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Hierarchical automated machine learning (AutoML) for advanced unconventional reservoir characterization
Recent advances in machine learning (ML) have transformed the landscape of energy exploration, including hydrocarbon, CO 2 storage, and hydrogen....
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How far are we with automated machine learning? characterization and challenges of AutoML toolkits
Automated Machine Learning aka AutoML toolkits are low/no-code software that aim to democratize ML system application development by ensuring rapid...
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Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer
Early postoperative mortality risk prediction is crucial for clinical management of gastric cancer. This study aims to predict 90-day mortality in...
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Machine Learning—Automated Machine Learning (AutoML) for Disease Prediction
The selection and tuning of feature selection, feature engineering, and classification or regression algorithms is a major challenge in machine...
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Automated machine learning: past, present and future
Automated machine learning (AutoML) is a young research area aiming at making high-performance machine learning techniques accessible to a broad set...
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Automated Machine Learning (AutoML): The Future of Computational Intelligence
Computer science controls every task in today’s environment, and everything in the sector attempts to automate the task. The basic essence of...
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AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network
Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data...
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Online AutoML: an adaptive AutoML framework for online learning
Automated Machine Learning (AutoML) has been used successfully in settings where the learning task is assumed to be static. In many real-world...
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Machine learning model matters its accuracy: a comparative study of ensemble learning and AutoML using heart disease prediction
Ensemble machine learning is the concept of using multiple models to gain better performance from the combination of weak individual models. New...
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A systematic literature review on AutoML for multi-target learning tasks
Automated machine learning (AutoML) aims to automate machine learning (ML) tasks, eliminating human intervention from the learning process as much as...
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Automated machine learning with dynamic ensemble selection
Automated machine learning (AutoML) has been developed for automatically building effective machine learning pipelines. However, existing AutoML...
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Predicting the Recurrence of Common Bile Duct Stones After ERCP Treatment with Automated Machine Learning Algorithms
BackgroundRecurrence of common bile duct stones (CBDs) commonly happens after endoscopic retrograde cholangiopancreatography (ERCP). The clinical...
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Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis
Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's...
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Automated Machine Learning-Based Landslide Susceptibility Map** for the Three Gorges Reservoir Area, China
Machine learning (ML)-based landslide susceptibility map** (LSM) has achieved substantial success in landslide risk management applications....
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MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma
ObjectivesIt is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous...
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Comparison of machine learning algorithms for slope stability prediction using an automated machine learning approach
Evaluation of slope failures, which cause significant loss of life and property comparable to natural disasters such as earthquakes, floods and...
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Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China
Predicting groundwater potential is crucial for identifying the spatial distribution of groundwater in a region. It serves as an essential guide for...
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A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning
Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very...
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Automated machine learning for early prediction of acute kidney injury in acute pancreatitis
BackgroundAcute kidney injury (AKI) represents a frequent and grave complication associated with acute pancreatitis (AP), substantially elevating...
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Groundwater Potential Map** in Hubei Region of China Using Machine Learning, Ensemble Learning, Deep Learning and AutoML Methods
Freshwater scarcity has become more widespread on a global scale in recent years. Surface water resources are no longer sufficient to meet the...
