Deep Neural Network Structure to Improve Individual Performance based Author Classification

This paper proposed an improved method for author name disambiguation problem, both homonym and synonym. The data prepared is the distance data of each pair of author’s attributes, Levenshtein distance are used. Using Deep Neural Networks, we found large gains on performance. The result shows that level of accuracy is 99.6% with a low number of hidden layers

Download Full-text

Author Name Disambiguation by Using Deep Neural Network

Intelligent Information and Database Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-319-05476-6_13 ◽

2014 ◽

pp. 123-132 ◽

Cited By ~ 23

Author(s):

Hung Nghiep Tran ◽

Tin Huynh ◽

Tien Do

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Name Disambiguation ◽

Author Name Disambiguation

Download Full-text

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

Nature Communications ◽

10.1038/s41467-021-25427-4 ◽

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.

Download Full-text

Single-cell conventional pap smear image classification using pre-trained deep neural network architectures

BMC Biomedical Engineering ◽

10.1186/s42490-021-00056-6 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Mohammed Aliy Mohammed ◽

Fetulhak Abdurahman ◽

Yodit Abebe Ayalew

Keyword(s):

Neural Network ◽

Cervical Cancer ◽

Computer Vision ◽

Single Cell ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Pap Smear ◽

Experimental Result ◽

Network Architectures ◽

Average Accuracy

Abstract Background Automating cytology-based cervical cancer screening could alleviate the shortage of skilled pathologists in developing countries. Up until now, computer vision experts have attempted numerous semi and fully automated approaches to address the need. Yet, these days, leveraging the astonishing accuracy and reproducibility of deep neural networks has become common among computer vision experts. In this regard, the purpose of this study is to classify single-cell Pap smear (cytology) images using pre-trained deep convolutional neural network (DCNN) image classifiers. We have fine-tuned the top ten pre-trained DCNN image classifiers and evaluated them using five class single-cell Pap smear images from SIPaKMeD dataset. The pre-trained DCNN image classifiers were selected from Keras Applications based on their top 1% accuracy. Results Our experimental result demonstrated that from the selected top-ten pre-trained DCNN image classifiers DenseNet169 outperformed with an average accuracy, precision, recall, and F1-score of 0.990, 0.974, 0.974, and 0.974, respectively. Moreover, it dashed the benchmark accuracy proposed by the creators of the dataset with 3.70%. Conclusions Even though the size of DenseNet169 is small compared to the experimented pre-trained DCNN image classifiers, yet, it is not suitable for mobile or edge devices. Further experimentation with mobile or small-size DCNN image classifiers is required to extend the applicability of the models in real-world demands. In addition, since all experiments used the SIPaKMeD dataset, additional experiments will be needed using new datasets to enhance the generalizability of the models.

Download Full-text

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

Faraday Discussions ◽

10.1039/d1fd00050k ◽

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,...

Download Full-text

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

Journal of X-Ray Science and Technology ◽

10.3233/xst-210955 ◽

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.

Download Full-text

USE OF GENETIC ALGORITHM IN DEEP NEURAL NETWORKS CONFIGURATION FOR THE PURPOSES OF COMPUTER ATTACKS CLASSIFICATION

10.22250/isu.2020.66.104-117 ◽

2020 ◽

pp. 104-117

Author(s):

O.S. Amosov ◽

◽

S.G. Amosova ◽

D.S. Magola ◽

◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Network Architecture ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Classification Problem ◽

Neural Network Architecture ◽

Computer Attacks ◽

Neural Network Technology

The task of multiclass network classification of computer attacks is given. The applicability of deep neural network technology in problem solving has been considered. Deep neural network architecture was chosen based on the strategy of combining a set of convolution and recurrence LSTM layers. Op-timization of neural network parameters based on genetic algorithm is proposed. The presented results of modeling show the possibility of solving the network classification problem in real time.

Download Full-text

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

Chemical Science ◽

10.1039/c8sc04578j ◽

2019 ◽

Vol 10 (15) ◽

pp. 4129-4140 ◽

Cited By ~ 9

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.

Download Full-text

Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments

Complexity ◽

10.1155/2019/5296123 ◽

2019 ◽

Vol 2019 ◽

pp. 1-21 ◽

Cited By ~ 1

Author(s):

Shixi Tang ◽

Jinan Gu ◽

Keming Tang ◽

Wei Ding ◽

Zhengyang Shang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Prediction Model ◽

Deep Neural Networks ◽

Complete Solution ◽

Partial Solution ◽

Complex Environments ◽

Solution Theory ◽

Analysis Model ◽

Neural Network Structure

The robot dynamic model is often rarely known due to various uncertainties such as parametric uncertainties or modeling errors existing in complex environments. It is a key problem to find the relationship between the changes of neural network structure and the changes of input and output environments and their mutual influences. Firstly, this paper defined the conceptions of neural network solution, neural network eigen solution, neural network complete solution, and neural network partial solution and the conceptions of input environments, output environments, and macrostructure of neural networks. Secondly, an eigen solution theory of general neural networks was proposed and proven including consistent approximation theorem, eigen solution existence theorem, consistency theorem of complete solution, the partial solution, and none solution theorem of neural networks. Lastly, to verify the eigen solution theory of neural networks, the proposed theory was applied to a novel prediction and analysis model of controller parameters of grinding robot in complex environments with deep neural networks and then build prediction model with deep learning neural networks for controller parameters of grinding robot. The morphological subfeature graph with multimoment was constructed to describe the block surface morphology using rugosity, standard deviation, skewness, and kurtosis. The results of theoretical analysis and experimental test show that the output traits have an optional effect with joint action. When the input features functioning in prediction increase, higher predicted accuracy can be obtained. And when the output traits involving in prediction increase, more output traits can be predicted. The proposed prediction and analysis model with deep neural networks can be used to find and predict the inherent laws of the data. Compared with the traditional prediction model, the proposed model can predict output features simultaneously and is more stable.

Download Full-text

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

Anesthesiology ◽

10.1097/aln.0000000000002186 ◽

2018 ◽

Vol 129 (4) ◽

pp. 649-662 ◽

Cited By ~ 48

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.

Download Full-text