#7363. Target discrimination, concentration prediction, and status judgment of electronic nose system based on large-scale measurement and multi-task deep learning

September 2026publication date
Proposal available till 13-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Instrumentation;
Metals and Alloys;
Materials Chemistry;
Surfaces, Coatings and Films;
Condensed Matter Physics;
Electrical and Electronic Engineering;
Electronic, Optical and Magnetic Materials;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №7363.1 Contract №7363.2 Contract №7363.3 Contract №7363.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
Pattern recognition is the core component of the electronic nose (E-nose). Traditional machine learning algorithms highly rely on the feature data selected manually for model training and testing. A complete experiment must be performed before the data can be further processed. To realize the automatic extraction of response features and simplify the models training and application process, a multi-task convolutional neural network (MTL-CNN) with a dual-block knowledge-sharing structure is designed to train a model for the E-nose system. This model can simultaneously perform three different classification tasks, for the purposes of target discrimination, concentration prediction, and state judgment.
Keywords:
Concentration prediction; Convolutional neural networks; Electronic nose; Gas type recognition; Multi-task deep learning

Contacts :
0