• Ruchi Pandey
• Professor.Shahana Gajala Qureshi

The Internet of Things (IOT) is a prominent research area that provides many interesting solutions to various problems experience by various departments. Smart homes applications is one such branch that evolve from IOT with the huge challenges of data storage and handling. Activity recognition is the major challenge in smart homes application that consolidates multiclass learning. The effectiveness of ensemble learner in handling multiclass problem and collective dissicion delivered prompt its uses in the smart homes application. In this paper we deal with activities recognition problems on various ensemble learners including bagging, boosting and random forest. The standard van Kasteren dataset contains three housedata with eight activities of different days. We perform our experiment on the pre-processed collected data and applied six learners i.e. three from individual learner and three from ensemble learners. On performing the extensive experiment it was found that the group of ensemble learner outcast the simple learners.

IOT, Ensemble Learner, Bagging, Boosting, Random Forest

"Multiple Activity recognition through combining classifier", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.792-802, June-2018, Available :http://www.jetir.org/papers/JETIR1805553.pdf

"Multiple Activity recognition through combining classifier", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp792-802, June-2018, Available at : http://www.jetir.org/papers/JETIR1805553.pdf