Stress classification for gender bias in reading

Nandita Sharma; Tom Gedeon

doi:10.1007/978-3-642-24965-5_39

Stress classification for gender bias in reading

Nandita Sharma^*, Tom Gedeon

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

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.

Original language	English
Title of host publication	Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Pages	348-355
Number of pages	8
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-24965-5_39
Publication status	Published - 2011
Event	18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China Duration: 13 Nov 2011 → 17 Nov 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	7064 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Neural Information Processing, ICONIP 2011
Country/Territory	China
City	Shanghai
Period	13/11/11 → 17/11/11

Access to Document

10.1007/978-3-642-24965-5_39

Cite this

Sharma, N., & Gedeon, T. (2011). Stress classification for gender bias in reading. In Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings (PART 3 ed., pp. 348-355). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7064 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-24965-5_39

@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",

}

Sharma, N & Gedeon, T 2011, Stress classification for gender bias in reading. in Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 7064 LNCS, pp. 348-355, 18th International Conference on Neural Information Processing, ICONIP 2011, Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24965-5_39

Stress classification for gender bias in reading. / Sharma, Nandita; Gedeon, Tom.
Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 3. ed. 2011. p. 348-355 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7064 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Stress classification for gender bias in reading

AU - Sharma, Nandita

AU - Gedeon, Tom

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - artificial neural networks

KW - classification

KW - galvanic skin response

KW - gender stress

KW - reading

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U2 - 10.1007/978-3-642-24965-5_39

DO - 10.1007/978-3-642-24965-5_39

M3 - Conference contribution

SN - 9783642249648

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 348

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BT - Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings

T2 - 18th International Conference on Neural Information Processing, ICONIP 2011

Y2 - 13 November 2011 through 17 November 2011

ER -

Stress classification for gender bias in reading

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