Narrative Review
Narrative reviews synthesize existing research to provide a comprehensive overview of a specific topic, aiming to identify key trends, gaps, and future research directions. Current research focuses on applying narrative reviews across diverse fields, employing various model architectures like graph neural networks, large language models, and diffusion models to analyze complex data and improve model interpretability and efficiency. This approach is crucial for advancing scientific understanding and informing the development of practical applications in areas such as medicine, engineering, and manufacturing.
Papers
A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection
Muath Alsuhaibani, Ali Pourramezan Fard, Jian Sun, Farida Far Poor, Peter S. Pressman, Mohammad H. Mahoor
Motion Planning for Robotics: A Review for Sampling-based Planners
Liding Zhang, Kuanqi Cai, Zewei Sun, Zhenshan Bing, Chaoqun Wang, Luis Figueredo, Sami Haddadin, Alois Knoll
Machine Learning Aided Modeling of Granular Materials: A Review
Mengqi Wang, Krishna Kumar, Y. T. Feng, Tongming Qu, Min Wang
Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review
Ahmad Obeid, Said Boumaraf, Anabia Sohail, Taimur Hassan, Sajid Javed, Jorge Dias, Mohammed Bennamoun, Naoufel Werghi