China
Africa
Explore chapters and articles related to this topic
Artificial Neural Networks and Hyperspectral Images for Quality Control in Foods
Published in N.C. Basantia, Leo M.L. Nollet, Mohammed Kamruzzaman, Hyperspectral Imaging Analysis and Applications for Food Quality, 2018
Luis Condezo-Hoyos, Wilson Castro
Recent applications of HSI coupled to chemometric tools as partial least square regression (PLSR) in texture prediction of foods are reported by Dai et al. [38] for prawn, Wu et al. [39] for salmon fillets, and Leiva-Valenzuela et al. [45] for blueberries. About application of HSI to cheeses texture properties during ripening, only one work was found which uses HSI and linear regression for a reflectance at 1,387 nm, obtaining R2 = 0.846 [46]. Then, in the next section, as an application example for ANN and hyperspectral images, is shown the hardness modeling of Swiss-type cheese during ripening process using functions and scripts implemented in GNU Octave, version 4.2.0. Octave is a free software of MATLAB® available for MacOs, BDS y Windows; therefore, Octave functions and procedures are compatible with MATLAB.
Gaussian Process Regression
Published in Robert B. Gramacy, Surrogates, 2020
Finally, how about categorical Y(x)? This is less common in the computer surrogate modeling literature, but GP classification remains popular in ML. See Chapter 3 of Rasmussen and Williams (2006). Software is widely available in Python (e.g., GPy40) and MATLAB/Octave (see gpstuff41Vanhatalo et al. (2012)). R implementation is provided in kernlab (Karatzoglou et al., 2018) and plgp (Gramacy, 2014). Bayesian optimization under constraints (§7.3) sometimes leverages classification surrogates to model binary constraints. GP classifiers work well here (Gramacy and Lee, 2011), but so too do other common nonparametric classifiers like random forests (Breiman, 2001). See §7.3.2 for details.
Recent advances in the use of remote labs in fluid mechanics
Published in Ataur Rahman, Vojislav Ilic, Blended Learning in Engineering Education, 2018
GNU Octave: GNU Octave was designed for numerical computations. Both linear and non-linear problems can be solved numerically using an interactive command-line interface. This is an open source redistributable software. Anyone using it can contribute to the development of GNU Octave by modifying it (Eaton et al., 1997). GNU Octave has been used by many researchers to design mathematical models for high-level numerical simulation.
Emergent patterns in deterministic modelling
Published in International Journal of Mathematical Education in Science and Technology, 2021
The mathematics of these two models and the code associated with running them have not been previously available to many instructors due to the proprietary nature of the MATLAB software. An important step in developing these instructional resources was adapting the code to Octave. The code for each of these models was developed and tested in Octave 5.1.0 (Eaton et al., 2019). Octave is available under the GNU General Public License and is freely redistributable software. It can be distributed, adapted, and modified as appropriate for any educational situation, yet maintains drop-in compatibility with many MATLAB scripts. Instructors and students can continue to use Octave software regardless of whether they remain at the institution, and their experience will transfer well to proprietary programs like MATLAB should the need arise.
Corrosion grade classification: a machine learning approach
Published in Indian Chemical Engineer, 2020
Guillermo Sanchez, William Aperador, Alexander Cerón
SVM is a classification method developed by Vapnik and Cortes [20]. One of the most relevant contributions was made by C. Chang and C. Lin: the LIBSVM project, a multilingual library with the main SVM algorithms. This library has become one of the most used and winner of the challenges like the Causation and Prediction challenge in 2008 and the Active Learning Challenge (2nd place) in 2010 [21]. LIBSVM contains algorithms for classification, regression and one class SVM. LIBSVM can be used with C++, Java and Python languages. Also, Scilab, Octave and MATLAB frameworks are supported.
A simple kinetic model applied to anaerobic digestion of cow manure
Published in Environmental Technology, 2021
Iván López, Martín Benzo, Mauricio Passeggi, Liliana Borzacconi
The SAD-TSFO model applied to a continuous digester was solved numerically using Octave 4.2.1 (https://www.gnu.org/software/octave/). Octave is an open source software that is useful for numerical calculations. Differential equations were solved using the ode45 routine, and fminsearch was used for the optimization procedures.