) supported on the Pt/gamma -Al(2)O(3) industrial catalyst was investigated to bring about the complete oxidation of 2-Propanol. Catalytic studies were carried out under atmospheric pressure in a fixed bed reactor. X-ray diffraction (XRD), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM) and ICP-AES ...
Sharif Mhd Saeed - - 2010
Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, ...
Anderson Billie - - 2010
Most discoveries of cancer biomarkers involve construction of a single model to determine predictions of survival.. 'Data-mining' techniques, such as artificial neural networks (ANNs), perform better than traditional methods, such as logistic regression. In this study, the quality of multiple predictive models built on a molecular data set for colorectal ...
Valavanis Ioannis K - - 2010
Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more ...
Piaggi Paolo - - 2010
Obesity is unanimously regarded as a global epidemic and a major contributing factor to the development of many common illnesses. Laparoscopic Adjustable Gastric Banding (LAGB) is one of the most popular surgical approaches worldwide. Yet, substantial variability in the results and significant rate of failure can be expected, and it ...
Amaral Jorge L M JL Dept. of Electronics and Telecommunications Engineering, Rio de Janeiro State University, 20550-013, RJ, Brazil. - - 2010
The purpose of this study is to develop an automatic classifier based on Artificial Neural Networks (ANNs) to help the diagnostic of Chronic Obstructive Pulmonary Disease (COPD) using forced oscillation measurements (FOT). The classifier inputs are the parameters provided by the FOT and the output is the indication if the ...
Guerra A - - 2010
A neural model based on a numerical molecular representation using CODES program to predict oral absorption of any structure is described. This model predicts both high and low-absorbed compounds with a global accuracy level of 74%. CODES/ANN methodology shows promising utilities not only as a conventional in silico tool in ...
Khataee A R - - 2010
The potential of a macroalgae Chara sp. was investigated as a viable biomaterial for biological treatment of Malachite Green (MG) solution. The effects of operational parameters such as temperature, pH, initial dye concentration, reaction time and amount of algae on biological decolorization efficiency were studied. Biological treatment of MG solution ...
Lee Hak Jong - - 2010
PURPOSE: We developed a multiple logistic regression model, an artificial neural network (ANN), and a support vector machine (SVM) model to predict the outcome of a prostate biopsy, and compared the accuracies of each model. METHOD: One thousand and seventy-seven consecutive patients who had undergone transrectal ultrasound (TRUS)-guided prostate biopsy ...
Khataee A R - - 2009
In this paper biosorption of triphenylmethane dye, C.I. Basic Green 4 (BG4), by Chlamydomonas species was investigated. The results obtained from batch experiments revealed the ability of Chlamydomonas sp. to remove BG4. The effects of operational parameters such as initial dye concentration, temperature, pH, reaction time and algal concentration on ...
Planckaert N - - 2010
The capabilities of artificial neural networks (ANNs) have been investigated for the analysis of nuclear resonant scattering (NRS) data obtained at a synchrotron source. The major advantage of ANNs over conventional analysis methods is that, after an initial training phase, the analysis is fully automatic and practically instantaneous, which allows ...
Chen L J - - 2009
Excessively applied manure contains a considerable amount of nutrient content such as nitrogen and phosphorus that could potentially pollute groundwater and soil. The present paper evaluated the use of nonlinear regression methods, such as artificial neural networks (ANN), for developing near infrared reflectance spectroscopy calibration models to predict nutrient content ...
Chakrabarti Swapan S Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA. - - 2009
Artificial neural networks (ANNs) are trained using high-throughput screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a methionine aminopeptidases inhibition study, consisted of a library of 43,347 compounds, and ...
Patra Jagdish C - - 2009
In this article, in the first part, we propose an artificial neural network-based intelligent technique to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARIs) for diabetes mellitus using two molecular descriptors, i.e., the electronegativity and molar volume of functional groups present in the main ARI lead ...
May R J - - 2010
Data splitting is an important consideration during artificial neural network (ANN) development where hold-out cross-validation is commonly employed to ensure generalization. Even for a moderate sample size, the sampling methodology used for data splitting can have a significant effect on the quality of the subsets used for training, testing and ...
Garkani-Nejad Zahra - - 2010
QSAR analysis for modeling the antileishmanial activity screening of a series of 49 nitro derivatives of Hydrazides were carried out using different Chemometrics methods. First, a large number of descriptors were calculated using Hyperchem, Mopac and Dragon softwares. Then, a suitable number of these descriptors were selected using multiple linear ...
Stephan Carsten - - 2010
OBJECTIVES: To carry out an internal validation of the retrospectively trained artificial neural network (ANN) 'ProstataClass'. METHODS: A prospectively collected database of 393 patients undergoing 8-12 core prostate biopsy was analyzed. Data of these patients were applied to the online available ANN 'ProstataClass' using the Elecsys total prostate-specific antigen (tPSA) ...
