The week's pick
Random Articles
Reseach Article
An Efficient Feature Selection Technique using Genetic Algorithm for Activity Recognition of Elder People
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 3 |
| Year of Publication: 2017 |
| Authors: Shreya Gupta |
10.5120/ijca2017915910
|
Shreya Gupta . An Efficient Feature Selection Technique using Genetic Algorithm for Activity Recognition of Elder People. International Journal of Computer Applications. 179, 3 ( Dec 2017), 37-42. DOI=10.5120/ijca2017915910
Abstract
Activity recognition technique is critical for the elderly people. To avoid the need of full time care giving service, the actual trend is to encourage elderly to stay living autonomously in their homes as long as possible. Reliable activity recognition techniques can improve life safety of the elderly and boost their confidence by immediately alerting fall cases to caregivers. Machine learning techniques are extensively used to predict the activity recognition of elder people. This paper has proposed the integration of the feature selection technique and genetic algorithms to improve the accuracy rate for detection of activity recognition of elder people as well as substantial features of activity recognition of elder people. The experimental results brings about the proposed technique that clearly shown the fact that proposed technique outperforms over the existing methods.
References
- S. Sasikala, S.A.A. Balamurugana, S. Geetha, “A novel adaptive feature selector for supervised classification”, Information Processing Letters 117 (2017) 25–34.
- M.E. Borsuk and C.A. Stow, “Bayesian parameter estimation in a mixed-order model of BOD decay,” Water Research, vol. 34(6),pp.1830-1836, 2000.
- Z. Zhu, Y.S. Ong, M. Dash, Wrapper–filter feature selection algorithm using a memetic framework, IEEE Trans. Syst. Man Cybern., Part B 10(4) (2006) 392–404.
- S. Y. Muhammad, M. Makhtar, A. Rozaimee, A. A. Aziz, A. A. Jamal, “Classification Model for Activity recognition of elder people using Machine Learning Techniques”, International Journal of Software Engineering and Its Applications Vol. 9, No. 6 (2015), pp. 45-52.
- S.C. Azhar, A.Z. Arisa, M.K.Yusoffa, M.F. Ramli, H. Juahir, “Classification of river activity recognition of elder people using multivariate analysis”, International Conference on Environmental Forensics 2015 (iENFORCE2015).
- J.R. Barclay, H. Tripp, C.J. Bellucci, G. Warner,A.M. Helton, “Do waterbody classifications predict activity recognition of elder people?”, Journal of Environmental Management 183 (2016) 1-12.
- Z. Xing, Q. Fu, D. Liu, “Activity recognition of elder people Evaluation by the Fuzzy Comprehensive Evaluation based on EW Method”, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).
- K.Z. Mao, “Feature subset selection for support vector machines through discriminative function pruning analysis,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 1, pp. 60–67, Feb. 2004.
- Y. Wang, C. Li and Y. Zuo, “A Selection Model for Optimal Fuzzy Clustering Algorithm and Number of Clusters Based on Competitive Comprehensive Fuzzy Evaluation”, IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 17, NO. 3, JUNE 2009.
- S. Wechmongkhonkon, N. Poomtong, S. Areerachakul, “Application of Artificial Neural Network to Classification Surface Activity recognition of elder people,” International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering Vol:6, No:9, 2012.
- A. Woznica, P. Nguyen, A. Kalousis, "Model mining for robust feature selection", KDD '12 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and machine learning, ACM New York, NY, USA, PP 913-921, 2012.
- L. Khuan, N. Hamzah and R. Jailani, “Recognition of Activity recognition of elder people Index(WQI) Based on Artificial Neural Network(ANN)”,Conference onResearch and Development Proceedings, Malasia, 2002, pp. 157-161.
- Singh, Dilbag, Deepak Garg, and Husanbir Singh Pannu. "Efficient Landsat image fusion using fuzzy and stationary discrete wavelet transform." The Imaging Science Journal 65, no. 2 (2017): 108-114.
- S.W. Athukorala, L.S. Weerasinghe, M. Jayasooria, D. Rajapakshe, L. Fernando, M. Raffeeze, N.P. Miguntanna, “Ananlysis of Activity recognition of elder people Variation In Kelani River, Sri Lanka Using Principal Componet Analysis,” SAITM Research Symposium on Engineering Advancements2013.
- T. Wijesinghe, “Status of Activity recognition of elder people of Kelani River, Central Environmental Authority, Sri Lanka,” IIIRR online publication pp.255,2010.
- Y. Park, K. H. Cho, J. Park, S. M. Cha, and J. H. Kim, “Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.,” Sci. Total Environ., vol. 502, pp. 31–41, Jan. 2015.
- S. Maiti and R. K. Tiwari, “A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level recognition,” Environ. Earth Sci., vol. 71, no.7, pp. 3147–3160, 2013.
- M.J. Diamantopoulou, V.Z. Antonopoulos and D.M. Papamichail “The Use of a Neural Network Technique for the Recognition of Activity recognition of elder people Parameters of Axios River in Northern Greece”, Journal 0fOperational Research, Springer-Verlag, Jan 2005, pp. 115-125.
- Singh, Dilbag, and Vijay Kumar. "Modified gain intervention filter based dehazing technique." Journal of Modern Optics 64, no. 20 (2017): 2165-2178.
- Singh, Dilbag, and Vijay Kumar. "Dehazing of remote sensing images using improved restoration model based dark channel prior." The Imaging Science Journal (2017): 1-11.
- Singh, Dilbag, and Vijay Kumar. "Dehazing of Remote Sensing Images using Fourth-order Partial Differential Equations Based Trilateral Filter." IET Computer Vision (2017).
Index Terms
Keywords