Machine Learning Algorithm
Machine learning algorithms are computational tools designed to learn patterns from data and make predictions or decisions without explicit programming. Current research emphasizes improving algorithm efficiency and interpretability, exploring various model architectures such as decision trees, neural networks (including LSTMs and GRUs), random forests, and support vector machines, as well as novel approaches based on information theory and Bayesian optimization. These advancements are impacting diverse fields, from healthcare (disease prediction, medical image analysis) and finance (option pricing) to engineering (combustion control, structural anomaly detection) and environmental science (contaminant monitoring), improving accuracy, efficiency, and decision-making in numerous applications.
Papers - Page 4
An effective and efficient green federated learning method for one-layer neural networks
Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas, Elena Hernández-Pereira, Beatriz Pérez-SánchezDMC4ML: Data Movement Complexity for Machine Learning
Chen Ding, Christopher Kanan, Dylan McKellips, Toranosuke Ozawa, Arian Shahmirza, Wesley Smith
Vulgar Remarks Detection in Chittagonian Dialect of Bangla
Tanjim Mahmud, Michal Ptaszynski, Fumito MasuiAI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach
Elham Nasarian, Danial Sharifrazi, Saman Mohsenirad, Kwok Tsui, Roohallah Alizadehsani