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
Perceived Fairness of the Machine Learning Development Process: Concept Scale Development
Anoop Mishra, Deepak Khazanchi
Time Series Embedding Methods for Classification Tasks: A Review
Yasamin Ghahremani, Vangelis Metsis
A review on development of eco-friendly filters in Nepal for use in cigarettes and masks and Air Pollution Analysis with Machine Learning and SHAP Interpretability
Bishwash Paneru, Biplov Paneru, Tanka Mukhiya, Khem Narayan Poudyal
A Review Paper of the Effects of Distinct Modalities and ML Techniques to Distracted Driving Detection
Anthony. Dontoh, Stephanie. Ivey, Logan. Sirbaugh, Armstrong. Aboah
Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database
Jeff J.H. Kim, George R. Nahass, Yang Dai, Theja Tulabandhula
The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions
Maryam Ebrahimi, Rajeev Sahay, Seyyedali Hosseinalipour, Bita Akram
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence
Minsu Kim, Jaehyun Oh, Sang-Young Lee, Junghwan Kim
Technical Report for the Forgotten-by-Design Project: Targeted Obfuscation for Machine Learning
Rickard Brännvall, Laurynas Adomaitis, Olof Görnerup, Anass Sedrati
Modelling of automotive steel fatigue lifetime by machine learning method
Oleh Yasniy, Dmytro Tymoshchuk, Iryna Didych, Nataliya Zagorodna, Olha Malyshevska
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data
Mohammad Ghabel Rahmat, Majid Khalilian
Towards Human-Guided, Data-Centric LLM Co-Pilots
Evgeny Saveliev, Jiashuo Liu, Nabeel Seedat, Anders Boyd, Mihaela van der Schaar
Contributions to the Decision Theoretic Foundations of Machine Learning and Robust Statistics under Weakly Structured Information
Christoph Jansen
Enhancing the Reliability in Machine Learning for Gravitational Wave Parameter Estimation with Attention-Based Models
Hibiki Iwanaga, Mahoro Matsuyama, Yousuke Itoh
Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning
Vahid Amiri, Mahmood Ahmadi
Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version
Muhammad Rajabinasab, Anton D. Lautrup, Arthur Zimek
Surgical Visual Understanding (SurgVU) Dataset
Aneeq Zia, Max Berniker, Rogerio Nespolo, Conor Perreault, Ziheng Wang, Benjamin Mueller, Ryan Schmidt, Kiran Bhattacharyya, Xi Liu, Anthony Jarc