Systematic Review
Systematic reviews synthesize existing research on a specific topic to provide a comprehensive and unbiased overview, guiding future research and informing practice. Current research focuses on applying systematic review methodologies across diverse fields, leveraging machine learning and large language models to automate tasks like literature searching, data extraction, and analysis, particularly in areas like healthcare, finance, and AI development. This approach enhances the efficiency and scalability of evidence synthesis, leading to more robust conclusions and improved decision-making in various scientific and practical domains.
Papers
Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection
Vishnu KN, Cota Navin Gupta
Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation
Shuai Wang, Harrisen Scells, Martin Potthast, Bevan Koopman, Guido Zuccon
Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review
Mahsa Golchoubian, Moojan Ghafurian, Kerstin Dautenhahn, Nasser Lashgarian Azad
Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review
Rabia Asghar, Sanjay Kumar, Paul Hynds, Arslan Shaukat