Abstract
Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions.
Highlights
Data are ubiquitous, whether they are on blogs, social media platforms, discussion forums, reviews, literature, or research studies
To reveal the examination patterns and future scope in the field of Android security, 27 point arrangements were found as depicted in Figures 15(a) and 15(b). e semantic relationship between 27 theme arrangements and three core research areas assists with recognizing research patterns inside each center exploration area of Android security, as depicted in Figures 10 and 11
This study proposed another literature review method to deal with this challenge. is study unveiled a framework called the Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature
Summary
Whether they are on blogs, social media platforms, discussion forums, reviews, literature, or research studies. Manual systematic reviews [1] or semiautomated [2,3,4] are two methods that can be employed for systematic reviews. Elaborating present trends and forecasting future directions from the existing literature is challenging and time-consuming for systematic manual reviews. Deployment of machine learning techniques within semiautomated review methods can facilitate researchers to gain a dynamic review of any literature of choice. Is manuscript offers an empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature using Simulating Expert comprehension for Analyzing Research trends (SEAR) framework. E framework leverages information modeling techniques to simulate how humans read, understand, interpret the meaning of words, and map the semantic relationship in text.
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