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
Continual Object Detection: A review of definitions, strategies, and challenges
Angelo G. Menezes, Gustavo de Moura, Cézanne Alves, André C. P. L. F. de Carvalho
Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research
Shreyas Gawde, Shruti Patil, Satish Kumar, Pooja Kamat, Ketan Kotecha, Ajith Abraham
Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review
Fahad Alhomayani, Mohammad H. Mahoor
Review on Panoramic Imaging and Its Applications in Scene Understanding
Shaohua Gao, Kailun Yang, Hao Shi, Kaiwei Wang, Jian Bai
A review of ontologies for smart and continuous commissioning
Sara Gilani, Caroline Quinn, J. J. McArthur
Deep Learning and Computer Vision Techniques for Microcirculation Analysis: A Review
Maged Abdalla Helmy Mohamed Abdou, Trung Tuyen Truong, Eric Jul, Paulo Ferreira