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Yanagawa F - - 2009
The aim of this study was to predict the permeability through porous poly (2-hydroxyethyl methacrylate) (pHEMA) membranes of fluorescein isothiocyanate-labeled dextran molecular weight 4400 (FD-4) as a model of peptide and protein drug movement. Homogeneous standard membranes were prepared by redox polymerization. Permeability data were predicted by an artificial neural ...
Chang Chia-Lin - - 2009
Neuroprostheses, implantable or non-invasive ones, are promising techniques to enable paralyzed individuals with conditions, such as spinal cord injury or spina bifida (SB), to control their limbs voluntarily. Direct cortical control of invasive neuroprosthetic devices and robotic arms have recently become feasible for primates. However, little is known about designing ...
Wang Bin - - 2009
A method for quantitative analysis of diclofenac sodium powder on the basis of near-infrared (NIR) spectroscopy is investigated by using of orthogonal projection to latent structures (O-PLS) combined with artificial neural network (ANN). 148 batches of different concentrations diclofenac sodium samples were divided into three groups: 80 training samples, 46 ...
Albaugh Daniel R - - 2009
A back-propagation artificial neural network (ANN) was used to create a 10-fold leave-10%-out cross-validated ensemble model of high performance liquid chromatography retention index (HPLC-RI) for a data set of 498 diverse druglike compounds. A 10-fold multiple linear regression (MLR) ensemble model of the same data was developed for comparison. Molecular ...
Fatemi Mohammad Hossein - - 2009
Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for ...
Mateo F - - 2009
To study the ability of multi-layer perceptron artificial neural networks (MLP-ANN) and radial-basis function networks (RBFNs) to predict ochratoxin A (OTA) concentration over time in grape-based cultures of Aspergillus carbonarius under different conditions of temperature, water activity (a(w)) and sub-inhibitory doses of the fungicide carbendazim. A strain of A. carbonarius ...
Pal Moumita P - - 2009
This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques--(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)--were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ...
Li Ye - - 2010
Polymerase chain reaction (PCR) is one of the most powerful techniques in a variety of clinical and biological research fields. In this paper, a chemometrics approach, combining experimental design (ED) and artificial neural network (ANN), was proposed for optimization of PCR amplification of lycopene cyclase gene carRA in Blakeslea Trispora. ...
Liu Yu - - 2009
The purpose of this study was to develop an artificial neural network (ANN) for predicting lower extremity joint torques using the ground reaction force (GRF) and related parameters derived by the GRF during counter-movement jump (CMJ) and squat jump (SJ). Ten student athletes performed CMJ and SJ. Force plate and ...
Nayak Richi - - 2009
Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in ...
Sacha G M - - 2009
A technique that combines a theoretical description of the electrostatic interaction and artificial neural networks (ANNs) is used to solve an inverse problem in scanning probe microscopy setups. Electrostatic interaction curves calculated by the generalized image charge method are used to train and validate the ANN in order to estimate ...
Abdul Rahman Mohd Basyaruddin - - 2009
In this study, an artificial neural network (ANN) trained by backpropagation algorithm, Levenberg-Marquadart, was applied to predict the yield of enzymatic synthesis of dioctyl adipate. Immobilized Candida antarctica lipase B was used as a biocatalyst for the reaction. Temperature, time, amount of enzyme, and substrate molar ratio were the four ...
Zou Jinming - - 2009
The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modern drug discovery research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set ...
Fatemi M H - - 2009
In this work, the degradability rate constants of 98 alkenes by OH radicals were predicted from theoretically derived descriptors, which were calculated from the molecular structure alone by applying a quantitative structure-property relationship (QSPR) approach. For the selection of the most relevant descriptors, stepwise multiple linear regression (MLR) and genetic ...
Jalali-Heravi Mehdi - - 2009
This chapter covers a part of the spectrum of neural-network uses in analytical chemistry. Different architectures of neural networks are described briefly. The chapter focuses on the development of three-layer artificial neural network for modeling the anti-HIV activity of the HETP derivatives and activity parameters (pIC(50)) of heparanase inhibitors. The ...
Obinata Goro - - 2009
Functional electrical stimulation (FES) is useful to improve the gait of patients with peroneal nerve palsy or spastic hemiparesis after stroke. So as to apply FES to such patients, we have to have estimators for detecting the timing of phase switching in walking motion. We designed a wearable device for ...
Eken Cenker - - 2009
Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a ...
Wang Jing - - 2009
In recent years, artificial neural network is advocated in modeling complex multivariable relationships due to its ability of fault tolerance; while decision tree of data mining technique was recommended because of its richness of classification arithmetic rules and appeal of visibility. The aim of our research was to compare the ...
