1 |
S. Hafner et al., "To live alone and to be depressed, an alarming combination for the renin-angiotensin-aldosterone-system (RAAS)," Psychoneuroendocrinology, vol. 37, no. 2, pp. 230-237, 2012.
DOI
|
2 |
N. Eyal, S. A. Hurst, O. F. Norheim, and D. Wikler, Inequalities in health: concepts, measures, and ethics, Oxford University Press, 2013.
|
3 |
B. Risteska Stojkoska, K. Trivodaliev, and D. Davcev, "Internet of things framework for home care systems," Wireless Communications and Mobile Computing, vol. 2017, Article ID 8323646, 2017.
|
4 |
B. L. R. Stojkoska and K. V. Trivodaliev, "A review of Internet of Things for smart home: Challenges and solutions," Journal of Cleaner Production, vol. 140, pp. 1454-1464, 2017.
DOI
|
5 |
N. K. Suryadevara and S. C. Mukhopadhyay, "Smart homes: design, implementation and issues," Springer, vol. 14, 2015.
|
6 |
M. Amiribesheli, A. Benmansour, and A. Bouchachia, "A review of smart homes in healthcare," Journal of Ambient Intelligence and Humanized Computing, vol. 6, no. 4, pp. 495-517, 2015.
DOI
|
7 |
J. Dahmen, D. J. Cook, X. Wang, and W. Honglei, "Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats," Journal of Reliable Intelligent Environments, vol. 3, pp. 83-98, 2017.
DOI
|
8 |
G.-J. Ra and I.-Y. Lee, "A Study on KSI-based Authentication Management and Communication for Secure Smart Home Environments," KSII Transactions on Internet and Information Systems (TIIS), vol. 12, no. 2, pp. 892-905, 2018.
DOI
|
9 |
M. Theoharidou, N. Tsalis, and D. Gritzalis, "Smart Home Solutions: Privacy Issues," Handbook of Smart Homes, Health Care and Well-Being, pp. 67-81, 2017.
|
10 |
M. Khan, B. N. Silva, C. Jung, and K. Han, "A context-Aware smart home control system based on ZigBee sensor network," KSII Transactions on Internet and Information Systems (TIIS), vol. 11, no. 2, pp. 1057-1069, 2017.
DOI
|
11 |
O. D. Lara and M. A. Labrador, "A survey on human activity recognition using wearable sensors," IEEE Communications Surveys and Tutorials, vol. 15, no. 3, pp. 1192-1209, 2013.
DOI
|
12 |
C. Zhu, W. Sheng, and M. Liu, "Wearable sensor-based behavioral anomaly detection in smart assisted living systems," IEEE Transactions on Automation Science and Engineering, vol. 12, no. 4, pp. 1225-1234, 2015.
DOI
|
13 |
Q. Ni, A. B. García Hernando, and I. Pau de la Cruz, "A context-aware system infrastructure for monitoring activities of daily living in smart home," Journal of Sensors, vol. 2016, 2016.
|
14 |
J. Dahmen, B. L. Thomas, D. J. Cook, and X. Wang, "Activity Learning as a Foundation for Security Monitoring in Smart Homes," Sensors, vol. 17, no. 4, p. 737, 2017.
DOI
|
15 |
P. Rashidi, D. J. Cook, L. B. Holder, and M. Schmitter-Edgecombe, "Discovering activities to recognize and track in a smart environment," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 4, pp. 527-539, 2011.
DOI
|
16 |
U. Bakar, H. Ghayvat, S. Hasanm, and S. Mukhopadhyay, "Activity and anomaly detection in smart home: A survey," Next Generation Sensors and Systems, Springer, pp. 191-220, 2015.
|
17 |
S. Enno-Edzard, F. Thomas, E. Marco, F. Melina, and H. Andreas, "Modeling individual healthy behavior using home automation sensor data: Results from a field trial," Journal of Ambient Intelligence and Smart Environments, vol. 5, no. 5, pp. 503-523, 2013.
DOI
|
18 |
E. Nazerfard and D. J. Cook, "CRAFFT: An Activity Prediction Model based on Bayesian Networks," Journal of Ambient Intelligence and Humanized Computing, vol. 6, no. 2, pp. 193-205, 2015.
DOI
|
19 |
J. Tonejc, S. Güttes, A. Kobekova, "Machine Learning Methods for Anomaly Detection in BACnet Networks," Journal of Universal Computer Science, vol. 22, no. 9, pp. 1203-1224, 2016.
|
20 |
V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection: A survey," ACM Computing Surveys (CSUR), vol. 41, no. 3, p. 15, 2009.
|
21 |
J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques, Elsevier, 2011.
|
22 |
G. F. Cooper and E. Herskovits, "A Bayesian method for the induction of probabilistic networks from data," Machine Learning, vol. 9, no. 4, pp. 309-347, 1992.
DOI
|
23 |
A. Abu-Samah, N. N. A. Razak, F. M. Suhaimi, U. K. Jamaludin, and J. G. Chase, "Towards Personalized Intensive Care Decision Support Using a Bayesian Network: A Multicenter Glycemic Control Study," IEIE Transactions on Smart Signal, vol. 8, no. 3, pp. 202-209, 2019.
|
24 |
F. J. Ordonez, P. de Toledo, and A. Sanchis, "Sensor-based Bayesian detection of anomalous living patterns in a home setting," Personal and Ubiquitous Computing, vol. 19, no. 2, pp. 259-270, 2015.
DOI
|
25 |
S. Mascaro, A. E. Nicholso, and K. B. Korb, "Anomaly detection in vessel tracks using Bayesian networks," International Journal of Approximate Reasoning, vol. 55, no. 1, pp. 84-98, 2014.
