R. Varatharajan

TL;DR: A new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications and uses MapReduce based prediction model to predict the heart diseases is proposed.

TL;DR: A new systematic approach is used for the diabetes diseases and the related medical data is generated by using the UCI Repository dataset and the medical sensors for predicting the people who has affected with diabetes severely and a new classification algorithm called Fuzzy Rule based Neural Classifier is proposed for diagnosing the disease and the severity.

TL;DR: This paper uses dynamic time warping (DTW) algorithm to compare the various shapes of foot movements collected from the wearable IoT devices to evaluate the effectiveness of the DTW method for Alzheimer disease diagnosis.

TL;DR: The proposed MKL with ANFIS based deep learning method follows two-fold approach and has produced high sensitivity, high specificity and less Mean Square Error for the for the KEGG Metabolic Reaction Network dataset.

TL;DR: A Bayesian hidden Markov model (HMM) with Gaussian Mixture (GM) Clustering approach is used to model the DNA copy number change across the genome and is compared with various existing approaches such as Pruned Exact Linear Time method, binary segmentation method and segment neighborhood method.