AIMC Topic: Machine Learning

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Scene-dependent sound event detection based on multitask learning with deformable large kernel attention convolution.

PloS one
Sound event detection (SED) and acoustic scene classification (ASC) are closely related tasks in environmental sound analysis. Given the interrelationship between sound events and scenes, some previous studies have proposed using the multitask learni...

Landslide susceptibility mapping using an entropy index-based negative sample selection strategy: A case study of Luolong county.

PloS one
Landslides constitute a significant geological hazard in China, particularly in high-altitude regions like the Himalayas, where the challenging environmental conditions impede field surveys. This research utilizes the IOE model to refine non-landslid...

An active machine learning framework for automatic boxing punch recognition and classification using upper limb kinematics.

PloS one
Boxing punch type classification and kinematic analysis are essential for coaches and athletes, providing critical insights into punch variety and effectiveness, which are vital for performance improvement. Existing methods for punch recognition and ...

Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish.

PloS one
INTRODUCTION: Accurate identification of graft loss in Electronic Medical Records of kidney transplant recipients is essential but challenging due to inconsistent and not mandatory International Classification of Diseases (ICD) codes. We developed an...

Identifying high-dose opioid prescription risks using machine learning: A focus on sociodemographic characteristics.

Journal of opioid management
OBJECTIVE: The objective of this study was to leverage machine learning techniques to analyze administrative claims and socioeconomic data, with the aim of identifying and interpreting the risk factors associated with high-dose opioid prescribing.

Reported liver toxicity of food chemicals in rats extrapolated to humans using virtual human-to-rat hepatic concentration ratios generated by pharmacokinetic modeling with machine learning-derived parameters.

The Journal of toxicological sciences
Pharmacokinetic data are not generally available for evaluating the toxicological potential of food chemicals. A simplified physiologically based pharmacokinetic (PBPK) model has been established to evaluate internal exposures to chemicals in rats or...

Exploring and Identifying Key Factors in Predicting Dyslexia in Children: Advanced Machine Learning Algorithms From Screening to Diagnosis.

Clinical psychology & psychotherapy
INTRODUCTION: The current study aimed to develop and validate a machine learning (ML)-based predictive models for early dyslexia detection in children by integrating neurocognitive, linguistic and behavioural predictors.

Enhancing Medical Image Classification through Transfer Learning and CLAHE Optimization.

Current medical imaging
INTRODUCTION: This paper examines the impact of transfer learning and CLAHE (Contrast Limited Adaptive Histogram Equalization) optimization on the classification of medical images, specifically brain images.

Combining Machine Learning and Electrophysiology for Insect Odorant Receptor Studies.

Methods in molecular biology (Clifton, N.J.)
Insects rely on olfaction in many aspects of their life, and odorant receptors are key proteins in this process. Whereas a plethora of insect odorant receptor sequences is available, most of them are still orphan or uncompletely characterized, since ...

Predictive Machine Learning Models for Olfaction.

Methods in molecular biology (Clifton, N.J.)
A classical problem in neuroscience, biology, and chemistry is linking the chemical structure of odorants to their olfactory perception. This difficulty arises from the subjective nature of odor perception, incomplete understanding of the physiologic...