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
Machine Intelligence-Driven Classification of Cancer Patients-Derived Extracellular Vesicles using Fluorescence Correlation Spectroscopy: Results from a Pilot Study
Abicumaran Uthamacumaran, Mohamed Abdouh, Kinshuk Sengupta, Zu-hua Gao, Stefano Forte, Thupten Tsering, Julia V Burnier, Goffredo Arena
Machine learning to assess relatedness: the advantage of using firm-level data
Giambattista Albora, Andrea Zaccaria
Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels
J. A. Montanez-Barrera, J. M. Barroso-Maldonado, A. F. Bedoya-Santacruz, Adrian Mota-Babiloni
Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues
Francesco La Rosa, Maxence Wynen, Omar Al-Louzi, Erin S Beck, Till Huelnhagen, Pietro Maggi, Jean-Philippe Thiran, Tobias Kober, Russell T Shinohara, Pascal Sati, Daniel S Reich, Cristina Granziera, Martina Absinta, Meritxell Bach Cuadra