scholarly journals Quantum entropy and exact 4D/5D connection

2015 ◽  
Vol 2015 (1) ◽  
Author(s):  
João Gomes
Keyword(s):  
Quantum Entropy

scholarly journals Density Estimation by Maximum Quantum Entropy

1996 ◽  
pp. 161-174
Author(s):  
R. N. Silver ◽  
T. Wallstrom ◽  
H. F. Martz
Keyword(s):  
Density Estimation ◽  
Quantum Entropy

Local quantum entropy

1971 ◽  
Vol 4 (2) ◽  
pp. 101-115 ◽  
Author(s):  
J. G. Gilson
Keyword(s):  
Quantum Entropy ◽  
Local Quantum

Quantum Entropy

2009 ◽  
pp. 543-546
Author(s):  
Dominik Janzing
Keyword(s):  
Quantum Entropy

scholarly journals MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1033
Author(s):  
Ali M. Hasan ◽  
Hamid A. Jalab ◽  
Rabha W. Ibrahim ◽  
Farid Meziane ◽  
Ala’a R. AL-Shamasneh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Short Term Memory ◽  
Local Binary Patterns ◽  
Quantum Entropy ◽  
Brain Scans ◽  
Quantum Calculus ◽  
Mri Brain ◽  
Brain Scan ◽  
The Brain

Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.

QEC: A Quantum Entropy Chip and Its Applications

2020 ◽  
Vol 28 (6) ◽  
pp. 1471-1484
Author(s):  
Jungmin Park ◽  
Seongjoon Cho ◽  
Taejin Lim ◽  
Mark Tehranipoor
Keyword(s):  
Quantum Entropy
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