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Artificial neural networks

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https://www.readbyqxmd.com/read/27910759/acidified-hot-water-extraction-of-adenosine-cordycepin-and-polysaccharides-from-the-chinese-caterpillar-mushroom-ophiocordyceps-sinensis-cs1197-ascomycetes-application-of-an-artificial-neural-network-and-evaluation-of-antioxidant-and-antibacterial-activities
#1
M G Shashidhar, B Manohar
The effects of process variables (temperature, time, and pH) on the extraction of adenosine, cordycepin, and polysaccharides from Ophiocordyceps sinensis CS1197 were studied using response surface methodology (RSM) and an artificial neural network (ANN). The ANN model resulted in low root mean square errors (0.022, 0.079, 0.018) and high R2 values (0.995, 0.934, 0.997) for adenosine, cordycepin, and polysaccharide yields, respectively, which implied good agreement between the predicted and actual data. An overall desirability of 0...
2016: International Journal of Medicinal Mushrooms
https://www.readbyqxmd.com/read/27907231/use-of-autopsy-to-determine-live-or-stillbirth-new-approaches-in-decision-support-systems
#2
Riza Yilmaz, Okan Erkaymaz, Erdogan Kara, Kivanc Ergen
Fetal deaths are important cases for forensic medicine, as well as for criminal and civil law. From a legal perspective, the determination of whether a deceased infant was stillborn is a difficult process. Such a determination is generally made during autopsy; however, no standardized procedures for this determination exist. Therefore, new facilitative approaches are needed. In this study, three new support systems based on 10 autopsy parameters were tested for their ability to correctly determine whether deceased infants were alive or stillborn...
December 1, 2016: Journal of Forensic Sciences
https://www.readbyqxmd.com/read/27899332/artificial-neural-network-for-suppression-of-banding-artifacts-in-balanced-steady-state-free-precession-mri
#3
Ki Hwan Kim, Sung-Hong Park
The balanced steady-state free precession (bSSFP) MR sequence is frequently used in clinics, but is sensitive to off-resonance effects, which can cause banding artifacts. Often multiple bSSFP datasets are acquired at different phase cycling (PC) angles and then combined in a special way for banding artifact suppression. Many strategies of combining the datasets have been suggested for banding artifact suppression, but there are still limitations in their performance, especially when the number of phase-cycled bSSFP datasets is small...
November 26, 2016: Magnetic Resonance Imaging
https://www.readbyqxmd.com/read/27894042/investigation-of-co-combustion-characteristics-of-sewage-sludge-and-coffee-grounds-mixtures-using-thermogravimetric-analysis-coupled-to-artificial-neural-networks-modeling
#4
Jiacong Chen, Jingyong Liu, Yao He, Limao Huang, Shuiyu Sun, Jian Sun, KenLin Chang, Jiahong Kuo, Shaosong Huang, Xunan Ning
Artificial neural network (ANN) modeling was applied to thermal data obtained by non-isothermal thermogravimetric analysis (TGA) from room temperature to 1000°C at three different heating rates in air to predict the TG curves of sewage sludge (SS) and coffee grounds (CG) mixtures. A good agreement between experimental and predicted data verified the accuracy of the ANN approach. The results of co-combustion showed that there were interactions between SS and CG, and the impacts were mostly positive. With the addition of CG, the mass loss rate and the reactivity of SS were increased while charring was reduced...
November 18, 2016: Bioresource Technology
https://www.readbyqxmd.com/read/27892600/artificial-neural-networks-and-geometric-morphometric-methods-as-a-means-for-classification-a-case-study-using-teeth-from-carcharhinus-sp-carcharhinidae
#5
K J Soda, D E Slice, G J P Naylor
Over the past few decades, geometric morphometric methods have become increasingly popular and powerful tools to describe morphological data while over the same period artificial neural networks have had a similar rise in the classification of specimens to preconceived groups. However, there has been little research into how well these two systems operate together, particularly in comparison to preexisting techniques. In this study, geometric morphometric data and multilayer perceptrons, a style of artificial neural network, were used to classify shark teeth from the genus Carcharhinus to species...
