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
NiaAutoARM: Automated generation and evaluation of Association Rule Mining pipelines
Uroš Mlakar, Iztok Fister Jr., Iztok Fister
Unified dimensionality reduction techniques in chronic liver disease detection
Anand Karna, Naina Khan, Rahul Rauniyar, Prashant Giridhar Shambharkar
Functional Risk Minimization
Ferran Alet, Clement Gehring, Tomás Lozano-Pérez, Kenji Kawaguchi, Joshua B. Tenenbaum, Leslie Pack Kaelbling
Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations
Yang Yang, Chen Cheng, Yunhao Fan, Gia-Wei Chern
Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniques
Tuğçe Gökdemir, Jakub Rydzewski
Stroke Prediction using Clinical and Social Features in Machine Learning
Aidan Chadha
Machine Learning for Sentiment Analysis of Imported Food in Trinidad and Tobago
Cassandra Daniels, Koffka Khan
A Comparative Study of Machine Unlearning Techniques for Image and Text Classification Models
Omar M. Safa, Mahmoud M. Abdelaziz, Mustafa Eltawy, Mohamed Mamdouh, Moamen Gharib, Salaheldin Eltenihy, Nagia M. Ghanem, Mohamed M. Ismail
Real-time classification of EEG signals using Machine Learning deployment
Swati Chowdhuri, Satadip Saha, Samadrita Karmakar, Ankur Chanda
Disparate Model Performance and Stability in Machine Learning Clinical Support for Diabetes and Heart Diseases
Ioannis Bilionis, Ricardo C. Berrios, Luis Fernandez-Luque, Carlos Castillo
Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review
Jaouhar Fattahi
Accelerating process control and optimization via machine learning: A review
Ilias Mitrai, Prodromos Daoutidis
An Overview and Discussion of the Suitability of Existing Speech Datasets to Train Machine Learning Models for Collective Problem Solving
Gnaneswar Villuri, Alex Doboli
Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies
Qi Liu, Wanjing Ma