Social Internet of Things (SIoT) is considered as one of the most recently evolved topics that connects people and object, object and object as well as people and people. The SIoT and big data provide an exact representation of IoT and social system for human progression characterization. Numerous machine learning techniques are employed to classify the data gathered from SIoT in a more powerful way. In this article, a deep neural network based marine predator (DRNN-MP) algorithm is proposed in classifying big data. Here, an adaptive Savitzky-Golay filter is employed for selecting the subset and to eliminate undesirable data, as well as different noises. The big data databases are reduced using a Hadoop map reducing framework thereby enhancing the performances of the proposed approach. In addition to this, a modified relief technique is employed to select optimal features thereby performing better classification. The testing and training process based on the proposed approach for optimal classification of features from the big data considers four different databases namely coronary illness, GPS trajectories, localization data, and water treatment plant obtained from UCI machine learning repository. In addition to this, the proposed approach is evaluated for diverse performance measures namely accuracy, precision, specificity, sensitivity, throughput, and energy consumption. Finally, the proposed approach is compared for various metrics to illustrate the effectiveness of the system and the results demonstrated that the accuracy of our work is 98.25%.
K E Y W O R D Sbig data, database, deep recurrent neural network, marine predator, modified relief, social internet of things
INTRODUCTIONInternet of things (IoT) illustrates a separate universe of heterogeneous objects namely actuators, sensors as well as smartphones in which each object and everything possesses a sovereign identity. 1 Nowadays, developing an IoT enabled technology and providing solutions are considered as tedious tasks. On the other hand, IoT deals with sharing and pervasive collection of data regarding a common intention. In IoT, various attribute factors namely integer value and variable define the data thereby permitting specific functions to be performed using a predetermined interface. 2Numerous research scholars are fascinated peculiarly in determining the risk problems emerging while integrating and discovering the data withinIoT. In recent years, the social Internet of Things (SIoT) has been established which is another utilization of IoT. 3 They constitute various distinctive characteristics including diverse communication protocols, operating systems, related standards, and various operational platforms; however, all such differences are neglected. 4
