H1N1 is the worldwide emerging infectious disease that spreads through human the same way the seasonal flu viruses spread which is one of the major health problems in the state of Tamil Nadu. This cross-sectional research is studied using R software to analyze the epidemiological features of H1N1 in Vellore district, Tamil Nadu, India in the year 2009. A total of 433 cases of H1N1 were reported during the period August 2009 to July 2010 in and around Tamil Nadu. The data are analyzed under three categories: demographical, seasonal, and social habits of the infected population. Demographical data were collected from National Informatics Centre, Chennai and Department of Health Services, Vellore. Seasonal information from Vellore Agriculture Department and social habits were collected from admitted patients. We have used Spatial Autoregressive model to predict future H1N1 incidence. It is observed that the number of infected individuals increased during monsoon and postmonsoon season whereas it is less during summer and winter. We have found that most of them were in the age group of 0–20 with a common symptom as cough and sore throat. The highest number of cases was reported with individuals having wheezing and it is observed that the infected host takes 3–6 days to recover from the disease. Our study, clearly reported the prevalence of H1N1 outbreak in Vellore district, India. It also helps the authorities to take a quick decision on the prevention strategies. © 2017 Elsevier B.V.

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.

&nbsp;

&nbsp;

VIT University

Handbook of Statistics Disease Modelling and Public Health, Part B

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

Most of the advancements are now carried out by interconnecting physical devices with computers; this is what known as Internet of Things (IoT). The major problems facing by blind people fall in the category of navigating through indoor and outdoor environments consisting of various obstacles and recognition of person in front of them. Identification of objects or person only with perceptive and audio information is difficult. An intelligent, portable, less expensive, self-contained navigation and face recognition system is highly demanded for blind people. This helps blind people to navigate with the help of a Smartphone, global positioning system (GPS) and a system equipped with ultrasonic sensors. Face recognition can be done using neural learning techniques with feature extraction and training modules. The images of friends, relatives are stored in the database of user Smartphone. Whenever a person comes in front of the blind user, the application with the help of neural network gives the voice aid to the user. Thus this system can replace the regular imprecise use of guide dogs as well as white sticks to help the navigation and face recognition process for people with impaired vision.In this paper, we have proposed a novel image recognition and navigation system which provides precise and quick messages in the form of audio to visually challenged people so that they can navigate easily. The performance of the proposed method is comparatively analyzed with the help of ROC analysis. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.

Cluster Computing

Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things

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

AbstractPresent study focuses on measurement, prediction and mapping of noise levels in important places of Vellore city located in Tamil Nadu state, India. Weekday and weekend noise levels at six locations (S1 to S6) are recorded during morning, afternoon and evening times. Sampling locations includes educational (S1), commercial (S2 and S4), tourist and recreational (S3), and mixed (S5 and S6) zones. Assessment in Vellore city showed maximum Leq level in S4 and S5 and lowest is recorded in S3 throughout the study. Highest Leq levels are recorded in evening (57.52–78.41 dB(A)) and afternoon (58.76–76.39 dB(A)) time during weekday and weekend, respectively. Along with Leq, noise descriptors are also included and discussed in this study. Weekend has higher mean value of TNI thanWeekday. CoRTN model is employed to predict the L10 values. Results showed good performance of model with marginal difference between measured and predicted L10 levels. Further noise maps are created separately for weekday and weekend using ArcGIS software. Noise maps showed alarming noise levels near sampling sites and it is suggested to take immediate preventive measures using barriers or by providing alternative traffic routes.

Fulltext

Noise Mapping

Assessment, Prediction and Mapping of Noise Levels in Vellore City, India

Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis

Machine learning algorithms play a vital role in the prediction of an outbreak of diseases based on climate change. Dengue outbreak is caused by improper maintenance of water storages, lack of urbanization, deforestation, lack of vaccination and awareness. Moreover, a number of dengue cases are varying based on climate season. There is a need to develop the prediction model for modeling the dengue outbreak based climate change. To model the dengue outbreak, Gaussian process regression (GPR) model is applied in this paper that uses the seasonal average of various climate parameters such as maximum temperature, minimum temperature, precipitation, wind, relative humidity and solar. The number of dengue cases and climate data for each block of Tamil Nadu, India are collected from Integrated Disease Surveillance Project and Global Weather Data for SWAT Inc respectively. Local Moran’s I spatial autocorrelation is used in this paper for geographical visualization of hotspot regions. The outbreak of dengue and its hot spot regions are geographically visualized with the help of ArcGIS 10.1 software. The day wise big climate data is collected and stored in the Hadoop cluster computing environment. MapReduce framework is used to reduce the day wise climate data into seasonal climate averages such as winter, summer, and monsoon. The seasonal climate data and number of dengue incidence (health data) are integrated based on the geolocation (latitude and longitude). GPR is used to develop the prediction model for dengue based on the integrated data (climate and health data). The proposed Gaussian process based prediction model is compared with various machine learning approaches such as multiple regression, support vector machine and random forests. Experimental results demonstrate the effectiveness of our Gaussian process based prediction framework. © Springer Science+Business Media New York 2017.

A Gaussian process based big data processing framework in cluster computing environment

Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm

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.

International Journal of Advanced Intelligence Paradigms

Health data analytics using scalable logistic regression with stochastic gradient descent

Big data plays a vital role in the prediction of diseases that occur due to climate change. For such predictions, scalable data storage platforms and efficient change detection algorithms are required to monitor the climate change. However, traditional data storage techniques and algorithms are not applicable to process the huge amount of climate data. This paper presents a scalable data processing framework with a novel change detection algorithm. The large volume of climate data is stored on Hadoop Distributed File System (HDFS) and MapReduce algorithm is applied to calculate the seasonal average of climate parameters. Spatial autocorrelation based climate change detection algorithm is proposed in this paper to monitor the changes in the seasonal climate. The proposed climate change detection algorithm is compared with various existing approaches such as pruned exact linear time method, binary segmentation method, and segment neighborhood method. © 2017

Journal	Data powered by TypesetHandbook of Statistics Disease Modelling and Public Health, Part B
Publisher	Data powered by TypesetElsevier
ISSN	0169-7161
Open Access	0