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Artificial neural networks

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https://www.readbyqxmd.com/read/28441429/correction-comparison-of-prediction-model-for-cardiovascular-autonomic-dysfunction-using-artificial-neural-network-and-logistic-regression-analysis
#1
Zi-Hui Tang, Juanmei Liu, Fangfang Zeng, Zhongtao Li, Xiaoling Yu, Linuo Zhou
[This corrects the article DOI: 10.1371/journal.pone.0070571.].
2017: PloS One
https://www.readbyqxmd.com/read/28438906/novel-screening-tool-for-stroke-using-artificial-neural-network
#2
Vida Abedi, Nitin Goyal, Georgios Tsivgoulis, Niyousha Hosseinichimeh, Raquel Hontecillas, Josep Bassaganya-Riera, Lucas Elijovich, Jeffrey E Metter, Anne W Alexandrov, David S Liebeskind, Andrei V Alexandrov, Ramin Zand
BACKGROUND AND PURPOSE: The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting. METHODS: Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.5 hours of symptoms onset, in 2 tertiary care stroke centers were randomized for inclusion in the model...
April 24, 2017: Stroke; a Journal of Cerebral Circulation
https://www.readbyqxmd.com/read/28437616/neural-network-and-nearest-neighbour-algorithms-for-enhancing-sampling-of-molecular-dynamics
#3
Raimondas Galvelis, Yuji Sugita
The free energy calculations of complex chemical and biological systems with molecular dynamics (MD) are inefficient due to multiple local minima separated by high-energy barriers. The minima can be escaped using an enhanced sampling method such as metadynamics, which apply bias (i.e. importance sampling) along a set of collective variables (CV), but the maximum number of CVs (or dimensions) is severely limited. We propose a high-dimensional bias potential method (NN2B) based on two machine learning algorithms: the nearest neighbour density estimator (NNDE) and the artificial neural network (ANN) for the bias potential approximation...
April 24, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28436147/rapid-detection-of-six-phosphodiesterase-type-5-enzyme-inhibitors-in-health-care-products-using-thin-layer-chromatography-and-surface-enhanced-raman-spectroscopy-combined-with-bp-neural-network
#4
Xiaopeng Hu, Guozhen Fang, Ailing Han, Yunpeng Fu, Rui Tong, Shuo Wang
A novel facile method for the detection of the phosphodiesterase type 5 enzyme inhibitors added illegally into health products was established using thin-layer chromatography and surface enhanced Raman spectroscopy combined with BP neural network. When the detecting conditions were optimized in detail, a repetitive adding procedure of silver colloids with the total amount keeping constant was used to improve the enhancement effect of surface enhanced Raman spectroscopy. According to the main Raman peaks and the retention factor of analyte, the data predictive model was established...
April 24, 2017: Journal of Separation Science
https://www.readbyqxmd.com/read/28434057/predictive-control-of-intersegmental-tarsal-movements-in-an-insect
#5
Alicia Costalago-Meruelo, David M Simpson, Sandor M Veres, Philip L Newland
In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ...
April 22, 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/28431389/prediction-of-size-fractionated-airborne-particle-bound-metals-using-mlr-bp-ann-and-svm-analyses
#6
Xiang'zi Leng, Jinhua Wang, Haibo Ji, Qin'geng Wang, Huiming Li, Xin Qian, Fengying Li, Meng Yang
Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters...
April 6, 2017: Chemosphere
https://www.readbyqxmd.com/read/28430263/energetic-materials-identification-by-laser-induced-breakdown-spectroscopy-combined-with-artificial-neural-network
#7
Amir Hossein Farhadian, Masoud Kavosh Tehrani, Mohammad Hossein Keshavarz, Seyyed Mohammad Reza Darbani
In this study, for the first time to the best of our knowledge, a combination of the laser-induced breakdown spectroscopy (LIBS) technique and artificial neural network (ANN) analysis has been implemented for the identification of energetic materials, including TNT, RDX, black powder, and propellant. Also, aluminum, copper, inconel, and graphite have been used for more accurate investigation and comparison. After the LIBS test and spectrum acquisition on all samples in both air and argon ambient, optimized neural networks were designed by LIBS data...
