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
Self-consistent Validation for Machine Learning Electronic Structure
Gengyuan Hu, Gengchen Wei, Zekun Lou, Philip H. S. Torr, Wanli Ouyang, Han-sen Zhong, Chen Lin
Random features and polynomial rules
Fabián Aguirre-López, Silvio Franz, Mauro Pastore
On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection
Marco Cantone, Claudio Marrocco, Alessandro Bria
COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions
Mahathir Mohammad Bishal, Md. Rakibul Hassan Chowdory, Anik Das, Muhammad Ashad Kabir
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, Torben Frøstrup Hansen, Uffe Kock Wiil, Lars Henrik Jensen, Abdolrahman Peimankar
Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems
Liang Zhang, Zhelun Chen
Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production
Patrick Oliver Schenk, Christoph Kern
EcoVal: An Efficient Data Valuation Framework for Machine Learning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Hong Ming Tan, Bowei Chen, Mohan Kankanhalli
Machine Learning in management of precautionary closures caused by lipophilic biotoxins
Andres Molares-Ulloa, Enrique Fernandez-Blanco, Alejandro Pazos, Daniel Rivero
Scheduling for On-Board Federated Learning with Satellite Clusters
Nasrin Razmi, Bho Matthiesen, Armin Dekorsy, Petar Popovski
Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning
Cangqing Wang
Predicting the Emergence of Solar Active Regions Using Machine Learning
Spiridon Kasapis, Irina N. Kitiashvili, Alexander G. Kosovichev, John T. Stefan, Bhairavi Apte
Optimal feature rescaling in machine learning based on neural networks
Federico Maria Vitrò, Marco Leonesio, Lorenzo Fagiano
Forecasting high-impact research topics via machine learning on evolving knowledge graphs
Xuemei Gu, Mario Krenn
Intelligent Diagnosis of Alzheimer's Disease Based on Machine Learning
Mingyang Li, Hongyu Liu, Yixuan Li, Zejun Wang, Yuan Yuan, Honglin Dai
Sugarcane Health Monitoring With Satellite Spectroscopy and Machine Learning: A Review
Ethan Kane Waters, Carla Chia-Ming Chen, Mostafa Rahimi Azghadi
From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations
Kapilya Gangadharan, K. Malathi, Anoop Purandaran, Barathi Subramanian, Rathinaraja Jeyaraj
Out-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. Delfino
Locality Sensitive Hashing for Network Traffic Fingerprinting
Nowfel Mashnoor, Jay Thom, Abdur Rouf, Shamik Sengupta, Batyr Charyyev
Convolutional Neural Networks for signal detection in real LIGO data
Ondřej Zelenka, Bernd Brügmann, Frank Ohme