Ahadian Samad - - 2009
Molecular dynamics simulations were performed to evaluate the penetration of two different fluids (i.e., a Lennard-Jones fluid and a polymer) through a designed nanochannel. For both fluids, the length of permeation as a function of time was recorded for various wall-fluid interactions. A novel methodology, namely, the artificial neural network ...
Noori Roohollah - - 2010
Artificial neural networks (ANNs) are suitable for modeling solid waste generation. In the present study, four training functions, including resilient backpropagation (RP), scale conjugate gradient (SCG), one step secant (OSS), and Levenberg-Marquardt (LM) algorithms have been used. The main goal of this research is to develop an ANN model with ...
Togun Necla - - 2010
Anticholinesterase poisoning is an important health problem in our country, and a complete understanding of its underlying mechanisms is essential for the emergency physician. So, this study focused on two purposes. First one was aimed to investigate the biochemical analysis to determine the tissue levels of malondialdehyde (MDA), glutathione and ...
Amani Amir - - 2010
PURPOSE: The aim of this study was to identify the dominant factors affecting the stability of nanoemulsions, using artificial neural networks (ANNs). METHODS: A nanoemulsion preparation of budesonide containing polysorbate 80, ethanol, medium chain triglycerides and saline solution was designed, and the particle size of samples with various compositions, prepared ...
Singh Kunwar P - - 2010
The paper describes linear and nonlinear modeling of the wastewater data for the performance evaluation of an up-flow anaerobic sludge blanket (UASB) reactor based wastewater treatment plant (WWTP). Partial least squares regression (PLSR), multivariate polynomial regression (MPR) and artificial neural networks (ANNs) modeling methods were applied to predict the levels ...
Zhang Chuanwei - - 2009
Model-based infrared reflectrometry (MBIR) has been introduced recently for characterization of high-aspect-ratio deep trench structures in microelectronics. The success of this technique relies heavily on accurate modeling of trench structures and fast extraction of trench parameters. In this paper, we propose a modeling method named corrected effective medium approximation (CEMA) ...
Akdenur B - - 2009
In this study, electromyography signals sampled from children undergoing orthodontic treatment were used to estimate the effect of an orthodontic trainer on the anterior temporal muscle. A novel data normalization method, called the correlation- and covariance-supported normalization method (CCSNM), based on correlation and covariance between features in a data set, ...
Poynton M R - - 2009
This study compared the blood concentrations of remifentanil obtained in a previous clinical investigation with the predicted remifentanil concentrations produced by different pharmacokinetic models: a non-linear mixed effects model created by the software NONMEM; an artificial neural network (ANN) model; a support vector machine (SVM) model; and multi-method ensembles. The ...
Njubi D M - - 2010
The study is focused on the capability of artificial neural networks (ANNs) to predict next month and first lactation 305-day milk yields (FLMY305) of Kenyan Holstein-Friesian (KHF) dairy cows based on a few available test days (TD) records in early lactation. The developed model was compared with multiple linear regressions ...
Barmpalexis Panagiotis - - 2010
Artificial neural networks (ANNs) were employed in the optimization of a nimodipine zero-order release matrix tablet formulation, and their efficiency was compared to that of multiple linear regression (MLR) on an external validation set. The amounts of PEG-4000, PVP K30, HPMC K100 and HPMC E50LV were used as independent variables ...
Lin Chao-Cheng - - 2010
OBJECTIVE: Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment. METHOD: ...
Parkinson Robert J - - 2009
The extensive data requirements of three-dimensional inverse dynamics and joint modelling to estimate spinal loading prevent the implementation of these models in industry and may hinder development of advanced injury prevention standards. This work examines the potential of feed forward artificial neural networks (ANNs) as a data reduction approach and ...
Akdemir Bayram - - 2009
The aorta is the largest vessel in the systemic circuit. Its diameter is very important to guess for child before adult age, due to growing up body. Aortic diameter, one of the cardiac values, changes in time. Evaluation of the cardiac structures and generating a valid regional curve requires a ...
Khataee A R - - 2009
C.I. Basic Red 46, commonly used as a textile dye, was photocatalytically removed using supported TiO2 nanoparticles irradiated by a 30 W UV-C lamp in a batch reactor. The investigated photocatalyst was industrial Degussa P25 (crystallite mean size 21 nm) immobilized on glass beads by a heat attachment method. The ...
Elragal Hassan M - - 2009
This paper proposes a technique to improve Artificial Neural Network (ANN) prediction accuracy using Particle Swarm Optimization (PSO) combiner. A hybrid system consists of two stages with the first stage containing two ANNs. The first ANN predictor is a multi-layer feed-forward network trained with error back-propagation and the second predictor ...
Kachrimanis K - - 2010
In the present study, a simple method, based on diffuse reflectance FTIR spectroscopy (DRIFTS) and artificial neural network (ANN) modeling is developed for the simultaneous quantitative analysis of mebendazole polymorphs A-C in powder mixtures. Spectral differences between the polymorphs are elucidated by computationally assisted band assignments on the basis of ...
