scholarly journals Ensemble-based support vector machine classifiers as an efficient tool for quality assessment of beef fillets from electronic nose data

2016 ◽  
Vol 8 (18) ◽  
pp. 3711-3721 ◽  
Author(s):  
Fady Mohareb ◽  
Olga Papadopoulou ◽  
Efstathios Panagou ◽  
George-John Nychas ◽  
Conrad Bessant
Keyword(s):  
Support Vector Machine ◽  
Quality Assessment ◽  
Electronic Nose ◽  
Support Vector ◽  
Efficient Tool ◽  
The Past ◽  
Food Freshness ◽  
Potential Tool

Over the past years, the application of electronic nose devices has been investigated as a potential tool for assessing food freshness.

scholarly journals Model Quality Assessment Method Based on Support Vector Machine

2021 ◽  
Vol 25 (1) ◽  
pp. 35-39
Author(s):  
Łukasz Glodek ◽  
Szymon Bysko ◽  
Witold Nocoń
Keyword(s):  
Support Vector Machine ◽  
Quality Assessment ◽  
Industry 4.0 ◽  
Assessment Method ◽  
Support Vector ◽  
Model Quality ◽  
Digital Twin ◽  
Model Quality Assessment ◽  
Virtual Commissioning ◽  
Quality Assessment Method

This paper proposes a model quality assessment method based on Support Vector Machine, which can be used to develop a digital twin. This work is strongly connected with Industry 4.0, in which the main idea is to integrate machines, devices, systems, and IT. One of the goals of Industry 4.0 is to introduce flexible assortment changes. Virtual commissioning can be used to create a simulation model of a plant or conduct training for maintenance engineers. On a branch of virtual commissioning is a digital twin. The digital twin is a virtual representation of a plant or a device. Thanks to the digital twin, different scenarios can be analyzed to make the testing process less complicated and less time-consuming. The goal of this work is to propose a coefficient that will take into account expert knowledge and methods used for model quality assessment (such as Normalized Root Mean Square Error – NRMSE, Maximum Error – ME). NRMSE and ME methods are commonly used for this purpose, but they have not been used simultaneously so far. Each of them takes into consideration another aspect of a model. The coefficient allows deciding whether the model can be used for digital twin appliances. Such an attitude introduces the ability to test models automatically or in a semi-automatic way.

scholarly journals Pork Quality Assessment Through Image Segmentation and Support Vector Machine Implementation

2018 ◽  
Vol 5 (2) ◽  
pp. 15-21
Author(s):  
Raver John S. De Guzman ◽  
◽  
David Nathaniel N. Niro ◽  
Aldous Cesar F. Bueno ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Image Segmentation ◽  
Quality Assessment ◽  
Support Vector ◽  
Pork Quality

scholarly journals Effective Usage of Support Vector Machine in Face Detection

2020 ◽  
Vol 9 (3) ◽  
pp. 1336-1340
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Facial Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Biometric Technology ◽  
Multimedia Contents ◽  
The Past ◽  
Vector Machines ◽  
Facial Recognition System

With the rapid growth in Technology in terms of multimedia contents such as Biometrics, Facial recognition etc. Facial detection got much attention over the past few years. Face recognition describes a biometric technology that attempts to establish an identity. In this paper, I would like to review about a facial recognition system using machine learning especially, using support vector machines. In any case, point of this exploration is to give extensive writing survey over face acknowledgment alongside its applications. Furthermore, after top to bottom conversation, a portion of the significant discoveries are given in end.

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