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"Artificial neural network"

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https://www.readbyqxmd.com/read/28227976/user-intent-prediction-with-a-scaled-conjugate-gradient-trained-artificial-neural-network-for-lower-limb-amputees-using-a-powered-prosthesis
#1
Richard B Woodward, John A Spanias, Levi J Hargrove, Richard B Woodward, John A Spanias, Levi J Hargrove, John A Spanias, Richard B Woodward, 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/28227973/improving-odorant-chemical-class-prediction-with-multi-layer-perceptrons-using-temporal-odorant-spike-responses-from-drosophila-melanogaster-olfactory-receptor-neurons
#2
Luqman R Bachtiar, Richard D Newcomb, Andrew V Kralicek, Charles P Unsworth, Luqman R Bachtiar, Richard D Newcomb, Andrew V Kralicek, Charles P Unsworth, Charles P Unsworth, Andrew V Kralicek, Richard D Newcomb, Luqman R Bachtiar
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/28227925/principal-component-analysis-can-decrease-neural-networks-performance-for-incipient-falls-detection-a-preliminary-study-with-hands-and-feet-accelerations
#3
Fiorenzo Artoni, Dario Martelli, Vito Monaco, Silvestre Micera, 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
https://www.readbyqxmd.com/read/28227738/tuning-electrical-stimulation-for-thalamic-visual-prosthesis-an-autoencoder-based-approach
#4
Amr Jawwad, Hossam H Abolfotuh, Bassem Abdullah, Hani M K Mahdi, Seif Eldawlatly, Amr Jawwad, Hossam H Abolfotuh, Bassem Abdullah, Hani M K Mahdi, Seif Eldawlatly, Amr Jawwad, Hani M K Mahdi, Bassem Abdullah, Seif Eldawlatly, Hossam H Abolfotuh
Visual prosthesis holds hope of vision restoration for millions with retinal degenerative diseases. Machine learning techniques such as artificial neural networks could help in improving prosthetic devices as they could learn how the brain encodes information and imitate that code. This paper introduces an autoencoder-based approach for tuning thalamic visual prostheses. The objective of the proposed approach is to estimate electrical stimuli that are equivalent to a given natural visual stimulus, in a way such that they both elicit responses that are as similar as possible when introduced to a Lateral Geniculate Nucleus (LGN) population...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227645/myoelectric-intuitive-control-and-transcutaneous-electrical-stimulation-of-the-forearm-for-vibrotactile-sensation-feedback-applied-to-a-3d-printed-prosthetic-hand
#5
Enrique I Germany, Esteban J Pino, Pablo E Aqueveque, Enrique I Germany, Esteban J Pino, Pablo E Aqueveque, Enrique I Germany, Pablo E Aqueveque, Esteban J Pino
This paper presents the development of a myoelectric prosthetic hand based on a 3D printed model. A myoelectric control strategy based on artificial neural networks is implemented on a microcontroller for online position estimation. Position estimation performance achieves a correlation index of 0.78. Also a study involving transcutaneous electrical stimulation was performed to provide tactile feedback. A series of stimulations with controlled parameters were tested on five able-body subjects. A single channel stimulator was used, positioning the electrodes 8 cm on the wrist over the ulnar and median nerve...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227230/a-biologically-inspired-image-classifier-adaptive-feature-detection
#6
Jeffrey C Ames, Konstantinos P Michmizos, Jeffrey C Ames, Konstantinos P Michmizos, Jeffrey C Ames, Konstantinos P Michmizos
Today's artificial neural networks use computational models and algorithms inspired by the knowledge of the brain in the '90s. Powerful as they are, artificial networks are impressive but their domain specificity and reliance on vast numbers of labeled examples are obvious limitations. About a decade ago, spiking neural networks (SNNs) emerged as a new formalism that takes advantage of the spike timing and are particularly versatile when depicting spatio-temporal representations. The challenge now is to design rules for SNNs that can help them interact with their environment just like humans do...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227033/advanced-analytics-for-outcome-prediction-in-intensive-care-units
#7
Ali Jalali, Dieter Bender, Mohamed Rehman, Vinay Nadkanri, C Nataraj, Ali Jalali, Dieter Bender, Mohamed Rehman, Vinay Nadkanri, C Nataraj, Mohamed Rehman, Ali Jalali, Vinay Nadkanri, Dieter Bender, C Nataraj
