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Online social networks (OSNs) have become so popular that it has changed the Internet to a more collaborative environment. Now, a third of the world’s population participates in OSNs, forming communities, and producing and consuming media in different ways. The recent boom of artificial intelligence technologies provides new opportunities to help improve the processing and mining of social data. In this article, an algorithm that can detect communities in the OSNs using the concepts of granular computing in rough sets is proposed. In this information model, a social network as a rough set granular social network (RGSN) is modeled. A new community detection algorithm named granular-based community detection (GBCD) is implemented. This article also defines and uses two measures, namely, a granular community factor and an object community factor. The proposed algorithm is evaluated on four real-world data sets as well as computer-generated data sets. The model is compared with other state-of-the-art community detection algorithms for the values of modularity, normalized mutual information (NMI), Omega index, accuracy, specificity, sensitivity, and $F1$ -measure. The cumulative performance of the GBCD algorithm is found to be 3.99, which outperforms other state-of-the-art community detection algorithms.

Nowadays, wearable techniques are widely used in the Internet of Things (IoT). The discussed IoT devices are used in various applications such as smart home, security management, education institutions and so on. Among the various application, IoT devices are used widely in health care application for reducing the risk factors. So, in this paper, introduces the wearable sensors based on the Internet of Things (WS-IoT) for sports person continuous health monitoring system. The goal of this paper is to define the health clinics for sports medicine and performance services of the sports team to further the use of the technology to help athletes return to play in different fields of sport. With the help of wearable tracking devices to collect the health details and track the exercise records. To analyze and monitoring sports person health effective optimization machine learning techniques are introduced. The created system efficiency is evaluated using experimental results and discussion.

Online Social networks have gained much prominence in the recent years such that it has become an unavoidable means of daily communication. The element of trust in social networks has been studied ever since the inception of online social networks. Trust in online social networks is extremely fragile in nature due to the virtual connections between users in the network. The level of trustworthiness of each user in a social network varies and is usually computed using reputation level of the users. This paper focuses on identifying the features that determine the trust of a user in online social networks using benchmark datasets. We propose a new probabilistic reputation feature model that is better than the raw reputation features. The enhanced trust prediction framework has been tested and validated on three benchmark datasets namely Wikipedia election dataset, Epinions dataset and Slashdot dataset. The proposed probabilistic feature enhances the overall accuracy, F1 score, and area under the ROC for the classifier results significantly. The results have been compared with other state of the art techniques and are found to be efficient.

There has been a surge in the research of complex network analysis in the recent years. This paper engages with online social network, which is the most popular complex network in the modern world. Network communities help to understand the organization of real world networks. Accordingly, this paper proposes and validates a novel algorithm for overlapping community detection in online social networks. We focus on the stability-plasticity problem in complex networks and attempt to solve it using a Fuzzy Adaptive resonance theory inspired algorithm. The algorithm consists of two stages namely prediction stage and comparison stage. The proposed algorithms make use of network measures such as Edge betweenness, Betweenness centrality, and pair betweenness. The algorithm has been tested and compared with other algorithms using benchmark datasets, artificial datasets and real network datasets. The experimental results obtained were better than other overlapping community detection algorithms. The entropy of the proposed model has been evaluated using Overlapping normalized information, omega index, F-score and the cumulative performance value is 2.42 out of 3, which is better than other community detection algorithm.

There has been lot of research endeavours in detecting overlapping community structures in complex networks. This paper concentrates on one of the most popular complex networks of recent times—online social networks. Some of the existing methodologies in community detection for online social networks are discussed. The goal of the proposed algorithm is to solve the stability–plasticity dilemma and to suggest a new technique for detecting modular and granular overlaps in community detection. The stability–plasticity dilemma is solved using a Fuzzy ART-inspired algorithm for overlapping community detection for detecting modular and granular overlaps. The algorithm is designed by making use of network measures such as vertex betweenness, betweenness centrality, and split betweenness. The validation metrics used for testing the algorithm were sensitivity, specificity, accuracy and normalized mutual information. The algorithm has been tested and validated using benchmark datasets and real network datasets and gives a cumulative performance of 3.1/4.0.

Scalability and on-demand features of cloud computing have revolutionised IT industry in a large scale. Cloud service providers takes the entire burdens of computing, especially managing resources and file storage. Securing the file stored in cloud storage from internal and external attacks has become a primary concern for cloud storage providers as well as cloud users. In this paper, the threats involved in file storage and a secure way of protecting the stored files using a novel ensemble of security strategies is presented. This proposed strategy helps service providers as well as users to secure the file in cloud storage more efficiently.

Recommendation system filters the information and shows only the data that are relevant to the user. In this paper, we have considered external factors in recommendation such as scrutability, algorithm accuracy and user interface as well as internal factors such as similarity, diversity, negative similarity, serendipity. The characteristics of each factor in similarity based recommendation system have been clearly examined and a model Fuzzy Inference System has been developed. Factors that influence in determining the recommendation are discussed and evaluated using rule based fuzzy inference model.

Online social networks have gained importance in the past few years and have become an inseparable form of communication. People use online social networks for making friends, gaining knowledge, for entertainment and to review products. This increased influence of social networks has led to the emergence of a research area named online social network analysis. In this paper we propose a model fuzzy inference system that analyze and quantify the network measures used in the analysis of online social networks. We have divided the network measures into two classes namely connection based measures and network structure based measure. We have linked the connection based measures with the network structure based measure and have produced a fuzzy inference system for online social network analysis.

The rapid growth of huge data has become a challenge to the data analysts in recent time. As the data is growing exponentially many techniques are on the rise, for processing the real time data. Many data processing models like Hadoop, Apache YARN, Mapreduce, Storm,and Akka are leading the Big Data domain. This paper analyses and compares all the data processing models stated above. Researchers are trying to increase the efficiency of the algorithms used in the data processing. In this paper, we propose two algorithms namely JATS and SD, which will enhance the efficiency of the storm data processing architecture.

Hadoop has become the most used framework for data intensive processes such as mapreduce. Business intelligence and technology forecasting has played a big part in enhancing the popularity of Hadoop framework. In this paper we have proposed an improvement for apache Hadoop yarn (also known as Hadoop 2.0) architecture. Container nodes, application master, resource manager and the pool of resources are arranged and used in a more efficient way in this framework. We are proposing two new algorithms for selection of application master and for selection of container nodes.

Social networking generates a huge amount of data which can throw useful insights if processed at the right time. Social computing is another emerging technique where social networking is made use for computational purpose. Cloud storage plays an important role in social computing. Privacy in social computing is still a debated issue. The privacy concerns and possible loopholes are discussed in detail. In this paper we are proposing some mathematical models to compute the probability of staying in social network and proposing an algorithm named firefly inspired algorithm for establishing connections FIAEC.