AIMC Topic: Machine Learning

Clear Filters Showing 17661 to 17670 of 34417 articles

Interval-Based Least Squares for Uncertainty-Aware Learning in Human-Centric Multimedia Systems.

IEEE transactions on neural networks and learning systems
Machine learning (ML) methods are popular in several application areas of multimedia signal processing. However, most existing solutions in the said area, including the popular least squares, rely on penalizing predictions that deviate from the targe...

Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts.

Biomolecules
For any molecule, network, or process of interest, keeping up with new publications on these is becoming increasingly difficult. For many cellular processes, the amount molecules and their interactions that need to be considered can be very large. Au...

Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network.

Sensors (Basel, Switzerland)
Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and havi...

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.

Scientific reports
The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction mo...

Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures.

NPJ systems biology and applications
The utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of appl...

Tracking strategy changes using machine learning classifiers.

Behavior research methods
In complex tasks, high performers often have better strategies than low performers, even with similar amounts of practice. Relatively little research has examined how people form and change strategies in tasks that permit a large set of strategies. O...

Kernel Methods for Predicting Yields of Chemical Reactions.

Journal of chemical information and modeling
The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in ...

Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions.

World journal of surgery
INTRODUCTION: Patients undergoing laparotomy for emergency general surgery (EGS) conditions, constitute a high-risk group with poor outcomes. These patients have a high prevalence of comorbidities. This study aims to identify patient factors, physiol...

Sensor-based particle mass prediction of lightweight packaging waste using machine learning algorithms.

Waste management (New York, N.Y.)
Sensor-based material flow characterization (SBMC) promises to improve the performance of future-generation sorting plants by enabling new applications like automatic quality monitoring or process control. Prerequisite for this is the derivation of m...

Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model.

Sensors (Basel, Switzerland)
Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to mo...