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.
3418papers
Papers - Page 73
February 14, 2024
Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach
Ricco Noel Hansen Flyckt, Louise Sjodsholm, Margrethe Høstgaard Bang Henriksen, Claus Lohman Brasen, Ali Ebrahimi, Ole Hilberg+4Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems
Liang Zhang, Zhelun ChenConnecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production
Patrick Oliver Schenk, Christoph KernEcoVal: An Efficient Data Valuation Framework for Machine Learning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Hong Ming Tan, Bowei Chen, Mohan KankanhalliMachine Learning in management of precautionary closures caused by lipophilic biotoxins
Andres Molares-Ulloa, Enrique Fernandez-Blanco, Alejandro Pazos, Daniel RiveroScheduling for On-Board Federated Learning with Satellite Clusters
Nasrin Razmi, Bho Matthiesen, Armin Dekorsy, Petar PopovskiIntelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning
Cangqing Wang, Jiangchuan GongPredicting the Emergence of Solar Active Regions Using Machine Learning
Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan, Bhairavi Apte
February 13, 2024
Optimal feature rescaling in machine learning based on neural networks
Federico Maria Vitrò, Marco Leonesio, Lorenzo FagianoForecasting high-impact research topics via machine learning on evolving knowledge graphs
Xuemei Gu, Mario KrennIntelligent Diagnosis of Alzheimer's Disease Based on Machine Learning
Mingyang Li, Hongyu Liu, Yixuan Li, Zejun Wang, Yuan Yuan, Honglin DaiSugarcane Health Monitoring With Satellite Spectroscopy and Machine Learning: A Review
Ethan Kane Waters, Carla Chia-Ming Chen, Mostafa Rahimi Azghadi
February 12, 2024
From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations
Kapilya Gangadharan, K. Malathi, Anoop Purandaran, Barathi Subramanian, Rathinaraja Jeyaraj, Soon Ki JungOut-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control
Ghada Zamzmi, Kesavan Venkatesh, Brandon Nelson, Smriti Prathapan, Paul H. Yi, Berkman Sahiner, Jana G. DelfinoLocality Sensitive Hashing for Network Traffic Fingerprinting
Nowfel Mashnoor, Jay Thom, Abdur Rouf, Shamik Sengupta, Batyr CharyyevConvolutional Neural Networks for signal detection in real LIGO data
Ondřej Zelenka, Bernd Brügmann, Frank OhmeMachine Learning for Stochastic Parametrisation
Hannah M. Christensen, Salah Kouhen, Greta Miller, Raghul Parthipan