On line measurement of crystallinity of nylon 6 nanocomposites by laser Raman spectroscopy and neural networks

2004 ◽  
Vol 92 (1) ◽  
pp. 474-483 ◽  
Author(s):  
Z. Ergungor ◽  
C. Batur ◽  
M. Cakmak
Keyword(s):  
Neural Networks ◽  
Raman Spectroscopy ◽  
Nylon 6 ◽  
Laser Raman Spectroscopy ◽  
Laser Raman ◽  
Line Measurement ◽  
On Line

On-line Tritium Process Gas Analysis by Laser Raman Spectroscopy at TSTA

1992 ◽  
Vol 21 (2P2) ◽  
pp. 465-470 ◽  
Author(s):  
S. O’hira ◽  
H. Nakamura ◽  
S. Konishi ◽  
T. Hayashi ◽  
K. Okuno ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Gas Analysis ◽  
Laser Raman Spectroscopy ◽  
Laser Raman ◽  
Process Gas ◽  
On Line

scholarly journals Review of Laser Raman Spectroscopy for Surgical Breast Cancer Detection: Stochastic Backpropagation Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6260
Author(s):  
Ragini Kothari ◽  
Yuman Fong ◽  
Michael C. Storrie-Lombardi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Neural Networks ◽  
Raman Spectroscopy ◽  
Learning Strategies ◽  
Breast Cancer Diagnosis ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Laser Raman Spectroscopy ◽  
Laser Raman

Laser Raman spectroscopy (LRS) is a highly specific biomolecular technique which has been shown to have the ability to distinguish malignant and normal breast tissue. This paper discusses significant advancements in the use of LRS in surgical breast cancer diagnosis, with an emphasis on statistical and machine learning strategies employed for precise, transparent and real-time analysis of Raman spectra. When combined with a variety of “machine learning” techniques LRS has been increasingly employed in oncogenic diagnostics. This paper proposes that the majority of these algorithms fail to provide the two most critical pieces of information required by the practicing surgeon: a probability that the classification of a tissue is correct, and, more importantly, the expected error in that probability. Stochastic backpropagation artificial neural networks inherently provide both pieces of information for each and every tissue site examined by LRS. If the networks are trained using both human experts and an unsupervised classification algorithm as gold standards, rapid progress can be made understanding what additional contextual data is needed to improve network classification performance. Our patients expect us to not simply have an opinion about their tumor, but to know how certain we are that we are correct. Stochastic networks can provide that information.

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