New Machine
Research on "new machines" broadly encompasses the development and application of machine learning across diverse fields, aiming to improve efficiency, accuracy, and decision-making. Current efforts focus on refining model architectures like convolutional neural networks, gradient boosting machines, and transformers for tasks ranging from image and signal processing to complex prediction and control problems. This research is significant because it drives advancements in various sectors, including healthcare, energy, manufacturing, and transportation, by enabling automated processes, improved diagnostics, and more efficient resource allocation.
Papers
Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis data
Djavan De Clercq, Adam Mahdi
CardioGenAI: A Machine Learning-Based Framework for Re-Engineering Drugs for Reduced hERG Liability
Gregory W. Kyro, Matthew T. Martin, Eric D. Watt, Victor S. Batista
FlaKat: A Machine Learning-Based Categorization Framework for Flaky Tests
Shizhe Lin, Ryan Zheng He Liu, Ladan Tahvildari
Computer-Controlled 3D Freeform Surface Weaving
Xiangjia Chen, Lip M. Lai, Zishun Liu, Chengkai Dai, Isaac C. W. Leung, Charlie C. L. Wang, Yeung Yam
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model
Tin Nguyen, Lam Pham, Phat Lam, Dat Ngo, Hieu Tang, Alexander Schindler