Sorting Search Results of Literature Digital Libraries: Recent Developments and Future Research Directions

Literature review is a fundamental part of a research process, and systematic protocols for this activity have been used for a long time, mainly in the field of health. Specifically in the Computer Science Education area, the use of systematic literature review has grown. One of the steps in a systematic literature review (SLR) is the selection of academic databases in which to search for articles. There are several databases with academic documents that may be relevant to SLR, for example: Google Scholar, which indexes different types of documents, such as articles, dissertations, theses, and others; Scopus and Web of Science are large databases that index articles from different conferences and journals. ACM Digital Library and IEEE Xplore are also important sources of information in the field of Computer Education. These tools have different characteristics, some charge a fee, others have only information about the title and authors and do not have access to the full article, others have advanced features, with many filters. In this context, this article presents the following research questions: RQ1) What metadata can be extracted automatically from the databases?; RQ2) What kind of visualization tools are available?; RQ3) Do the documents returned by the databases cover the research topic?; RQ4) Do the databases have papers from the main CSE venues?; and RQ5) How many databases are required to perform a literature review in CSE? To answer these questions we used five academic databases: Google Scholar, Scopus, Web of Science, ACM Digital Library, and IEEE xplore. Regarding the results, Scopus and Web of Science have the best visualization of the documents and a robust query engine, however those academic databases are not free. ACM Digital library, IEEE Xplore, Scopus and Web of Science allow the automatic download of the papers’ metadata (author, title, abstract, affiliation and others). Specifically in the field of Computer Science Education, the ACM Digital Library and the IEEE Xplore have important papers from conferences (SIGCSE and FIE) and journals (ACM Transaction on Education and IEEE Transaction on Education). In this full paper, the results will be presented to help researchers to choose the most appropriate academic databases based on their requirements and available options.

Many scientists now manage the bulk of their bibliographic information electronically, thereby organizing their publications and citation material from digital libraries. However, a library has been described as “thought in cold storage,” and unfortunately many digital libraries can be cold, impersonal, isolated, and inaccessible places. In this Review, we discuss the current chilly state of digital libraries for the computational biologist, including PubMed, IEEE Xplore, the ACM digital library, ISI Web of Knowledge, Scopus, Citeseer, arXiv, DBLP, and Google Scholar. We illustrate the current process of using these libraries with a typical workflow, and highlight problems with managing data and metadata using URIs. We then examine a range of new applications such as Zotero, Mendeley, Mekentosj Papers, MyNCBI, CiteULike, Connotea, and HubMed that exploit the Web to make these digital libraries more personal, sociable, integrated, and accessible places. We conclude with how these applications may begin to help achieve a digital defrost, and discuss some of the issues that will help or hinder this in terms of making libraries on the Web warmer places in the future, becoming resources that are considerably more useful to both humans and machines.

In today's digital age, the integration of business management principles with computer science and technology has become increasingly vital for organizations seeking sustainable growth and competitive advantage. This study delves into the multifaceted relationship between business management and computer science, exploring integration strategies, challenges, and the resultant implications. The objective of this research is to comprehensively analyze the integration of business management and computer science, shedding light on effective strategies, potential challenges, and the overall impact on organizational performance and innovation. This study adopts a library research approach, encompassing a thorough review of existing literature, scholarly articles, case studies, and industry reports. Information is gathered from reputable databases such as PubMed, IEEE Xplore, ACM Digital Library, and Google Scholar. The collected data are analyzed qualitatively to identify patterns, trends, and insights into the integration of business management and computer science. The findings of this research highlight various integration strategies employed by organizations to leverage computer science and technology for enhancing business management practices. These strategies encompass the adoption of data analytics, artificial intelligence, machine learning, blockchain, and other emerging technologies to streamline operations, optimize decision-making processes, and gain competitive advantage. Moreover, the study identifies challenges associated with integration, including data security concerns, skills gap, resistance to change, and ethical implications. Despite these challenges, successful integration initiatives have demonstrated significant improvements in organizational efficiency, innovation capability, and market positioning

Databases are the discovery mechanisms used for searching and browsing scholarly information. Citings to Science, Technology, Engineering, and Mathematics (STEM) databases used by computer scientists, engineers, and mathematicians were analyzed for coverage and impact in the literature. This study uses the Web of Science (WoS) database to conduct a cited reference search to ACM Digital Library, Engineering Village, IEEE Xplore, and MathSciNet. The respective citation counts were compared by document types, publication year, language, country, organization, WoS category, and source title. The resulting low citation counts and lack of reference formatting agreement to these databases were in line with the predicted hypothesis. The impact of STEM databases in citations appears to be undercounted since they are not often included in citations. This article is intended to spark debate on the practice of citing databases in scholarly publications.

