Bibliometric analysis
The objective of this study is to conduct a systematic examination of research trends and hotspots in the domain of autonomous vehicles leveraging deep learning, through a bibliometric analysis. By scrutinizing research publications from various countries spanning 2017 to 2023, this paper aims to summarize effective research methodologies and identify potential innovative pathways to foster further advancements in AVs research. A total of 1,239 publications from the core collection of scientific networks were retrieved and utilized to construct a clustering network.
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This study utilizes a bibliometric analysis to explore the research landscape of student attrition within the computer sciences (CS) field. As a widely-adopted technique for evaluating published scientific articles, bibliometric analysis proves invaluable in examining vast and growing research streams. By applying this method, the study uncovers key patterns, trends, and insights in the student attrition literature, offering a comprehensive understanding of the factors influencing retention in CS and informing future research and interventions in the domain.
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