AIMC Topic: Machine Learning

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A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition.

Sensors (Basel, Switzerland)
Classical machine learning techniques have dominated Music Emotion Recognition. However, improvements have slowed down due to the complex and time-consuming task of handcrafting new emotionally relevant audio features. Deep learning methods have rece...

An adaptive data-driven architecture for mental health care applications.

PeerJ
BACKGROUND: In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven arc...

Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images.

PloS one
Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Mach...

Generalizability and robustness evaluation of attribute-based zero-shot learning.

Neural networks : the official journal of the International Neural Network Society
In the field of deep learning, large quantities of data are typically required to effectively train models. This challenge has given rise to techniques like zero-shot learning (ZSL), which trains models on a set of "seen" classes and evaluates them o...

Predicting Hydrocarbon Primary Biodegradation in Soil and Sediment Systems Using System Parameterization and Machine Learning.

Environmental toxicology and chemistry
Technical complexity associated with biodegradation testing, particularly for substances of unknown or variable composition, complex reaction products, or biological materials (UVCB), necessitates the advancement of non-testing methods such as quanti...

PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for Recurrent Stroke.

Stroke
BACKGROUND: Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individ...

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.

BMC medical informatics and decision making
Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better ...

Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data.

Scientific reports
Ultrasound imaging is a widely used technique for fatty liver diagnosis as it is practically affordable and can be quickly deployed by using suitable devices. When it is applied to a patient, multiple images of the targeted tissues are produced. We p...

Explainable artificial intelligence analysis of brachytherapy boost receipt in cervical cancer during the COVID-19 era.

Brachytherapy
PURPOSE: Brachytherapy is a critical component of the standard-of-care curative radiotherapy regimen for women with locally advanced cervical cancer (LACC). However, existing literature suggests that many patients will not receive the brachytherapy b...

Transfer learning from rating prediction to Top-k recommendation.

PloS one
Recommender system has made great strides in two major research fields, rating prediction and Top-k recommendation. In essence, rating prediction is a regression task, which aims to predict users scores on other items, while Top-k is a classification...