AIMC Topic: Machine Learning

Clear Filters Showing 8991 to 9000 of 34417 articles

Advancing Point-of-Care Testing by Application of Machine Learning Techniques and Artificial Intelligence.

Chest
The promise of artificial intelligence has generated enthusiasm among patients, health care professionals, and technology developers who seek to leverage its potential to enhance the diagnosis and management of an increasing number of chronic and acu...

Online chicken carcass volume estimation using depth imaging and 3-D reconstruction.

Poultry science
Variability in the size of slaughtered chickens remains a longstanding challenge in the standardization of the poultry industry. To address this issue, we present a novel approach that uses volume as a grading metric for chicken carcasses. This innov...

Harnessing collective structure knowledge in data augmentation for graph neural networks.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are a...

StochCA: A novel approach for exploiting pretrained models with cross-attention.

Neural networks : the official journal of the International Neural Network Society
Utilizing large-scale pretrained models is a well-known strategy to enhance performance on various target tasks. It is typically achieved through fine-tuning pretrained models on target tasks. However, naï ve fine-tuning may not fully leverage knowle...

From data to harvest: Leveraging ensemble machine learning for enhanced crop yield predictions across Canada amidst climate change.

The Science of the total environment
Accurate crop yield predictions are crucial for farmers and policymakers. Despite the widespread use of ensemble machine learning (ML) models in computer science, their application in crop yield prediction remains relatively underexplored. This study...

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest.

The Science of the total environment
The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challengin...

Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.

International journal of neural systems
Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalizatio...

Automated Sleep Detection in Movement Disorders Using Deep Brain Stimulation and Machine Learning.

Movement disorders : official journal of the Movement Disorder Society
BACKGROUND: Automated sleep detection in movement disorders may allow monitoring sleep, potentially guiding adaptive deep brain stimulation (DBS).

Machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectra for antimicrobial resistance prediction: A systematic review of recent advancements and future development.

Journal of chromatography. A
BACKGROUND: The use of matrix-assisted laser desorption/ionization time-of-flight mass spectra (MALDI-TOF MS) combined with machine learning techniques has recently emerged as a method to address the public health crisis of antimicrobial resistance. ...

An optimal fast fractal method for breast masses diagnosis using machine learning.

Medical engineering & physics
This article introduces a fast fractal method for classifying breast cancerous lesions in mammography. While fractal methods are valuable for extracting information, they often come with a high computational load and time consumption. This paper demo...