AIMC Topic: Machine Learning

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AI-based disease category prediction model using symptoms from low-resource Ethiopian language: Afaan Oromo text.

Scientific reports
Automated disease diagnosis and prediction, powered by AI, play a crucial role in enabling medical professionals to deliver effective care to patients. While such predictive tools have been extensively explored in resource-rich languages like English...

Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.

Nature communications
Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechanics (QM) methods in crystallographic refinement, showed promise in improving the structural quality ...

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.

PloS one
BACKGROUND: Parkinson's Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cogn...

Predicting malaria outbreak in The Gambia using machine learning techniques.

PloS one
Malaria is the most common cause of death among the parasitic diseases. Malaria continues to pose a growing threat to the public health and economic growth of nations in the tropical and subtropical parts of the world. This study aims to address this...

Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics.

PloS one
Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a ch...

Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients.

ESC heart failure
AIMS: In recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceiv...

Regularization, early-stopping and dreaming: A Hopfield-like setup to address generalization and overfitting.

Neural networks : the official journal of the International Neural Network Society
In this work we approach attractor neural networks from a machine learning perspective: we look for optimal network parameters by applying a gradient descent over a regularized loss function. Within this framework, the optimal neuron-interaction matr...

Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review.

World neurosurgery
BACKGROUND: Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use artificial intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and criti...

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Plant communications
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target...