Babaei Sepideh - - 2009
Cardiac auscultatory proficiency of physicians is crucial for accurate diagnosis of many heart diseases. Plenty of diverse abnormal heart sounds with identical main specifications and different details representing the ambient noise are indispensably needed to train, assess and improve the skills of medical students in recognizing and distinguishing the primary ...
Chronopoulos Kostas I - - 2008
In this work artificial neural network (ANN) models are developed to estimate meteorological data values in areas with sparse meteorological stations. A more traditional interpolation model (multiple regression model, MLR) is also used to compare model results and performance. The application site is a canyon in a National Forest located ...
Mori Kentaro - - 2008
Experience with dissection of the cavernous sinus and the temporal bone is essential for training in skull base surgery, but the opportunities for cadaver dissection are very limited. A modification of a commercially available prototype three-dimensional (3D) skull base model, made by a selective laser sintering method and incorporating surface ...
Chen L J - - 2008
Nutrients in animal manure are valuable inputs in agronomic crop production. Timely and reliable information on animal manure nutrient content will facilitate the utilization of manure as organic fertilizer and reduce any associated potential environmental problems. The objective of this study was to investigate the feasibility of using multiple linear ...
Sun Yuchun - - 2009
This study explored a method for fabricating removable complete denture aided by CAD&RP technology. 3D crossing section scanner and laser scanner were respectively applied to obtain the surface data of artificial teeth, edentulous models and rims made in clinic. The vertical and horizontal relations of models were recorded before scanning ...
Jancić-Stojanović B - - 2009
Artificial neural networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes the information. In the past few years, coupling of experimental design (ED) and ANN became useful tool in the method optimization. This paper presents the application of ED-ANN in analysis ...
Kasiri M B - - 2008
In this study, estimation capacities of response surface methodology (RSM) and artificial neural network (ANN) in a heterogeneous photo-Fenton process were investigated. The zeolite Fe-ZSM5 was used as heterogeneous catalyst of the process for degradation of C.I. Acid Red 14 azo dye. The efficiency of the process was studied as ...
Hannula Manne - - 2008
In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor ...
Mazurek Sylwester - - 2009
The FT-Raman quantification of atorvastatin calcium in tablets was performed using the partial least squares (PLS), principal component regression (PCR) and counter-propagation artificial neural networks (CP-ANN) methods. To compare the predictive abilities of the elaborated models, the relative standard errors of prediction (RSEP) were calculated. The application of PLS, PCR ...
Green Michael - - 2009
Artificial neural network (ANN) ensembles have long suffered from a lack of interpretability. This has severely limited the practical usability of ANNs in settings where an erroneous decision can be disastrous. Several attempts have been made to alleviate this problem. Many of them are based on decomposing the decision boundary ...
Johnson David H - - 2009
PURPOSE: Demonstrate the ability of an artificial neural network (ANN), trained on a formulation screen of measured second virial coefficients to predict protein self-interactions for untested formulation conditions. MATERIALS AND METHODS: Protein self-interactions, quantified by the second virial coefficient, B22, were measured by self-interaction chromatography (SIC). The B22 values of ...
Sun Chih-Hung - - 2008
We have developed a simple and scalable bottom-up approach for fabricating moth-eye antireflective coatings on GaAs substrates. Monolayer, non-close-packed silica colloidal crystals are created on crystalline GaAs wafers by a spin-coating-based single-layer reduction technique. These colloidal monolayers can be used as etching masks during a BCl(3) dry-etch process to generate ...
da Costa Albuquerque Clarissa Daisy - - 2008
Biomass is an important variable in biosurfactant production process. However, such bioprocess variable, usually, is collected by sampling and determined by off-line analysis, with significant time delay. Therefore, simple and reliable on-line biomass estimation procedures are highly desirable. An artificial neural network model (ANN) is presented for the on-line estimation ...
Forberg Jakob L - - 2009
INTRODUCTION: The aim of this study was to compare different methods to predict acute coronary syndrome (ACS) using only data from a single electrocardiogram (ECG) in the emergency department (ED). METHOD: We compared the ACS prediction abilities of classical ECG criteria, human expert ECG interpretation, a logistic regression model and ...
Huang Ri-Bo - - 2009
Predicting the bioactivity of peptides and proteins is an important challenge in drug development and protein engineering. In this study we introduce a novel approach, the so-called "physics and chemistry-driven artificial neural network (Phys-Chem ANN)", to deal with such a problem. Unlike the existing ANN approaches, which were designed under ...
Marengo Emilio - - 2009
This paper reports the development of calibration models for quality control in the production of ethylene/propylene/1-butene terpolymers by the use of multivariate tools and FT-IR spectroscopy. 1-Butene concentration prediction is achieved in terpolymers by coupling FT-IR spectroscopy to multivariate regression tools. A dataset of 26 terpolymers (14 coming from a ...