In this paper we present a new expert knowledge based clinical decision support system for prediction of intensive care units outcome based on the physiological measurements collected during the first 48 hours of the patient's admission to the ICU. The developed CDSS algorithm is composed of several stages. First, we categorize the collected data based on the physiological organ that they represent. We then extract clinically relevant features from each data category and then rank these features based on their mutual information with the outcome...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226786/neural-decoding-of-code-modulated-visual-evoked-potentials-by-spatio-temporal-inverse-filtering-for-brain-computer-interfaces
#8
Jun-Ichi Sato, Yoshikazu Washizawa, Jun-Ichi Sato, Yoshikazu Washizawa, Jun-Ichi Sato, Yoshikazu Washizawa
This study addresses neural decoding of a code modulated visual evoked potentials (c-VEPs). c-VEP was recently developed, and applied to brain computer interfaces (BCIs). c-VEP BCI exhibits faster communication speed than existing VEP-based BCIs. In c-VEP BCI, the canonical correlation analysis (CCA) that maximizes the correlation between an averaged signal and single trial signals is often used for the spatial filter. However, CCA does not utilize information of given PN sequence, and hence, the filtered signal may not have properties of PN sequence...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226519/novel-method-to-characterize-upper-limb-movements-based-on-paraconsistent-logic-and-myoelectric-signals
#9
Gabriela W Favieiro, Karina O A Moura, Alexandre Balbinot, Gabriela W Favieiro, Karina O A Moura, Alexandre Balbinot, Karina O A Moura, Gabriela W Favieiro, Alexandre Balbinot
This paper presents a novel method that investigates the use of Paraconsistent Artificial Neural Network (PANN) and upper-limb electromyography signals for classification of movements, due to their intrinsic ability to deal with imprecise, inconsistent and paracomplete data. The preliminary study presents promising results in terms of processing time and accuracy. The average classification accuracy for the developed paraconsistent logic method was 76,0±9,1% for 17 distinguish movements and a classification average processing time of 14 ms per movement...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28225828/systematic-analysis-of-non-structural-protein-features-for-the-prediction-of-ptm-function-potential-by-artificial-neural-networks
#10
Henry M Dewhurst, Matthew P Torres
Post-translational modifications (PTMs) provide an extensible framework for regulation of protein behavior beyond the diversity represented within the genome alone. While the rate of identification of PTMs has rapidly increased in recent years, our knowledge of PTM functionality encompasses less than 5% of this data. We previously developed SAPH-ire (Structural Analysis of PTM Hotspots) for the prioritization of eukaryotic PTMs based on function potential of discrete modified alignment positions (MAPs) in a set of 8 protein families...
2017: PloS One
https://www.readbyqxmd.com/read/28224450/artificial-neural-network-based-model-enhances-risk-stratification-and-reduces-non-invasive-cardiac-stress-imaging-compared-to-diamond-forrester-and-morise-risk-assessment-models-a-prospective-study
#11
Hussain A Isma'eel, George E Sakr, Mustapha Serhan, Nader Lamaa, Ayman Hakim, Paul C Cremer, Wael A Jaber, Torkom Garabedian, Imad Elhajj, Antoine B Abchee
BACKGROUND: Coronary artery disease (CAD) accounts for more than half of all cardiovascular events. Stress testing remains the cornerstone for non-invasive assessment of patients with possible or known CAD. Clinical utilization reviews show that most patients presenting for evaluation of stable CAD by stress testing are categorized as low risk prior to the test. Attempts to enhance risk stratification of individuals who are sent for stress testing seem to be more in need today. The present study compares artificial neural networks (ANN)-based prediction models to the other risk models being used in practice (the Diamond-Forrester and the Morise models)...
February 21, 2017: Journal of Nuclear Cardiology: Official Publication of the American Society of Nuclear Cardiology
https://www.readbyqxmd.com/read/28223914/decoding-lower-limb-muscle-activity-and-kinematics-from-cortical-neural-spike-trains-during-monkey-performing-stand-and-squat-movements
#12
Xuan Ma, Chaolin Ma, Jian Huang, Peng Zhang, Jiang Xu, Jiping He
Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1)...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28222194/forecasting-outpatient-visits-using-empirical-mode-decomposition-coupled-with-back-propagation-artificial-neural-networks-optimized-by-particle-swarm-optimization
#13
Daizheng Huang, Zhihui Wu
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series...
