Classics of Deep Learning Approach for Human Behaviour Ontology: A Survey
 |
International Journal of Computer Trends and Technology (IJCTT) | 
|
| © 2017 by IJCTT Journal | ||
| Volume-51 Number-1 |
||
| Year of Publication : 2017 | ||
| Authors : H. R. Divakar, Dr.B.R.Prakash | ||
| DOI : 10.14445/22312803/IJCTT-V51P107 |
H. R. Divakar, Dr.B.R.Prakash "Classics of Deep Learning Approach for Human Behaviour Ontology: A Survey". International Journal of Computer Trends and Technology (IJCTT) V51(1):46-51, September 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
As being a human with all technological equipments we cannot predict the different mode of communication, reaction and predicted behaviour for the current situation. So, in case of health centre to represent their current health status using social networks, having different conventional methods, among them we have Ontology is one of the technology derived from WordSet where we can retrieve information form semantic web for the cross reference of the given input regarding health social activity of a person based on the posts in online social networks allowing access between persons and places, for this we have a tool called SMASH ontology which works on the basis of semantic web. Where it access the middle ware for the human personal behaviour factor and social co-relation factor, the best prediction accuracy permit the development of semantic parsers to ontology specific queries for deep learning as ORBM. Now a day’s many online health information sites having human behaviour predictions done with the some of the tools like RBM, CRBM, SRBM, ORBM and follows.
References
[1] Xiaoqian Liu and Tingshao Zhu. 2016. Deep learning for constructing microblog behaviour representation to identify social media user’s personality. PeerJ Computer. Sci. 2:e81; DOI 10.7711/peerj-cs.81.
[2] N. Phan, D. Dou, B. peniewski, and D. Kil. 2015. Social restricted Boltzmann machine: Human behaviour prediction in health social networks. In ASONAM’15.
[3] N. Phan, D. Dou, H. Wang, B. peniewski, and D. Kil. 2015. Ontology-based Deep Learning for Human Behaviour Prediction in Health Social Networks. In ACM-BCB’15.
[4] N.Phan et al., 2016. Ontology-based deep learning for human behaviour prediction with explanations in health social networks, Information Sciences.
[5] Charissa Ann Ronao, Sung-Bae Cho. 2016. Human activity recognition with smartphone sensors using deep learning neural networks. Elsevier.
[6] Edward Grefenstette, Phil Blunsom, Nando de Freitas and Karl Moritz Hermann. 2014. A Deep Architecture for Semantic Parsing. Proceedings of the ACL workshop on Semantic Parsing, pages 22-27.
[7] H. Min et al, Applying an Ontology-guided Machine Learning Methodology to SEER-MHOS Dataset.
[8] Li Deng. Three Classes of Deep Learning Architectures and Their Applications: A Tutorial Survey.
[9] Zhang X, Hu B, Chen J, Moore P. 2013. Ontology-based context modeling for emotions recognition in an intelligent web. World Wide Web-internet and Web Information Systems 16(4):497-513 DOI 10.10007/s11280-012-0181-5.
[10] Chen J, Hu B, Moore P, Zhang X, Ma X. 2015. Electroencephalogram-based emotion assessment system using ontology and data mining techniques. Applied Soft Computing 30:663-674 DOI 10.1016/j.asoc.2015.01.007.
[11] Hujje L, Jia J, Quan G, Yuanyuan X, Jie H, Lianhong C, Ling F. 2014a. Psychological stress detection from cross-media microblog data using deep sparse neural network. In: Proceedings of IEEE international conference on multimedia expo. Piscataway: IEEE.
[12] Gao R, Hao B, Li H, GAO Y, Zhu T. 2013. Developing simplified Chinese psychological linguistic analysis dictionary for microblog. In: International conference on brain health informatics.
[13] Bengio Y. 2009. Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1):1-127 DOI 10.1561/2200000006.
[14] P. Smolensky, Information processing in dynamical systems: foundations of harmony theory, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1, MIT Press, 1986, pp. 194-281.
