Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

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

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

International Journal of Ambient Computing and Intelligence

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

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

Honeypot Internet of Things (IoT) (HIoTPOT) keep a secret eye on IoT devices and analyzes the various recent threats which are dangerous to IoT devices. In this paper, implementation of a research honeypot is presented which is used to learn the recent tactics and ethics used by black hat community to attack on IoT devices. As IoT is open and easy for accessing, all the intruders are highly attracted towards IoT. Recently Telnet based attacks are very famous on IoT devices to get easy access and attack on other devices. To reduce these kinds of threats, it is necessary to know in details about intruder, therefore the aim of this research work is to implement novel based secret eye server known as HIoTPOT which will make the IoT environment more safe and secure. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Wireless Personal Communications

HIoTPOT: Surveillance on IoT Devices against Recent Threats

Computer Methods and Programs in Biomedicine

A MapReduce approach to diminish imbalance parameters for big deoxyribonucleic acid dataset

Fulltext

International Journal of Infectious Diseases

Climate change and disease dynamics - A big data perspective

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.

Journal	International Journal of Ambient Computing and Intelligence
Publisher	IGI Global
ISSN	1941-6237
Open Access	0