• P. Ilakkiya
• Dr. B. Indrani

The main objective of this paper is to implement a Machine Learning algorithm for medical IoT big data analytics. Machine Learning (ML) is one of the groups of modern programming methodologies in Artificial Intelligence. It can learn linear and non-linear data representation in huge complex datasets. ML can explore hidden information of the data successfully and report the data variations. Various problems of AI are solved using ML mechanisms in several fields like time-series data, computer vision, and NLP. One of the recent industries generating voluminous increasingly, day by day is the medical industry. Conventional AI methods have been used in earlier research works that are slow in data analytics, not cost-effective and the prediction accuracy is also less. Hence, this paper aimed to implement and experiment the Transfer Learning Method for analyzing the bigdata generated from Medical Internet of Things (IoT). The transfer Learning method uses knowledge of the source-domain to analyze medical bigdata. From the experimental results, it is identified that the proposed Transfer Learning method outperforms than conventional AI methods.

Machine Learning, IoT, Bigdata Analytics, Medical IoT, Prediction.

"A Roar Towards IoT Big data Analytics: A Transfer Learning Context Framework for Medical Internet of Things", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 8, page no.1143-1163, August-2018, Available :http://www.jetir.org/papers/JETIR1809A72.pdf

"A Roar Towards IoT Big data Analytics: A Transfer Learning Context Framework for Medical Internet of Things", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 8, page no. pp1143-1163, August-2018, Available at : http://www.jetir.org/papers/JETIR1809A72.pdf