Machine Learning
Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.
Papers
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
Vispi Karkaria, Anthony Goeckner, Rujing Zha, Jie Chen, Jianjing Zhang, Qi Zhu, Jian Cao, Robert X. Gao, Wei Chen
Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis
Lorenzo Peracchio, Giovanna Nicora, Enea Parimbelli, Tommaso Mario Buonocore, Roberto Bergamaschi, Eleonora Tavazzi, Arianna Dagliati, Riccardo Bellazzi
Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
Christian Nauck, Michael Lindner, Nora Molkenthin, Jürgen Kurths, Eckehard Schöll, Jörg Raisch, Frank Hellmann
Does Negative Sampling Matter? A Review with Insights into its Theory and Applications
Zhen Yang, Ming Ding, Tinglin Huang, Yukuo Cen, Junshuai Song, Bin Xu, Yuxiao Dong, Jie Tang
AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning
Le Yang, Miao Tian, Duan Xin, Qishuo Cheng, Jiajian Zheng
A Synergistic Approach to Wildfire Prevention and Management Using AI, ML, and 5G Technology in the United States
Stanley Chinedu Okoro, Alexander Lopez, Austine Unuriode
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey
Chaoyu Zhang
Informed Meta-Learning
Katarzyna Kobalczyk, Mihaela van der Schaar
A Machine Learning Approach to Detect Customer Satisfaction From Multiple Tweet Parameters
Md Mahmudul Hasan, Dr. Shaikh Anowarul Fattah
An Empirical Study of Challenges in Machine Learning Asset Management
Zhimin Zhao, Yihao Chen, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan
Hierarchical energy signatures using machine learning for operational visibility and diagnostics in automotive manufacturing
Ankur Verma, Seog-Chan Oh, Jorge Arinez, Soundar Kumara
Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs
Anika Hannemann, Jan Ewald, Leo Seeger, Erik Buchmann
Machine Learning Reveals Large-scale Impact of Posidonia Oceanica on Mediterranean Sea Water
Celio Trois, Luciana Didonet Del Fabro, Vladimir A. Baulin
A Bio-Medical Snake Optimizer System Driven by Logarithmic Surviving Global Search for Optimizing Feature Selection and its application for Disorder Recognition
Ruba Abu Khurma, Esraa Alhenawi, Malik Braik, Fatma A. Hashim, Amit Chhabra, Pedro A. Castillo