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"Artificial neural network"

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https://www.readbyqxmd.com/read/28328498/a-framework-for-patient-state-tracking-by-classifying-multiscalar-physiologic-waveform-features
#1
Benjamin Vandendriessche, Mustafa Abas, Thomas Dick, Kenneth Loparo, Frank Jacono
OBJECTIVE: State-of-the-art algorithms that quantify nonlinear dynamics in physiologic waveforms are underutilized clinically due to their esoteric nature. We present a generalizable framework for classifying multiscalar waveform features, designed for patient-state tracking directly at the bedside. METHODS: An artificial neural network classifier was designed to evaluate multiscale waveform features against a fingerprint database of multifractal synthetic time series...
March 17, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28326009/improving-eeg-based-driver-fatigue-classification-using-sparse-deep-belief-networks
#2
Rifai Chai, Sai Ho Ling, Phyo Phyo San, Ganesh R Naik, Tuan N Nguyen, Yvonne Tran, Ashley Craig, Hung T Nguyen
This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28325448/diagnosis-of-autism-through-eeg-processed-by-advanced-computational-algorithms-a-pilot-study
#3
Enzo Grossi, Chiara Olivieri, Massimo Buscema
BACKGROUND: Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28325033/user-intent-prediction-with-a-scaled-conjugate-gradient-trained-artificial-neural-network-for-lower-limb-amputees-using-a-powered-prosthesis
#4
Richard B Woodward, John A Spanias, Levi J Hargrove
Powered lower limb prostheses have the ability to provide greater mobility for amputee patients. Such prostheses often have pre-programmed modes which can allow activities such as climbing stairs and descending ramps, something which many amputees struggle with when using non-powered limbs. Previous literature has shown how pattern classification can allow seamless transitions between modes with a high accuracy and without any user interaction. Although accurate, training and testing each subject with their own dependent data is time consuming...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324778/measurement-of-weld-penetration-depths-in-thin-structures-using-transmission-coefficients-of-laser-generated-lamb-waves-and-neural-network
#5
Lei Yang, I Charles Ume
The Laser/EMAT ultrasonic (LEU) technique has shown the capability to measure weld penetration depths in thick structures based on ray-tracing of laser-generated bulk and surface waves. The ray-tracing method is not applicable to laser-generated Lamb waves when the LEU technique is used to measure weld penetration depths in thin structures. In this work, transmission coefficients of Lamb waves present in the LEU signals are investigated against varying weld penetration depths. An artificial neural network is developed to use transmission coefficients of sensitive Lamb waves and LEU signal energy to predict weld penetration depths accurately...
February 28, 2017: Ultrasonics
https://www.readbyqxmd.com/read/28324707/chemical-structure-based-predictive-model-for-the-oxidation-of-trace-organic-contaminants-by-sulfate-radical
#6
Tiantian Ye, Zongsu Wei, Richard Spinney, Chong-Jian Tang, Shuang Luo, Ruiyang Xiao, Dionysios D Dionysiou
Second-order rate constants [Formula: see text] for the reaction of sulfate radical anion (SO4(•-)) with trace organic contaminants (TrOCs) are of scientific and practical importance for assessing their environmental fate and removal efficiency in water treatment systems. Here, we developed a chemical structure-based model for predicting [Formula: see text] using 32 molecular fragment descriptors, as this type of model provides a quick estimate at low computational cost. The model was constructed using the multiple linear regression (MLR) and artificial neural network (ANN) methods...
March 6, 2017: Water Research
https://www.readbyqxmd.com/read/28319760/improved-prediction-of-higher-heating-value-of-biomass-using-an-artificial-neural-network-model-based-on-proximate-analysis
#7
Harun Uzun, Zeynep Yıldız, Jillian L Goldfarb, Selim Ceylan
As biomass becomes more integrated into our energy feedstocks, the ability to predict its combustion enthalpies from routine data such as carbon, ash, and moisture content enables rapid decisions about utilization. The present work constructs a novel artificial neural network model with a 3-3-1 tangent sigmoid architecture to predict biomasses' higher heating values from only their proximate analyses, requiring minimal specificity as compared to models based on elemental composition. The model presented has a considerably higher correlation coefficient (0...
