@inproceedings{d928d00b17d549d18b400e61c46c5ba6,
title = "Stress classification for gender bias in reading",
abstract = "The paper investigates classification of stress in reading for males and females based on an artificial neural network model (ANN). An experiment was conducted, with stressful and non-stressful reading material as stimuli, to obtain galvanic skin response (GSR) signals, a good indicator of stress. GSR signals formed the input of the ANN with stressed and non-stressed states as the two output classes. Results show that stress in reading for males compared to females are significantly different (p < 0.01), with males showing different patterns in GSR signals to females.",
keywords = "artificial neural networks, classification, galvanic skin response, gender stress, reading",
author = "Nandita Sharma and Tom Gedeon",
year = "2011",
doi = "10.1007/978-3-642-24965-5_39",
language = "English",
isbn = "9783642249648",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 3",
pages = "348--355",
booktitle = "Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings",
edition = "PART 3",
note = "18th International Conference on Neural Information Processing, ICONIP 2011 ; Conference date: 13-11-2011 Through 17-11-2011",
}