A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection
S Masmoudi, H Elghazel, D Taieb, O Yazar… - Science of the Total …, 2020 - Elsevier
Air pollution is considered one of the biggest threats for the ecological system and human
existence. Therefore, air quality monitoring has become a necessity in urban and industrial …
existence. Therefore, air quality monitoring has become a necessity in urban and industrial …
Real-time prediction system of train carriage load based on multi-stream fuzzy learning
When a train leaves a platform, knowing the carriage load (the number of passengers in
each carriage) of this train will support train managers to guide passengers at the next …
each carriage) of this train will support train managers to guide passengers at the next …
[HTML][HTML] Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy
SB Junior, SM Mastelini, APAC Barbon… - … processing in agriculture, 2020 - Elsevier
Near Infrared (NIR) spectroscopy is an analytical technology widely used for the non-
destructive characterisation of organic samples, considering both qualitative and …
destructive characterisation of organic samples, considering both qualitative and …
A convolutional neural network approach for estimating tropical cyclone intensity using satellite-based infrared images
JS Combinido, JR Mendoza… - 2018 24th International …, 2018 - ieeexplore.ieee.org
Existing techniques for satellite-based tropical cyclone (TC) intensity estimation involve an
explicit feature extraction step to model TC intensity on a set of relevant TC features or …
explicit feature extraction step to model TC intensity on a set of relevant TC features or …
Acoustic investigation of speech pathologies based on the discriminative paraconsistent machine (DPM)
Background Voice disorders are related to both modest and severe health problems,
including discomfort, pain, difficulty speaking, dysphagia and also cancer. Widely adopted …
including discomfort, pain, difficulty speaking, dysphagia and also cancer. Widely adopted …
Multiple voice disorders in the same individual: investigating handcrafted features, multi-label classification algorithms, and base-learners
Non-invasive acoustic analyses of voice disorders have been at the forefront of current
biomedical research. Usual strategies, essentially based on machine learning (ML) …
biomedical research. Usual strategies, essentially based on machine learning (ML) …
Predicting on multi-target regression for the yield of sweet potato by the market class of its roots upon vegetation indices
Single-target regression can accurately predict the crop's performance but fails to generalize
problems with more than one true and cross-validatable solution. An alternative to output …
problems with more than one true and cross-validatable solution. An alternative to output …
Using meta-learning for multi-target regression
Choosing the most suitable algorithm to perform a machine learning task for a new problem
is a recurrent and complex task. In multi-target regression tasks, when problem …
is a recurrent and complex task. In multi-target regression tasks, when problem …
Improved prediction of soil properties with multi-target stacked generalisation on EDXRF spectra
Energy dispersive X-ray fluorescence (EDXRF) is one of the most quick, environmentally
friendly and least expensive spectroscopic analytical methodologies for assessing soil …
friendly and least expensive spectroscopic analytical methodologies for assessing soil …
A multitask approach to learn molecular properties
Z Tan, Y Li, W Shi, S Yang - Journal of Chemical Information and …, 2021 - ACS Publications
The endeavors to pursue a robust multitask model to resolve intertask correlations have
lasted for many years. A multitask deep neural network, as the most widely used multitask …
lasted for many years. A multitask deep neural network, as the most widely used multitask …