Külahci Fatih - - 2009
Apart from the linear monitoring studies concerning the relationship between radon and earthquake, an artificial neural networks (ANNs) model approach is presented starting out from non-linear changes of the eight different parameters during the earthquake occurrence. A three-layer Levenberg-Marquardt feedforward learning algorithm is used to model the earthquake prediction process ...
Dogan Emrah - - 2009
Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resources' variables. The feed-forward neural network modeling technique is the most widely used ANN type in water resources applications. The main purpose of the study is to investigate the abilities of an artificial neural networks' (ANNs) model ...
Ruiz Jonatan R - - 2008
OBJECTIVE: To develop an artificial neural network (ANN)-equation to estimate maximal oxygen uptake (VO(2max)) from 20m shuttle run test (20 mSRT) performance (stage), sex, age, weight, and height in young persons. METHODS: The 20 mSRT was performed by 193 (122 boys and 71 girls) adolescents aged 13-19 years. All the ...
Torrecilla José S - - 2008
We present an optimised artificial neural network (ANN) model for predicting the melting point of a group of 97 imidazolium salts with varied anions. Each cation and anion in the model is described using molecular descriptors. Our model has a mean prediction error of 1.30%, a regression coefficient of 0.99 ...
Colak M Cengiz - - 2008
OBJECTIVE: Eight different learning algorithms used for creating artificial neural network (ANN) models and the different ANN models in the prediction of coronary artery disease (CAD) are introduced. METHODS: This work was carried out as a retrospective case-control study. Overall, 124 consecutive patients who had been diagnosed with CAD by ...
Palani Sundarambal - - 2008
Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast ...
Kjaer J - - 2008
OBJECTIVES: Genotypic interpretation systems extrapolate observed associations in datasets to predict viral susceptibility to antiretroviral drugs (ARVs) for given isolates. We aimed to develop and validate an approach using artificial neural networks (ANNs) that employ descriptors of physiochemical properties for mutations in HIV-1 protease (PR) and reverse transcriptase (RT) to ...
Peng S-Y - - 2008
Risk-stratification models based on pre-operative patient and disease characteristics are useful for providing individual patients with an insight into the potential risk of complications and mortality, for aiding the clinical decision for surgery vs non-surgical therapy, and for comparing the quality of care between different surgeons or hospitals. Our study ...
Wang Bin - - 2009
A new method orthogonal projection to latent structures (O-PLS) combined with artificial neural networks is investigated for non-destructive determination of Ampicillin powder via near-infrared (NIR) spectroscopy. The modern NIR spectroscopy analysis technique is efficient, simple and non-destructive, which has been used in chemical analysis in diverse fields. Be a preprocessing ...
Amani Amir - - 2008
The purpose of this study was to use Artificial Neural Networks (ANNs) in identifying factors, in addition to surfactant and internal phase content, that influence the particle size of nanoemulsions. The phase diagram and rheometric characteristics of a nanoemulsion system containing polysorbate 80, ethanol, medium chain triglycerides and normal saline ...
Witthayapanyanon A - - 2008
An accurate determination of the hydrophilic-lipophilic nature of surfactants plays an essential role in guiding the formulation of microemulsion with the goal of achieving low interfacial tension (IFT) and high solubilization. While several empirical models have been proposed as simple tools for predicting surfactant characteristics and microemulsion conditions, only a ...
Robinson Christopher J - - 2008
OBJECTIVE: The objective of this investigation was to test the ability of a feedforward artificial neural network (ANN) to differentiate patients who have pelvic organ prolapse (POP) from those who retain good pelvic organ support. STUDY DESIGN: Following institutional review board approval, patients with POP (n = 87) and controls ...
Vlachos Antonios - - 2008
Rice importance resides in its high consumption mainly in Asia and Africa and less in the EU. Several cultivars, both GM and non-GM, have established themselves in various regions depending mainly on the climatic and soil conditions. A high number of analytical, enzymic, and genomic analyses (instrumental) in conjunction with ...
Lin Chao-Cheng - - 2008
Although one third to one half of refractory schizophrenic patients responds to clozapine, however, there are few evidences currently that could predict clozapine response before the use of the medication. The present study aimed to train and validate artificial neural networks (ANN), using clinical and pharmacogenetic data, to predict clozapine ...
Perdiguero-Alonso Diana - - 2008
Due to the complexity of host-parasite relationships, discrimination between fish populations using parasites as biological tags is difficult. This study introduces, to our knowledge for the first time, random forests (RF) as a new modelling technique in the application of parasite community data as biological markers for population assignment of ...
Zhang Yong - - 2008
It is important to monitor quality of tobacco during the production of cigarette. Therefore, in order to scientifically control the tobacco raw material and guarantee the cigarette quality, fast and accurate determination routine chemical of constituents of tobacco, including the total sugar, reducing sugar, Nicotine, the total nitrogen and so ...
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