March 9, 2017: Bioresource Technology
https://www.readbyqxmd.com/read/28319649/optimization-of-extraction-of-linarin-from-flos-chrysanthemi-indici-by-response-surface-methodology-and-artificial-neural-network
#8
Hongye Pan, Qing Zhang, Keke Cui, Guoquan Chen, Xuesong Liu, Longhu Wang
The extraction of linarin from Flos chrysanthemi indici by ethanol was investigated. Two modeling techniques, response surface methodology and artificial neural network, were adopted to optimize the process parameters, such as, ethanol concentration, extraction period, extraction frequency, and solvent to material ratio. We showed that both methods provided good predictions, but artificial neural network provided a better and more accurate result. The optimum process parameters include, ethanol concentration of 74%, extraction period of 2 h, extraction three times, solvent to material ratio of 12 mL/g...
March 20, 2017: Journal of Separation Science
https://www.readbyqxmd.com/read/28319275/mrf-ann-a-machine-learning-approach-for-automated-er-scoring-of-breast-cancer-immunohistochemical-images
#9
T Mungle, S Tewary, D K DAS, I Arun, B Basak, S Agarwal, R Ahmed, S Chatterjee, C Chakraborty
Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation-maximization optimization is employed to segment the ER cells...
March 20, 2017: Journal of Microscopy
https://www.readbyqxmd.com/read/28296020/memristive-devices-with-highly-repeatable-analog-states-boosted-by-graphene-quantum-dots
#10
Changhong Wang, Wei He, Yi Tong, Yishu Zhang, Kejie Huang, Li Song, Shuai Zhong, Rajasekaran Ganeshkumar, Rong Zhao
Memristive devices, having a huge potential as artificial synapses for low-power neural networks, have received tremendous attention recently. Despite great achievements in demonstration of plasticity and learning functions, little progress has been made in the repeatable analog resistance states of memristive devices, which is, however, crucial for achieving controllable synaptic behavior. The controllable behavior of synapse is highly desired in building neural networks as it helps reduce training epochs and diminish error probability...
March 15, 2017: Small
https://www.readbyqxmd.com/read/28287986/deepx-deep-learning-accelerator-for-restricted-boltzmann-machine-artificial-neural-networks
#11
Lok-Won Kim
Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs...
March 8, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28287830/artificial-neural-network-system-to-predict-the-postoperative-outcome-of-percutaneous-nephrolithotomy
#12
Alireza Aminsharifi, Dariush Irani, Shima Pooyesh, Hamid Parvin, Sakineh Dehghani, Khalilolah Yousofi, Ebrahim Fazel, Fatemeh Zibaie
PURPOSE: To construct, train, and apply an artificial neural network (ANN) system for prediction of different outcome variables of percutaneous nephrolithotomy (PCNL). We calculated predictive accuracy, sensitivity, and precision for each outcome variable. METHODS: During the study period, all adult patients who underwent PCNL at our institute were enrolled in the study. Preoperative and postoperative variables were recorded, and stone-free status was assessed perioperatively with computed tomography scans...
March 13, 2017: Journal of Endourology
https://www.readbyqxmd.com/read/28283971/comparative-study-of-adsorptive-removal-of-cr-vi-ion-from-aqueous-solution-in-fixed-bed-column-by-peanut-shell-and-almond-shell-using-empirical-models-and-ann
#13
Munmun Banerjee, Nirjhar Bar, Ranjan Kumar Basu, Sudip Kumar Das
Cr(VI) is a toxic water pollutant, which causes cancer and mutation in living organisms. Adsorption has become the most preferred method for removal of Cr(VI) due to its high efficiency and low cost. Peanut and almond shells were used as adsorbents in downflow fixed bed continuous column operation for Cr(VI) removal. The experiments were carried out to scrutinise the adsorptive capacity of the peanut shells and almond shells, as well as to find out the effect of various operating parameters such as column bed depth (5-10 cm), influent flow rate (10-22 ml min(-1)) and influent Cr(VI) concentration (10-20 mg L(-1)) on the Cr(VI) removal...
March 10, 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/28283297/a-new-approach-for-the-estimation-of-phytoplankton-cell-counts-associated-with-algal-blooms
#14
Majid Nazeer, Man Sing Wong, Janet Elizabeth Nichol
This study proposes a method for estimating phytoplankton cell counts associated with an algal bloom, using satellite images coincident with in situ and meteorological parameters. Satellite images from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI) and HJ-1 A/B Charge Couple Device (CCD) sensors were integrated with the meteorological observations to provide an estimate of phytoplankton cell counts. All images were atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) atmospheric correction method with a possible error of 1...
