Human Activity Recognition using Active Learning Methodology
K.R. Baskaran1, M.N. Saroja2
1Dr. K.R. Baskaran, Professor, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
2M.N. Saroja, Assistant Professor, Department of Information Technology, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 07 October 2019 | Revised Manuscript received on 21 October 2019 | Manuscript Published on 26 December 2019 | PP: 404-406 | Volume-8 Issue-12S October 2019 | Retrieval Number: L110110812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1101.10812S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In current technology, presenting detailed and exact information about one’s daily activities is the major task in artificial intelligence. This paper represents the multiple classification techniques used to monitor the behaviours of aging people. It can also play an important role in health care monitoring system and surveillance systems. Human Activity Recognition (HAR) dataset is used for evaluating and comparing the prediction accuracy of the dictionary learning algorithm, Naive Bayes and J48 algorithms. Based on the classification, J48 algorithm is superior compared to other classifier algorithms. J48 and Naïve Bayes machine learning algorithms are evaluated on WEKA tool and their efficiency is compared with Dictionary learning algorithm for achieving better results on the given dataset.
Keywords: Machine Learning, HAR, Dictionary Learning, ADL Problem.
Scope of the Article: Machine Learning