In the last five years, there has been a significant shift in how user interface (UI) and user experience (UX) design are approached within enterprise systems, reflecting the growing demand for more intuitive, adaptive, and inclusive solutions. This study employs a narrative review based on 1,500 initial records screened from Scopus, IEEE Xplore, ACM Digital Library, and Google Scholar (2019–2024). After rigorous selection, 82 empirical studies were included, focusing on user-centered design (UCD), adaptive interfaces, and inclusive practices in enterprise environments.. The review draws upon academic sources indexed in Scopus, IEEE Xplore, ACM Digital Library, and Google Scholar. Keywords including "Enterprise Systems," "User Experience," "Interface Design," and "Adaptive User Interfaces" were utilized to identify relevant literature, with inclusion criteria focusing on empirical studies from the last decade. Findings from 82 included studies show that UCD practices enhance usability and user satisfaction, with some reporting 20–30% higher usability scores and faster task completion rates when end-users are actively involved throughout development.. Adaptive interfaces employing machine learning have demonstrated potential to increase task efficiency and user engagement by personalizing content and layout. Moreover, inclusive design strategies, such as universal accessibility features and assistive technologies, contribute to improved user experiences across ability levels. However, systemic barriers like organizational resistance and limited training still hinder optimal implementation. The review highlights the need for strategic design interventions, ongoing usability assessments, and context-sensitive adaptations. As enterprise systems continue to evolve, future research must explore long-term effects of adaptive design and develop unified frameworks for inclusive, responsive interfaces. These efforts are vital to ensure equitable access and effectiveness of enterprise technologies across global and cross-sectoral contexts.

This systematic literature review on the issues faced by female computer science undergraduates sought to examine the reported reasons for female evasion from computer science major. A full range of indexed journals was surveyed using the ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, Web of Science, and Springer databases. Of the 818 articles retrieved from the digital libraries, only 24 papers were selected for data extraction. The several issues cited as reasons for female evasion from computer science undergraduate courses were divided into six major categories and described. Initiatives that have been implemented to minimize the dropout rate among undergraduate computer science female students were also addressed. Pointing out the main issues faced by female computer science students and identifying the limitations of the initiatives taken to solve them is the first step for future work, proposing good ways around them and outlining specific solutions for the classroom, making education professionals and even classmates aware of these problem. Attention to these issues may pique the researchers’ interest, while pursuing a graduate STEM degree, in working to make the experience of female undergraduate students more positive, thus decreasing their chances of evasion. Moreover, based on the results of this research, it is possible to make theory-based academic, managerial and administrative decisions concerning gender issues.

Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Background:There is increased interest in using artificial intelligence (AI) to provide participation-focused pediatric re/habilitation. Existing reviews on the use of AI in participation-focused pediatric re/habilitation focus on interventions and do not screen articles based on their definition of participation. AI-based assessments may help reduce provider burden and can support operationalization of the construct under investigation. To extend knowledge of the landscape on AI use in participation-focused pediatric re/habilitation, a scoping review on AI-based participation-focused assessments is needed.Objective:To understand how the construct of participation is captured and operationalized in pediatric re/habilitation using AI.Methods:We conducted a scoping review of literature published in Pubmed, PsycInfo, ERIC, CINAHL, IEEE Xplore, ACM Digital Library, ProQuest Dissertation and Theses, ACL Anthology, AAAI Digital Library, and Google Scholar. Documents were screened by 2–3 independent researchers following a systematic procedure and using the following inclusion criteria: (1) focuses on capturing participation using AI; (2) includes data on children and/or youth with a congenital or acquired disability; and (3) published in English. Data from included studies were extracted [e.g., demographics, type(s) of AI used], summarized, and sorted into categories of participation-related constructs.Results:Twenty one out of 3,406 documents were included. Included assessment approaches mainly captured participation through annotated observations (n = 20; 95%), were administered in person (n = 17; 81%), and applied machine learning (n = 20; 95%) and computer vision (n = 13; 62%). None integrated the child or youth perspective and only one included the caregiver perspective. All assessment approaches captured behavioral involvement, and none captured emotional or cognitive involvement or attendance. Additionally, 24% (n = 5) of the assessment approaches captured participation-related constructs like activity competencies and 57% (n = 12) captured aspects not included in contemporary frameworks of participation.Conclusions:Main gaps for future research include lack of: (1) research reporting on common demographic factors and including samples representing the population of children and youth with a congenital or acquired disability; (2) AI-based participation assessment approaches integrating the child or youth perspective; (3) remotely administered AI-based assessment approaches capturing both child or youth attendance and involvement; and (4) AI-based assessment approaches aligning with contemporary definitions of participation.

