AIMC Topic: Machine Learning

Clear Filters Showing 19511 to 19520 of 34417 articles

Robust machine learning method for imputing missing values in audiograms collected in children.

International journal of audiology
OBJECTIVE: To assess the accuracy and reliability of a machine learning (ML) algorithm for predicting the full audiograms of hearing-impaired children relative to the common approach (CA).

An effective SteinGLM initialization scheme for training multi-layer feedforward sigmoidal neural networks.

Neural networks : the official journal of the International Neural Network Society
Network initialization is the first and critical step for training neural networks. In this paper, we propose a novel network initialization scheme based on the celebrated Stein's identity. By viewing multi-layer feedforward sigmoidal neural networks...

Optimizing predictive strategies for acute kidney injury after major vascular surgery.

Surgery
BACKGROUND: Postoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitig...

Machine learning, artificial intelligence and mechanical circulatory support: A primer for clinicians.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
Artificial intelligence (AI) refers to the ability of machines to perform intelligent tasks, and machine learning (ML) is a subset of AI describing the ability of machines to learn independently and make accurate predictions. The application of AI co...

Trends in application of advancing computational approaches in GPCR ligand discovery.

Experimental biology and medicine (Maywood, N.J.)
G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefo...

A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems.

Sensors (Basel, Switzerland)
Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle wi...

Advances in machine learning for directed evolution.

Current opinion in structural biology
Machine learning (ML) can expedite directed evolution by allowing researchers to move expensive experimental screens in silico. Gathering sequence-function data for training ML models, however, can still be costly. In contrast, raw protein sequence d...

Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images.

Annals of the New York Academy of Sciences
We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom-built OCT system based on an akinetic swept laser at ...

A review on current advances in machine learning based diabetes prediction.

Primary care diabetes
Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it properly. The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hype...

LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes.

Sensors (Basel, Switzerland)
Human Activity Recognition (HAR) employing inertial motion data has gained considerable momentum in recent years, both in research and industrial applications. From the abstract perspective, this has been driven by an acceleration in the building of ...