Exploiting social media data by extracting key information from it is one of the great challenges in data mining and knowledge discovery. Every election campaign has an online presence of voters which uses these social media platform to express their sentiments and opinions towards political parties, leaders and important topics. This paper presents a novel data collection technique for prediction of election outcomes and a topic modeling method for extracting topics. Data collection technique used RSS (Rich Site Summary) feeds of news articles and trending keywords from Twitter simultaneously and constructed an intelligent prediction model based primarily on the volume of tweets and sentiment of users. This paper effort to improve electoral predictions using social media data based dynamic keyword methodology.
Different techniques for electoral prediction based on social media data has been investigated based on existing literature and isolate the factors which improve our methodology. Meaningful inferences such as the popularity of leaders and parties during different intervals, trending issues, and important factors are extracted from the data set. The election outcomes are compared with traditional methods used by survey agencies for exit polls and validation of results showed that social media data can predict with better accuracy. The research has identified that data collection technique and timing play an important role in yielding better accuracy in predicting outcomes and extracting meaningful inferences.

Data clustering is a basic technique to show the structure of a data set. K-means clustering is a widely acceptable method of data clustering, which follow a partitioned approach for dividing the given data set into non-overlapping groups. Unfortunately, it has the pitfall of randomly choosing the initial cluster centers. Due to its gradient nature, this algorithm is highly sensitive to the initial seed value. In this paper, we propose a kernel density-based method to compute an initial seed value for the k-means algorithm. The idea is to select an initial point from the denser region because they truly reflect the property of the overall data set. Subsequently, we are avoiding the selection of outliers as an initial seed value. We have verified the proposed method on real data sets with the help of different internal and external validity measures. The experimental analysis illustrates that the proposed method has better performance over the k-means, k-means++ algorithm, and other recent initialization methods.

Risk identification and assessment in today’s sce-nario play a vital role in any software/web application devel-opment industry. Many process models deliver the process related to development life cycle, but the risk assessment at an early stage is still an issue and is a significant subject for research. In this paper, an approach based on MVC architecture by embedding spiral process, which is verified and validated by V-shape model is proposed. By using this approach development efficiency will increase due to less burdened working team(s), reduces stressful maintenance effort that causes reduction in risk factors because of beautifully distributed human effort to improve software quality. Besides, the efficiency of our approach is manifested by the preliminary experiment.

Linux systems use Encrypting File System (EFS) for providing confidentiality and integrity services to files stored on disk in a secure, efficient and transparent manner. Distributed encrypting file system should also provide support for secure remote access, multiuser file sharing, possible use by non-privileged users, portability, incremental backups etc. Existing kernel-space EFS designed at file system level provides all necessary features, but they are not portable and cannot be mounted by non-privileged users. Existing user-space EFS have performance limitations and does not provide support for file sharing.
Through this paper, modifications in the design and implementation of two existing user-space EFS, for performance gain and file sharing support, has been presented. Performance gain has been achieved in both the proposed approaches using fast and modern ciphers. File sharing support in proposed approaches has been provided with Public Key Infrastructure (PKI) integration using GnuPG PKI module and Linux Pluggable Authentication Module (PAM) framework. Cryptographic metadata is being stored as extended attributes in file's Access Control List (ACL) to make file sharing task easier and seamless to the end user.