November 28, 2016: Journal of Morphology
https://www.readbyqxmd.com/read/27890153/the-arm-force-field-method-to-predict-manual-arm-strength-based-on-only-hand-location-and-force-direction
#6
Nicholas J La Delfa, Jim R Potvin
This paper describes the development of a novel method (termed the 'Arm Force Field' or 'AFF') to predict manual arm strength (MAS) for a wide range of body orientations, hand locations and any force direction. This method used an artificial neural network (ANN) to predict the effects of hand location and force direction on MAS, and included a method to estimate the contribution of the arm's weight to the predicted strength. The AFF method predicted the MAS values very well (r(2) = 0.97, RMSD = 5.2 N, n = 456) and maintained good generalizability with external test data (r(2) = 0...
March 2017: Applied Ergonomics
https://www.readbyqxmd.com/read/27890144/classification-of-a-driver-s-cognitive-workload-levels-using-artificial-neural-network-on-ecg-signals
#7
Amir Tjolleng, Kihyo Jung, Wongi Hong, Wonsup Lee, Baekhee Lee, Heecheon You, Joonwoo Son, Seikwon Park
An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated...
March 2017: Applied Ergonomics
https://www.readbyqxmd.com/read/27884247/machine-learning-algorithms-to-automate-morphological-and-functional-assessments-in-2d-echocardiography
#8
Sukrit Narula, Khader Shameer, Alaa Mabrouk Salem Omar, Joel T Dudley, Partho P Sengupta
BACKGROUND: Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. OBJECTIVES: This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). METHODS: Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system...
November 29, 2016: Journal of the American College of Cardiology
https://www.readbyqxmd.com/read/27881212/building-machines-that-learn-and-think-like-people
#9
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, Samuel J Gershman
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it...
November 24, 2016: Behavioral and Brain Sciences
https://www.readbyqxmd.com/read/27877120/fronto-parietal-contributions-to-phonological-processes-in-successful-artificial-grammar-learning
#10
Dariya Goranskaya, Jens Kreitewolf, Jutta L Mueller, Angela D Friederici, Gesa Hartwigsen
Sensitivity to regularities plays a crucial role in the acquisition of various linguistic features from spoken language input. Artificial grammar learning paradigms explore pattern recognition abilities in a set of structured sequences (i.e., of syllables or letters). In the present study, we investigated the functional underpinnings of learning phonological regularities in auditorily presented syllable sequences. While previous neuroimaging studies either focused on functional differences between the processing of correct vs...
2016: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/27877107/training-deep-spiking-neural-networks-using-backpropagation
#11
Jun Haeng Lee, Tobi Delbruck, Michael Pfeiffer
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials...
2016: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/27876537/artificial-neural-networks-approach-to-pharmacokinetic-model-selection-in-dce-mri-studies
#12
Mohammad-Reza Mohammadian-Behbahani, Ali-Reza Kamali-Asl
PURPOSE: In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like computation time and general fitting problems. This article proposes an approach based on Artificial Neural Networks (ANNs) for solving these problems. METHODS: A set of three physiologically and mathematically nested models generated from the Tofts model were assumed: Model I, II and III...
November 19, 2016: Physica Medica: PM
https://www.readbyqxmd.com/read/27876174/artificial-neural-networks-predicting-head-ct-findings-in-elderly-patients-presenting-with-minor-head-injury-after-a-fall
#13
Michael W Dusenberry, Charles K Brown, Kori L Brewer
OBJECTIVES: To construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patients≥age 65years who have incurred minor head injury after a fall. METHODS: An ANN was created in the Python programming language using a population of 514 patients ≥ age 65 years presenting to the ED with minor head injury after a fall. The patient dataset was divided into three parts: 60% for "training", 20% for "cross validation", and 20% for "testing"...
November 3, 2016: American Journal of Emergency Medicine
https://www.readbyqxmd.com/read/27876006/predictors-of-in-hospital-mortality-following-major-lower-extremity-amputations-in-type-2-diabetic-patients-using-artificial-neural-networks
#14
Ana Lopez-de-Andres, Valentin Hernandez-Barrera, Roberto Lopez, Pablo Martin-Junco, Isabel Jimenez-Trujillo, Alejandro Alvaro-Meca, Miguel Angel Salinero-Fort, Rodrigo Jimenez-Garcia
BACKGROUND: Outcome prediction is important in the clinical decision-making process. Artificial neural networks (ANN) have been used to predict the risk of post-operative events, including survival, and are increasingly being used in complex medical decision making. We aimed to use ANN analysis to estimate predictive factors of in-hospital mortality (IHM) in patients with type 2 diabetes (T2DM) after major lower extremity amputation (LEA) in Spain. METHODS: We design a retrospective, observational study using ANN models...
