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IEEE Final Year Project Topic for CSE

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Effective Features to Classify Big Data Using Social Internet of Things

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IEEE Project Abstract

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.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.

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