scholarly journals Deep neural network learning of complex binary sorption equilibria from molecular simulation data

2019 ◽  
Vol 10 (16) ◽  
pp. 4377-4388 ◽  
Author(s):  
Yangzesheng Sun ◽  
Robert F. DeJaco ◽  
J. Ilja Siepmann
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Molecular Simulation ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Molecular Simulations ◽  
Simulation Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Sorption Equilibria

We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling.

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

Inclusion of Multiple Cycling of the Potential into Deep Neural Network Classification of Voltammetric Reaction Mechanisms

2021 ◽  
Author(s):  
Luke Gundry ◽  
Gareth Kennedy ◽  
Alan Bond ◽  
Jie Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reaction Mechanisms ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Neural Network Classification ◽  
Initial Cycle

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

Automated vessel segmentation in lung CT and CTA images via deep neural networks

2021 ◽  
pp. 1-15
Author(s):  
Wenjun Tan ◽  
Luyu Zhou ◽  
Xiaoshuo Li ◽  
Xiaoyu Yang ◽  
Yufei Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Vessel Segmentation ◽  
Multi Scale ◽  
Vascular Segmentation ◽  
Study Results ◽  
Lung Ct

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation ratio and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

scholarly journals Extensive deep neural networks for transferring small scale learning to large scale systems

2019 ◽  
Vol 10 (15) ◽  
pp. 4129-4140 ◽  
Author(s):  
Kyle Mills ◽  
Kevin Ryczko ◽  
Iryna Luchak ◽  
Adam Domurad ◽  
Chris Beeler ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Small Scale ◽  
Large Scale Systems ◽  
Energy Entropy ◽  
Large Systems ◽  
Number Of Particles

We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with scaling.

scholarly journals Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality

2018 ◽  
Vol 129 (4) ◽  
pp. 649-662 ◽  
Author(s):  
Christine K. Lee ◽  
Ira Hofer ◽  
Eilon Gabel ◽  
Pierre Baldi ◽  
Maxime Cannesson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hospital Mortality ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Operating Characteristics ◽  
Physical Status ◽  
Physical Status Classification ◽  
Receiver Operating Characteristics Curve ◽  
Status Classification

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality. Methods The data used to train and validate the algorithm consists of 59,985 patients with 87 features extracted at the end of surgery. Feed-forward networks with a logistic output were trained using stochastic gradient descent with momentum. The deep neural networks were trained on 80% of the data, with 20% reserved for testing. The authors assessed improvement of the deep neural network by adding American Society of Anesthesiologists (ASA) Physical Status Classification and robustness of the deep neural network to a reduced feature set. The networks were then compared to ASA Physical Status, logistic regression, and other published clinical scores including the Surgical Apgar, Preoperative Score to Predict Postoperative Mortality, Risk Quantification Index, and the Risk Stratification Index. Results In-hospital mortality in the training and test sets were 0.81% and 0.73%. The deep neural network with a reduced feature set and ASA Physical Status classification had the highest area under the receiver operating characteristics curve, 0.91 (95% CI, 0.88 to 0.93). The highest logistic regression area under the curve was found with a reduced feature set and ASA Physical Status (0.90, 95% CI, 0.87 to 0.93). The Risk Stratification Index had the highest area under the receiver operating characteristics curve, at 0.97 (95% CI, 0.94 to 0.99). Conclusions Deep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods.

Local Learning Algorithms for Sequential Tasks in Neural Networks

1998 ◽  
Vol 2 (6) ◽  
pp. 221-227 ◽  
Author(s):  
Anthony Robins ◽  
◽  
Marcus Frean ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithms ◽  
Learning Task ◽  
Sequential Learning ◽  
Local Learning ◽  
Neural Network Learning ◽  
Network Learning ◽  
Sequential Task ◽  
Global And Local

In this paper, we explore the concept of sequential learning and the efficacy of global and local neural network learning algorithms on a sequential learning task. Pseudorehearsal, a method developed by Robins19) to solve the catastrophic forgetting problem which arises from the excessive plasticity of neural networks, is significantly more effective than other local learning algorithms for the sequential task. We further consider the concept of local learning and suggest that pseudorehearsal is so effective because it works directly at the level of the learned function, and not indirectly on the representation of the function within the network. We also briefly explore the effect of local learning on generalization within the task.

Adaptive Control Based on Neural Network for Ship Sterring Autopilot

2012 ◽  
Vol 503-504 ◽  
pp. 1239-1242
Author(s):  
Guan Shan Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reference Model ◽  
Learning Algorithm ◽  
Adaptive Controller ◽  
Membership Functions ◽  
Neural Network Learning ◽  
Network Learning ◽  
Ship Steering ◽  
Course Control

The Autopilot is importance for a ship to navigate safely and economically, so we proposes an intelligent reference modeling adaptive controller for ship steering based on neural networks. In order to satisfy the requirements of ship’s course control under various sea status, we used fuzzy logic and neural networks to design the feedback controller, used multilayer perceptron neural network to design the reference model and the identification network. In order to enhance adaptive characteristics of the controller，the parameters of membership functions and connection weights etc were revised online with neural network learning algorithm. The results of simulation shown that the performance of the ship controller is valuable and effective.

scholarly journals Predictive Model for Brain Stroke in CT using Deep Neural Network

2020 ◽  
Vol 9 (1) ◽  
pp. 2011-2017
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Principal Component ◽  
World Population ◽  
Neural Network Learning ◽  
Machine Learning Classification ◽  
Network Learning ◽  
Brain Stroke ◽  
Aging World

The increasing in the incidence of stroke with aging world population would quickly place an economic burden on society. In proposed method we use different machine learning classification algorithms like Decision Tree, Deep Neural Network Learning, Maximum Expectization , Random Forest and Gaussian Naïve Bayesian Classifier is used with associated number of attributes to estimate the occurrence of stroke disease. The present research, mainly PCA (Principal Component Analysis) algorithm is used to limit the performance and scaling used to be adopted to extract splendid context statistics from medical records. We used those reduced features to determine whether or not the patient has a stroke disorder. We compared proposed method Deep neural network learning classifier with other machine-learning methods with respect to accuracy, sensitivity and specificity that yields 86.42%, 74.89 and 88.49% respectively. Hence it can be with the aid of both patients and medical doctors to treat viable stroke.

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