2017: PloS One
https://www.readbyqxmd.com/read/28219431/assessment-of-triglyceride-and-cholesterol-in-overweight-people-based-on-multiple-linear-regression-and-artificial-intelligence-model
#14
Jing Ma, Jiong Yu, Guangshu Hao, Dan Wang, Yanni Sun, Jianxin Lu, Hongcui Cao, Feiyan Lin
BACKGROUND: The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. METHODS: A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL)...
February 20, 2017: Lipids in Health and Disease
https://www.readbyqxmd.com/read/28214992/data-mining-in-hiv-aids-surveillance-system-application-to-portuguese-data
#15
Alexandra Oliveira, Brígida Mónica Faria, A Rita Gaio, Luís Paulo Reis
The Human Immunodeficiency Virus (HIV) is an infectious agent that attacks the immune system cells. Without a strong immune system, the body becomes very susceptible to serious life threatening opportunistic diseases. In spite of the great progresses on medication and prevention over the last years, HIV infection continues to be a major global public health issue, having claimed more than 36 million lives over the last 35 years since the recognition of the disease. Monitoring, through registries, of HIV-AIDS cases is vital to assess general health care needs and to support long-term health-policy control planning...
April 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28212422/probability-matching-in-perceptrons-effects-of-conditional-dependence-and-linear-nonseparability
#16
Michael R W Dawson, Maya Gupta
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation...
2017: PloS One
https://www.readbyqxmd.com/read/28212138/deep-learning-in-mammography-diagnostic-accuracy-of-a-multipurpose-image-analysis-software-in-the-detection-of-breast-cancer
#17
Anton S Becker, Magda Marcon, Soleen Ghafoor, Moritz C Wurnig, Thomas Frauenfelder, Andreas Boss
OBJECTIVES: The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mammography data set. MATERIALS AND METHODS: In this retrospective, Health Insurance Portability and Accountability Act-compliant study, all patients undergoing mammography in 2012 at our institution were reviewed (n = 3228). All of their prior and follow-up mammographies from a time span of 7 years (2008-2015) were considered as a reference for clinical diagnosis...
February 16, 2017: Investigative Radiology
https://www.readbyqxmd.com/read/28212113/age-and-gender-dependency-of-physiological-networks-in-sleep
#18
Dagmar Krefting, Christoph Jansen, Thomas Penzel, Fang Han, Jan Kantelhardt
Recently, time delay stability analysis of biosignals has been successfully applied as a multivariate time series analysis method to assess the human physiological network in young adults. The degree of connectivity between different network nodes is described by the so-called link strength. Based on polysomnographic recordings (PSGs), it could be shown that the network changes with the sleep stage. Here, we apply the method to a large set of healthy controls spanning six decades of age. As it is well known, that the overall sleep architecture is dependent both on age and on gender, we particularly address the question, if these changes are also found in the network dynamics...
February 17, 2017: Physiological Measurement
https://www.readbyqxmd.com/read/28201916/artificial-neural-network-for-the-configuration-problem-in-solids
#19
Hyunjun Ji, Yousung Jung
A machine learning approach based on the artificial neural network (ANN) is applied for the configuration problem in solids. The proposed method provides a direct mapping from configuration vectors to energies. The benchmark conducted for the M1 phase of Mo-V-Te-Nb oxide showed that only a fraction of configurations needs to be calculated, thus the computational burden significantly decreased, by a factor of 20-50, with R(2) = 0.96 and MAD = 0.12 eV. It is shown that ANN can also handle the effects of geometry relaxation when properly trained, resulting in R(2) = 0...
February 14, 2017: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28196722/comparison-of-models-for-predicting-the-changes-in-phytoplankton-community-composition-in-the-receiving-water-system-of-an-inter-basin-water-transfer-project
#20
Qinghui Zeng, Yi Liu, Hongtao Zhao, Mingdong Sun, Xuyong Li
Inter-basin water transfer projects might cause complex hydro-chemical and biological variation in the receiving aquatic ecosystems. Whether machine learning models can be used to predict changes in phytoplankton community composition caused by water transfer projects have rarely been studied. In the present study, we used machine learning models to predict the total algal cell densities and changes in phytoplankton community composition in Miyun reservoir caused by the middle route of the South-to-North Water Transfer Project (SNWTP)...
February 10, 2017: Environmental Pollution
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