AIMC Topic: Machine Learning

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Identifying mislabelled samples: Machine learning models exceed human performance.

Annals of clinical biochemistry
BACKGROUND: It is difficult for clinical laboratories to identify samples that are labelled with the details of an incorrect patient. Many laboratories screen for these errors with delta checks, with final decision-making based on manual review of re...

Novel approach for predicting groundwater storage loss using machine learning.

Journal of environmental management
Comprehensive national estimates of groundwater storage loss (GSL) are needed for better management of natural resources. This is especially important for data scarce regions with high pressure on groundwater resources. In Iran, almost all major grou...

Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy.

ACS nano
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent succe...

Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems.

Sensors (Basel, Switzerland)
Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-a...

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods.

Sensors (Basel, Switzerland)
Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situa...

Wheat Ear Recognition Based on RetinaNet and Transfer Learning.

Sensors (Basel, Switzerland)
The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, an...

Prioritizing and characterizing functionally relevant genes across human tissues.

PLoS computational biology
Knowledge of genes that are critical to a tissue's function remains difficult to ascertain and presents a major bottleneck toward a mechanistic understanding of genotype-phenotype links. Here, we present the first machine learning model-FUGUE-combini...

Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms.

American journal of epidemiology
Unlike parametric regression, machine learning (ML) methods do not generally require precise knowledge of the true data generating mechanisms. As such, numerous authors have advocated for ML methods to estimate causal effects. Unfortunately, ML algor...

A systematic review of machine learning and automation in burn wound evaluation: A promising but developing frontier.

Burns : journal of the International Society for Burn Injuries
BACKGROUND: Visual evaluation is the most common method of evaluating burn wounds. Its subjective nature can lead to inaccurate diagnoses and inappropriate burn center referrals. Machine learning may provide an objective solution. The objective of th...