Healthcare is one of the most important sectors of our society, and during the COVID-19 pandemic a new challenge emerged—how to support people safely and effectively at home regarding their health-related problems. In this regard chatbots or conversational agents (CAs) play an increasingly important role, and are spreading rapidly. They can enhance not only user interaction by delivering quick feedback or responses, but also hospital management, thanks to several of their features. Considerable research is focused on making CAs more reliable, accurate, and robust. However, a critical aspect of chatbots is how to make them inclusive, in order to effectively support the interaction of users unfamiliar with technology, such as the elderly and people with disabilities. In this study, we investigate the current use of chatbots in healthcare, exploring their evolution over time and their inclusivity. The study was carried out on four digital libraries (ScienceDirect, IEEE Xplore, ACM Digital Library, and Google Scholar) on research articles published in the last 5 years, with a total of 21 articles describing chatbots implemented and actually used in the eHealth clinical area. The results showed a notable improvement in the use of chatbots in the last few years but also highlight some design issues, including poor attention to inclusion. Based on the findings, we recommend a different kind of approach for implementing chatbots with an inclusive accessibility-by-design approach.

This study aims to systematically review mobile applications and their impact on customer service in the tourism sector from 2017 to 2021. For this, the use of the DEMATEL method in its neutrosophic variant is proposed. The search strategy identified 257,399 articles from digital libraries such as Scopus, IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Microsoft Academic, EBSCOhost, ProQuest, ScienceDirect, and ARDI. Likewise, only 70 articles based on exclusion criteria were considered using the PRISMA Flowchart. The results of the systematic review have focused on recent studies of mobile applications and their impact on customer service in tourism and also provide a mapping of the extracted studies, metrics, trends, and validation methods to compare relevance to their settings and situations. The applicability and importance of multiple decision-making methods for solving complex problems were demonstrated. In addition, the effectiveness of using neutrosophy to reach valid conclusions when faced with real-life problems was manifested.

In recent years, numerous Machine Learning (ML) models, including Deep Learning (DL) and classic ML models, have been developed to detect software vulnerabilities. However, there is a notable lack of comprehensive and systematic surveys that summarize, classify, and analyze the applications of these ML models in software vulnerability detection. This absence may lead to critical research areas being overlooked or under-represented, resulting in a skewed understanding of the current state of the art in software vulnerability detection. To close this gap, we propose a comprehensive and systematic literature review that characterizes the different properties of ML-based software vulnerability detection systems using six major Research Questions (RQs). Using a custom web scraper, our systematic approach involves extracting a set of studies from four widely used online digital libraries: ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. We manually analyzed the extracted studies to filter out irrelevant work unrelated to software vulnerability detection, followed by creating taxonomies and addressing RQs. Our analysis indicates a significant upward trend in applying ML techniques for software vulnerability detection over the past few years, with many studies published in recent years. Prominent conference venues include the International Conference on Software Engineering (ICSE), the International Symposium on Software Reliability Engineering (ISSRE), the Mining Software Repositories (MSR) conference, and the ACM International Conference on the Foundations of Software Engineering (FSE), whereas Information and Software Technology (IST), Computers & Security (C&S), and Journal of Systems and Software (JSS) are the leading journal venues. Our results reveal that 39.1% of the subject studies use hybrid sources, whereas 37.6% of the subject studies utilize benchmark data for software vulnerability detection. Code-based data are the most commonly used data type among subject studies, with source code being the predominant subtype. Graph-based and token-based input representations are the most popular techniques, accounting for 57.2% and 24.6% of the subject studies, respectively. Among the input embedding techniques, graph embedding and token vector embedding are the most frequently used techniques, accounting for 32.6% and 29.7% of the subject studies. Additionally, 88.4% of the subject studies use DL models, with recurrent neural networks and graph neural networks being the most popular subcategories, whereas only 7.2% use classic ML models. Among the vulnerability types covered by the subject studies, CWE-119, CWE-20, and CWE-190 are the most frequent ones. In terms of tools used for software vulnerability detection, Keras with TensorFlow backend and PyTorch libraries are the most frequently used model-building tools, accounting for 42 studies for each. In addition, Joern is the most popular tool used for code representation, accounting for 24 studies. Finally, we summarize the challenges and future directions in the context of software vulnerability detection, providing valuable insights for researchers and practitioners in the field.