DOI
|
26 |
P. Daneshjoo, H. H. S. Javadi, and H. R. Sharifi, "Sink Location Service Based on Fano Plane in Wireless Sensor Networks," Journal of Communication Engineering (JCE), vol. 5, no. 10, 2016.
|
27 |
L. Xiao, Y. Chen, and C. K. Chang, "Bayesian model averaging of bayesian network classifiers for intrusion detection," in Proc. of Computer Software and Applications Conference Workshops (COMPSACW), 2014 IEEE 38th International, pp. 128-133, 2014.
|
28 |
A. Gruber and I. Ben-Gal, "Using targeted Bayesian network learning for suspect identification in communication networks," International Journal of Information Security, vol. 17, no. 2, pp. 169-181, 2018.
DOI
|
29 |
D. Gutchess, N. Checka, and M. S. Snorrason, "Learning patterns of human activity for anomaly detection," Intelligent Computing: Theory and Applications. V SPIE Orlando FL USA, vol. 6560, pp. 65600Y-12, 2007.
|
30 |
J. Loane, B. O'Mullane, B. Bortz, and R. B. Knapp, "Interpreting presence sensor data and looking for similarities between homes using cluster analysis," in Proc. of Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2011 5th International Conference on, pp. 438-445, 2011.
|
31 |
Y. Song, Z. Wen, C.-Y. Lin, and R. Davis, "One-Class Conditional Random Fields for Sequential Anomaly Detection," in Proc. of International Joint Conferences on Artificial Intelligence, pp. 1685-1691, 2013.
|
32 |
F. Cardinaux, S. Brownsell, M. Hawley, and D. Bradley, "Modelling of behavioural patterns for abnormality detection in the context of lifestyle reassurance," Iberoamerican Congress on Pattern Recognition, pp. 243-251, 2008.
|
33 |
M. Novak, M. Binas, and F. Jakab, "Unobtrusive anomaly detection in presence of elderly in a smart-home environment," in Proc. of 2012 ELEKTRO, pp. 341-344, 2012.
|
34 |
E. Hoque and J. Stankovic, "Semantic anomaly detection in daily activities," in Proc. of the 2012 ACM Conference on Ubiquitous Computing, pp. 633-634, 2012.
|
35 |
D. Heckerman, D. Geiger, and D. M. Chickering, "Learning Bayesian networks: The combination of knowledge and statistical data," Machine Learning, vol. 20, no. 3, pp. 197-243, 1995.
DOI
|
36 |
S. Hela, B. Amel, and R. Badran, "Early anomaly detection in smart home: A causal association rule-based approach," Artificial Intelligence in Medicine, vol. 91, pp. 57-71, 2018.
DOI
|
37 |
J. Yin, Q. Yang, and J. J. Pan, "Sensor-based abnormal human-activity detection," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 8, pp. 1082-1090, 2008.
DOI
|
38 |
A. R. M. Forkan, I. Khalil, Z. Tari, S. Foufou, and A. Bouras, "A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living," Pattern Recognition, vol. 48, no. 3, pp. 628-641, 2015.
DOI
|
39 |
K. Park, Y. Lin, V. Metsis, Z. Le, and F. Makedon, "Abnormal human behavioral pattern detection in assisted living environments," in Proc. of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments, p. 1-8, 2010.
|
40 |
J. H. Shin, B. Lee, and K. S. Park, "Detection of abnormal living patterns for elderly living alone using support vector data description," IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 3, pp. 438-448, 2011.
DOI
|
41 |
Koller D., Friedman N., Probabilistic Graphical Model, MIT Press, Massachusetts, 2009.
|
42 |
H. Amirkhani, M. Rahmati, P. J. Lucas, and A. Hommersom, "Exploiting Experts' Knowledge for Structure Learning of Bayesian Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 11, pp. 2154-2170, 2017.
DOI
|
43 |
H. Amirkhani and M. Rahmati, "Expectation maximization based ordering aggregation for improving the K2 structure learning algorithm," Intelligent Data Analysis, vol. 19, pp. 1003-1018, Sep. 2015.
DOI
|
44 |
Y. Han, M. Han, S. Lee, A. Sarkar, and Y.-K. Lee, "A framework for supervising lifestyle diseases using long-term activity monitoring," Sensors, vol. 12, no. 5, pp. 5363-5379, 2012.
DOI
|
45 |
P. Spirtes and C. Glymour, "An algorithm for fast recovery of sparse causal graphs," Social Science Computer Review, vol. 9, no. 1, pp. 62-72, 1991.
DOI
|
46 |
A. Ankan and A. Panda, "Mastering Probabilistic Graphical Models Using Python," in Proc. of THE 14th PYTHON IN SCIENCE CONF (SCIPY 2015), 2015.
|
47 |
R. Daly, Q. Shen, and S. Aitken, "Learning Bayesian networks: approaches and issues," The Knowledge Engineering Review, vol. 26, no. 2, pp. 99-157, 2011.
DOI
|
48 |
T. Van Kasteren, A. Noulas, G. Englebienne, and B. Krose, "Accurate activity recognition in a home setting," in Proc. of the 10th international conference on Ubiquitous computing, pp. 1-9, 2008.
|
49 |
D. J. Cook, "Learning setting-generalized activity models for smart spaces," IEEE Intelligent Systems, vol. 27, no. 1, pp. 32-38, 2012.
DOI
|
50 |
K. P. Murphy, "Performance evaluation of binary classifiers," Technical Report, University of British Columbia, Report, 2007.
|