Big Data
Big data research focuses on managing, analyzing, and extracting insights from massive datasets, primarily using machine learning and deep learning techniques. Current research emphasizes efficient algorithms and architectures, such as transformers, recurrent neural networks (like LSTMs), and various tree-based models, often within object-oriented programming frameworks to improve scalability and maintainability. This field is crucial for advancements in diverse sectors, including finance (risk management), healthcare (disease prediction), and environmental science (e.g., forest fire prediction), by enabling more accurate and timely decision-making through data-driven models.
Papers
Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives
Kwok P Chun, Thanti Octavianti, Nilay Dogulu, Hristos Tyralis, Georgia Papacharalampous, Ryan Rowberry, Pingyu Fan, Mark Everard, Maria Francesch-Huidobro, Wellington Migliari, David M. Hannah, John Travis Marshall, Rafael Tolosana Calasanz, Chad Staddon, Ida Ansharyani, Bastien Dieppois, Todd R Lewis, Juli Ponce, Silvia Ibrean, Tiago Miguel Ferreira, Chinkie Peliño-Golle, Ye Mu, Manuel Delgado, Elizabeth Silvestre Espinoza, Martin Keulertz, Deepak Gopinath, Cheng Li
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models
Keyu Chen, Ziqian Bi, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Liu, Ming Li, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Pohsun Feng