scholarly journals Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images

2014 ◽  
Vol 6 (4) ◽  
pp. 2912-2939 ◽  
Author(s):  
Marc Wieland ◽  
Massimiliano Pittore
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Performance Evaluation ◽  
Satellite Images ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Urban Pattern

Performance Evaluation of Different Machine Learning Algorithms in Prediction of Pandemic in Early Stages

2021 ◽  
Vol 34 (01) ◽  
Author(s):  
Hemalatha S ◽  
◽  
Nivedha S ◽  
Dr. M Sangeetha ◽  
Dr. K. E Kannammal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Early Stages

scholarly journals Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 557 ◽  
Author(s):  
Cristiano Fragassa ◽  
Matej Babic ◽  
Carlos Perez Bergmann ◽  
Giangiacomo Minak
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Mechanical Properties ◽  
Pattern Recognition ◽  
Cast Iron ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tensile Behaviour ◽  
Cast Alloys ◽  
Pattern Recognition Analysis

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

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