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Due to the effect of citation impact on The Higher Education (THE) world university ranking system, most of the researchers are looking for some helpful techniques to increase their citation record. This paper by reviewing the relevant articles extracts 33 different ways for increasing the citations possibilities. The results show that the article visibility has tended to receive more download and citations. This is probably the first study to collect over 30 different ways to improve the citation record. Further study is needed to explore and expand these techniques in specific fields of study in order to make the results more precisely.
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... Bibliometrics is one of the frequently used terms in research evaluation metrics. Bibliometrics is a set of methods for quantitatively analyzing academic literature and scholarly communications [38,39]. In bibliometric analysis, we rigorously explored and analyzed extensive scientific data. ...
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Every data and kind of data need a physical drive to store it. There has been an explosion in the volume of images, video, and other similar data types circulated over the internet. Users using the internet expect intelligible data, even under the pressure of multiple resource constraints such as bandwidth bottleneck and noisy channels. Therefore, data compression is becoming a fundamental problem in wider engineering communities. There has been some related work on data compression using neural networks. Various machine learning approaches are currently applied in data compression techniques and tested to obtain better lossy and lossless compression results. A very efficient and variety of research is already available for image compression. However, this is not the case for video compression. Because of the explosion of big data and the excess use of cameras in various places globally, around 82% of the data generated involve videos. Proposed approaches have used Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and various variants of Autoencoders (AEs) are used in their approaches. All newly proposed methods aim to increase performance (reducing bitrate up to 50% at the same data quality and complexity). This paper presents a bibliometric analysis and literature survey of all Deep Learning (DL) methods used in video compression in recent years. Scopus and Web of Science are well-known research databases. The results retrieved from them are used for this analytical study. Two types of analysis are performed on the extracted documents. They include quantitative and qualitative results. In quantitative analysis, records are analyzed based on their citations, keywords, source of publication, and country of publication. The qualitative analysis provides information on DL-based approaches for video compression, as well as the advantages, disadvantages, and challenges of using them.
... To carry out a bibliographic study we can use several bibliographic databases [104,105] such as the web of science (WoS), Scopus, Springer, Google Scholar or Science Direct. For our case study, we focus on the WoS search engine, our motivations are the following: (a) WoS is a bibliometric analysis tool that allows evaluating statistical indicators of publications; (b) unlike Scopus, WoS contains more multidisciplinary publications with a high impact in each field [106]; (c) in contrast to Scopus, WoS contains more multidisciplinary publications with high impact in each field, also, we exclude Scopus to avoid duplicate documents, and Google Scholar for the reduced performance compared to the quality of the search obtained. ...
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The purpose of this article is to study the issues of industrial maintenance, one of the critical drivers of Industry 4.0 (I4.0), which has contributed to the advent of new industrial challenges. In this context, predictive maintenance 4.0 (PdM4.0) has seen a significant progress, providing several potential advantages among which: increase of productivity, especially by improving both availability and quality and ensuring cost-saving through automated processes for production systems monitoring, early detection of failures, reduction of machine downtime, and prediction of equipment life. In the research work carried out, we focused on bibliometric analysis to provide beneficial guidelines that may help researchers and practitioners to understand the key challenges and the most insightful scientific issues that characterize a successful application of Artificial Intelligence (AI) to PdM4.0. Even though, most of the exploited articles focus on AI techniques applied to PdM, they do not include predictive maintenance practices and their organization. Using Biblioshiny, VOSviewer, and Power BI tools, our main contribution consisted of performing a Bibliometric study to analyze and quantify the most important concepts, application areas, methods, and main trends of AI applied to real-time predictive maintenance. Therefore, we studied the current state of research on these new technologies, their applications, associated methods, related roles or impacts in developing I4.0. The result shows the most common productive sources, institutes, papers, countries, authors, and their collaborative networks. In this light, American and Chinese institutes dominate the scientific debate, while the number of publications in I4.0 and PdM4.0 is exponentially growing, particularly in the field of data-driven, hybrid models, and digital twin frameworks applied for prognostic diagnostic or anomaly detection. Emerging topics such as Machine Learning and Deep Learning also significantly impacted PdM4.0 development. Subsequently, we analyzed factors that may hinder the successful use of AI-based systems in I4.0, including the data collection process, potential influence of ethics, socio-economic issues, and transparency for all stakeholders. Finally, we suggested our definition of trustful AI for I4.0.
... However, the most prominent global ranking bodies are Google Scholar (Webometrics), Scopus (QS World University), and Web of Science (Times Higher Education & Academic Ranking of World Universities). Through these databases, it allows scholars to have citation accounts, find out the identities of academics who refer to their research papers, and also extend the networks with other researchers (Ale Ebrahim et al., 2013). Besides, the value of the H-index will be updated automatically from time to time for each journal, proceeding and book published in the database. ...
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... 1262 out of the 2096 papers were cited, with a total of 17,832 citations and an average of 8.5 citations. Citation counts are generally considered a measure of utilization and contribution to published articles [43] so that they reflect the importance and influence of articles in the academic community [44]. WoS describes a highly cited paper in COVID-19 and psychological and behavioral research as follows: as of May/June 2021, the highly cited paper has received enough citations to place it in the top 1% of its academic field, based on the high citation threshold for the field and publication year. ...
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The COVID-19 pandemic outbreak in December 2019 has spread globally. The ongoing psychological and behavioral effects of the COVID-19 pandemic, which poses a major challenge to humanity, are of concern to researchers. To understand the academic community’s attention, focus and research collaboration on psychological and behavioral research during the COVID-19 pandemic, we conducted a macro analysis using a bibliometric approach. Using the topic selection strategy of TS = (“COVID-19” OR “coronavirus disease 2019” OR “SARS-CoV-2” OR “2019-nCoV”) AND TS = (“behavio*”) AND TS = (“psycholog*”), 2096 high-quality research articles and reviews were downloaded as data from the Web of Science core collection on 16 November 2021. Through analysis and visualization, the following conclusions are drawn in this study: (1) The popularity and importance of psychological and behavioral research under COVID-19 has increased significantly and needs further attention; (2). Related research focuses on eight hotspots, with quarantine, health care workers, the elderly, students, pregnant women, family, consumers, social media and emergency preparedness knowledge as the focus of the research object; and (3) Research collaboration is relatively high at the author, organizational and national levels. However, low-income countries need to get more attention. Furthermore, this article would help researchers make decisions for the research of psychological and behavioral issues under COVID-19 and planning for future prospects to contribute to academic development and applied methodology.
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Chapter
We are living in a publish or perish world, where publishing research is no longer the only requirement imposed on scholars. Maintaining high author and article-level research metrics has gained momentum and these are considered important gauges when applying for jobs in academia, for academic promotions as well as for securing of research funds. Similarly, journals are depending on journal-level metrics to establish their place within the publishing scientific community. The higher the journal metrics, the more prestigious the journal is considered. Research metrics mainly depend on the number of citations an article or a journal achieves over time. Nowadays the digital footprint is also being considered as an essential sector for research metrics. This chapter will discuss author- and article-level metrics, how to interpret them, the influence of the digital platforms and outlets as well as what the future holds. These are very important facts that any scholar should be well acquainted with!
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