AIMC Topic: Machine Learning

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Graph convolutional neural network applied to the prediction of normal boiling point.

Journal of molecular graphics & modelling
In this article, we describe training and validation of a machine learning model for the prediction of organic compound normal boiling points. Data are drawn from the experimental literature as captured in the NIST Thermodynamics Research Center (TRC...

The emergence of a concept in shallow neural networks.

Neural networks : the official journal of the International Neural Network Society
We consider restricted Boltzmann machine (RBMs) trained over an unstructured dataset made of blurred copies of definite but unavailable "archetypes" and we show that there exists a critical sample size beyond which the RBM can learn archetypes, namel...

Machine learning-based modeling in food processing applications: State of the art.

Comprehensive reviews in food science and food safety
Food processing is a complex, multifaceted problem that requires substantial human interaction to optimize the various process parameters to minimize energy consumption and ensure better-quality products. The development of a machine learning (ML)-ba...

Developing Affordable, Portable and Simplistic Diagnostic Sensors to Improve Access to Care.

Sensors (Basel, Switzerland)
Ophthalmology is a highly technical specialty, especially in the area of diagnostic equipment. While the field is innovative, the access to cutting-edge technology is limited with reference to the global population. A significant way to improve overa...

Task-Coupling Elastic Learning for Physical Sign-Based Medical Image Classification.

IEEE journal of biomedical and health informatics
Physical signs of patients indicate crucial evidence for diagnosing both location and nature of the disease, where there is a sequential relationship between the two tasks. Thus their joint learning can utilize intrinsic association by transferring r...

Anomalous Gait Feature Classification From 3-D Motion Capture Data.

IEEE journal of biomedical and health informatics
The gait kinematics of an individual is affected by various factors, including age, anthropometry, gender, and disease. Detecting anomalous gait features aids in the diagnosis and treatment of gait-related diseases. The objective of this study was to...

Multiparametric Quantitative US Examination of Liver Fibrosis: A Feature-Engineering and Machine-Learning Based Analysis.

IEEE journal of biomedical and health informatics
Quantitative ultrasound (QUS), which attempts to extract quantitative features from the US radiofrequency (RF) or envelope data for tissue characterization, is becoming a promising technique for noninvasive assessments of liver fibrosis. However, the...

Interpretable Classification of Bacterial Raman Spectra With Knockoff Wavelets.

IEEE journal of biomedical and health informatics
Deep neural networks and other machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions. However, the difficulty of interpreting such models is a limitation, especi...

Establishment and External Validation of a Hypoxia-Derived Gene Signature for Robustly Predicting Prognosis and Therapeutic Responses in Glioblastoma Multiforme.

BioMed research international
OBJECTIVE: Hypoxia presents a salient feature investigated in most solid tumors that holds key roles in cancer progression, including glioblastoma multiforme (GBM). Here, we aimed to construct a hypoxia-derived gene signature for identifying the high...