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This paper is a systematic review that aims to collect and review studies on library users’ smartphone use to access library services and resources. Another objective of the systematic review is to identify the challenges and benefits (reported in the reviewed literature) regarding using smartphones to access library resources and services.

The researchers searched the relevant literature from four databases: Scopus, Library and Information Science Technology Abstracts, Web of Science and Google Scholar. A total of 20 studies were selected, out of the 2,416 retrieved titles, for review in light of the PRISMA guidelines. This systematic review also used the quality assessment guidelines of Boynton and Greenhalgh (2004), to analyze the quality of the publications reviewed in the study.

The results highlighted that library users used smartphones to access a wide range of library services, including directions help, fresh arrivals, library news notifications, room and course reservations, library account management, circulation and the Online Public Access Catalogue (OPAC). The libraries offered their users multiple mobile-supported services related to searching for resources and account management. The challenges, as well as the benefits associated with the use of smartphones for accessing and using library services, have also been reported. Most reviewed studies scored high (9–13 out of 13) in the quality assessment.

This research has some limitations. First, only the English-language research studies were included in the review. Therefore, it is possible that a significant study conducted in a language other than English was missed. Second, researchers performed a comprehensive search to collect the maximum number of relevant studies; there is a chance that an important study was missed during the search and retrieval process.

This research fills the research gap by advancing the literature on mobile device use for accessing library services and resources. The findings might help library professionals understand the current trends in accessing library services through smartphones. This also helps them plan the latest mobile-supported library services to meet the ever-changing demands of library users. Policymakers could also benefit from this research, as it could help them understand current trends in library services and facilitate them in making appropriate policies, funding allocations and implementing mobile services in libraries.

The researchers found that the use of mobile phones to access library services and resources impacts society in a discernible way. It provides a rich environment as users access a wide range of library services, including directions help, fresh arrivals, library news notifications, room and course reservations, library account management, circulation and the OPAC without any hindrances or hurdles.

This is the first review, to the best of the authors’ knowledge, that has systematically collected and reviewed the literature on the topic under study. This study also shares the theoretical and practical implications of the research and future research directions for researchers.

The use of mobile technologies and learning devices has been increasing in every field of life, and library and information sciences are no exception. This study aims to explore the perceptions of library and information science (LIS) students regarding mobile learning (m-learning) along with their primary purposes, social media applications, advantages, disadvantages, barriers, impact and overall satisfaction with using these devices.

A survey method was used, and data was collected from the students of 12 library schools in Pakistan for a total sample of 250 students.

Their main purposes of usage, their needs, advantages, disadvantages, barriers, impacts and the level of overall satisfaction were also identified. The inferential statistics (t-test and ANOVA) also identified the difference of opinion on the basis of gender, programs, types and number of m-learning devices. Overall, the findings highlight the need for academic libraries to give mobile access (launching mobile applications) to better use library services. To ensure this, libraries must keep an eye on new advances in mobile technology, researchers’ needs and related electronic library services and observe how these services are being used.

Theoretical and practical implications have been highlighted to understand the perceptions of LIS students about m-learning devices.

The mobile library service providers and management need to improve their services by offering services that fit the diverse needs of their users and should know how to attract modern library users.

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.