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

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

Computers & Electrical Engineering

As health data are very sensitive, there is a need to prevent and control the health data with end-to-end security solutions. In general, a number of authentication and authorization schemes are available to prevent and protect the sensitive data, which are collected with the help of wearable Internet of Things (IoT) devices. The transport layer security (TLS) protocol is designed to transfer the data from source to destination in more reliable manner. This protocol enables a user to overcome the no lost or reordered messages. The more challenge with TLS is to tolerate unreliability. In order to overcome this issue, Datagram transport layer security (DTLS) protocol has been designed and used in low-power wireless constrained networks. The DTLS protocol consists of a base protocol, record layer, handshake protocol, ChangeCipherSpec and alert protocol. The complex issue with the DTLS protocol is the possibility of an attacker could send a number of ClientHello messages to a server. This scenario would cause a denial-of-service (DOS) attack against the server. This DoS attack enables new connection between the attacker and server, increasing attacker bandwidth, and allocation of resources for every ClientHello message. In order to overcome this issue, we have proposed a smart gateway-based authentication and authorization method to prevent and protect more sensitive physiological data from an attacker and malicious users. The enhanced smart gateway-based DTLS is demonstrated with the help of Contiki Network Simulator. The packet loss ratio is calculated for the CoAP, host identity protocol, CoAP-DTLS and CoAP-enhanced DTLS to evaluate the performance of the proposed work. Data transmission and handshake time are also calculated to evaluate the efficiency of the enhanced DTLS. © 2017, Springer Science+Business Media, LLC.

The Journal of Supercomputing

Enhanced DTLS with CoAP-based authentication scheme for the internet of things in healthcare application

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

Twitter, a social blogging site which became the tremendous topic in today’s environment, which made several organizations and public to develop their identity and overwhelming through this social website. But unfortunately, twitter facing great challenges due to spammers who break the reputation of the website from deliberate users to stop using it. Researchers have proposed many techniques to overcome the issues faced by the spammers. As far researchers find a new path so as the spammers develop new techniques to travel in that path. So far, many algorithms were proposed to detect the spammers and some extraction techniques have developed to increase the potential of detection rate. In this paper, the main focus is about feature extraction of our data with a hybrid approach of combining logistic regression with dimensional reduction technique using principal component analysis. Our dataset contains 17 million users’ tweets with 159 features included in it. Then we are going to extract particular features from it which would be helpful for the further process of increasing the classification accuracy. For the classification process, our work extended for the process of classification of data using some machine learning techniques. From the proposed work the detection rate could be increased by using particular features for the classification process. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Cluster Computing

Feature extraction using LR-PCA hybridization on twitter data and classification accuracy using machine learning algorithms

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.

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

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.

International Journal of Ambient Computing and Intelligence

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.

Journal	Data powered by TypesetComputers & Electrical Engineering
Publisher	Data powered by TypesetElsevier BV
ISSN	0045-7906
Open Access	0