News publishers have decreased disseminating news through conventional newspapers and have migrated to the use of digital means like websites and purpose-built mobile applications. It is observed that news recommendation systems can automatically process lengthy articles and identify similar articles for readers considering predefined criteria. The objectives of the current work are to identify and classify the challenges in news recommendation domain, to identify state-of-the-art approaches and classify on the application domain, to identify datasets used for evaluation and their sources, the evaluation approaches used and to highlight the challenges explicitly addressed. The literature is thoroughly studied over the time span of 2001-2019 and shortlisted 81 related studies, broadly classified into six categories and discussed. The analysis showed that 60% of news recommendation system adopted a hybrid approach, 66% studies little talk about datasets, and addresses a few challenges from a long list of challenges in the news domain. This article is the first in the field to draw a comprehensive big picture of news recommendation and explore different dimensions covered in the studies. The last section presents the future research opportunities that lead to improving the recommendation of news articles in the news domain.

As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.

Context: mobile application development is rapidly evolving with substantial economic and scientific interest. One of the primary reasons for mobile application development failure is the increasing number of mobile platforms; some organizations endorse mobile application development before understanding the associated development challenges of each target platform. Objective: the objective of this paper is to identify the challenges of native, web, and hybrid mobile applications, which can undermine the successful development of such applications. Method: we adopted a two-phase research approach: at first, the challenges were identified via a systematic literature review (SLR); and then, the identified challenges were validated through conducting interviews with practitioners. Results: through both research approaches, we identified nine challenges vital to the success of mobile application development and four additional challenges from interviews not reported in the literature. A comparison of the challenges (native, web, and hybrid) identified in SLR indicates that there are slightly more differences than similarities between the challenges. On the other hand, the challenges (native, web, and hybrid) identified in interviews indicates that there are more similarities than differences between the challenges. Our results show a weak negative correlation between the ranks obtained from the SLR and the interviews ([rs(9) = -.034], p = 0.932). The results obtained from our t-test (i.e., t = 0.868, p = 0.402 > 0.05) depicts that there is no significant difference between the findings of SLR and interviews. Conclusions: mobile application development organizations should try to address the identified challenges when developing mobile applications (native, web, or hybrid) to increase the probability of mobile application success.

The web is being loaded daily with a huge volume of data, mainly unstructured textual data, which increases the need for information extraction and NLP systems significantly. Named‐entity recognition task is a key step towards efficiently understanding text data and saving time and effort. Being a widely used language globally, English is taking over most of the research conducted in this field, especially in the biomedical domain. Unlike other languages, Arabic suffers from lack of resources. This work presents a BERT‐based model to identify biomedical named entities in the Arabic text data (specifically disease and treatment named entities) that investigates the effectiveness of pretraining a monolingual BERT model with a small‐scale biomedical dataset on enhancing the model understanding of Arabic biomedical text. The model performance was compared with two state‐of‐the‐art models (namely, AraBERT and multilingual BERT cased), and it outperformed both models with 85% F1‐score.

Purpose: Software developers extensively use stack overflow (SO) for knowledge sharing on software development. Thus, software engineering researchers have started mining the structured/unstructured data present in certain software repositories including the Q&A software developer community SO, with the aim to improve software development. The purpose of this paper is show that how academics/practitioners can get benefit from the valuable user-generated content shared on various online social networks, specifically from Q&A community SO for software development. Design/methodology/approach: A comprehensive literature review was conducted and 166 research papers on SO were categorized about software development from the inception of SO till June 2016. Findings: Most of the studies revolve around a limited number of software development tasks; approximately 70 percent of the papers used millions of posts data, applied basic machine learning methods, and conducted investigations semi-automatically and quantitative studies. Thus, future research should focus on the overcoming existing identified challenges and gaps. Practical implications: The work on SO is classified into two main categories; “SO design and usage” and “SO content applications.” These categories not only give insights to Q&A forum providers about the shortcomings in design and usage of such forums but also provide ways to overcome them in future. It also enables software developers to exploit such forums for the identified under-utilized tasks of software development. Originality/value: The study is the first of its kind to explore the work on SO about software development and makes an original contribution by presenting a comprehensive review, design/usage shortcomings of Q&A sites, and future research challenges.