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
Segmentation of Industrial Burner Flames: A Comparative Study from Traditional Image Processing to Machine and Deep Learning
Steven Landgraf, Markus Hillemann, Moritz Aberle, Valentin Jung, Markus Ulrich
The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security
Harriet Farlow, Matthew Garratt, Gavin Mount, Tim Lynar
Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati
Andani Madodonga, Vukosi Marivate, Matthew Adendorff
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities
Masoud Mehrabi Koushki, Ibrahim AbuAlhaol, Anandharaju Durai Raju, Yang Zhou, Ronnie Salvador Giagone, Huang Shengqiang