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Unbiased inference for echocardiogram urgency prediction using double machine learning. [PDF]
PLoS ONEThe increased utilization of echocardiography in clinical practice has witnessed a substantial rise, underscoring its pivotal role as a diagnostic tool for various cardiovascular conditions. However, due to the relative scarcity of echocardiography tests,
Yiqun Jiang +4 more
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A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning [PDF]
SensorsCausal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focus on
Keyue Yan +5 more
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Causal effect analysis of serving performance using double machine learning [PDF]
BMC Sports Science, Medicine and RehabilitationServing performance is widely recognized as a critical factor influencing match outcomes in professional tennis. To evaluate its contribution to winning probability, this study analyzes ATP men’s singles matches (2013–2024) and estimates the causal ...
Jiacai Ma, Fuzhu Zou
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A Double machine learning trend model for citizen science data
Methods in Ecology and Evolution, 2023 Citizen and community science datasets are typically collected using flexible protocols. These protocols enable large volumes of data to be collected globally every year; however, the consequence is that these protocols typically lack the structure ...
Daniel Fink +10 more
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Robust double machine learning model with application to omics data [PDF]
BMC BioinformaticsBackground Recently, there has been a growing interest in combining causal inference with machine learning algorithms. Double machine learning model (DML), as an implementation of this combination, has received widespread attention for their expertise in
Xuqing Wang +3 more
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Enabling intrusion detection systems with dueling double deep Q-learning [PDF]
Digital Transformation and Society, 2022 Purpose – In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g.
Youakim Badr
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Bending obstacles when moving a mobile robot
Системный анализ и прикладная информатика, 2023 The issues of modeling when navigating around obstacles of a mobile robot using machine learning methods are considered: Q-learning, SARSA algorithm, deep Q-learning and double deep Q-learning.
A. V. Sidorenko, N. A. Saladukha
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Multi-Modal Feature Fusion-Based Machine Learning to Detect Abnormal Mechanical Ventilation
IEEE Access, 2023 Mechanical ventilation (MV) is a critical life-supportive technique for saving patients with acute respiratory failure. Abnormal ventilation happens frequently due to patient-ventilator dyssynchrony (PVD), condensation in the circuit, increased airway ...
Huaqing Zhang +4 more
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Sensors, 2020 Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using ...
José Manuel Lozano Domínguez +3 more
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Energies, 2023 Electricity peak load forecasting plays an important role in electricity generation capacity planning to ensure reliable power supplies. To achieve high forecast accuracy, multiple machine learning models have been implemented to forecast the monthly ...
Warut Pannakkong +3 more
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