Cloud computing is a technology that provides resources and utility services based on user demand. Due to this demand, efficient cloud security protocols are highly required, especially at the time of data communication for user authentication and data aggregation. The data communication scenarios are majorly affected by the security threats in the cloud computing environment. This article provides a practical approach to developing an efficient and empirical cloud framework in terms of cloud protocol. The framework uses fuzzy c-means (FCM) algorithm to group data, and calculation is done individually or associatively to rank the text data. Uploaded data are passed to a simple additive weighting (SAW) algorithm for ranking and making decision selection. The framework executes in three phases, namely data preprocessing, clustering, and automatic data security with an alert mechanism. The process is completely automated so there is no need of considering the individual files for the processing and the data held will be appropriately correlated with the sharing inter-cloud environment. To inspect security issues, the proposed framework is secured by three different security algorithms. The encryption process is completed by Rivest Cipher 6 (RC6); the substitution process is done by Advanced Encryption Standard (AES); and key generation is done by RC6, AES, and Rivest-Shamir-Adleman (RSA) approaches collectively. Based on the given situations, these standard approaches were automatically applied separately or collectively. Unauthorized access trapping and data deletion mechanism are also provided in the proposed framework. The experimental results with a comparative study depicted the effectiveness of the proposed work.

No abstract

Mobile networks, in particular, are composed of wireless cellular communication nodes (MANET). Communication between these mobile nodes is not under centric systems. MANET is a network of randomly traveling nodes that self-configure and self-organize. Routing is a fundamental topic of MANET, and performance analysis of routing protocols is the focus of this study. AODV, DSR, and WRP are three routing protocols that are compared in this article. Glomosim will be used for simulation. The throughput, average end-to-end latency, and packet delivery ratio of various routing systems are all examined. Two scenarios depending on mobility and node density are considered in this research. As node density rises, PDR and throughput rise with it. Low node density resulted in the shortest delay. AODV has a higher packet delivery ratio and throughput in both scenarios, while WRP has the shortest delay. The authors also analyzed the average energy consumption with a best routing protocol that was decided by the result and conclude the efficiency of the ad-hoc network.

No abstract

Useful information can be extracted through the analysis of Facebook posts. Text analysis and image analysis can play a vital role towards this. To predict the users' involvement, text data and image data can be incorporated using some machine learning models. These models can be used to perform testing on advertisements that are posted on Facebook for users' involvement prediction. Count of share and comments with sentiment analysis are included as users' involvement. This chapter contributes to understand the users' involvement on social media along with finding out the best machine learning model for prediction of users' involvement. The procedure of prediction with both text data and image data by suitable models is also discussed. This chapter produces a predictive model for posts of Facebook to predict users' involvement that will be based on the number of shares and comments on the post. The best models are obtained by using the combination of image data and text data. Further, it demonstrated that random models are surpassed by the models that are integrated for prediction.