April 20, 2017: Applied Optics
https://www.readbyqxmd.com/read/28427684/rapid-and-high-capacity-ultrasonic-assisted-adsorption-of-ternary-toxic-anionic-dyes-onto-mof-5-activated-carbon-artificial-neural-networks-partial-least-squares-desirability-function-and-isotherm-and-kinetic-study
#8
Hanieh Askari, Mehrorang Ghaedi, Kheibar Dashtian, Mohammad Hossein Ahmadi Azghandi
The present paper focused on the ultrasonic assisted simultaneous removal of fast green (FG), eosin Y (EY) and quinine yellow (QY) from aqueous media following using MOF-5 as a metal organic framework and activated carbon hybrid (AC-MOF-5). The structure and morphology of AC-MOF-5 was identified by SEM, FTIR and XRD analysis. The interactive and main effects of variables such as pH, initial dyes concentration (mgL(-1)), adsorbent dosage (mg) and sonication time (min) on removal percentage were studied by central composite design (CCD), subsequent desirability function (DF) permit to achieved real variable experimental condition...
July 2017: Ultrasonics Sonochemistry
https://www.readbyqxmd.com/read/28426739/artificial-neural-network-and-sarima-based-models-for-power-load-forecasting-in-turkish-electricity-market
#9
Ömer Özgür Bozkurt, Göksel Biricik, Ziya Cihan Tayşi
Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule...
2017: PloS One
https://www.readbyqxmd.com/read/28422856/a-cross-sectional-evaluation-of-meditation-experience-on-electroencephalography-data-by-artificial-neural-network-and-support-vector-machine-classifiers
#10
Yu-Hao Lee, Ya-Ju Hsieh, Yung-Jong Shiah, Yu-Huei Lin, Chiao-Yun Chen, Yu-Chang Tyan, JiaCheng GengQiu, Chung-Yao Hsu, Sharon Chia-Ju Chen
To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-sectional analysis to evaluate the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant normalizing scaling are manipulated as the evaluating variables for the detection of accuracy...
April 2017: Medicine (Baltimore)
https://www.readbyqxmd.com/read/28420977/development-and-training-of-a-neural-controller-for-hind-leg-walking-in-a-dog-robot
#11
Alexander Hunt, Nicholas Szczecinski, Roger Quinn
Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal's body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few "synthetic nervous systems" have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28420275/beta-hebbian-learning-as-a-new-method-for-exploratory-projection-pursuit
#12
Héctor Quintián, Emilio Corchado
In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback...
March 16, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28420077/recognition-of-the-duration-and-prediction-of-insect-prevalence-of-stored-rough-rice-infested-by-the-red-flour-beetle-tribolium-castaneum-herbst-using-an-electronic-nose
#13
Sai Xu, Zhiyan Zhou, Keliang Li, Sierra Mari Jamir, Xiwen Luo
The purpose of this research is to explore the feasibility of applying an electronic nose for the intelligent monitoring of injurious insects in a stored grain environment. In this study, we employed an electronic nose to sample rough rice that contained three degrees of red flour beetle (Tribolium castaneum Herbst) infestation for different durations-light degree (LD), middle degree (MD), and heavy degree (HD)-and manually investigated the insect situation at the same time. Manual insect situation investigation shows that, in all three rice treatments, the insect amounts gradually decreased after infestation...
April 14, 2017: Sensors
https://www.readbyqxmd.com/read/28418055/application-of-machine-statistical-learning-artificial-intelligence-and-statistical-experimental-design-for-the-modeling-and-optimization-of-methylene-blue-and-cd-ii-removal-from-a-binary-aqueous-solution-by-natural-walnut-carbon
#14
H Mazaheri, M Ghaedi, M H Ahmadi Azqhandi, A Asfaram
Analytical chemists apply statistical methods for both the validation and prediction of proposed models. Methods are required that are adequate for finding the typical features of a dataset, such as nonlinearities and interactions. Boosted regression trees (BRTs), as an ensemble technique, are fundamentally different to other conventional techniques, with the aim to fit a single parsimonious model. In this work, BRT, artificial neural network (ANN) and response surface methodology (RSM) models have been used for the optimization and/or modeling of the stirring time (min), pH, adsorbent mass (mg) and concentrations of MB and Cd(2+) ions (mg L(-1)) in order to develop respective predictive equations for simulation of the efficiency of MB and Cd(2+) adsorption based on the experimental data set...
