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
The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease -- A Machine Learning-Based Fusion Approach
Hafsa Binte Kibria, Abdul Matin
Language Diversity: Visible to Humans, Exploitable by Machines
Gábor Bella, Erdenebileg Byambadorj, Yamini Chandrashekar, Khuyagbaatar Batsuren, Danish Ashgar Cheema, Fausto Giunchiglia
A novel shape-based loss function for machine learning-based seminal organ segmentation in medical imaging
Reza Karimzadeh, Emad Fatemizadeh, Hossein Arabi
Prediction of transport property via machine learning molecular movements
Ikki Yasuda, Yusei Kobayashi, Katsuhiro Endo, Yoshihiro Hayakawa, Kazuhiko Fujiwara, Kuniaki Yajima, Noriyoshi Arai, Kenji Yasuoka