scholarly journals Deep Network Pharmacology: Targeting Glutamate Systems as Integrative Treatments for Jump-Starting Neural Networks and Recovery Trajectories

2021 ◽  
Keyword(s):  
Neural Networks ◽  
Network Pharmacology ◽  
Deep Network

scholarly journals SVM-Based Deep Stacking Networks

2019 ◽  
Vol 33 ◽  
pp. 5273-5280 ◽  
Author(s):  
Jingyuan Wang ◽  
Kai Feng ◽  
Junjie Wu
Keyword(s):  
Neural Networks ◽  
High Performance ◽  
Optimal Solution ◽  
Data Sets ◽  
Complex Data ◽  
Layer By Layer ◽  
Deep Network ◽  
Local View ◽  
Linear Svm ◽  
Network Approaches

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models.

scholarly journals Understanding unconventional preprocessors in deep convolutional neural networks for face identification

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Chollette C. Olisah ◽  
Lyndon Smith
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Color Space ◽  
Back Propagation ◽  
Face Identification ◽  
Deep Convolutional Neural Networks ◽  
Softmax Classifier ◽  
Deep Network

Abstract Deep convolutional neural networks have achieved huge successes in application domains like object and face recognition. The performance gain is attributed to different facets of the network architecture such as: depth of the convolutional layers, activation function, pooling, batch normalization, forward and back propagation and many more. However, very little emphasis is made on the preprocessor’s module of the network. Therefore, in this paper, the network’s preprocessing module is varied across different preprocessing approaches while keeping constant other facets of the deep network architecture, to investigate the contribution preprocessing makes to the network. Commonly used preprocessors are the data augmentation and normalization and are termed conventional preprocessors. Others are termed the unconventional preprocessors, they are: color space converters; grey-level resolution preprocessors; full-based and plane-based image quantization, Gaussian blur, illumination normalization and insensitive feature preprocessors. To achieve fixed network parameters, CNNs with transfer learning is employed. The aim is to transfer knowledge from the high-level feature vectors of the Inception-V3 network to offline preprocessed LFW target data; and features is trained using the SoftMax classifier for face identification. The experiments show that the discriminative capability of the deep networks can be improved by preprocessing RGB data with some of the unconventional preprocessors before feeding it to the CNNs. However, for best performance, the right setup of preprocessed data with augmentation and/or normalization is required. Summarily, preprocessing data before it is fed to the deep network is found to increase the homogeneity of neighborhood pixels even at reduced bit depth which serves for better storage efficiency.

scholarly journals Adapting Deep Network Features to Capture Psychological Representations: An Abridged Report

2017 ◽  
Author(s):  
Joshua C. Peterson ◽  
Joshua T. Abbott ◽  
Thomas L. Griffiths
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Object Classification ◽  
Deep Network ◽  
Similarity Judgments ◽  
Animal Images ◽  
Image Representations ◽  
The Relationship ◽  
Psychological Experiments

Deep neural networks have become increasingly successful at solving classic perception problems (e.g., recognizing objects), often reaching or surpassing human-level accuracy. In this abridged report of Peterson et al. [2016], we examine the relationship between the image representations learned by these networks and those of humans. We find that deep features learned in service of object classification account for a significant amount of the variance in human similarity judgments for a set of animal images. However, these features do not appear to capture some key qualitative aspects of human representations. To close this gap, we present a method for adapting deep features to align with human similarity judgments, resulting in image representations that can potentially be used to extend the scope of psychological experiments and inform human-centric AI.

scholarly journals Multi-View Deep Network: A Deep Model Based on Learning Features From Heterogeneous Neural Networks for Sentiment Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 86984-86997
Author(s):  
Hossein Sadr ◽  
Mir Mohsen Pedram ◽  
Mohammad Teshnehlab
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Deep Network ◽  
Model Based ◽  
Deep Model

scholarly journals Benchmarking deep network architectures for ethnicity recognition using a new large face dataset

2020 ◽  
Vol 31 (7-8) ◽  
Author(s):  
Antonio Greco ◽  
Gennaro Percannella ◽  
Mario Vento ◽  
Vincenzo Vigilante
Keyword(s):  
Neural Networks ◽  
Network Architectures ◽  
Related Factors ◽  
Deep Network ◽  
Soft Biometrics ◽  
Face Images ◽  
Race Effect ◽  
Effective Training ◽  
The One ◽  
Better Than

