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 an MLOps Architecture for XAI in Industrial Applications
Leonhard Faubel, Thomas Woudsma, Leila Methnani, Amir Ghorbani Ghezeljhemeidan, Fabian Buelow, Klaus Schmid, Willem D. van Driel, Benjamin Kloepper, Andreas Theodorou, Mohsen Nosratinia, Magnus Bång
From Text to Trends: A Unique Garden Analytics Perspective on the Future of Modern Agriculture
Parag Saxena
Machine Learning Meets Advanced Robotic Manipulation
Saeid Nahavandi, Roohallah Alizadehsani, Darius Nahavandi, Chee Peng Lim, Kevin Kelly, Fernando Bello
Identification of pneumonia on chest x-ray images through machine learning
Eduardo Augusto Roeder
Shedding Light on the Ageing of Extra Virgin Olive Oil: Probing the Impact of Temperature with Fluorescence Spectroscopy and Machine Learning Techniques
Francesca Venturini, Silvan Fluri, Manas Mejari, Michael Baumgartner, Dario Piga, Umberto Michelucci
Model-free tracking control of complex dynamical trajectories with machine learning
Zheng-Meng Zhai, Mohammadamin Moradi, Ling-Wei Kong, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
A Comprehensive Survey on Rare Event Prediction
Chathurangi Shyalika, Ruwan Wickramarachchi, Amit Sheth
Combining low-dose CT-based radiomics and metabolomics for early lung cancer screening support
Joanna Zyla, Michal Marczyk, Wojciech Prazuch, Marek Socha, Aleksandra Suwalska, Agata Durawa, Malgorzata Jelitto-Gorska, Katarzyna Dziadziuszko, Edyta Szurowska, Witold Rzyman, Piotr Widlak, Joanna Polanska
Towards a Prediction of Machine Learning Training Time to Support Continuous Learning Systems Development
Francesca Marzi, Giordano d'Aloisio, Antinisca Di Marco, Giovanni Stilo
A Model-Based Machine Learning Approach for Assessing the Performance of Blockchain Applications
Adel Albshri, Ali Alzubaidi, Ellis Solaiman
DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning
Spencer Giddens, Fang Liu
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks
Vasileios Perifanis, Nikolaos Pavlidis, Selim F. Yilmaz, Francesc Wilhelmi, Elia Guerra, Marco Miozzo, Pavlos S. Efraimidis, Paolo Dini, Remous-Aris Koutsiamanis
Asteroids co-orbital motion classification based on Machine Learning
Giulia Ciacci, Andrea Barucci, Sara Di Ruzza, Elisa Maria Alessi
Model Leeching: An Extraction Attack Targeting LLMs
Lewis Birch, William Hackett, Stefan Trawicki, Neeraj Suri, Peter Garraghan
Metastatic Breast Cancer Prognostication Through Multimodal Integration of Dimensionality Reduction Algorithms and Classification Algorithms
Bliss Singhal, Fnu Pooja
Multi-fidelity climate model parameterization for better generalization and extrapolation
Mohamed Aziz Bhouri, Liran Peng, Michael S. Pritchard, Pierre Gentine
A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches
Maisha Maliha, Golnaz Habibi, Mohammed Atiquzzaman
Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties
Shokirbek Shermukhamedov, Dilorom Mamurjonova, Michael Probst
Performance of the Pre-Trained Large Language Model GPT-4 on Automated Short Answer Grading
Gerd Kortemeyer