Gregori Dario - - 2011
We aim at evaluating how data-mining statistical techniques can be applied on medical records and administrative data of diabetes and how they differ in terms of capabilities of predicting outcomes (e.g. death). Data on 3,892 outpatient patients with a diagnosis of type 2 diabetes from the San Giovanni Battista Hospital ...
Golmohammadi Hassan - - 2009
The main aim of the present work was development of a quantitative structure-property relationship method using an artificial neural network (ANN) for predicting gas-to-olive oil partition coefficients of organic compounds. As a first step, a multiple linear regression (MLR) model was developed; the descriptors appearing in this model were considered ...
Balabin Roman M - - 2009
Artificial neural network (ANN) approach has been applied to estimate the density functional theory (DFT) energy with large basis set using lower-level energy values and molecular descriptors. A total of 208 different molecules were used for the ANN training, cross validation, and testing by applying BLYP, B3LYP, and BMK density ...
Goudarzi Nasser - - 2009
A quantitative structure-property relationship (QSPR) study was conducted to predict the adsorption coefficients of some pesticides. The successive projection algorithm feature selection (SPA) strategy was used as descriptor selection and model development method. Modeling of the relationship between selected molecular descriptors and adsorption coefficient data was achieved by linear (multiple ...
Riveros Toni - - 2009
The first reported hybrid artificial neural network-genetic algorithm (ANN-GA) approach for the optimization of on-capillary dipeptide derivatization is presented. More specifically, genetic optimization proved valuable in the determination of effective network structure with three defined parameter inputs: (i) phthalic anhydride injection volume, (ii) time of injection, and (iii) voltage, for ...
Pan Peng-min - - 2009
The weight of shelled shrimp is an important parameter for grading process. The weight prediction of shelled shrimp by contour area is not accurate enough because of the ignorance of the shrimp thickness. In this paper, a multivariate prediction model containing area, perimeter, length, and width was established. A new ...
Staudenmayer John - - 2009
The aim of this investigation was to develop and test two artificial neural networks (ANN) to apply to physical activity data collected with a commonly used uniaxial accelerometer. The first ANN model estimated physical activity metabolic equivalents (METs), and the second ANN identified activity type. Subjects (n = 24 men ...
Nurmi Pauliina - - 2010
In this study, the applicability of three modelling approaches was determined in an effort to describe complex relationships between process parameters and to predict the performance of an integrated process, which consisted of a fluidized bed bioreactor for Fe(3+) regeneration and a gravity settler for precipitative iron removal. Self-organizing maps ...
Fatemi M H - - 2009
Multiple linear regression and artificial neural networks (ANNs) as feature mapping techniques were used for the prediction of the biomagnification factor (BMF) of some organochlorine pollutants. As independent variables, or compound descriptors, the Abraham descriptors often employed in linear free energy relationships were used. Much better results were obtained from ...
Dastorani Mohammad T - - 2010
Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and ...
Aber S - - 2009
In the present work, the removal of Cr(VI) from synthetic and real wastewater using electrocoagulation (EC) process was studied. The influence of anode material, initial Cr(VI) concentration, initial pH of solution, type of electrolyte, current density and time of electrolysis was investigated. During 30 min of electrocoagulation, maximum removal efficiencies ...
Wang Dechao - - 2009
OBJECTIVE: HIV treatment failure is commonly associated with drug resistance and the selection of a new regimen is often guided by genotypic resistance testing. The interpretation of complex genotypic data poses a major challenge. We have developed artificial neural network (ANN) models that predict virological response to therapy from HIV ...
Fatemi Mohammad Hossein - - 2009
Multiple linear regression (MLR) and artificial neural network (ANN) were used to predict the migration factors of benzene derivatives in MEKC. Some topological and electronic descriptors were calculated for each solute in the data set, and then the stepwise MLR method was used to select more significant descriptors and MLR ...
Cazzaniga Massimo - - 2009
OBJECTIVE: Models based on logistic regression analysis are proposed as noninvasive tools to predict cirrhosis in chronic hepatitis C (CHC) patients. However, none showed to be sufficiently accurate to replace liver biopsy. Artificial neural networks (ANNs), providing a prediction based on nonlinear algorithms, can improve the diagnosis of cirrhosis, a ...
Tokatli Figen - - 2009
Aspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial ...
Ho Cheng-I - - 2010
A methodology based on the integration of a seismic-based artificial neural network (ANN) model and a geographic information system (GIS) to assess water leakage and to prioritize pipeline replacement is developed in this work. Qualified pipeline break-event data derived from the Taiwan Water Corporation Pipeline Leakage Repair Management System were ...
Ali Hany S M - - 2009
This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting drug nanoprecipitation using microfluidic reactors. The input variables examined were saturation levels of prednisolone, solvent and antisolvent flow rates, microreactor inlet angles and internal diameters, while particle size was the single output. ANNs ...
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