Abstract Although in recent years we have witnessed an explosion of the scientific research in the recognition of facial soft biometrics such as gender, age and expression with deep neural networks, the recognition of ethnicity has not received the same attention from the scientific community. The growth of this field is hindered by two related factors: on the one hand, the absence of a dataset sufficiently large and representative does not allow an effective training of convolutional neural networks for the recognition of ethnicity; on the other hand, the collection of new ethnicity datasets is far from simple and must be carried out manually by humans trained to recognize the basic ethnicity groups using the somatic facial features. To fill this gap in the facial soft biometrics analysis, we propose the VGGFace2 Mivia Ethnicity Recognition (VMER) dataset, composed by more than 3,000,000 face images annotated with 4 ethnicity categories, namely African American, East Asian, Caucasian Latin and Asian Indian. The final annotations are obtained with a protocol which requires the opinion of three people belonging to different ethnicities, in order to avoid the bias introduced by the well-known other race effect. In addition, we carry out a comprehensive performance analysis of popular deep network architectures, namely VGG-16, VGG-Face, ResNet-50 and MobileNet v2. Finally, we perform a cross-dataset evaluation to demonstrate that the deep network architectures trained with VMER generalize on different test sets better than the same models trained on the largest ethnicity dataset available so far. The ethnicity labels of the VMER dataset and the code used for the experiments are available upon request at https://mivia.unisa.it.

A note on the applications of one primary function in deep neural networks

2021 ◽  
Author(s):  
Hengjie Chen ◽  
Zhong Li
Keyword(s):  
Neural Networks ◽  
Mathematical Knowledge ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Continuous Functions ◽  
Middle Point ◽  
Deep Network ◽  
Rectified Linear Unit ◽  
The Mean ◽  
The Difference

By applying fundamental mathematical knowledge, this paper proves that the function [Formula: see text] is an integer no less than [Formula: see text] has the property that the difference between the function value of middle point of arbitrarily two adjacent equidistant distribution nodes on [Formula: see text] and the mean of function values of these two nodes is a constant depending only on the number of nodes if and only if [Formula: see text] By them, we establish an important result about deep neural networks that the function [Formula: see text] can be interpolated by a deep Rectified Linear Unit (ReLU) network with depth [Formula: see text] on the equidistant distribution nodes in interval [Formula: see text] and the error of approximation is [Formula: see text] Then based on the main result that has just been proven and the Chebyshev orthogonal polynomials, we construct a deep network and give the error estimate of approximation to polynomials and continuous functions, respectively. In addition, this paper constructs one deep network with local sparse connections, shared weights and activation function [Formula: see text] and discusses its density and complexity.

scholarly journals Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals

2021 ◽  
Author(s):  
Sadegh Arefnezhad ◽  
Arno Eichberger ◽  
Matthias Frühwirth ◽  
Clemens Kaufmann ◽  
Maximilian Moser ◽  
...  
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Traffic Accidents ◽  
Bayesian Optimization ◽  
Video Observation ◽  
Traditional Methods ◽  
Ecg Signals ◽  
Deep Network ◽  
Driver Drowsiness ◽  
Automated Modes

Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were de-fined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, Heart Rate Variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated modes, respectively. However, the best obtained balanced accuracies using traditional methods are about 62% and 64%. We conclude that designed deep networks working with wavelet scalogram images of ECG signals significantly outperform KNN and random forest classifiers which are trained on HRV-based features.

scholarly journals Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 480
Author(s):  
Sadegh Arefnezhad ◽  
Arno Eichberger ◽  
Matthias Frühwirth ◽  
Clemens Kaufmann ◽  
Maximilian Moser ◽  
...  
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Traffic Accidents ◽  
Bayesian Optimization ◽  
Video Observation ◽  
Traditional Methods ◽  
Ecg Signals ◽  
Deep Network ◽  
Driver Drowsiness ◽  
Automated Modes

Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were defined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, heart rate variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated modes, respectively. However, the best obtained balanced accuracies using traditional methods are about 62% and 64%. We conclude that designed deep networks working with wavelet scalogram images of ECG signals significantly outperform KNN and random forest classifiers which are trained on HRV-based features.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural
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