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
Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection
Niklas Risse, Marcel Böhme
ml_edm package: a Python toolkit for Machine Learning based Early Decision Making
Aurélien Renault, Youssef Achenchabe, Édouard Bertrand, Alexis Bondu, Antoine Cornuéjols, Vincent Lemaire, Asma Dachraoui
COVID-19 Probability Prediction Using Machine Learning: An Infectious Approach
Mohsen Asghari Ilani, Saba Moftakhar Tehran, Ashkan Kavei, Arian Radmehr
Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach
Mohsen Asghari Ilani, Saba Moftakhar Tehran, Ashkan Kavei, Hamed Alizadegan
From Radiologist Report to Image Label: Assessing Latent Dirichlet Allocation in Training Neural Networks for Orthopedic Radiograph Classification
Jakub Olczak, Max Gordon
Predicting Solar Energy Generation with Machine Learning based on AQI and Weather Features
Arjun Shah, Varun Viswanath, Kashish Gandhi, Nilesh Madhukar Patil
Multiple testing for signal-agnostic searches of new physics with machine learning
Gaia Grosso, Marco Letizia
Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey
Haixin Wang, Yadi Cao, Zijie Huang, Yuxuan Liu, Peiyan Hu, Xiao Luo, Zezheng Song, Wanjia Zhao, Jilin Liu, Jinan Sun, Shikun Zhang, Long Wei, Yue Wang, Tailin Wu, Zhi-Ming Ma, Yizhou Sun
Extraction of Research Objectives, Machine Learning Model Names, and Dataset Names from Academic Papers and Analysis of Their Interrelationships Using LLM and Network Analysis
S. Nishio, H. Nonaka, N. Tsuchiya, A. Migita, Y. Banno, T. Hayashi, H. Sakaji, T. Sakumoto, K. Watabe
MAC protocol classification in the ISM band using machine learning methods
Hanieh Rashidpour, Hossein Bahramgiri
Rage Music Classification and Analysis using K-Nearest Neighbour, Random Forest, Support Vector Machine, Convolutional Neural Networks, and Gradient Boosting
Akul Kumar
Understanding the Skills Gap between Higher Education and Industry in the UK in Artificial Intelligence Sector
Khushi Jaiswal, Ievgeniia Kuzminykh, Sanjay Modgil
Extending Machine Learning Based RF Coverage Predictions to 3D
Muyao Chen, Mathieu Châteauvert, Jonathan Ethier
Chatbots and Zero Sales Resistance
Sauro Succi
Unsupervised Machine Learning Hybrid Approach Integrating Linear Programming in Loss Function: A Robust Optimization Technique
Andrew Kiruluta, Andreas Lemos
Augmenting train maintenance technicians with automated incident diagnostic suggestions
Georges Tod, Jean Bruggeman, Evert Bevernage, Pieter Moelans, Walter Eeckhout, Jean-Luc Glineur
Machine Learning with Physics Knowledge for Prediction: A Survey
Joe Watson, Chen Song, Oliver Weeger, Theo Gruner, An T. Le, Kay Hansel, Ahmed Hendawy, Oleg Arenz, Will Trojak, Miles Cranmer, Carlo D'Eramo, Fabian Bülow, Tanmay Goyal, Jan Peters, Martin W. Hoffman
Circuit design in biology and machine learning. I. Random networks and dimensional reduction
Steven A. Frank
EEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach
Biplov Paneru, Bipul Thapa, Bishwash Paneru, Sanjog Chhetri Sapkota