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
Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook
Ziying Song, Lin Liu, Feiyang Jia, Yadan Luo, Guoxin Zhang, Lei Yang, Li Wang, Caiyan Jia
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Lingchao Mao, Hairong Wang, Leland S. Hu, Nhan L Tran, Peter D Canoll, Kristin R Swanson, Jing Li
Quantitative Technology Forecasting: a Review of Trend Extrapolation Methods
Peng-Hung Tsai, Daniel Berleant, Richard S. Segall, Hyacinthe Aboudja, Venkata Jaipal R. Batthula, Sheela Duggirala, Michael Howell
Image-based Deep Learning for Smart Digital Twins: a Review
Md Ruman Islam, Mahadevan Subramaniam, Pei-Chi Huang