Evaluating Twitter Data to Discover User’s Perception About Social Internet of Things

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.     

Adaptive Framework for Privacy Preserving in Online Social Networks

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.

     

Artificial Intelligence Based Smart Routing in Software Defined Networks

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.     

Design and implementation of CfoTS networks for industrial fault detection and correction mechanism

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...     

Distributed multichannel assignment with congestion control in wireless mesh networks

In Multichannel Wireless Mesh Network architecture, topology discovery, traffic profiling, channel assignment and routing are essential. From the existing work done so far, we can observe that no work has been carried out on the combined solution of multichannel assignment with routing protocol and congestion control. In this paper, we propose to design a Distributed Multichannel Assignment with Congestion control (DMAC) routing protocol. In this protocol, a traffic‐aware metric provides the solution for multichannel assignment and congestion control. Hence, the proposed protocol can improve the throughput and channel utilization to a very high extent. The proposed algorithm avoids self‐interference by not assigning a channel to any link whose incident links have already been assigned channels. By our simulation results, we show that our proposed protocol attains high throughput and delivery ratio along with reduced delay. Copyright © 2011 John Wiley & Sons, Ltd.     

Secure architecture to circumvent collision using RSSI measurement in WSN: a cross layer design approach

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...     

CARI – a heuristic approach to machine-part cell formation using correlation analysis and relevance index

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.     

Threshold prediction for segmenting tumour from brain MRI scans

In recent decades, region growing methods in image segmentation plays a vital role in medical image processing. Nonetheless, the method needs more advancement to cope up with the images of current acquisition devices. This paper attempts to solve the problem of maintaining diversity among wide image specifications by optimizing the threshold. In order to accomplish this, we introduce a hybrid framework of Artificial Bee Colony and Genetic Algorithm in a region growing variant, in which gradient and intensity levels are used for segmentation. Eventually, the proposed work is subjected to classify the tumor and non‐tumor images, followed by the segmentation of tumor region in MRI images. Classification methodologies such as feed forward back propagation neural network, radial basis neural network, support vector machine with quadratic programming and adaptive neuro‐fuzzy inference system are considered for experimental investigation in which support vector machine with quadratic programming is found to be dominant than other methodologies. Proposed region growing method outperforms well on the classified image, when compared with the region growing variant and standard region growing method. The results are demonstrated with the aid of wide set of performance measures. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 129–137, 2014