TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data
Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction features and social surveys for finding human stress behavior. For this, we gathered mobile phone...
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| Main Authors: | , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2016-10-01.
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| Subjects: | |
| Online Access: | Get fulltext |
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| LEADER | 02323 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs5802_4576 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Padmaja, B. |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a V. Rama Prasad, V. |e author |
| 700 | 1 | 0 | |a V. N, K. |e author |
| 245 | 0 | 0 | |a TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data |
| 260 | |b Institute of Advanced Engineering and Science, |c 2016-10-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/5802 | ||
| 520 | |a Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction features and social surveys for finding human stress behavior. For this, we gathered mobile phone call logs data set containing 111444 voice calls of 131 adult members of a living community for a period of more than 5 months. And we identified that top 5 social network measures like hierarchy, density, farness, reachability and eigenvector of individuals have profound influence on individuals stress levels in a social network. If an ego lies in the shortest path of all other alters then the ego receives more information and hence is more stressed. In this paper, we have used TreeNet machine learning algorithm for its speed and immune to outliers. We have tested our results with another Random Forest classifier as well and yet, we found TreeNet to be more efficient. This research can be of vital importance to economists, professionals, analysts, and policy makers. | ||
| 540 | |a Copyright (c) 2016 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Reality Mining, Social Network Analysis, Sensor data, Human Stress | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 4, No 1: October 2016; 148-154 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v4.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/5802/4576 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/5802/4576 |z Get fulltext |