March 7, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/28282877/impact-of-calcium-and-magnesium-in-groundwater-and-drinking-water-on-the-health-of-inhabitants-of-the-slovak-republic
#15
Stanislav Rapant, Veronika Cvečková, Katarína Fajčíková, Darina Sedláková, Beáta Stehlíková
This work aims to evaluate the impact of the chemical composition of groundwater/drinking water on the health of inhabitants of the Slovak Republic. Primary data consists of 20,339 chemical analyses of groundwater (34 chemical elements and compounds) and data on the health of the Slovak population expressed in the form of health indicators (HI). Fourteen HIs were evaluated including life expectancy, potential years of lost life, relative/standardized mortality for cardiovascular and oncological diseases, and diseases of the gastrointestinal and respiratory systems...
March 8, 2017: International Journal of Environmental Research and Public Health
https://www.readbyqxmd.com/read/28279828/intelligent-evaluation-of-color-sensory-quality-of-black-tea-by-visible-near-infrared-spectroscopy-technology-a-comparison-of-spectra-and-color-data-information
#16
Qin Ouyang, Yan Liu, Quansheng Chen, Zhengzhu Zhang, Jiewen Zhao, Zhiming Guo, Hang Gu
Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables...
March 3, 2017: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
https://www.readbyqxmd.com/read/28274431/artificial-neural-network-genetic-algorithm-to-optimize-wheat-germ-fermentation-condition-application-to-the-production-of-two-anti-tumor-benzoquinones
#17
Zi-Yi Zheng, Xiao-Na Guo, Ke-Xue Zhu, Wei Peng, Hui-Ming Zhou
Methoxy-ρ-benzoquinone (MBQ) and 2, 6-dimethoxy-ρ-benzoquinone (DMBQ) are two potential anticancer compounds in fermented wheat germ. In present study, modeling and optimization of added macronutrients, microelements, vitamins for producing MBQ and DMBQ was investigated using artificial neural network (ANN) combined with genetic algorithm (GA). A configuration of 16-11-1 ANN model with Levenberg-Marquardt training algorithm was applied for modeling the complicated nonlinear interactions among 16 nutrients in fermentation process...
July 15, 2017: Food Chemistry
https://www.readbyqxmd.com/read/28272810/deep-learning-for-computational-chemistry
#18
REVIEW
Garrett B Goh, Nathan O Hodas, Abhinav Vishnu
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models...
March 8, 2017: Journal of Computational Chemistry
https://www.readbyqxmd.com/read/28269711/improving-odorant-chemical-class-prediction-with-multi-layer-perceptrons-using-temporal-odorant-spike-responses-from-drosophila-melanogaster-olfactory-receptor-neurons
#19
Luqman R Bachtiar, Richard D Newcomb, Andrew V Kralicek, Charles P Unsworth
In this work, we examine the possibility of improving the prediction performance of an olfactory biosensor through the use of temporal spiking data. We present an Artificial Neural Network (ANN), in the form of an optimal hybrid Multi-Layer Perceptron (MLP) system for the classification of chemical odorants from olfactory receptor neuron spike responses of the Drosophila melanogaster fruit fly (DmOrs). The data used in this study contains the responses to 34 odorants from 6 individual DmOrs, of which we exploit the temporal spiking responses of a 500ms odorant stimulus window...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269666/principal-component-analysis-can-decrease-neural-networks-performance-for-incipient-falls-detection-a-preliminary-study-with-hands-and-feet-accelerations
#20
Fiorenzo Artoni, Dario Martelli, Vito Monaco, Silvestre Micera
Fall-related accidents constitute a major problem for elderly people and a burden to the health-care national system. It is therefore important to design devices (e.g., accelerometers) and machine learning algorithms able to recognize incipient falls as quickly and reliably as possible. Blind source separation (BSS) methods are often used as a preprocessing step before classification, however the effects of BSS on classification performance are not well understood. The aim of this work is to preliminarily characterize the effect that two methods, namely Principal and Independent Component Analysis (PCA and ICA) and their combined use have on the performance of a neural network in detecting incipient falls...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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