keyword
MENU ▼
Read by QxMD icon Read
search

"Artificial neural network"

keyword
https://www.readbyqxmd.com/read/28431389/prediction-of-size-fractionated-airborne-particle-bound-metals-using-mlr-bp-ann-and-svm-analyses
#1
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
#2
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
#3
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
#4
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
#5
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/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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
https://www.readbyqxmd.com/read/28398138/examining-human-behavior-in-video-games-the-development-of-a-computational-model-to-measure-aggression
#14
Richard Lamb, Leonard Annetta, Douglas Hoston, Marina Shapiro, Benjamin Matthews
Video games with violent content have raised considerable concern in popular media and within academia. Recently, there has been considerable attention regarding the claim of the relationship between aggression and video game play. The authors of this study propose the use of a new class of tools developed via computational models to allow examination of the question; is there is a relationship between violent video games and aggression. The purpose of this study is to computationally model and compare the General Aggression Model with the Diathesis Mode of Aggression related to the play of violent content in video games...
April 11, 2017: Social Neuroscience
https://www.readbyqxmd.com/read/28397061/a-neural-controller-for-online-laser-power-adjustment-during-the-heat-therapy-process-in-the-presence-of-nanoparticles
#15
S Ehsan Razavi
The present research evaluated the efficiency of a control approach to control the temperature of a breast tumor mass in the presence of nanoparticles exposed to laser radiation. However, if the radiation is carried out in open loop manner it may result in excessive temperature rise healthy cells that exist in the vicinity of tumor's cells. This may lead to the death of healthy cells. So, using closed loop control methods is necessary to guarantee the preservation of healthy cells during the period of radiation...
April 10, 2017: Australasian Physical & Engineering Sciences in Medicine
https://www.readbyqxmd.com/read/28396167/comparative-study-of-ann-and-rsm-for-simultaneous-optimization-of-multiple-targets-in-fenton-treatment-of-landfill-leachate
#16
Mohammad Reza Sabour, Allahyar Amiri
In this study, two modeling methods, namely response surface methodology (RSM) and artificial neural networks (ANN), were applied to investigate the Fenton process performance in landfill leachate treatment. For this purpose, three targets were used to cover different aspects of post-treatment products such as supernatant and sludge: mass content ratio (MCR) and mass removal efficiency (MRE). It was observed that coagulation was dominant mechanism in all responses. The proposed models were evaluated based on correlation coefficient (R(2)), root mean square error (RMSE) and average error (AE) and both models seemed satisfactory...
April 7, 2017: Waste Management
https://www.readbyqxmd.com/read/28394938/words-from-spontaneous-conversational-speech-can-be-recognized-with-human-like-accuracy-by-an-error-driven-learning-algorithm-that-discriminates-between-meanings-straight-from-smart-acoustic-features-bypassing-the-phoneme-as-recognition-unit
#17
Denis Arnold, Fabian Tomaschek, Konstantin Sering, Florence Lopez, R Harald Baayen
Sound units play a pivotal role in cognitive models of auditory comprehension. The general consensus is that during perception listeners break down speech into auditory words and subsequently phones. Indeed, cognitive speech recognition is typically taken to be computationally intractable without phones. Here we present a computational model trained on 20 hours of conversational speech that recognizes word meanings within the range of human performance (model 25%, native speakers 20-44%), without making use of phone or word form representations...
2017: PloS One
https://www.readbyqxmd.com/read/28394270/learning-traffic-as-images-a-deep-convolutional-neural-network-for-large-scale-transportation-network-speed-prediction
#18
Xiaolei Ma, Zhuang Dai, Zhengbing He, Jihui Ma, Yong Wang, Yunpeng Wang
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network...
April 10, 2017: Sensors
https://www.readbyqxmd.com/read/28391919/ethanol-mediated-as-iii-adsorption-onto-zn-loaded-pinecone-biochar-experimental-investigation-modeling-and-optimization-using-hybrid-artificial-neural-network-genetic-algorithm-approach
#19
Mohd Zafar, N Van Vinh, Shishir Kumar Behera, Hung-Suck Park
Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box-Behnken design. The effect of EtOH on As(III) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches...
April 2017: Journal of Environmental Sciences (China)
https://www.readbyqxmd.com/read/28389809/computer-aided-diagnosis-of-malignant-or-benign-thyroid-nodes-based-on-ultrasound-images
#20
Qin Yu, Tao Jiang, Aiyun Zhou, Lili Zhang, Cheng Zhang, Pan Xu
The objective of this study is to evaluate the diagnostic value of combination of artificial neural networks (ANN) and support vector machine (SVM)-based CAD systems in differentiating malignant from benign thyroid nodes with gray-scale ultrasound images. Two morphological and 65 texture features extracted from regions of interest in 610 2D-ultrasound thyroid node images from 543 patients (207 malignant, 403 benign) were used to develop the ANN and SVM models. Tenfold cross validation evaluated their performance; the best models showed accuracy of 99% for ANN and 100% for SVM...
April 7, 2017: European Archives of Oto-rhino-laryngology
keyword
keyword
97342
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"