AIMC Topic: Electronic Nose

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An Olfactory Sensor Array for Predicting Chemical Odor Characteristics from Mass Spectra with Deep Learning.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
Machine learning techniques are useful for applications such as electronic nose (e-nose) systems to classify or identify the target odor. In recent years, deep learning is regarded as one of the most powerful machine learning methods. It enables rese...
Deep Learning Models, Chemical Odorants Smell Electronic Nose
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Machine Learning in Human Olfactory Research.

Chemical senses Jan 1, 2019
The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data-driven approaches that complement hypothesis-driven research. Contemporary developments in computational and...
Humans Machine Learning Databases, Factual Biomarkers Odorants Volatile Organic Compounds Smell Electronic Nose
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Qualitative analysis of biological tuberculosis samples by an electronic nose-based artificial neural network.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease Jul 1, 2017
OBJECTIVE: To apply an e-nose system for monitoring headspace volatiles in biological samples from Egyptian patients with active pulmonary tuberculosis (TB) and healthy controls (HCs) and compare them with standard sputum analysis.
Humans Neural Networks, Computer Female Aged Young Adult Adult Sensitivity and Specificity Male Middle Aged Case-Control Studies Adolescent Socioeconomic Factors Mass Screening Principal Component Analysis Egypt Sputum Tuberculosis, Pulmonary Electronic Nose
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