Paper Info
Title
Features Selection Model for Internet of E-Health Things Using Big Data.
Abstract
Internet of Things (IoT) plays a key role in connecting the e-health system with the cyber world through new services and seamless interconnection between heterogeneous devices. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Therefore, keeping in view the needs above, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real-time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy, and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.
Year
Venue
Keywords
2017
IEEE Global Communications Conference
IoT,Big Data,ABC algorithm,feature selection
Field
DocType
ISSN
Algorithm design,Parallel algorithm,Computer science,Real-time computing,Feature extraction,Systems architecture,Throughput,Big data,Distributed computing,Scalability,The Internet
Conference
2334-0983
Citations 
PageRank 
References 
1
0.34
0
Authors
6
Name
Order
Citations
PageRank
Sadia Din18719.55
Anand Paul252746.32
Nadra Guizani327432.70
Syed Hassan Ahmed450565.70
Murad Khan515022.14
muhammad mazhar ullah rathore630121.15