AIMC Topic: Machine Learning

Clear Filters Showing 5171 to 5180 of 34417 articles

Galactose-Induced Cataracts in Rats: A Machine Learning Analysis.

International journal of medical sciences
Rat models are widely used to study cataracts due to their cost-effectiveness and prominent physiological and genetic similarities to humans The objective of this study was to identify genes involved in cataractogenesis due to galactose exposure in ...

Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels.

BioMed research international
Research suggests that dreams play a role in the regulation of emotional processing and memory consolidation; electroencephalography (EEG) is useful for studying them, but manual annotation is time-consuming and prone to bias. This study was aimed at...

Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.

PloS one
The healthcare industry is generating a massive volume of data, promising a potential goldmine of information that can be extracted through machine learning (ML) techniques. The Intensive Care Unit (ICU) stands out as a focal point within hospitals a...

Addressing imbalanced data classification with Cluster-Based Reduced Noise SMOTE.

PloS one
In recent years, the challenge of imbalanced data has become increasingly prominent in machine learning, affecting the performance of classification algorithms. This study proposes a novel data-level oversampling method called Cluster-Based Reduced N...

One-shot learning for generalization in medical image classification across modalities.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Generalizability is one of the biggest challenges hindering the advancement of medical sensing technologies across multiple imaging modalities. This issue is further impaired when the imaging data is limited in scope or of poor quality. To tackle thi...

Methods for estimating resting energy expenditure in intensive care patients: A comparative study of predictive equations with machine learning and deep learning approaches.

Computer methods and programs in biomedicine
BACKGROUND: Accurate estimation of resting energy expenditure (REE) is critical for guiding nutritional therapy in critically ill patients. While indirect calorimetry (IC) is the gold standard for REE measurement, it is not routinely feasible in clin...

Bayesian Optimization-Enhanced Reinforcement learning for Self-adaptive and multi-objective control of wastewater treatment.

Bioresource technology
Controllers of wastewater treatment plants (WWTPs) often struggle to maintain optimal performance due to dynamic influent characteristics and the need to balance multiple operational objectives. In this study, Reinforcement Learning (RL) algorithms a...

Machine learning-based evolution of water quality prediction model: An integrated robust framework for comparative application on periodic return and jitter data.

Environmental pollution (Barking, Essex : 1987)
Accurate water quality prediction is paramount for the sustainable management of surface water resources. Current deep learning models face challenges in reliably forecasting water quality due to the non-stationarity of environmental conditions and t...

Analysis and validation of programmed cell death genes associated with spinal cord injury progression based on bioinformatics and machine learning.

International immunopharmacology
BACKGROUND: Spinal cord injury (SCI) is a severe condition affecting the central nervous system. It is marked by a high disability rate and potential for death. Research has demonstrated that programmed cell death (PCD) plays a significant role in th...

A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors.

Sensors (Basel, Switzerland)
Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limited number and geographical coverage of advanced, highly accurate sensors monitoring air pollutants, ...