Corrigendum to “A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals” [Biomed. Signal Process. Control 8 (2013) 740–754]

2014 ◽  
Vol 10 ◽  
pp. 144
Author(s):  
Rajiv Ranjan Singh ◽  
Sailesh Conjeti ◽  
Rahul Banerjee
Keyword(s):  
Neural Network ◽  
Process Control ◽  
Stress Level ◽  
Comparative Evaluation ◽  
Physiological Signals ◽  
Signal Process ◽  
Neural Network Classifiers ◽  
Level Analysis

Rancang Bangun Sistem Pakar Prediksi Stres Belajar Dengan Neural Network Algoritma Backpropagation

2016 ◽  
Vol 7 (2) ◽  
pp. 105-112
Author(s):  
Adhi Kusnadi ◽  
Idul Putra
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Stress Level ◽  
Network Structure ◽  
Human Being ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Stress Prediction ◽  
Index Terms ◽  
Hidden Nodes

Stress will definitely be experienced by every human being and the level of stress experienced by each individual is different. Stress experienced by students certainly will disturb their study if it is not handled quickly and appropriately. Therefore we have created an expert system using a neural network backpropagation algorithm to help counselors to predict the stress level of students. The network structure of the experiment consists of 26 input nodes, 5 hidden nodes, and 2 the output nodes, learning rate of 0.1, momentum of 0.1, and epoch of 5000, with a 100% accuracy rate. Index Terms - Stress on study, expert system, neural network, Stress Prediction

scholarly journals Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Jack Y. Araz ◽  
Michael Spannowsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Top Quark ◽  
Feature Space ◽  
Training Sample ◽  
Bayesian Techniques ◽  
Training Sample Size ◽  
Event Reconstruction ◽  
Neural Network Classifiers ◽  
Quark Jets

Abstract Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

Relationship between rework of engineering drawing tasks and stress level measured from physiological signals

2021 ◽  
Vol 124 ◽  
pp. 103560
Author(s):  
Jeonghyeun Chae ◽  
Sungjoo Hwang ◽  
Wonkyoung Seo ◽  
Youngcheol Kang
Keyword(s):  
Stress Level ◽  
Physiological Signals ◽  
Engineering Drawing ◽  
Drawing Tasks

scholarly journals Neural network classifiers for images of genetic conditions with cutaneous manifestations

2021 ◽  
pp. 100053 ◽  
Author(s):  
Dat Duong ◽  
Rebekah L. Waikel ◽  
Ping Hu ◽  
Cedrik Tekendo-Ngongang ◽  
Benjamin D. Solomon
Keyword(s):  
Neural Network ◽  
Cutaneous Manifestations ◽  
Neural Network Classifiers
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