Transfer learning is a technique utilized in deep learning applications to transmit learned inference to a different target domain. The approach is mainly to solve the problem of a few training datasets resulting in model overfitting, which affects model performance. The study was carried out on publications retrieved from various digital libraries such as SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, and Google Scholar, which formed the Primary studies. Secondary studies were retrieved from Primary articles using the backward and forward snowballing approach. Based on set inclusion and exclusion parameters, relevant publications were selected for review. The study focused on transfer learning pretrained NLP models based on the deep transformer network. BERT and GPT were the two elite pretrained models trained to classify global and local representations based on larger unlabeled text datasets through self-supervised learning. Pretrained transformer models offer numerous advantages to natural language processing models, such as knowledge transfer to downstream tasks that deal with drawbacks associated with training a model from scratch. This review gives a comprehensive view of transformer architecture, self-supervised learning and pretraining concepts in language models, and their adaptation to downstream tasks. Finally, we present future directions to further improvement in pretrained transformer-based language models.

Interpretability in the medical field: A systematic mapping and review study

In the last decade, neurofuzzy networks have received considerable attention from academia. These systems strike a tradeoff between the performance of artificial neural networks and the interpretability of fuzzy inference systems expressed through fuzzy rules. Many researchers, however, are still dissatisfied with the performance of single neuro-fuzzy systems and are constantly working to improve them using ensemble techniques. This study conducts a systematic mapping of relevant studies on the application of ensemble techniques to neuro-fuzzy systems. Many aspects of the study are highlighted, including publication years, sources, contribution types, and application domains. As result, 48 articles published from 2000 to Mars 2022 were selected from six digital libraries (Science Direct, IEEE Xplore, ACM Digital Library, PubMed, Wiley, and Google Scholar. The findings revealed that the number of studies employing ensemble techniques with neuro-fuzzy systems is low and unstable. As the most used neuro-fuzzy system, ANFIS, from the Takagi-Sugeno-Kang category, is being studied in a wide range of application domains, most notably finance, medicine, and ecology.

Images captured through the glass often consist of undesirable specular reflections. These reflections detected in front of the glass remarkably reduce the quality and visibility of the scenes behind it. The process of reflection removal from images through the glass has many important applications in computer vision projects. Recently deep learning-based methods are being utilized for reflection removal so widely. In this article, we proposed a systematic literature review on the topic of single-image reflection removal using deep learning methods which were published between the years 2015 to 2021. A total number of 1600 research papers were extracted from five different online databases and digital libraries (IEEE Xplore, Google Scholar, Science Direct, SpringerLink and ACM Digital Library). After following the study selection procedure, 25 research papers were selected for this systematic review. The selected research papers were then analyzed to answer 7 key research questions that we have come up with to comprehensively explore the use of deep learning and neural networks for single-image reflection removal. After reading this article, future researchers will have a solid idea in the research field and will be able to work on their own research. The results provided in this proposed systematic review illustrate the main challenges that are encountered by researchers in this field and recommend encouraging directions for future research work. This review will also be helpful for researchers in discovering accessible datasets that can be used as benchmarks for comparing their proposed deep learning techniques with other studies in this research area.