Effective Features to Classify Big Data Using Social Internet of Things

IEEE Access

Placido R. Pinheiro¹  Ashish Khanna²  Deepak Gupta²  S. K. Lakshmanaprabu³  Victor Hugo C. De Albuquerque⁴  Joel J. P. C. Rodrigues⁵  K. Shankar⁶ 

[1] /MG, Brazil;Assistant Maharaja Agrasen Institute of Technology, GGSIP University, Delhi, India;Department of Electronics and Instrumentation Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India;Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza/CE, Brazil;National Institute of Telecommunications, Santa Rita do Sapuca&x00ED;School of Computing, Kalasalingam Academy of Research and Education, KrishnanKoil, India;

关键词: Internet of Things;    social Internet of Things;    machine Learning;    big data;    feature selection;   

DOI  :  10.1109/ACCESS.2018.2830651

来源: DOAJ

【 摘 要 】

Social Internet of Things (SIoT) supports many novel applications and networking services for the IoT in a more powerful and productive way. In this paper, we have introduced a hierarchical framework for feature extraction in SIoT big data using map-reduced framework along with a supervised classifier model. Moreover, a Gabor filter is used to reduce noise and unwanted data from the database, and Hadoop Map Reduce has been used for mapping and reducing big databases, to improve the efficiency of the proposed work. Furthermore, the feature selection has been performed on a filtered data set by using Elephant Herd Optimization. The proposed system architecture has been implemented using Linear Kernel Support Vector Machine-based classifier to classify the data and for predicting the efficiency of the proposed work. From the results, the maximum accuracy, specificity, and sensitivity of our work is 98.2%, 85.88%, and 80%, moreover analyzed time and memory, and these results have been compared with the existing literature.

【 授权许可】

Unknown