Social Internet of Things (SIoT) is a young paradigm that integrates Internet of Things and Social Networks. Social Internet of Things is defined as a social network of intelligent objects. SIoT has led to autonomous decision making and communication between object peers. SIoT has created and opened many research avenues in the recent years and it is vital to understand the impact of SIoT in the real world. In this paper, we have mined twitter to evaluate the user awareness and impact of SIoT among the public. We use R for mining twitter and perform extensive sentiment analysis using supervised and semi supervised algorithms to evaluate the user’s perception about SIoT. Experimental results show that the proposed Fragment Vector model, a semi supervised classification algorithm is better when compared to supervised classification algorithms namely Improved Polarity Classifier (IPC) and SentiWordNet Classifier (SWNC). We also evaluate the combined performance of IPC and SWNC and propose a hybrid classifier (IPC?+?SWNC). Our analysis was challenged by limited number of tweets with respect to our study. Experimental results using R has produced evidences of its social influences. 相似文献
In our current digital life, social networks play a vital role to share the data. A group of hazards that related to the privacy parameters settings of the universal online social network like facebook. The settings corresponding to the user’s shared data as well as the data sharing with privacy. A middleware server can split the users with regular social relationships within a same group. The community detection methodology normally request for the full access to the detailed social communications within the users. It is very sensitive to share the personal information like personal images which cause the uncertainty in case of privacy. In this paper, we suggest an Adaptive Framework for privacy preserving in Online Social Networks. It also affords the flexibility in which a third-party server can frequently choose the related sub graph. The emotional detection is constructed with the neuro-fuzzy technique that the fundamental emotions are identified through the singular value decomposition and produces the output of 22 emotions. The experimental results provide the real-time dataset that demonstrate the non-aggregated analysis and practical suggestions highlight the interferences to encourage the secured online social network usage.
In a non-static information exchange network, routing is an overly complex task to perform, which has to satisfy all the needs of the network. Software Defined Network (SDN) is the latest and widely used technology in the future communication networks, which would provide smart routing that is visible universally. The various features of routing are supported by the information centric network, which minimizes the congestion in the dataflow in a network and provides the content awareness through its mined mastery. Due to the advantages of the information centric network, the concepts of the information-centric network has been used in the paper to enable an optimal routing in the software-defined networks. Although there are many advantages in the information-centric network, there are some disadvantages due to the non-static communication properties, which affects the routing in SDN. In this regard, artificial intelligence methodology has been used in the proposed approach to solve these difficulties. A detailed analysis has been conducted to map the content awareness with deep learning and deep reinforcement learning with routing. The novel aligned internet investigation technique has been proposed to process the deep reinforcement learning. The performance evaluation of the proposed systems has been conducted among various existing approaches and results in optimal load balancing, usage of the bandwidth, and maximization in the throughput of the network. 相似文献
The Journal of Supercomputing - In the industry, large- and small-scale manufacturers and even original equipment manufacturers are facing a major problem in monitoring large data. Because the... 相似文献
Multimedia Tools and Applications - ‘Wireless sensor networks’ (WSNs) follow layered architecture for the fruitful and reliable working of distributed WSNs. The region of WSN is... 相似文献
Machine-part cell formation is the process of identifying part families and the appropriate machine cell for each part family. Grouping efficacy (GE), the widely used measure for assessing the goodness of the machine-part cells depends on identification of correct part families and the appropriate machine cell for each part family. In this paper, a heuristic based on correlation analysis and relevance index is proposed for the formation of machine-part cells. Computational performance of the proposed heuristic on a set of group technology data-set available in the literature is also presented. GE of the solutions produced by the proposed heuristic is equal to the best efficacy reported in the literature for 63% of the test instances and improved the GE for 6% of the total test instances. 相似文献