[15] T. Gruber. A translation approach to portable ontology specifications, Knowl. Acquisition 5(2) (1993) 199-220.
[16] R. M. Ryan and E. L. Deci. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psycology, 25(1):54-67, 2000.
[17] N. A. Christakis. The hidden influence of social networks. In TED2010.2010. URL http://www.ted.com/talks/nicholas_christakis_the_hidden_influence_of_social_networks.
[18] T. R. Gruber. Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-computer Studies, 43(5):907-928, 1995.
[19] R. Studer, V. R. Benjamins, and D. Fensel. Knowledge engineering: principles and methods. Data & knowledge engineering, 25(1):161-197, 1998.
[20] Plotz. T., Hammerla. N. Y. & Olivier. P (2011). Feature learning for activity recognition in ubiquitous computing. In proceedings of international conference on artificial intelligence (IJCAI): Vol. 2 (pp. 1729-1734).
[21] LeCun. Y., Bengio. Y. & Hintion. G. (2015). Deep learning Nature. 521(7553), 436-444.
[22] Duong. T., Phung. D., Bui. H. & Venkatesh. S. (2009). Efficient duration and hierarchical modeling for human activity recognition. Artificial Intelligence, 173(7-8), 830-856.
[23] Bhattacharya. S., Nurmi. P., Hammerla. N. & Plotz. T. (2014). Using unlabeled data in a sparse-coding framework for human activity recognition. Pervasive and mobile computing. 15, 242-262.
[24] Li. Y., Shi. D., Ding. B. & Liu. D. (2014). Unsupervised feature learning for human activity recognition using smartphone sensors. Mining Intelligence and knowledge Exploration. 8891, 99-107.
[25] Vollmer. C., Gross. H. M. & Eggert. J. P. (2013). Learning features for activity recognition with shift-invariant sparse coding. In Proceedings of international conference on artificial neural networks and machine learning (ICANN): Vol. 8131 (pp. 367-374).
[26] LeCun. Y., Bengio. Y. (1998). Convolutional networks for images, speech, and time-series. In The handbook of brain theory and neural networks (pp. 255-258). The MIT Press.
[27] Zeng. M., Nguyen. L. T., Yu. B., Mengshoel. O. J., Zhu. J., Wu. P. & Zang. J. (2014). Convolutional neural networks for human activity recognition using mobile sensors. In Proceedings of international conference on mobile computing, applications and services (MobiCASE) (pp. 197-205).
[28] Zheng. Y., Liu. Q., Chen. E., Ge. Y. & Zhao. J. L. (2014). Time series classification using multi-channels deep convolutional neural networks. In Web-age information management. Lecture notes in computer science: Vol. 8485 (pp. 298-310). Springer.
[29] Lee. Y. S. & Cho. S. B. (2011). Activity recognition using hierarchical hidden markow models on a smartphone with 3D accelerometer. Hybrid Artificial Intelligent Systems. 6678. 460-467.
[30] Tom Kwiatkowski, Eunsol Choi, uoav Artzi, and Luke Zettlemoyer. 2013. Scaling semantic parsers with on-the-fly ontology matching. In proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1545-1556, Seattle, Washington, USA, October. Association for Computational Linguistics.
[31] Karl Moritz Hermann and Phil Blunsom. 2014a. Multilingual Distributed Representation without Word Alignment. In proceedings of the 2nd International Conference on Learning Represenations, Banff, Canada, April.
[32] Karl Moritz Hermann and Phil Blunsom. 2014b. Multilingual Models for Compositional Distributional Semantics. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, USA, June. Association for Computational Linguistics.
[33] Quingqing Cai and Alexander Yates. 2013. Large-scale Semantic Parsing via Schema Matching and Lexicon Extension. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL).
Keywords
Semantic Web, Ontology, SMASH Ontology, Social networks, Deep learning, RBM, CRBM, SRBM, ORBM.