AIMC Topic: Machine Learning

Clear Filters Showing 13721 to 13730 of 34417 articles

Simulator, machine learning, and artificial intelligence: Time has come to assist prenatal ultrasound diagnosis.

Journal of clinical ultrasound : JCU
In this Commentary authors investigated and extended the role of simulator in assisting obstetric sonographers in training program. The interconnection of different digitalized technologies such as digital data, artificial neuronal and convolutional ...

Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study.

Intensive care medicine
PURPOSE: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU).

A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels.

Scientific reports
Solar energy is a very efficient alternative for generating clean electric energy. However, pollution on the surface of solar panels reduces solar radiation, increases surface transmittance, and raises the surface temperature. All these factors cause...

Differentially private knowledge transfer for federated learning.

Nature communications
Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exc...

Modeling dissolved oxygen concentration using machine learning techniques with dimensionality reduction approach.

Environmental monitoring and assessment
Oxygen is crucial to keep the life cycle balance in any aspect. Aquatic life is highly influenced by the levels of dissolved oxygen (DO). This calls for not just constant monitoring of the DO in aquatic systems, but to generate an accurate prediction...

Stochastic momentum methods for non-convex learning without bounded assumptions.

Neural networks : the official journal of the International Neural Network Society
Stochastic momentum methods are widely used to solve stochastic optimization problems in machine learning. However, most of the existing theoretical analyses rely on either bounded assumptions or strong stepsize conditions. In this paper, we focus on...

Discovery of a Novel DCAF1 Ligand Using a Drug-Target Interaction Prediction Model: Generalizing Machine Learning to New Drug Targets.

Journal of chemical information and modeling
DCAF1 functions as a substrate recruitment subunit for the RING-type CRL4 and the HECT family EDVP E3 ubiquitin ligases. The WDR domain of DCAF1 serves as a binding platform for substrate proteins and is also targeted by HIV and SIV lentiviral adapto...

Application of Machine Learning Algorithms for Tool Condition Monitoring in Milling Chipboard Process.

Sensors (Basel, Switzerland)
In this article, we present a novel approach to tool condition monitoring in the chipboard milling process using machine learning algorithms. The presented study aims to address the challenges of detecting tool wear and predicting tool failure in rea...

Application of Artificial Intelligence in Geriatric Care: Bibliometric Analysis.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) can improve the health and well-being of older adults and has the potential to assist and improve nursing care. In recent years, research in this area has been increasing. Therefore, it is necessary to underst...

Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology.

PloS one
Robust semantic segmentation of tumour micro-environment is one of the major open challenges in machine learning enabled computational pathology. Though deep learning based systems have made significant progress, their task agnostic data driven appro...