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
Vulgar Remarks Detection in Chittagonian Dialect of Bangla
Tanjim Mahmud, Michal Ptaszynski, Fumito Masui
AI 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
Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms
Mariapia Rita Iandolo, Francesca Razzano, Chiara Zarro, G. S. Yogesh, Silvia Liberata Ullo
Integration of Sentinel-1 and Sentinel-2 data for Earth surface classification using Machine Learning algorithms implemented on Google Earth Engine
Francesca Razzano, Mariapia Rita Iandolo, Chiara Zarro, G. S. Yogesh, Silvia Liberata Ullo
Machine learning for option pricing: an empirical investigation of network architectures
Laurens Van Mieghem, Antonis Papapantoleon, Jonas Papazoglou-Hennig
Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms
Qi Liu, Zheng Gong, Zhenya Huang, Chuanren Liu, Hengshu Zhu, Zhi Li, Enhong Chen, Hui Xiong