April 18, 2017: Physical Chemistry Chemical Physics: PCCP
https://www.readbyqxmd.com/read/28415481/modeling-of-drug-release-behavior-of-ph-and-temperature-sensitive-poly-nipaam-co-aac-ipn-hydrogels-using-response-surface-methodology-and-artificial-neural-networks
#15
Sanogo Brahima, Cihangir Boztepe, Asim Kunkul, Mehmet Yuceer
An interpenetrated polymer network (IPN) poly(NIPAAm-co-AAc) hydrogel was synthesized by two polymerization method: emulsion and solution polymerization. The pH- and temperature-sensitive hydrogel was loaded by swelling with riboflavin drug, a B2 vitamin. The release of riboflavin as a function of time has been achieved under different pH and temperature environments. The determination of experimental conditions and the analysis of drug delivery results were achieved using response surface methodology (RSM)...
June 1, 2017: Materials Science & Engineering. C, Materials for Biological Applications
https://www.readbyqxmd.com/read/28413358/the-soft-computing-based-approach-to-investigate-allergic-diseases-a-systematic-review
#16
Gennaro Tartarisco, Alessandro Tonacci, Paola Lucia Minciullo, Lucia Billeci, Giovanni Pioggia, Cristoforo Incorvaia, Sebastiano Gangemi
BACKGROUND: Early recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field. OBJECTIVE: The aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases...
2017: Clinical and Molecular Allergy: CMA
https://www.readbyqxmd.com/read/28412664/new-model-for-prediction-binary-mixture-of-antihistamine-decongestant-using-artificial-neural-networks-and-least-squares-support-vector-machine-by-spectrophotometry-method
#17
Shirin Mofavvaz, Mahmoud Reza Sohrabi, Alireza Nezamzadeh-Ejhieh
In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated...
April 5, 2017: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
https://www.readbyqxmd.com/read/28412403/a-further-development-of-the-qnar-model-to-predict-the-cellular-uptake-of-nanoparticles-by-pancreatic-cancer-cells
#18
Feng Luan, Lili Tang, Lihong Zhang, Shuang Zhang, Maykel Cruz Monteagudo, M Natália D S Cordeiro
Nanotechnology has led to the development of new nanomaterials with unique properties and a wide variety of applications. In the present study, we focused on the cellular uptake of a group of nanoparticles with a single metal core by pancreatic cancer cells, which has been studied by Yap et al. (Rsc Advances, 2012, 2 (2):8489-8496) using classification models. In this work, the development of a further Quantitative Nanostructure-Activity Relationship (QNAR) model was performed by linear multiple linear regression (MLR) and nonlinear artificial neural network (ANN) techniques to accurately predict the cellular uptake values of these compounds by dividing them into three groups...
April 12, 2017: Food and Chemical Toxicology
https://www.readbyqxmd.com/read/28407117/nnalign-a-platform-to-construct-and-evaluate-artificial-neural-network-models-of-receptor-ligand-interactions
#19
Morten Nielsen, Massimo Andreatta
Peptides are extensively used to characterize functional or (linear) structural aspects of receptor-ligand interactions in biological systems, e.g. SH2, SH3, PDZ peptide-recognition domains, the MHC membrane receptors and enzymes such as kinases and phosphatases. NNAlign is a method for the identification of such linear motifs in biological sequences. The algorithm aligns the amino acid or nucleotide sequences provided as training set, and generates a model of the sequence motif detected in the data. The webserver allows setting up cross-validation experiments to estimate the performance of the model, as well as evaluations on independent data...
April 12, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28406471/real-time-vision-based-road-sign-recognition-using-an-artificial-neural-network
#20
Kh Tohidul Islam, Ram Gopal Raj
Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are 'traffic light ahead' or 'pedestrian crossing' indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information...
April 13, 2017: Sensors
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