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Bibliometrics is one of the research fields in library and information science that deals with the analysis of academic entities. In this regard, to gauge the productivity and popularity of authors, publication counts and citation counts are common bibliometric measures. Similarly, the significance of a journal is measured using another bibliometric measure, impact factor. However, scarce attention has been paid to find the impact and productivity of conferences using these bibliometric measures. Moreover, the application of the existing techniques rarely finds the impact of conferences in a distinctive manner. The purpose of this paper is to propose and compare the DS-index with existing bibliometric indices, such as h-index, g-index and R-index, to study and rank conferences distinctively based on their significance.

The DS-index is applied to the self-developed large DBLP data set having publication data over 50 years covering more than 10,000 conferences.

The empirical results of the proposed index are compared with the existing indices using the standard performance evaluation measures. The results confirm that the DS-index performs better than other indices in ranking the conferences in a distinctive manner.

Scarce attention is paid to rank conferences in distinctive manner using bibliometric measures. In addition, exploiting the DS-index to assign unique ranks to the different conferences makes this research work novel.

This paper aims to present a framework to detect phishing websites using stacking model. Phishing is a type of fraud to access users’ credentials. The attackers access users’ personal and sensitive information for monetary purposes. Phishing affects diverse fields, such as e-commerce, online business, banking and digital marketing, and is ordinarily carried out by sending spam emails and developing identical websites resembling the original websites. As people surf the targeted website, the phishers hijack their personal information.

Features of phishing data set are analysed by using feature selection techniques including information gain, gain ratio, Relief-F and recursive feature elimination (RFE) for feature selection. Two features are proposed combining the strongest and weakest attributes. Principal component analysis with diverse machine learning algorithms including (random forest [RF], neural network [NN], bagging, support vector machine, Naïve Bayes and k-nearest neighbour) is applied on proposed and remaining features. Afterwards, two stacking models: Stacking1 (RF + NN + Bagging) and Stacking2 (kNN + RF + Bagging) are applied by combining highest scoring classifiers to improve the classification accuracy.

The proposed features played an important role in improving the accuracy of all the classifiers. The results show that RFE plays an important role to remove the least important feature from the data set. Furthermore, Stacking1 (RF + NN + Bagging) outperformed all other classifiers in terms of classification accuracy to detect phishing website with 97.4% accuracy.

This research is novel in this regard that no previous research focusses on using feed forward NN and ensemble learners for detecting phishing websites.

This research study proposes a feature-centric spam email detection model (FSEDM) based on content, sentiment, semantic, user and spam-lexicon features set. The purpose of this study is to exploit the role of sentiment features along with other proposed features to evaluate the classification accuracy of machine learning algorithms for spam email detection.

Existing studies primarily exploits content-based feature engineering approach; however, a limited number of features is considered. In this regard, this research study proposed a feature-centric framework (FSEDM) based on existing and novel features of email data set, which are extracted after pre-processing. Afterwards, diverse supervised learning techniques are applied on the proposed features in conjunction with feature selection techniques such as information gain, gain ratio and Relief-F to rank most prominent features and classify the emails into spam or ham (not spam).

Analysis and experimental results indicated that the proposed model with sentiment analysis is competitive approach for spam email detection. Using the proposed model, deep neural network applied with sentiment features outperformed other classifiers in terms of classification accuracy up to 97.2%.

This research is novel in this regard that no previous research focuses on sentiment analysis in conjunction with other email features for detection of spam emails.