scholarly journals Fabrication and characterization of an L3 nanocavity designed by an iterative machine-learning method

APL Photonics ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 036113
Author(s):  
Takeshi Shibata ◽  
Takashi Asano ◽  
Susumu Noda
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Fabrication And Characterization

scholarly journals Machine Learning Method to Differentiate Ataxias

2021 ◽  
Vol 15 (1) ◽  
pp. 53-67
Author(s):  
Gustavo Simões Carnivali ◽  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Neurodegenerative Diseases ◽  
Genetic Similarity ◽  
Machine Learning Method ◽  
Learning Method ◽  
Spinocerebellar Ataxias ◽  
New Entry

Spinocerebellar ataxias or SCAs, are a group of more than 37 genetically and clinically heterogeneous known neurodegenerative diseases. This work analyzes the level of genetic similarity between several ataxias, we identified proteins that are associated with more than one ataxia. A decision tree was trained to identify ataxias by identifying whether a new entry disease not yet identified and not classified can be grouped as an ataxia. Altogether 12 proteins from different ataxias were verified, all 12 proteins were analyzed in 500 different trees to better evaluate the method used. Of the 12 proteins tested, the method was correct for 10 different proteins or 83% of correct results. This identifier and the results obtained in the experiments allow a greater characterization of the diseases addressed, it also allows applications such as the reuse of treatments for similar diseases.

scholarly journals Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images

Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2706
Author(s):  
Haotian Wen ◽  
José María Luna-Romera ◽  
José C. Riquelme ◽  
Christian Dwyer ◽  
Shery L. Y. Chang
Keyword(s):  
Machine Learning ◽  
Morphological Characterization ◽  
Atomic Resolution ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning ◽  
Transmission Electron ◽  
Property Control ◽  
Quality Imaging

The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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