November 22, 2016: BMC Medical Research Methodology
https://www.readbyqxmd.com/read/27875902/nuisance-regression-of-high-frequency-fmri-data-de-noising-can-be-noisy
#15
Jingyuan E Chen, Gary Glover, Hesamoddin Jahanian
Recently, emerging studies have demonstrated the existence of brain resting state (RS) spontaneous activity at frequencies higher than the conventional 0.1 Hz. A few groups utilizing accelerated acquisitions have reported persisting signals beyond 1 Hz, which seems too high to be accommodated by the sluggish hemodynamic process underpinning blood-oxygen-level dependent contrasts (the upper limit of the canonical model is ~ 0.3 Hz). It is thus questionable whether the observed high-frequency (HF) functional connectivity (FC) originates from alternative mechanisms (e...
November 22, 2016: Brain Connectivity
https://www.readbyqxmd.com/read/27875137/visualizing-the-hidden-activity-of-artificial-neural-networks
#16
Paulo E Rauber, Samuel G Fadel, Alexandre X Falcao, Alexandru C Telea
In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons...
January 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/27873552/correlated-eeg-signals-simulation-based-on-artificial-neural-networks
#17
Nikola M Tomasevic, Aleksandar M Neskovic, Natasa J Neskovic
In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence)...
September 30, 2016: International Journal of Neural Systems
https://www.readbyqxmd.com/read/27871490/predictive-models-for-mortality-after-ruptured-aortic-aneurysm-repair-do-not-predict-futility-and-are-not-useful-for-clinical-decision-making
#18
Patrick C Thompson, Ronald L Dalman, E John Harris, Venita Chandra, Jason T Lee, Matthew W Mell
OBJECTIVE: The clinical decision-making utility of scoring algorithms for predicting mortality after ruptured abdominal aortic aneurysms (rAAAs) remains unknown. We sought to determine the clinical utility of the algorithms compared with our clinical decision making and outcomes for management of rAAA during a 10-year period. METHODS: Patients admitted with a diagnosis rAAA at a large university hospital were identified from 2005 to 2014. The Glasgow Aneurysm Score, Hardman Index, Vancouver Score, Edinburgh Ruptured Aneurysm Score, University of Washington Ruptured Aneurysm Score, Vascular Study Group of New England rAAA Risk Score, and the Artificial Neural Network Score were analyzed for accuracy in predicting mortality...
December 2016: Journal of Vascular Surgery
https://www.readbyqxmd.com/read/27867818/a-new-gene-regulatory-network-model-based-on-bp-algorithm-for-interrogating-differentially-expressed-genes-of-sea-urchin
#19
Longlong Liu, Tingting Zhao, Meng Ma, Yan Wang
BACKGROUND: Computer science and mathematical theories are combined to analyze the complex interactions among genes, which are simplified to a network to establish a theoretical model for the analysis of the structure, module and dynamic properties. In contrast, traditional model of gene regulatory networks often lack an effective method for solving gene expression data because of high durational and spatial complexity. In this paper, we propose a new model for constructing gene regulatory networks using back propagation (BP) neural network based on predictive function and network topology...
2016: SpringerPlus
https://www.readbyqxmd.com/read/27866986/modelling-of-in-vitro-and-in-vivo-performance-of-aerosol-emitted-from-different-vibrating-mesh-nebulisers-in-non-invasive-ventilation-circuit
#20
Hoda Rabea, Ahmed M A Ali, Randa Salah Eldin, Maha M Abdelrahman, Amira S A Said, Mohamed E Abdelrahim
Substituting nebulisers by another in non-invasive ventilation circuit (NIV) involves many process variables which must be adjusted to ensure patient optimum therapy. However, there is a doubt when nebulisers use the same technology. Data mining technology based on artificial neural networks and genetic algorithms were used here to model in-vitro inhalation process and predict bioavailability from inhaled doses delivered by three different vibrating mesh nebulisers (VMNs) in NIV. Modelling of data indicated that in-vitro performance of VMNs was dependent mainly on fine particle fraction, mass median aerodynamic diameter (MMAD), total emitted dose (TED) and to lesser extent on nebuliser type...
November 17, 2016: European Journal of Pharmaceutical Sciences
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