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
ADHD diagnosis based on action characteristics recorded in videos using machine learning
Yichun Li, Syes Mohsen Naqvi, Rajesh Nair
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates
Alice Williams, Boris Kovalerchuk
Counterfactual Fairness by Combining Factual and Counterfactual Predictions
Zeyu Zhou, Tianci Liu, Ruqi Bai, Jing Gao, Murat Kocaoglu, David I. Inouye
Learning Machines: In Search of a Concept Oriented Language
Veyis Gunes
Clustering of Indonesian and Western Gamelan Orchestras through Machine Learning of Performance Parameters
Simon Linke, Gerrit Wendt, Rolf Bader
Hazardous Asteroids Classification
Thai Duy Quy, Alvin Buana, Josh Lee, Rakha Asyrofi
From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?
Muhammad Arbab Arshad, Pallavi Kandanur, Saurabh Sonawani, Laiba Batool, Muhammad Umar Habib
NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques
Leand Thaqi, Arbnor Halili, Kamer Vishi, Blerim Rexha
A Critical Analysis on Machine Learning Techniques for Video-based Human Activity Recognition of Surveillance Systems: A Review
Shahriar Jahan, Roknuzzaman, Md Robiul Islam
Abstaining Machine Learning -- Philosophical Considerations
Daniela Schuster
CyberNFTs: Conceptualizing a decentralized and reward-driven intrusion detection system with ML
Synim Selimi, Blerim Rexha, Kamer Vishi
Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes
Vikram Sudarshan, Warren D. Seider
Objective Features Extracted from Motor Activity Time Series for Food Addiction Analysis Using Machine Learning
Mikhail Borisenkov, Andrei Velichko, Maksim Belyaev, Dmitry Korzun, Tatyana Tserne, Larisa Bakutova, Denis Gubin
Unveiling Processing--Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experiments
Mahsa Amiri, Zahra Zanjani Foumani, Penghui Cao, Lorenzo Valdevit, Ramin Bostanabad
Accelerating the discovery of steady-states of planetary interior dynamics with machine learning
Siddhant Agarwal, Nicola Tosi, Christian Hüttig, David S. Greenberg, Ali Can Bekar
Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine
Ahmed M Salih