AIMC Topic: Unsupervised Machine Learning
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Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data.
Human brain mapping
Oct 1, 2019
Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and studies seeking to investigate generalizability are scarce. To address these issues, we assessed withi...
Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques.
Ecotoxicology and environmental safety
Sep 30, 2019
Presence of missing data points in datasets is among main challenges in handling the toxicological data for nanomaterials. As the processing of missing data is an important part of data analysis, we have introduced a read-across approach that uses a ...
BIA: ehavior dentification lgorithm Using Unsupervised Learning Based on Sensor Data for Home Elderly.
IEEE journal of biomedical and health informatics
Sep 24, 2019
Behavior identification plays an important role in supporting homecare for the elderly living alone. In literature, plenty of algorithms have been designed to identify behaviors of the elderly by learning features or extracting patterns from sensor d...
Unsupervised Anomaly Detection With LSTM Neural Networks.
IEEE transactions on neural networks and learning systems
Sep 13, 2019
We investigate anomaly detection in an unsupervised framework and introduce long short-term memory (LSTM) neural network-based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM-based struc...
Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients.
The spine journal : official journal of the North American Spine Society
Sep 13, 2019
BACKGROUND CONTEXT: Traumatic spinal cord injury can have a dramatic effect on a patient's life. The degree of neurologic recovery greatly influences a patient's treatment and expected quality of life. This has resulted in the development of machine ...
Unsupervised machine learning using an imaging mass spectrometry dataset automatically reassembles grey and white matter.
Scientific reports
Sep 13, 2019
Current histological and anatomical analysis techniques, including fluorescence in situ hybridisation, immunohistochemistry, immunofluorescence, immunoelectron microscopy and fluorescent fusion protein, have revealed great distribution diversity of m...
Decoding Brain States From fMRI Signals by Using Unsupervised Domain Adaptation.
IEEE journal of biomedical and health informatics
Sep 11, 2019
With the development of deep learning in medical image analysis, decoding brain states from functional magnetic resonance imaging (fMRI) signals has made significant progress. Previous studies often utilized deep neural networks to automatically clas...
Unsupervised machine learning reveals mimicry complexes in bumblebees occur along a perceptual continuum.
Proceedings. Biological sciences
Sep 11, 2019
Müllerian mimicry theory states that frequency-dependent selection should favour geographical convergence of harmful species onto a shared colour pattern. As such, mimetic patterns are commonly circumscribed into discrete mimicry complexes, each cont...
Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence.
Neural networks : the official journal of the International Neural Network Society
Sep 9, 2019
This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. ...
Prediction of Potential miRNA-Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder.
Cells
Sep 6, 2019
The important role of microRNAs (miRNAs) in the formation, development, diagnosis, and treatment of diseases has attracted much attention among researchers recently. In this study, we present an unsupervised deep learning model of the variational aut...
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