As wearable medical sensors continuously generate enormous data, it is difficult to process and analyse. This paper focuses on developing scalable sensor data processing architecture in cloud computing to store and process body sensor data for healthcare applications. Proposed architecture uses big data technologies such as Apache Flume, Apache Pig and Apache HBase to collect and store huge sensor data in the Amazon web service. Apache Mahout implementation of MapReduce-based online stochastic gradient descent algorithm is used in the logistic regression to develop the scalable diagnosis model. Cleveland heart disease database (CHDD) is used to train the logistic regression model. Wearable body sensors are used to get the blood pressure, blood sugar level and heart rate of the patient to predict the heart disease status. Proposed prediction model efficiently classifies the heart disease with the accuracy of training and validation sample is 81.99% and 81.52%, respectively. Copyright © 2018 Inderscience Enterprises Ltd.

Daphne Lopez

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

Manogaran G

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

International Journal of Advanced Intelligence Paradigms

In the midst of all the applications empowered by the Internet of Things (IoT), constant health surveying framework is an especially essential one. The leg movement monitoring framework is utilized to recognize the beginning time of joint pain illness. Wearable sensor gadgets and Uric acid (UA) sensor utilized as a part of IoT health monitoring framework for joint inflammation ailment have been creating a gigantic measure of information on a nonstop premise with rapid, change of huge health facts, and identification of these infections present some significant issues. These can be overwhelmed by utilizing the proposed framework for health data transformation and alongside the calculation utilized for the discovery of human foot movement for the GOUT arthritis diagnosis. Sensitivity and specificity are computed to assess the Time wrapping calculation-based characterization display for Arthritis ailment. The exploratory outcomes demonstrated the adequacy of the dynamic time wrapping technique. At last, ROC examination is conducted to distinguish the most significance clinical and laboratory assessments to get joint inflammation (GOUT arthritis) and kidney ailment. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

International Journal of Computers and Applications

A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm

In the present era of Information Technology, almost all big and small scale companies are moving towards cloud to store and manage the data. Cloud computing is a routine of deploying a structure of distant servers speed up on the Internet to store, oversee, and manage information, rather than a neighbourhood server or a PC. The purpose for cloud procurement is reduced cost, adaptability, regular access and refreshed programming. Nowadays, healthcare frameworks are adjusting computerized stages and ending up being more patient-centered and data driven. In this paper, we present a planning empowered intermediary re-encryption method to defeat the security issues. This Technique will allow only limited access rights to an authorized agent to access the records for a specific time period. This technique will use a searchable encryption and proxy Re-encryption technique. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Mobile Networks and Applications

E-Health Cloud Security Using Timing Enabled Proxy Re-Encryption

Multimedia Tools and Applications

Score level based latent fingerprint enhancement and matching using SIFT feature

Cluster Computing

IoT based fall detection and ambient assisted system for the elderly

Journal of Medical Systems

Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals

Fulltext

International Journal of Infectious Diseases

Climate change and disease dynamics - A big data perspective

Procedia Computer Science

MetaCloudDataStorage Architecture for Big Data Security in Cloud Computing

Alternative selection of vaccination strategy has become a challenging task for the public health, and it is considered as a complex decision making problem. Decision makers often use linguistic variables to rate the alternatives. The objective of this research is to use fuzzy logic based VIKOR method for evaluating H1N1 Influenza vaccination strategies. The experimental design of the proposed decision making model is illustrated with a case study in Vellore, Tamil Nadu, India. The alternative of a vaccination strategy considered in this study includes the combination of “people”, “spatial” and “temporal”. © Springer International Publishing Switzerland 2015.

Advances in Intelligent Systems and Computing Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015)

Assessment of Vaccination Strategies Using Fuzzy Multi-criteria Decision Making

The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009-2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013-2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013-2014. © 2014 IEEE.

2014 IEEE International Conference on Big Data (Big Data)

Spatial big data analytics of influenza epidemic in Vellore, India

With increased usage of IT solutions, a huge volume of data is generated from different sources like social networks, CRM, and healthcare applications, to name a few. The size of the data that is generated grows exponentially. As cloud computing provides an optimized, shared, and virtualized IT infrastructure, it is better to leverage the cloud services for storing and processing such Big Data. Securing the data is one of the major challenges in all the domains. Though security and privacy have been talked about for decades, there is still a growing need for high end methods for securing the rampant growth of data. The privacy of personal data, and to be more specific the health data, continues to be an important issue worldwide. Most of the health data in today’s IT world is being computerized. A patient’s health data may portray the different attributes such as his physical and mental health, its severity, financial status, and much more. Moreover, the medical data that are collected from the patients are being shared with other stakeholders of interest like doctors, insurance companies, pharmacies, researchers, and other health care providers. Individuals raise concern about the privacy of their health data in such a shared environment.

Advances in Data Mining and Database Management Handbook of Research on Cloud Infrastructures for Big Data Analytics

Middleware for Preserving Privacy in Big Data

Health data analytics using scalable logistic regression with stochastic gradient descent

Journal	International Journal of Advanced Intelligence Paradigms
Publisher	Inderscience Publishers
ISSN	1755-0386
Open Access	0